Recent hadron experiments keep providing evidences of exotic hadrons with multi-quark components.
These multiquarks are self-arranged into various configurations such as diquarks, hadronic molecules and so on.
In this seminar, we discuss possible structures of tetra and pentaquarks with multi-flavor contents including recently observed T_cc, Pc and P_cs. Based on our recent studies in the quark model and hadron models, we discuss where and how different quark structures emerge.

Gamma-ray bursts and supernovae provide ideal environments for efficient energy channeling between different plasma species through collective processes such as collisionless shock waves. Extensively studied in astrophysical and laboratory environments, observations and kinetic simulations indicate strong electron heating in the precursor of collisionless shock waves propagating in unmagnetized electron-ion plasmas. We outline a theoretical model accounting for electron heating via a Joule-like process through the interplay between pitch-angle scattering in the microturbulence and the coherent electrostatic field induced by the difference in inertia between species. Using analytical kinetic estimates, semi-analytical Monte Carlo methods, and ab-initio Particle-In-Cell simulations, we demonstrate the validity of this model in the relativistic regime relevant to the afterglow emission of gamma-ray burst and extend it to characterize the electron-to-ion-temperature ratio in the downstream of nonrelativistic high-Mach numbers shock waves relevant for supernova remnants and laboratory experiments.

Abstract for the 1st hour: Enumerative Geometry has been a feature of mathematics from its beginnings, just think about the number of lines in the plane passing through 2 points. I will take you on a history of the subject and its relationship to other areas of mathematics and physics.

Abstract for the 2nd hour: Many problems in mathematics are solved by taking a limit and solving the limiting problem. Tropical geometry is a key technique that allows us to do this systematically. I will talk about the following problem. Take the complex projective plane S and an elliptic curve E in S. Count algebraic maps from the affine line into the complement S \ E. This counting problem is solved via tropical geometry as I will describe in this talk.

How ecological communities are assembled and maintained is a fundamental question in community ecology. To tackle this challenge in the heterogeneous world, we need to understand how community assembly patterns change with environmental gradients and how species coexist in temporally fluctuating environments. In the first of my talk, I introduce our study on how plant community assembly patterns change along with the largest global environmental gradient, the latitudinal gradient. Then, I will present how the seasonal variability of environments contributes to the coexistence of phytoplankton species in a lake. These results altogether uncover how spatiotemporal heterogeneity of environments forms ecological communities in nature.

Tricritical point (TCP) is the end-point of a line of three-phase coexistence (a triple line) at which three coexisting phases simultaneously become identical. A TCP can be observed in various systems, for example, the QCD phase diagram with the chiral limit and a metamagnet such as a FeCl2 crystal. In the AdS/CFT correspondence, a TCP associated with a chiral phase transition has been found in the D3/D7 model [1].
In this talk, I will discuss the recent study [2] of critical phenomena at a tricritical point which emerges in the D3/D7 model in the presence of a finite baryon number density and an external magnetic field. We found all the critical exponents defined in this paper take the mean-field values. I will also compare the results with our previous works about the critical phenomena at the TCP that emerges in the steady state [3,4].

The continuous space Schrödinger equation is reformulated in terms of spin Hamiltonians. For the kinetic energy operator, the critical concept facilitating the reduction in model complexity is the idea of position encoding. A binary encoding of position produces a spin-1/2 Heisenberg-like model and yields exponential improvement in space complexity when compared to classical computing. Encoding with a binary reflected Gray code (BRGC), and a Hamming distance 2 Gray code (H2GC) reduces the model complexity down to the XZ and transverse Ising model respectively. Any real potential is mapped to a series of k-local Ising models through the fast Walsh transform. As a first step, the encoded Hamiltonian is simulated for quantum adiabatic evolution. As a second step, the time evolution is discretized, resulting in a quantum circuit with a gate cost that is better than the Quantum Fourier transform. Finally, a simple application on an ion-based quantum computer is provided as proof of concept.

Dynamics of leptons such as electrons and neutrinos play an important role in the evolution of core-collapse supernovae (CCSN). Nevertheless, chirality as one of fundamental microscopic properties that could affect lepton transport, through e.g. weak interaction, has been mostly overlooked. In this talk, I will discuss how chiral effects such as the renowned chiral magnetic effect (CME), generating an electric charge current along magnetic fields with chirality imbalance, could result in the unstable modes of magnetic fields and inverse cascade, which potentially influence the matter evolution in CCSN and pulsar kicks. I will also show how an effective CME could be realized via the backreaction from neutrino radiation even in the absence of an axial charge characterizing an unequal number of right- and left-handed electrons.

People's incentives during an infectious disease outbreak influence their behaviour, and this behaviour can impact how the outbreak unfolds. Early on during an outbreak, people are at little personal risk of infection and hence may be unwilling to change their lifestyle to slow the spread of disease. As the number of cases grows, however, people may then voluntarily take extreme measures to limit their exposure. Political leaders also respond to the welfare and changing desires of their constituents, through public health policies that themselves shape the course of the epidemic and its ultimate health and economic repercussions. In this talk I will use ideas from the study of differential games to model how individuals’ and politicians’ incentives change during an outbreak, and the epidemiological and economic consequences that ensue when these incentives are acted upon. Motivated by the COVID-19 pandemic, I focus on physical distancing behaviour and the imposition of stay-at-home orders by politicians. I show that there is a fundamental difference in the political, economic, and health consequences of an infectious disease outbreak depending on the degree of asymptomatic transmission. If transmission occurs primarily by asymptomatic carriers, then politicians will be incentivized to impose stay-at-home orders earlier and for longer than individuals would like. Despite such orders being unpopular, however, they ultimately benefit all individuals. On the other hand, if the disease is transmitted primarily by symptomatic infections, then individuals are incentivized to stay at home earlier and for longer than politicians would like. In this case, politicians will be incentivized to impose back-to-work orders that, despite being unpopular, will again ultimately be to the benefit of all individuals.

This is joint work with David McAdams, Fuqua School of Business and Economics Department, Duke University.

Lattice gauge theory is a powerful computational approach in quantum field theory. It is also utilizable for investigating quantum phenomena in curved spacetimes, such as rotating frame, torsion, and gravitational backgrounds. In this talk, I would like to overview the formulation and results of lattice simulations in curved spacetimes.

iTHEMS-AIP Joint Colloquium

Optimal transport (OT) has recently gained a lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book "Computational Optimal Transport" (see related link below).