| Saturday 4 May 2024 | |
| Events for day: Wednesday 29 June 2022 |
| 11:00 - 12:00 Wednesday Weekly Seminar - google meet Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data School PARTICLES AND ACCELERATORS We present a QCD analysis to extract the fragmentation functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering datasets. This analysis includes the data from all available single inclusive electron-positron annihilation processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. We exploit the analytic derivative of the neural network for fitting of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD. The Monte Carlo method is implied for all sources of experimental un ... 13:00 - 14:00 Weekly Seminar Condensed Matter and Statistical Physics Group Fingerprinting local environments with application to machine learning interatomic potentials School PHYSICS Date: Wednesday, June 29, 2022 Time: 13:00-14:00 To join the webinar, please follow this link and log in as a guest: https://www.skyroom.online/ch/schoolofnanoscience/weeklyseminars Abstract: A class of supervised machine learning approaches aims at predicting a quantity from an input data vector. For example, it is common practice to recognize a person's face from the set of data points (pixels) in a digital image frame. The same techniques are effectively useful in computational condensed matter physics problems for the prediction of atomic contributions to a given physical ... 13:00 - 14:00 Weekly Seminar Fingerprinting local environments with application to machine learning interatomic potentials School NANO SCIENCES A class of supervised machine learning approaches aims at predicting a quantity from an input data vector. For example, it is common practice to recognize a person's face from the set of data points (pixels) in a digital image frame. The same techniques are effectively useful in computational condensed matter physics problems for the prediction of atomic contributions to a given physical quantity from the arrangement of the neighboring atoms of the individual atoms. Then one needs a "descriptor" that quantifies the environment of an atom such that it can be fed as input to a supervised machine learning tool. We review the basic ideas, techni ... 13:30 - 15:00 Weekly Seminar (Online) Thermodynamics-Gravity Conjecture https://www.skyroom.online/ch/soa/weekly-seminar School ASTRONOMY In this talk, I will review the deep connection between the law of thermodynamics and the gravitational field equations in any gravity theory. In deriving the gravitational field equations, the entropy expression plays a crucial role. As an example, I will apply this conjecture to derive the modified Friedmann equations describing the evolution of the universe, when the entropy of the boundary get modified due to the quantum fluctuations. To join the seminar, please click on the following link Read more 15:00 - 17:00 Mathematical Logic Weekly Seminar Non-degenerate n-linear Forms and n-dependence School MATHEMATICS In a joint work with Chernikov we have shown that the structure of non-degenerate bilinear forms on an NIP field are 2-dependent. One of the main ingredients is a composition lemma, which issues that NIP formulas remain 2-dependent when precomposed with binary functions. In this talk, we will discuss possible generalizations of non-degeneracy for n-linear forms and the decomposition lemma, present the structure of non-degenerate n-linear forms and draw the connection to n-dependent structures. (joint work with Chernikov) https://us06web.zoom.us/j/9086116889?pwd=WGRFOGZWZ1FOMXJrcWpJMWFqUFIvQT09 Meeting ID: 908 611 6 ... 18:00 - 19:30 Physics Colloquium Disease Ecology from Perspectives of Physics School PHYSICS Here I will review my recent works [1-14] on modeling interacting contagious dynamics, for example coupled SIR or SIS dynamics, in mean field approximations and also on different random generated or empirical complex networks. I show and discuss how our recent results have been improving our understanding and prediction of epidemic dynamics and disease ecology while raising new questions and challenges in physics of critical phenomena. Also I will briefly discuss SARS-COV-2 from the perspective of disease ecology and present my recent studies on "behavioral responses to the COVID-19 spread"; which focus on the analysis and mode ... |