Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-equilibrium evolution of Bose-Einstein condensate deformation in temporally controlled weak disorder

We consider a time-dependent extension of a perturbative mean-field approach to the dirty boson problem by considering how switching on and off a weak disorder potential affects the stationary state of an initially homogeneous Bose-Einstein condensate by the emergence of a disorder-induced condensate deformation. We find that in the switch on scenario the stationary condensate deformation turns out to be a sum of an equilibrium part and a dynamically-induced part, where the latter depends on the particular driving protocol. If the disorder is switched off afterwards, the resulting condensate deformation acquires an additional dynamically-induced part in the long-time limit, while the equilibrium part vanishes. Our results demonstrate that the condensate deformation represents an indicator of the generically non-equilibrium nature of steady states of a Bose gas in a temporally controlled weak disorder.
QR and LQ Decomposition Matrix Backpropagation Algorithms for Square, Wide, and Deep Matrices and Their Software Implementation

This article presents matrix backpropagation algorithms for the QR decomposition of matrices $A_{m, n}$, that are either square (m = n), wide (m n), with rank $k = min(m, n)$. Furthermore, we derive novel matrix backpropagation results for the pivoted (full-rank) QR decomposition and for the LQ decomposition of deep input matrices. Differentiable QR decomposition offers a numerically stable, computationally efficient method to solve least squares problems frequently encountered in machine learning and computer vision. Software implementation across popular deep learning frameworks (PyTorch, TensorFlow, MXNet) incorporate the methods for general use within the deep learning community. Furthermore, this article aids the practitioner in understanding the matrix backpropagation methodology as part of larger computational graphs, and hopefully, leads to new lines of research.
The Moran process on 2-chromatic graphs

Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring.
Investigation of Doubly Heavy Tetraquark Systems using Lattice QCD

We search for possibly existent bound states in the heavy-light tetraquark channels with quark content $ \bar{b}\bar{b}ud $, $ \bar{b}\bar{b}us $ and $ \bar{b}\bar{c}ud $ using lattice QCD. We carry out calculations on several gauge link ensembles with $ N_f=2+1 $ flavours of domain-wall fermions and consider a basis of local and non-local interpolators. Besides extracting the energy spectrum from the correlation matrices, we also perform a Luscher analysis to extrapolate our results to infinite volume.
Computational techniques for parameter estimation of gravitational wave signals

Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to extract the signal out of noisy interferometric measurements, obtain point estimates of the physical parameters responsible for producing the signal, and rigorously quantify their uncertainties. Different computational techniques have been devised depending on the source of the gravitational radiation and the gravitational waveform model used. Prominent sources of gravitational waves are binary black hole or neutron star mergers, the only objects that have been observed by detectors to date. But also gravitational waves from core‐collapse supernovae, rapidly rotating neutron stars, and the stochastic gravitational‐wave background are in the sensitivity band of the ground‐based interferometers and expected to be observable in future observation runs. As nonlinearities of the complex waveforms and the high‐dimensional parameter spaces preclude analytic evaluation of the posterior distribution, posterior inference for all these sources relies on computer‐intensive simulation techniques such as Markov chain Monte Carlo methods. A review of state‐of‐the‐art Bayesian statistical parameter estimation methods will be given for researchers in this cross‐disciplinary area of gravitational wave data analysis.
Context-theoretic Semantics for Natural Language: an Algebraic Framework

Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts it occurs in in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings. Together these properties form guidelines as to how to construct semantic representations within the framework. A context theory is an implementation of the framework; in an implementation strings are represented as vectors with the properties deduced from the theoretical analysis. We show how to incorporate logical semantics into context theories; this enables us to represent statistical information about uncertainty by taking weighted sums of individual representations. We also use the framework to analyse approaches to the task of recognising textual entailment, to ontological representations of meaning and to representing syntactic structure. For the latter, we give new algebraic descriptions of link grammar.
A New Parameterization of the Star Formation Rate Dense Gas Mass Relation: Embracing Gas Density Gradients

It is well established that a gas density gradient inside molecular clouds and clumps raises their star formation rate (SFR) compared with what they would experience from a gas reservoir of uniform density. This effect should be observed in the relation between dense-gas mass M dg and SFR of molecular clouds and clumps, with steeper gas density gradients yielding higher SFR/M dg ratios. The content of this paper is twofold. First, we build on the notion of a magnification factor introduced by Parmentier to redefine the dense-gas relation (i.e., the relation between M dg and SFR). Not only does the SFR/M dg ratio depend on the mean freefall time of the gas and on its (intrinsic) star formation efficiency per freefall time, but it also depends on the clump density profile (e.g., the logarithmic slope of the gas density profile and on the relative extent of the constant-density region at the clump center). Second, we show that nearby molecular clouds follow the newly defined dense-gas relation, provided that their dense-gas mass is defined based on a volume density criterion. We also find the same trend for the dense molecular clouds of the Central Molecular Zone (CMZ) of the Galaxy, although this one is scaled down by a factor of 10 compared with nearby clouds, even when including the Sgr C and Sgr B2 clouds. The respective locii of both nearby and CMZ clouds in the parameter space is discussed.
SUMBT+LaRL: End-to-end Neural Task-oriented Dialog System with Reinforcement Learning

The recent advent of neural approaches for developing each dialog component in task-oriented dialog systems has remarkably improved, yet optimizing the overall system performance remains a challenge. In this paper, we propose an end-to-end trainable neural dialog system with reinforcement learning, named SUMBT+LaRL. The SUMBT+ estimates user-acts as well as dialog belief states, and the LaRL models latent system action spaces and generates responses given the estimated contexts. We experimentally demonstrate that the training framework in which the SUMBT+ and LaRL are separately pretrained and then the entire system is fine-tuned significantly increases dialog success rates. We propose new success criteria for reinforcement learning to the end-to-end dialog system as well as provide experimental analysis on a different result aspect depending on the success criteria and evaluation methods. Consequently, our model achieved the new state-of-the-art success rate of 85.4% on corpus-based evaluation, and a comparable success rate of 81.40% on simulator-based evaluation provided by the DSTC8 challenge.
Modeling of Vertical Dipole Above Lossy Dielectric Half-Space: Characteristic Mode Theory

This work introduces a theoretical extension of the characteristic mode formulation for analysing the vertical electric dipole lying above a lossy dielectric half-space. As the conventional characteristic formulation fails to maintain the orthogonality of the characteristic field modes over the infinite sphere, an alternate modal formulation is proposed here to maintain the orthogonality for both the current and field modes. The modal results are found to match closely with its method of moment counterparts. Later, the modes of an isolated dipole with no ground plane have been used to predict the role of the lossy ground plane through a theory of the linear combination of the eigenvectors. The proposed formulations have been studied with different heights from the ground plane and are compared with the direct modal solutions to validate its accuracy. It helps to provide a thorough understanding of how the isolated modes interact among each other to constitute the perturbed modes in the presence of the lossy half-space. It can find application to include the lossy earth effect in the study of the lightning fields and the path loss modelling of the antennas over the lossy ground.
Simulating the dynamics and non-thermal emission of relativistic magnetized jets I. Dynamics

We have performed magnetohydrodynamic (MHD) simulations of relativistic jets from supermassive blackholes over a few tens of kpc for a range of jet parameters. One of the primary aims was to investigate the effect of different MHD instabilities on the jet dynamics and their dependence on the choice of jet parameters. We find that two dominant MHD instabilities affect the dynamics of the jet, small-scale Kelvin–Helmholtz (KH) modes and large-scale kink modes, whose evolution depends on internal jet parameters like the Lorentz factor, the ratio of the density and pressure to the external medium, and the magnetization and hence consequently on the jet power. Low power jets are susceptible to both instabilities, kink modes for jets with higher central magnetic field and KH modes for lower magnetization. Moderate power jets do not show appreciable growth of kink modes, but KH modes develop for lower magnetization. Higher power jets are generally stable to both instabilities. Such instabilities decelerate and decollimate the jet while inducing turbulence in the cocoon, with consequences on the magnetic field structure. We model the dynamics of the jets following a generalized treatment of the Begelman–Cioffi relations, which we present here. We find that the dynamics of stable jets match well with simplified analytic models of expansion of non-self-similar FRII jets, whereas jets with prominent MHD instabilities show a nearly self-similar evolution of the morphology as the energy is more evenly distributed between the jet head and the cocoon.
What Do You See?: Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors

EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network predicts inputs through model saliency explanations that highlight the input parts deemed important to arrive at a decision for a specific target. However, it remains challenging to quantify the correctness of their interpretability as current evaluation approaches either require subjective input from humans or incur high computation cost with automated evaluation. In this paper, we propose backdoor trigger patterns--hidden malicious functionalities that cause misclassification--to automate the evaluation of saliency explanations. Our key observation is that triggers provide ground truth for inputs to evaluate whether the regions identified by an XAI method are truly relevant to its output. Since backdoor triggers are the most important features that cause deliberate misclassification, a robust XAI method should reveal their presence at inference time. We introduce three complementary metrics for the systematic evaluation of explanations that an XAI method generates. We evaluate seven state-of-the-art model-free and model-specific post-hoc methods through 36 models trojaned with specifically crafted triggers using color, shape, texture, location, and size. We found six methods that use local explanation and feature relevance fail to completely highlight trigger regions, and only a model-free approach can uncover the entire trigger region. We made our code available at https://github.com/yslin013/evalxai.
Limit shapes of large skew Young tableaux and a modification of the TASEP process

We present a survey of points of view on the problem of the asymptotic shape of a path between two large Young diagrams, and introduce a modification of the TASEP process related to it. This representation allows to write explicitly the functional, counting the asymptotics of the number of Young tableau close to a given one, as well as to see the sine-process on the boundary shape of a large random Young diagram.
Scrambling and Lyapunov Exponent in Unitary Networks with Tunable Interactions

Anna Keselman, 2 Laimei Nie, 4 and Erez Berg Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106-4030 Microsoft Station Q, Santa Barbara, California 93106-6105, USA Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA Department of Physics and Institute for Condensed Matter Theory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot 76100, Israel (Dated: September 23, 2020)
New combinations in Australasian Zostera (Zosteraceae)

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Fast Navigation in a Large Hilbert Space Using Quantum Optimal Control

Arthur Larrouy, Sabrina Patsch , Rémi Richaud, Jean-Michel Raimond , Michel Brune , Christiane P. Koch , and Sébastien Gleyzes 1,* Laboratoire Kastler Brossel, Collège de France, CNRS, ENS-Université PSL, Sorbonne Université, 11, place Marcelin Berthelot, 75005 Paris, France Theoretische Physik, Universität Kassel, Heinrich-Plett-Straße 40, 34132 Kassel, Germany Dahlem Center for Complex Quantum Systems and Fachbereich Physik, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany
Effect of Ficus sycomorus L. Leaves on High Fat Diet-Fed Rats: Possible Mechanisms behind the Prevention of Obesity and Its Related Disorders

The main target of this study was to investigate the protective effect of Ficus sycomorus L. leaves (FSLs) against high fat diet (HFD)-induced obesity and the possible mechanisms behind this effect in experimental rats. 30 male albino rats (Sprague Dawley strain) weighing 98.61 ± 5.87 g were used and randomly divided into five equal groups including the control besides HFD, HFD + 2.5% FSLs, HFD + 5% FSLs and HFD + 7.5% FSLsfed groups. High fat diet was prepared using 20% fat (19% sheep tallow + 1% soybean oil), while basal diet contained 4% soybean oil. The experiment lasted for 6 weeks. Feed intake, body weight gain and liver weight/ body weight (%) were finally calculated. Leptin, pancreatic lipase, glucose, lipid profiles, liver enzymes, urea and creatinine were also determined in serum. Moreover, livers of sacrificed rats were histopathologically examined. Results indicated that feeding on high fat diet resulted in a significant increase in body weight, relative liver weight and the serum concentrations of leptin, pancreatic lipase, glucose, triglycerides, total cholesterol, LDL-C, VLDL-C, transaminases, alkaline phosphatase, urea and creatinine, while daily feed intake and serum HDL-C were significantly declined. Addition of dried FSLs to high fat diet led to a significant decrease in body weight gain, relative liver weight and the serum levels of leptin and pancreatic lipase. They also showed hypoglycemic and hypolipidemic effects, improved liver and kidney functions and reversed the marked lesion noticed in liver tissue. In general, the more the dose used, the better the effect noticed. Accordingly, it was concluded that Ficus sycomorus L. leaves could hinder weight gain and prevent its related disorders. So, dietary supplementation with Ficus sycomorus L. leaves should be implicated in the antiobesity strategies.
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't

The purpose of this article is to review the achievements made in the last few years towards the understanding of the reasons behind the success and subtleties of neural network-based machine learning. In the tradition of good old applied mathematics, we will not only give attention to rigorous mathematical results, but also the insight we have gained from careful numerical experiments as well as the analysis of simplified models. Along the way, we also list the open problems which we believe to be the most important topics for further study. This is not a complete overview over this quickly moving field, but we hope to provide a perspective which may be helpful especially to new researchers in the area.
On the distribution of lattice points on hyperbolic circles

We study the fine distribution of lattice points lying on expanding circles in the hyperbolic plane $\mathbb{H}$. The angles of lattice points arising from the orbit of the modular group $PSL_{2}(\mathbb{Z})$, and lying on hyperbolic circles, are shown to be equidistributed for generic radii. However, the angles fail to equidistribute on a thin set of exceptional radii, even in the presence of growing multiplicity. Surprisingly, the distribution of angles on hyperbolic circles turns out to be related to the angular distribution of $\mathbb{Z}^2$-lattice points (with certain parity conditions) lying on circles in $\mathbb{R}^2$, along a thin subsequence of radii. A notable difference is that measures in the hyperbolic setting can break symmetry - on very thin subsequences they are not invariant under rotation by $\frac{\pi}{2}$, unlike the Euclidean setting where all measures have this invariance property.
Dynamics Evolution of a Solar Active-region Filament from a Quasi-static State to Eruption: Rolling Motion, Untwisting Motion, Material Transfer, and Chirality

To better understand the magnetic structure and eruptive process of solar filaments, a solar active-region filament (labeled F2) eruption associated with a B-class flare was investigated by using high-resolution Hα data from the 1 m New Vacuum Solar Telescope (NVST), combined with EUV observations of the Solar Dynamics Observatory (SDO). The filament F2 was disturbed by another filament (labeled F1) eruption that experienced a whip-like motion. Before the filament F2 eruption, the Dopplergrams show that the southern and the northern parts of the filament F2 body exhibit blueshift and redshift along the filament spine, simultaneously. This implies that the filament F2 was rolling from one side to the other. During the filament F2 eruption, the Doppler shifts of the filament body are opposite of those before its eruption. This demonstrates that the filament body exhibits an untwisting motion, which can also be identified by tracing the movement of the eruptive filament threads. Moreover, it is found that the material of the filament F2 was transferred to the surrounding magnetic field loops, which is caused by magnetic reconnection between the filament F2 and the surrounding magnetic loops. According to the right-bearing threads of the filament F2 before its eruption, it can be deduced that the filament F2 is initially supported by a sheared arcade. The following observations reveal that the twisted magnetic structure of filament F2 formed in the eruption phase.
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search

Although BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models, it seems that no consensus can be reached on what is the optimal architecture, since there are many design choices available. In this work, we design a comprehensive search space for BERT based RC models and employ a modified version of efficient neural architecture search (ENAS) method to automatically discover the design choices mentioned above. Experiments on eight benchmark RC tasks show that our method is efficient and effective in finding better architectures than the baseline BERT based RC models. Ablation study demonstrates the necessity of our search space design and the effectiveness of our search method. We also show that our framework can also apply to other entity related tasks like coreference resolution and span based named entity recognition (NER).
Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective

User engagement with data privacy and security through consent banners has become a ubiquitous part of interacting with internet services. While previous work has addressed consent banners from either interaction design, legal, and ethics-focused perspectives, little research addresses the connections among multiple disciplinary approaches, including tensions and opportunities that transcend disciplinary boundaries. In this paper, we draw together perspectives and commentary from HCI, design, privacy and data protection, and legal research communities, using the language and strategies of “dark patterns” to perform an interaction criticism reading of three different types of consent banners. Our analysis builds upon designer, interface, user, and social context lenses to raise tensions and synergies that arise together in complex, contingent, and conflicting ways in the act of designing consent banners. We conclude with opportunities for transdisciplinary dialogue across legal, ethical, computer science, and interactive systems scholarship to translate matters of ethical concern into public policy.