Miracle-dz/UniFAR-FTUs huggingface dataset:
Black hole thermodynamics in the presence of nonlinear electromagnetic fields

As the interaction between the black holes and highly energetic infalling charged matter receives quantum corrections, the basic laws of black hole mechanics have to be carefully rederived. Using the covariant phase space formalism, we generalize the first law of black hole mechanics, both"equilibrium state"and"physical process"versions, in the presence of nonlinear electrodynamics fields, defined by Lagrangians depending on both quadratic electromagnetic invariants, $F_{ab}F^{ab}$ and $F_{ab}\,{\star F}^{ab}$. Derivation of this law demands a specific treatment of the Lagrangian parameters, similar to embedding of the cosmological constant into thermodynamic context. Furthermore, we discuss the validity of energy conditions, several complementing proofs of the zeroth law of black hole electrodynamics and some aspects of the recently generalized Smarr formula, its (non-)linearity and relation to the first law.
A Structural Approach to Default Modelling with Pure Jump Processes

ABSTRACT We present a general framework for the estimation of corporate default based on a firm’s capital structure, when its assets are assumed to follow a pure jump Lévy processes; this setup provides a natural extension to usual default metrics defined in diffusion (log-normal) models, and allows to capture extreme market events such as sudden drops in asset prices, which are closely linked to default occurrence. Within this framework, we introduce several pure jump processes featuring negative jumps only and derive practical closed formulas for equity prices, which enable us to use a moment-based algorithm to calibrate the parameters from real market data and to estimate the associated default metrics. A notable feature of these models is the redistribution of credit risk towards shorter maturity: this constitutes an interesting improvement to diffusion models, which are known to underestimate short-term default probabilities. We also provide extensions to a model featuring both positive and negative jumps and discuss qualitative and quantitative features of the results. For readers convenience, practical tools for model implementation and GitHub links are also included.
Cation Vibrational Spectra of Indole and Indole-Argon van der Waals Complex. A Zero Kinetic Energy Photoelectron Study

Zero kinetic energy (ZEKE) photoelectron spectra of indole obtained via nine vibrational levels of the S1(1Lb) manifold (namely, 00, 422, 412, 291, 391421, 281, 271, 261, and 372) are reported. The adiabatic ionization energy of indole has been determined to be Ia = 62592 ± 4 cm-1 (7.7604 ± 0.0005 eV). Cation fundamental vibrational modes up to 807 cm-1 above the D0 origin have been determined on the basis of the Δv = 0 propensity. The presence of the Δv = 1 excitation for the out-of-plane modes indicates the presence of a significant vibronic interaction in the ground state cation. For the van der Waals (vdW) complex of indole with Ar, it has been found that the red shift in the S1 origin amounts to 27 cm-1 upon the complex formation, whereas the decrease in Ia is 88 cm-1 with respect to indole. The ZEKE D0−S1 O00 photoelectron band of indole−Ar shows a low-frequency progression due to a vdW bending mode whose frequency has been found to be 13 cm-1. The assignments of low-frequency fundamental vibrationa.
Emoji-Based Transfer Learning for Sentiment Tasks

Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit the emotional information encoded in emojis to enhance the performance on a variety of sentiment tasks. This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task. We analyse the efficacy of the transfer under three conditions, i.e. i) the emoji content and ii) label distribution of the target task as well as iii) the difference between monolingually and multilingually learned source tasks. We find i.a. that the transfer is most beneficial if the target task is balanced with high emoji content. Monolingually learned source tasks have the benefit of taking into account the culturally specific use of emojis and gain up to F1 +0.280 over the baseline.
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search

The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area mostly focused on variations of $Q$-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,. This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.
The Effect of Dietary Fat on Insulin Secretion and Pancreatic β-Cell Mass in 90% Pancreatectomized Diabetic Rats

The prevalence of diabetes has increased to 8% of population. Unlike type 2 diabetes in the western countries, Korean diabetic patients are nonobese and have low serum insulin levels. As the increased prevalence of diabetes and the peculiar characteristics may be related to dietary fat contents, we determined their effects on insulin resistance, insulin secretion and pancreatic β⁃cell mass in 90% pancreatectomized (Px) diabetic rats in the present study. The rats were provided with low fat diet (LF, 10 energy% fat), moderate fat diet (MF, 25 energy% fat) and high fat diet (HF, 40 energy% fat) for 6 months. HF increased body weight and epidydimal fat pads parallel with increased food intake compared to LF and MF. Fasting serum glucose and insulin levels and homeostasis model assessment of insulin resistance were higher in HF, compared to LF and MF, indicating that HF increased insulin resistance. Rats fed LF and MF diets reduced insulin resistance, but only rats fed MF improved pancreatic β⁃cell mass and insulin secretion capacity, measured by hyperglycemic clamp and in situ pancreatic perfusion. LF had low insulin secretion capacity and pancreatic β⁃cell mass, indicating the increased possibility of diabetic prevalence and progression. MF increased β⁃cell mass by stimulating β⁃cell proliferation and neogenesis and reducing β⁃cell apoptosis. In conclusion, MF is effective for the prevention of prevalence and progression of diabetes.
Differences in the spatial landscape of urban mobility: Gender and socioeconomic perspectives

Many of our routines and activities are linked to our ability to move; be it commuting to work, shopping for groceries, or meeting friends. Yet, factors that limit the individuals’ ability to fully realise their mobility needs will ultimately affect the opportunities they can have access to (e.g. cultural activities, professional interactions). One important aspect frequently overlooked in human mobility studies is how gender-centred issues can amplify other sources of mobility disadvantages (e.g. socioeconomic inequalities), unevenly affecting the pool of opportunities men and women have access to. In this work, we leverage on a combination of computational, statistical, and information-theoretical approaches to investigate the existence of systematic discrepancies in the mobility diversity (i.e. the diversity of travel destinations) of (1) men and women from different socioeconomic backgrounds, and (2) work and non-work travels. Our analysis is based on datasets containing multiple instances of large-scale, official, travel surveys carried out in three major metropolitan areas in South America: Medellín and Bogotá in Colombia, and São Paulo in Brazil. Our results indicate the presence of general discrepancies in the urban mobility diversities related to the gender and socioeconomic characteristics of the individuals. Lastly, this paper sheds new light on the possible origins of gender-level human mobility inequalities, contributing to the general understanding of disaggregated patterns in human mobility.
Annotation Cleaning for the MSR-Video to Text Dataset

—The video captioning task is to describe the video contents with natural language by the machine. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benckmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difﬁculties to video captioning models for learning. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to contents of the video clips. The cleaned dataset is publicly available.
Knowledge Infused Policy Gradients for Adaptive Pandemic Control

COVID-19 has impacted nations differently based on their policy implementations The effective policy requires taking into account public information and adaptability to new knowledge Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC) Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy In this work, we take the early steps to address these challenges using Knowledge Infused Policy Gradient (KIPG) methods Prior work on knowledge infusion does not handle soft and hard imposition of varying forms of knowledge in disease information and guidelines to necessarily comply with Furthermore, the models do not attend to non-homogeneity in feature counts, manifesting as partial observability in informing the policy Additionally, interpretable structures are extracted post-learning instead of learning an interpretable model required for APC To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner The study establishes a theory for imposing hard and soft constraints and simulates it through experiments In comparison with knowledge-intensive baselines, we show quick sample efficient adaptation to new knowledge and interpretability in the learned policy, especially in a pandemic context © 2021 Copyright for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0) CEUR Workshop Proceedings (CEUR-WS org)
Leveraging Global Parameters for Flow-based Neural Posterior Estimation

Inferring the parameters of a stochastic model based on experimental observations is central to the scientiﬁc method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. This arises in many practical situations, such as when inferring the distance and power of a radio source (is the source close and weak or far and strong? ) or when estimating the ampliﬁer gain and underlying brain activity of an electrophysiological experiment. In this work, we present a method for cracking such in-determinacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters. Our method extends recent developments in simulation-based inference (SBI) based on normalizing ﬂows to Bayesian hierarchical models. We validate quantitatively our proposal on a motivating example amenable to analytical solutions, and then apply it to invert a well known non-linear model from computational neuroscience.
Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset

Knowledge of loudspeaker responses are useful in a number of applications, where a sound system is located inside a room that alters the listening experience depending on position within the room. Acquisition of sound fields for sound sources located in reverberant rooms can be achieved through labor intensive measurements of impulse response functions covering the room, or alternatively by means of reconstruction methods which can potentially require significantly fewer measurements. This paper extends evaluations of sound field reconstruction at low frequencies by introducing a dataset with measurements from four real rooms. The ISOBEL Sound Field dataset is publicly available, and aims to bridge the gap between synthetic and real-world sound fields in rectangular rooms. Moreover, the paper advances on a recent deep learning-based method for sound field reconstruction using a very low number of microphones, and proposes an approach for modeling both magnitude and phase response in a U-Net-like neural network architecture. The complex-valued sound field reconstruction demonstrates that the estimated room transfer functions are of high enough accuracy to allow for personalized sound zones with contrast ratios comparable to ideal room transfer functions using 15 microphones below 150 Hz.
A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile Users' Reactions to the COVID-19 Pandemic Through the Lens of App Popularity

The ongoing COVID-19 pandemic has profoundly impacted people's lives around the world, including how they interact with mobile technologies. In this paper, we seek to develop an understanding of how the dynamic trajectory of a pandemic shapes mobile phone users' experiences. Through the lens of app popularity, we approach this goal from a cross-country perspective. We compile a dataset consisting of six-month daily snapshots of the most popular apps in the iOS App Store in China and the US, where the pandemic has exhibited distinct trajectories. Using this longitudinal dataset, our analysis provides detailed patterns of app ranking during the pandemic at both category and individual app levels. We reveal that app categories' rankings are correlated with the pandemic, contingent upon country-specific development trajectories. Our work offers rich insights into how the COVID-19, a typical global public health crisis, has influence people's day-to-day interaction with the Internet and mobile technologies.
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective

The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), \ie~a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial perturbation (UAP), can be generated to fool the DNN for most images. A similar misalignment phenomenon has also been observed in the deep steganography task, where a decoder network can retrieve a secret image back from a slightly perturbed cover image. We attempt explaining the success of both in a unified manner from the Fourier perspective. We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content. We also perform feature layer analysis for providing deep insight on model generalization and robustness. Additionally, we propose two new variants of universal perturbations: (1) high-pass UAP (HP-UAP) being less visible to the human eye; (2) Universal Secret Adversarial Perturbation (USAP) that simultaneously achieves attack and hiding.
Optimization Issues in KL-Constrained Approximate Policy Iteration

Many reinforcement learning algorithms can be seen as versions of approximate policy iteration (API). While standard API often performs poorly, it has been shown that learning can be stabilized by regularizing each policy update by the KL-divergence to the previous policy. Popular practical algorithms such as TRPO, MPO, and VMPO replace regularization by a constraint on KL-divergence of consecutive policies, arguing that this is easier to implement and tune. In this work, we study this implementation choice in more detail. We compare the use of KL divergence as a constraint vs. as a regularizer, and point out several optimization issues with the widely-used constrained approach. We show that the constrained algorithm is not guaranteed to converge even on simple problem instances where the constrained problem can be solved exactly, and in fact incurs linear expected regret. With approximate implementation using softmax policies, we show that regularization can improve the optimization landscape of the original objective. We demonstrate these issues empirically on several bandit and RL environments.
Potential Singularity Formation of 3D Axisymmetric Navier-Stokes Equations with Degenerate Variable Diffusion Coefficients

In this paper, we present strong numerical evidences that the 3D axisymmetric Navier–Stokes equations with degenerate variable diffusion coefficients and smooth initial data of finite energy develop a potential finite time locally self-similar singularity at the origin. An important feature of this potential singularity is that the solution develops a two-scale traveling wave that travels towards the origin. The two-scale feature is characterized by the property that the center of the traveling wave approaches to the origin at a slower rate than the rate of the collapse of the singularity. The driving mechanism for this potential singularity is due to two antisymmetric vortex dipoles that generate a strong shearing layer in both the radial and axial velocity fields, which transport the solution first towards z = 0 and then towards the symmetry axis r = 0. The initial condition is designed in such a way that it generates a positive feedback loop that enforces a strong nonlinear alignment of vortex stretching, leading to a stable locally self-similar blowup at the origin. We perform careful resolution study and asymptotic scaling analysis to provide further support of the potential finite time locally self-similar blowup.
Analysis of Interpolation based Image In-painting Approaches

Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image in-painting in the literature. Errors and noise generated on the colour and grayscale formats of some of the commonly used standard images in the literature were corrected by using Cubic, Kriging, Radial based function and High dimensional model representation approaches and the results were compared using standard image comparison criteria, namely, PSNR (peak signal-to-noise ratio), SSIM (Structural SIMilarity), Mean Square Error (MSE). According to the results obtained from the study, the absolute superiority of the methods against each other was not observed. However, Kriging and RBF interpolation give better results both for numerical data and visual evaluation for image in-painting problems with large area losses.
CLUMP-3D: the lack of non-thermal motions in galaxy cluster cores

We report the non-thermal pressure fraction (Pnt/Ptot) obtained from a three-dimensional triaxial analysis of 16 galaxy clusters in the CLASH sample using gravitational lensing (GL) data primarily from Subaru and HST, X-ray spectroscopic imaging from Chandra, and Sunyaev–Zel’dovich effect (SZE) data from Planck and Bolocam. Our results span the approximate radial range 0.015–0.4 R200m (∼35–1000 kpc). At cluster-centric radii smaller than 0.1 R200m the ensemble average Pnt/Ptot is consistent with zero with an upper limit of 9 per cent, indicating that heating from active galactic nuclei and other relevant processes does not produce significant deviations from hydrostatic equilibrium (HSE). The ensemble average Pnt/Ptot increases outside of this radius to approximately 20 per cent at 0.4 R200m, as expected from simulations, due to newly accreted material thermalizing via a series of shocks. Also in agreement with simulations, we find significant cluster-to-cluster variation in Pnt/Ptot and little difference in the ensemble average Pnt/Ptot based on dynamical state. We conclude that on average, even for diverse samples, HSE-derived masses in the very central regions of galaxy clusters require only modest corrections due to non-thermal motions.
Superresolving Herschel imaging: a proof of concept using Deep Neural Networks

Wide-field submillimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here, we present an auto-encoder with a novel loss function to overcome this problem in the submillimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500 $\mu\mathrm{m}$ COSMOS data, with the superresolving target being the JCMT SCUBA-2 450 $\mu\mathrm{m}$ observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this auto-encoder. This is quantified through the point source fluxes and positions, the completeness, and the purity.
Revealing Gravitational Collapse in the Serpens G3–G6 Molecular Cloud Using Velocity Gradients

The relative role of turbulence, magnetic fields, and self-gravity in star formation is a subject of intensive debate. We present IRAM 30 m telescope observations of the 13CO (1–0) emission in the Serpens G3–G6 molecular cloud and apply to the data a set of statistical methods. These include the probability density functions of column density and the velocity gradients technique. We combine our data with the Planck 353 GHz polarized dust emission observations and Hershel H2 column density. We suggest that the Serpens G3–G6 south clump is undergoing a gravitational collapse. Our analysis reveals that the gravitational collapse happens at volume density n ≥ 103 cm−3. We estimate the plane-of-the-sky magnetic field strength to be approximately 120 μG using the traditional Davis–Chandrasekhar–Fermi method and 100 μG using a new technique proposed in Lazarian et al (). We find that the Serpens G3–G6 south clump’s total magnetic field energy significantly surpasses kinetic energy and gravitational energy. We conclude that the gravitational collapse could be successfully triggered in a supersonic and sub-Alfvénic cloud.