Miracle-dz/UniFAR-FTUs huggingface dataset:
Some Topics in Quantum Games

This work, based on the author's MA thesis, concentrates on simultaneous move quantum games of two players. A numerical algorithm based on the method of best response functions, designed to search for pure strategy Nash equilibrium in quantum games is constructed. It is then used show that there is a critical degree of entanglement above which a pure strategy Nash Equilibrium does not exist. The behavior of the two payoffs approach the cooperative classical ones. Bayesian quantum games are defined, and it is shown that under certain conditions, there {\it is} a pure strategy Nash equilibrium in such games even when entanglement is maximal. The basic ingredients of a quantum game based on a two-players {\it three} decisions classical games are presented. This requires the definition of trits (instead of bits) and quantum trits (instead of quantum bits). It is shown that in this quantum game, there is no classical commensurability in the sense that the classical strategies are not obtained as a special case of the quantum strategies. An entanglement operator transforming a pure two qutrit state into a maximally entangled one is explicitly constructed.
THE SLOAN BRIGHT ARCS SURVEY: FOUR STRONGLY LENSED GALAXIES WITH REDSHIFT > 2

We report the discovery of four very bright, strongly lensed galaxies found via systematic searches for arcs in Sloan Digital Sky Survey Data Release 5 and 6. These were followed up with spectroscopy and imaging data from the Astrophysical Research Consortium 3.5 m telescope at Apache Point Observatory and found to have redshift z > 2.0. With isophotal magnitudes r = 19.2–20.4 and 3″ diameter magnitudes r = 20.0–20.6, these systems are some of the brightest and highest surface brightness lensed galaxies known in this redshift range. In addition to the magnitudes and redshifts, we present estimates of the Einstein radii, which range from 5. ″0 to 12. ″7, and use those to derive the enclosed masses of the lensing galaxies.
Doubly nonlinear parabolic equations for a general class of Forchheimer gas flows in porous media

This paper is focused on the generalized Forchheimer flows of compressible fluids in porous media. The gravity effect and other general nonlinear forms of the source term and boundary flux are integrated into the model. We derive a doubly nonlinear parabolic equation for the so-called pseudo-pressure, and study its initial value problem subject to a general nonlinear Robin boundary condition. The growth rates in the source term and the boundary condition are arbitrarily large. The maximum of the solution, for positive time, is estimated in terms of certain Lebesgue norms of the initial and boundary data. The gradient estimates are obtained under a theoretical condition which, indeed, is relevant to the fluid flows in applications. In dealing with the complexity and generality of the equation and boundary condition, suitable trace theorems and Sobolev’s inequalities are utilized, and a well-adapted Moser’s iteration is implemented.
Entanglement-assisted scheme for nondemolition detection of the presence of a single photon

In this paper, we propose a resources-optimal linear-optical scheme for quantum nondemolition detection of single-photon presence. By measuring the state of ancillary photons, the presence of a photon in signal mode is revealed with a success probability of 1/2 without any disturbance to its state. We also show how to tune the setup to perform quantum nondemolition measurement of the signal photon state, and we provide tradeo between the extracted information and the signal state disturbance. Moreover, the optimality of resources and methods by which to increase the success probability are discussed.
GeV Emission in the Region of HESS J1809−193 and HESS J1813−178: Is HESS J1809−193 a Proton Pevatron?

HESS J1809−193 is an unidentified TeV source discovered by the High Energy Stereoscopic System and originally classified as a pulsar wind nebula (PWN) candidate associated with the pulsar PSR J1809−1917. However, a recent study of deep radio observations and the interstellar medium near the source has found evidence for a hadronic scenario for the gamma-rays. Here, a detailed study of the GeV emission in the region using data from the Fermi-LAT is presented. The GeV emission has an extended morphology in the region of the TeV emission and the overall spectrum can be accounted for by a cosmic-ray population having a simple power-law spectrum with energies extending up to 1 PeV. However, the spectrum at tens of TeV should be observed more deeply in the future to confirm its hadronic nature, and other scenarios involving combinations of leptonic and hadronic emission from several of the known supernova remnants in the region cannot be ruled out. The nearby TeV source HESS J1813−178, thought to be a PWN, is also studied in detail at GeV energies and we find a region of significant emission that is much more extended than the TeV emission and whose spectrum is softer than expected from a PWN but similar to those seen in several star-forming regions that are believed to accelerate protons. There is marginal evidence for a GeV point source at the location of the X-ray PWN, beside the extended emission.
A study of relative velocity statistics in Lagrangian perturbation theory with PINOCCHIO

Subject of this paper is a detailed analysis of the PINpointing Orbit-Crossing Collapsed HIerarchical Object (PINOCCHIO) algorithm for studying the relative velocity statistics of merging haloes in Lagrangian perturbation theory. Given a cosmological background model, a power spectrum of fluctuations as well as a Gaussian linear density contrast field δl is generated on a cubic grid, which is then smoothed repeatedly with Gaussian filters. For each Lagrangian particle at position q and each smoothing radius R, the collapse time, the velocities and ellipsoidal truncation are computed using Lagrangian perturbation theory. The collapsed medium is then fragmented into isolated objects by an algorithm designed to mimic the accretion and merger events of hierarchical collapse. Directly after the fragmentation process the mass function, merger histories of haloes and the statistics of the relative velocities at merging are evaluated. We reimplemented the algorithm in C++, recovered the mass function and optimized the construction of halo merging histories. When compared with the output of the Millennium Simulation our results suggest that the PINOCCHIO is well suited for studying relative velocities of merging haloes and is able to reproduce the pairwise velocity distribution.
Ultracold quantum gases and lattice systems: quantum simulation of lattice gauge theories

Abelian and non‐Abelian gauge theories are of central importance in many areas of physics. In condensed matter physics, Abelian U(1) lattice gauge theories arise in the description of certain quantum spin liquids. In quantum information theory, Kitaev's toric code is a Z(2) lattice gauge theory. In particle physics, Quantum Chromodynamics (QCD), the non‐Abelian SU(3) gauge theory of the strong interactions between quarks and gluons, is non‐perturbatively regularized on a lattice. Quantum link models extend the concept of lattice gauge theories beyond the Wilson formulation, and are well suited for both digital and analog quantum simulation using ultracold atomic gases in optical lattices. Since quantum simulators do not suffer from the notorious sign problem, they open the door to studies of the real‐time evolution of strongly coupled quantum systems, which are impossible with classical simulation methods. A plethora of interesting lattice gauge theories suggests itself for quantum simulation, which should allow us to address very challenging problems, ranging from confinement and deconfinement, or chiral symmetry breaking and its restoration at finite baryon density, to color superconductivity and the real‐time evolution of heavy‐ion collisions, first in simpler model gauge theories and ultimately in QCD.
Cost‐effectiveness of surgical treatment compared to medical treatment in patients with drug‐refractory epilepsy: A systematic review

Approximately 30% of epilepsy patients develop a drug‐refractory epilepsy, that is, seizures cannot be controlled with antiepileptic drugs. Surgery has been evaluated as an effective but costly form of treatment. The aim of this systematic review is to synthesize the available evidence on the cost‐effectiveness of surgical treatment compared to medical treatment for these patients.
The black hole and central stellar population of MCG–6-30-15

We present the first near-infrared integral field spectroscopy observations of the galaxy MCG--6-30-15. The H-band data studied in this paper cover the central 500 pc of the galaxy at the best resolution (0".1) so far. The spectra of the innermost regions are dominated by broad brackett series emission lines and non-stellar continuum, under which we are able to trace the distribution and kinematics of the stars and also the [Fe II] line emission. We find that there is a counter-rotating stellar core extending out to 125 pc, which appears to be associated with the [Fe II] emission. Based on the mass-to-light ratio, and the presence of this emission line, we estimate the age of the central stellar population to be of order of 65 Myr. We show that the gas needed to fuel the black hole is, at most, only 1 per cent of that needed to form these stars. We derive independent constraints on the black hole mass using the dynamical information and determine an upper limit for the black hole mass, Mbh < 6e7 Msun, that is consistent with other estimates.
Distances to Two Galactic Supernova Remnants: G32.8-0.1 and G346.6-0.2

There are either a near kinematic distance of 5.5 kpc or a far distance of 8.8 kpc for a Galactic supernova remnant (SNR) G32.8$-$0.1 derived by using the rotation curve of the Galaxy. Here we make sure that the remnant distance is the farther one 8.8 kpc through solving a group of equations for the shell-type remnants separately at the adiabatic-phase and the radiative-phase. For SNR G346.6$-$0.2 we determine its distance also the farther one 11 kpc rather than the nearer one 5.5 kpc.
The Clemens–Griffiths method over non-closed fields

We use the Clemens-Griffiths method to construct smooth projective threefolds, over any field $k$ admitting a separable quadratic extension, that are $k$-unirational and $\bar{k}$-rational but not $k$-rational. When $k=\mathbb{R}$, we can moreover ensure that their real locus is diffeomorphic to the real locus of a smooth projective $\mathbb{R}$-rational variety and that all their unramified cohomology groups are trivial.
Nonparametric Star Formation History Reconstruction with Gaussian Processes. I. Counting Major Episodes of Star Formation

The star formation histories (SFHs) of galaxies contain imprints of the physical processes responsible for regulating star formation during galaxy growth and quenching. We improve the Dense Basis SFH reconstruction method of Iyer & Gawiser, introducing a nonparametric description of the SFH based on the lookback times at which a galaxy assembles certain quantiles of its stellar mass. The method uses Gaussian processes to create smooth SFHs independent of any functional form, with a flexible number of parameters that is adjusted to extract the maximum amount of information from the SEDs being fit. Applying the method to reconstruct the SFHs of 48,791 galaxies with H < 25 at 0.5 < z < 3.0 across the five Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields, we study the evolution of galaxies over time. We quantify the fraction of galaxies that show multiple major episodes of star formation, finding that the median time between two peaks of star formation is , where tuniv is the age of the universe at a given redshift and remains roughly constant with stellar mass. Correlating SFHs with morphology allows us to compare the timescales on which the SFHs decline for different morphological classifications, ranging from for galaxies with spiral arms to for spheroids at 0.5 < z < 1.0 with 1010 < M* < 1010.5M⊙. The Gaussian process–based SFH description provides a general approach to reconstruct smooth, flexible, nonparametric SFH posteriors for galaxies that can be incorporated into Bayesian SED fitting codes to minimize the bias in estimating physical parameters due to SFH parameterization.
Acoustic Pornography Recognition Using Convolutional Neural Networks and Bag of Refinements

A large number of pornographic audios publicly available on the Internet seriously threaten the mental and physical health of children, but these audios are rarely detected and filtered. In this paper, we firstly propose a convolutional neural networks (CNN) based model for acoustic pornography recognition. Then, we research a collection of refinements and verify their effectiveness through ablation studies. Finally, we stack all refinements together to verify whether they can further improve the accuracy of the model. Experimental results on our newly-collected large dataset consisting of 224127 pornographic audios and 274206 normal samples demonstrate the effectiveness of our proposed model and these refinements. Specifically, the proposed model achieves an accuracy of 92.46% and the accuracy is further improved to 97.19% when all refinements are combined.
Nonlinear fractional damped wave equation on compact Lie groups

In this paper, we deal with the initial value fractional damped wave equation on G, a compact Lie group, with power-type nonlinearity. The aim of this manuscript is twofold. First, using the Fourier analysis on compact Lie groups, we prove a local in-time existence result in the energy space for the fractional damped wave equation on G. Moreover, a finite time blow-up result is established under certain conditions on the initial data. In the next part of the paper, we consider fractional wave equation with lower order terms, i.e., damping and mass with the same power type nonlinearity on compact Lie groups, and prove the global in-time existence of small data solutions in the energy evolution space.
On the p-typical de Rham–Witt complex over
W(k)

Hesselholt and Madsen in [7] define and study the (absolute, p-typical) de Rham-Witt complex in mixed characteristic, where p is an odd prime. They give as an example an elementary algebraic description of the de Rham-Witt complex over Z_(p). The main goal of this paper is to construct, for k a perfect ring of characteristic p > 2, a Witt complex over A = W(k) with an algebraic description which is completely analogous to Hesselholt and Madsen's description for Z_(p). Our Witt complex is not isomorphic to the de Rham-Witt complex; instead we prove that, in each level, the de Rham-Witt complex over W(k) surjects onto our Witt complex, and that the kernel consists of all elements which are divisible by arbitrarily high powers of p. We deduce an explicit description of the de Rham-Witt complex over W(k). We also deduce results concerning the de Rham-Witt complex over certain perfectoid rings.
Path integral approach to nonequilibrium potentials in multiplicative Langevin dynamics

We present a path integral formalism to compute potentials for nonequilibrium steady states, reached by a multiplicative stochastic dynamics. We develop a weak-noise expansion, which allows the explicit evaluation of the potential in arbitrary dimensions and for any stochastic prescription. We apply this general formalism to study noise-induced phase transitions. We focus on a class of multiplicative stochastic lattice models and compute the steady-state phase diagram in terms of the noise intensity and the lattice coupling. We obtain, under appropriate conditions, an ordered phase induced by noise. By computing entropy production, we show that microscopic irreversibility is a necessary condition to develop noise-induced phase transitions. This property of the nonequilibrium stationary state has no relation with the initial stages of the dynamical evolution, in contrast with previous interpretations, based on the short-time evolution of the order parameter.
SPIN EVOLUTION OF ACCRETING YOUNG STARS. II. EFFECT OF ACCRETION-POWERED STELLAR WINDS

We present a model for the rotational evolution of a young, solar-mass star interacting magnetically with an accretion disk. As in a previous paper (Paper I), the model includes changes in the star's mass and radius as it descends the Hayashi track, a decreasing accretion rate, and a prescription for the angular momentum transfer between the star and disk. Paper I concluded that, for the relatively strong magnetic coupling expected in real systems, additional processes are necessary to explain the existence of slowly rotating pre-main-sequence stars. In the present paper, we extend the stellar spin model to include the effect of a spin-down torque that arises from an accretion-powered stellar wind (APSW). For a range of magnetic field strengths, accretion rates, initial spin rates, and mass outflow rates, the modeled stars exhibit rotation periods within the range of 1–10 days in the age range of 1–3 Myr. This range coincides with the bulk of the observed rotation periods, with the slow rotators corresponding to stars with the lowest accretion rates, strongest magnetic fields, and/or highest stellar wind mass outflow rates. We also make a direct, quantitative comparison between the APSW scenario and the two types of disk-locking models (namely, the X-wind and Ghosh & Lamb type models) and identify some remaining theoretical issues for understanding young star spins.
Quantum Computing with p-Wave Superfluid Vortices

It is shown that Majorana fermions trapped in three vortices in a p -wave superfluid implement a qubit for universal quantum computing. Several similar ideas have already been proposed: Ivanov [Phys. Rev. Lett. 86 (2001) 268] and Zhang et al. [Phys. Rev. Lett. 99 (2007) 220502] have proposed schemes in which a qubit is implemented with two and four Majorana fermions, respectively, where a qubit operation is performed by exchanging the positions of Majorana fermions. The set of gates thus obtained is a discrete subset of the relevant unitary group. We propose, in this paper, a new scheme, where three Majorana fermions form a qubit. We show that continuous 1-qubit gate operations are possible by exchanging the positions of Majorana fermions complemented with dynamical phase change. Two-qubit gates are realized through the use of the coupling between Majorana fermions belonging to different qubits.
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-Agent Deep Reinforcement Learning Approach

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue.
A Survey of Knowledge Enhanced Pre-Trained Language Models

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tuning stage, they still have some limitations such as poor reasoning ability due to the lack of external knowledge. Research has been dedicated to incorporating knowledge into PLMs to tackle these issues. In this paper, we present a comprehensive review of Knowledge Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear insight into this thriving field. We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight these two main tasks of NLP. For NLU, we divide the types of knowledge into four categories: linguistic knowledge, text knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are categorized into KG-based and retrieval-based methods. Finally, we point out some promising future directions of KE-PLMs.
A Convergence Study of SGD-Type Methods for Stochastic Optimization

In this paper, we first reinvestigate the convergence of vanilla SGD method in the sense of $L^2$ under more general learning rates conditions and a more general convex assumption, which relieves the conditions on learning rates and do not need the problem to be strongly convex. Then, by taking advantage of the Lyapunov function technique, we present the convergence of the momentum SGD and Nesterov accelerated SGD methods for the convex and non-convex problem under $L$-smooth assumption that extends the bounded gradient limitation to a certain extent. The convergence of time averaged SGD was also analyzed.
RFFNet: Scalable and interpretable kernel methods via Random Fourier Features

Kernel methods provide a ﬂexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory require-ments hinder their applicability to large datasets, many approximate solvers were recently developed for scaling up kernel methods, such as random Fourier features. However, these scalable approaches are based on approximations of isotropic kernels, which are incapable of removing the inﬂuence of possibly irrelevant features. In this work, we design random Fourier features for automatic relevance determination kernels, widely used for variable selection, and propose a new method based on joint optimization of the kernel machine parameters and the kernel relevances. Additionally, we present a new optimization algorithm that efﬁciently tackles the re-sulting objective function, which is non-convex. Numerical validation on synthetic and real-world data shows that our approach achieves low prediction error and effectively identiﬁes relevant predictors. Our solution is modular and uses the PyTorch framework.