scholarly journals Quark Mass Models and Reinforcement Learning

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
T. R. Harvey ◽  
A. Lukas
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Particle Physics ◽  
Quark Mass ◽  
Policy Networks ◽  
Quark Masses

Abstract In this paper, we apply reinforcement learning to the problem of constructing models in particle physics. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic policy-based algorithm we show that neural networks can be successfully trained to construct Froggatt-Nielsen models which are consistent with the observed quark masses and mixing. The trained policy networks lead from random to phenomenologically acceptable models for over 90% of episodes and after an average episode length of about 20 steps. We also show that the networks are capable of finding models proposed in the literature when starting at nearby configurations.

scholarly journals Quark mass hierarchy and flavor mixing puzzles

2014 ◽  
Vol 29 (30) ◽  
pp. 1430067 ◽  
Author(s):  
Zhi-Zhong Xing
Keyword(s):  
Cp Violation ◽  
Electric Charge ◽  
Particle Physics ◽  
Quark Mass ◽  
Mass Hierarchy ◽  
Quark Masses ◽  
Lepton Flavor ◽  
Quark Flavor ◽  
Flavor Mixing ◽  
Highly Correlated

The fact that quarks of the same electric charge possess a mass hierarchy is a big puzzle in particle physics, and it must be highly correlated with the hierarchy of quark flavor mixing. This chapter is intended to provide a brief description of some important issues regarding quark masses, flavor mixing and CP-violation. A comparison between the salient features of quark and lepton flavor mixing structures is also made.

Water, Water, Here and There

2018 ◽  
Author(s):  
Steven E. Vigdor
Keyword(s):  
Quark Mass ◽  
Mass Difference ◽  
Baryon Number ◽  
Electroweak Phase Transition ◽  
Hydrogen Burning ◽  
Quark Masses ◽  
Grand Unified Theories ◽  
Down Quark ◽  
Unified Theories ◽  
The Stability

Chapter 4 deals with the stability of the proton, hence of hydrogen, and how to reconcile that stability with the baryon number nonconservation (or baryon conservation) needed to establish a matter–antimatter imbalance in the infant universe. Sakharov’s three conditions for establishing a matter–antimatter imbalance are presented. Grand unified theories and experimental searches for proton decay are described. The concept of spontaneous symmetry breaking is introduced in describing the electroweak phase transition in the infant universe. That transition is treated as the potential site for introducing the imbalance between quarks and antiquarks, via either baryogenesis or leptogenesis models. The up–down quark mass difference is presented as essential for providing the stability of hydrogen and of the deuteron, which serves as a crucial stepping stone in stellar hydrogen-burning reactions that generate the energy and elements needed for life. Constraints on quark masses from lattice QCD calculations and violations of chiral symmetry are discussed.

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

scholarly journals Quarkonia Formation in a Holographic Gravity–Dilaton Background Describing QCD Thermodynamics

Particles ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 159-177
Author(s):  
Rico Zöllner ◽  
Burkhard Kämpfer
Keyword(s):  
Quark Mass ◽  
Heavy Ion ◽  
Mass Dependence ◽  
Holographic Model ◽  
Spectral Functions ◽  
Special Aspect ◽  
Quark Masses ◽  
Ion Collisions ◽  
Heavy Quark Mass ◽  
Quark Mass Dependence

A holographic model of probe quarkonia is presented, where the dynamical gravity–dilaton background was adjusted to the thermodynamics of 2 + 1 flavor QCD with physical quark masses. The quarkonia action was modified to account for the systematic study of the heavy-quark mass dependence. We focused on the J/ψ and Υ spectral functions and related our model to heavy quarkonia formation as a special aspect of hadron phenomenology in heavy-ion collisions at LHC.

scholarly journals Diversity oriented Deep Reinforcement Learning for targeted molecule generation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tiago Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Deep Neural Networks ◽  
Chemical Space ◽  
Biological Properties ◽  
Training Process ◽  
Training Strategy ◽  
Inhibitory Power ◽  
Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

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