Miracle-dz/UniFAR-FTUs huggingface dataset:
First Cospatial Comparison of Stellar, Neutral-gas, and Ionized-gas Metallicities in a Metal-rich Galaxy: M83

We carry out a comparative analysis of the metallicities from the stellar, neutral-gas, and ionized-gas components in the metal-rich spiral galaxy M83. We analyze spectroscopic observations taken with the Hubble Space Telescope, the Large Binocular Telescope, and the Very Large Telescope. We detect a clear depletion of the H i gas, as observed from the H i column densities in the nuclear region of this spiral galaxy. We find column densities of log[N(H i) cm−2] < 20.0 at galactocentric distances of <0.18 kpc, in contrast to column densities of log[N(H i) cm−2] ∼ 21.0 in the galactic disk, a trend observed in other nearby spiral galaxies. We measure a metallicity gradient of −0.03 ± 0.01 dex kpc−1 for the ionized gas, comparable to the metallicity gradient of a local benchmark of 49 nearby star-forming galaxies of −0.026 ± 0.002 dex kpc−1. Our cospatial metallicity comparison of the multiphase gas and stellar populations shows excellent agreement outside of the nucleus of the galaxy, hinting at a scenario where the mixing of newly synthesized metals from the most massive stars in the star clusters takes longer than their lifetimes (∼10 Myr). Finally, our work shows that caution must be taken when studying the metallicity gradient of the neutral-gas component in star-forming galaxies, since this can be strongly biased, as these environments can be dominated by molecular gas. In these regions the typical metallicity tracers can provide inaccurate abundances, as they may trace both the neutral- and molecular-gas components.
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains

Robot gait optimization is the task of generating an optimal control trajectory under various internal and external constraints. Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments. Existing literatures often regard the gait generation as an optimization problem and solve the gait optimization from scratch for robots walking in a specific environment. However, such approaches do not consider the use of pre-acquired knowledge which can be useful in improving the quality and speed of motion generation in complex environments. To address the issue, this paper proposes a transfer learning-based evolutionary framework for multi-objective gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The advantage is that the generated gait can not only dynamically adapt to different environments and tasks, but also simultaneously satisfy multiple design specifications (e.g., speed, stability). The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and MOPSO. When transferring the pre-acquired knowledge from the plain terrain to various inclined and rugged ones, the proposed Tr-GO framework accelerates the evolution process by a minimum of 3-4 times compared with non-transferred scenarios.
Geometrically irreducible p-adic local systems are de Rham up to a twist

We prove that any geometrically irreducible $\overline{\mathbb{Q}}_p$-local system on a smooth algebraic variety over a $p$-adic field $K$ becomes de Rham after a twist by a character of the Galois group of $K$. In particular, for any geometrically irreducible $\overline{\mathbb{Q}}_p$-local system on a smooth variety over a number field the associated projective representation of the fundamental group automatically satisfies the assumptions of the relative Fontaine-Mazur conjecture. The proof uses $p$-adic Simpson and Riemann-Hilbert correspondences of Diao-Lan-Liu-Zhu and the Sen operator on the decompletions of those developed by Shimizu. Along the way, we observe that a $p$-adic local system on a smooth geometrically connected algebraic variety over $K$ is Hodge-Tate if its stalk at one closed point is a Hodge-Tate Galois representation. Moreover, we prove a version of the main theorem for local systems with arbitrary geometric monodromy, which allows us to conclude that the Galois action on the pro-algebraic completion of the fundamental group is de Rham.
Generalised Airy polynomials

We consider properties of semi-classical orthogonal polynomials with respect to the generalised Airy weight ω(x;t,λ)=xλexp−13x3+tx,x∈R+ with parameters λ > −1 and t∈R . We also investigate the zeros and recurrence coefficients of the polynomials. The generalised sextic Freud weight ω(x;t,λ)=|x|2λ+1exp−x6+tx2,x∈R arises from a symmetrisation of the generalised Airy weight and we study analogous properties of the polynomials orthogonal with respect to this weight.
Experimental generation of axisymmetric internal wave super-harmonics

In this paper, we present an experimental study of weakly non-linear interaction of axisymmetric internal gravity waves in a resonant cavity, supported by theoretical considerations. Contrary to plane waves in Cartesian coordinates, for which self-interacting terms are null in a linear stratifiation, the non-linear self-interaction of an internal wave mode in axisymmetric geometry is found to be efficient at producing super-harmonics, i.e. waves whose frequencies are integer multiples of the excitation frequency. Due to the range of frequencies tested in our experiments, the first harmonic frequency is below the cut-off imposed by the stratification so the lowest harmonic created can always propagate. The study shows that the super-harmonic wave field is a sum of standing waves satisfying both the dispersion relation for internal waves and the boundary conditions imposed by the cavity walls, while conserving the axisymmetry.
ALUMINUM AND SILICA SOLUBILITY IN SOILS

The applicability of equilibrium relationships to the mobility of Al and Si in open systems (soil columns) was examined in nine soils representing six soil orders. Gibbsite, halloysite, kaolinite, and montmorillonite appeared to control the solubility of Al and Si. X-ray diffraction measurements confirmed their presence in the soils. A model for describing soluble Al as a series of Al(OH)3−zz (z = 0–5) monomers was evaluated. The monomer model was in agreement with the electrophorectic properties of aqueous Al and the solubility of solid phase Al.
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints

The multi-armed bandits' framework is the most common platform to study strategies for sequential decision-making problems. Recently, the notion of fairness has attracted a lot of attention in the machine learning community. One can impose the fairness condition that at any given point of time, even during the learning phase, a poorly performing candidate should not be preferred over a better candidate. This fairness constraint is known to be one of the most stringent and has been studied in the stochastic multi-armed bandits' framework in a stationary setting for which regret bounds have been established. The main aim of this paper is to study this problem in a non-stationary setting. We present a new algorithm called Fair Upper Confidence Bound with Exploration Fair-UCBe algorithm for solving a slowly varying stochastic $k$-armed bandit problem. With this we present two results: (i) Fair-UCBe indeed satisfies the above mentioned fairness condition, and (ii) it achieves a regret bound of $O\left(k^{\frac{3}{2}} T^{1 - \frac{\alpha}{2}} \sqrt{\log T}\right)$, for some suitable $\alpha \in (0, 1)$, where $T$ is the time horizon. This is the first fair algorithm with a sublinear regret bound applicable to non-stationary bandits to the best of our knowledge. We show that the performance of our algorithm in the non-stationary case approaches that of its stationary counterpart as the variation in the environment tends to zero.
The stability of the b-family of peakon equations

In the present work we revisit the b-family model of peakon equations, containing as special cases the b = 2 (Camassa–Holm) and b = 3 (Degasperis–Procesi) integrable examples. We establish information about the point spectrum of the peakon solutions and notably find that for suitably smooth perturbations there exists point spectrum in the right half plane rendering the peakons unstable for b < 1. We explore numerically these ideas in the realm of fixed-point iterations, spectral stability analysis and time-stepping of the model for the different parameter regimes. In particular, we identify exact, stationary (spectrally stable) lefton solutions for b<−1 , and for −1<b<1 , we dynamically identify ramp-cliff solutions as dominant states in this regime. We complement our analysis by examining the breakup of smooth initial data into stable peakons for b > 1. While many of the above dynamical features had been explored in earlier studies, in the present work, we supplement them, wherever possible, with spectral stability computations.
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling

To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We show that: (i) our newly collected dataset is notably more effective at providing supervision for the dialogue contradiction detection task than existing NLI data including those aimed to cover the dialogue domain; (ii) Transformer models that explicitly hinge on utterance structures for dialogue contradiction detection are more robust and generalize well on both analysis and out-of-distribution dialogues than standard (unstructured) Transformers. We also show that our best contradiction detection model correlates well with human judgments and further provide evidence for its usage in both automatically evaluating and improving the consistency of state-of-the-art generative chatbots.
Chiral properties of the zero-field spiral state and field-induced magnetic phases of the itinerant kagome metal 
YMn6Sn6

Applying a magnetic field in the hexagonal plane of $\mathrm{Y}{\mathrm{Mn}}_{6}{\mathrm{Sn}}_{6}$ leads to a complex magnetic phase diagram of commensurate and incommensurate phases, one of which coexists with the topological Hall effect (THE) generated by a unique fluctuation-driven mechanism. Using unpolarized neutron diffraction, we report on the solved magnetic structure for two previously identified, but unknown, commensurate phases. These include a low-temperature, high-field fanlike phase and a room-temperature, low-field canted antiferromagnetic phase. An intermediate incommensurate phase between the fanlike and forced ferromagnetic phases is also identified as the last known phase of the in-plane field-temperature diagram. Additional characterization using synchrotron powder diffraction reveals extremely high-quality, single-phase crystals, which suggests that the presence of two incommensurate magnetic structures throughout much of the phase diagram is an intrinsic property of the system. Interestingly, polarized neutron diffraction shows that the centrosymmetric system hosts preferential chirality in the zero-field double-flat-spiral phase, which, along with the THE, is a topologically nontrivial characteristic.
Weak Convergence of a Collection of Random Functions Defined by the Eigenvectors of Large Dimensional Random Matrices

For each n, let Un be Haar distributed on the group of n × n unitary matrices. Let xn,1, . ,xn,m denote orthogonal nonrandom unit vectors in C n and let un,k = (uk, . . . , u n k) ∗ = U∗ nxn,k, k = 1, . , m. Define the following functions on [0,1]: X k,k n (t) = √ n ∑[nt] i=1(|uk|− 1 n ),X ′ n (t) = √ 2n ∑[nt] i=1 ū i ku i k′ , k < k ′. Then it is proven thatX n ,RXk,k ′ n , IXk,k′ n , considered as random processes in D[0, 1], converge weakly, as n → ∞, to m independent copies of Brownian bridge. The same result holds for the m(m + 1)/2 processes in the real case, where On is real orthogonal Haar distributed and xn,i ∈ R, with √ n in X n and √ 2n in X ′ n replaced with √ n 2 and √ n, respectively. This latter result will be shown to hold for the matrix of eigenvectors of Mn = (1/s)VnV T n where Vn is n × s consisting of the entries of {vij}, i, j = 1, 2, . , i.i. d. standardized and symmetrically distributed, with each xn,i = {±1/ √ n, . ,±1/√n}, and n/s→ y > 0 as n→ ∞. This result extends the result in J.W. Silverstein Ann. Probab. 18 1174-1194. These results are applied to the detection problem in sampling random vectors mostly made of noise and detecting whether the sample includes a nonrandom vector. The matrix Bn = θvnv ∗ n + Sn is studied where Sn is Hermitian or symmetric and nonnegative definite with either its matrix of eigenvectors being Haar distributed, or Sn = Mn, θ > 0 nonrandom, and vn is a nonrandom unit vector. Results are derived on the distributional behavior of the inner product of vectors orthogonal to vn with the eigenvector associated with the largest eigenvalue of Bn.
Ranks of Tensor Networks for Eigenspace Projections and the Curse of Dimensionality

The hierarchical (multi-linear) rank of an order-$d$ tensor is key in determining the cost of representing a tensor as a (tree) Tensor Network (TN). In general, it is known that, for a fixed accuracy, a tensor with random entries cannot be expected to be efficiently approximable without the curse of dimensionality, i.e., a complexity growing exponentially with $d$. In this work, we show that the ground state projection (GSP) of a class of unbounded Hamiltonians can be approximately represented as an operator of low effective dimensionality that is independent of the (high) dimension $d$ of the GSP. This allows to approximate the GSP without the curse of dimensionality.
AudioViewer: Learning to Visualize Sound

Sensory substitution can help persons with perceptual deficits. In this work, we attempt to visualize audio with video. Our long-term goal is to create sound perception for hearing impaired people, for instance, to facilitate feedback for training deaf speech. Different from existing models that translate between speech and text or text and images, we target an immediate and low-level translation that applies to generic environment sounds and human speech without delay. No canonical mapping is known for this artificial translation task. Our design is to translate from audio to video by compressing both into a common latent space with shared structure. Our core contribution is the development and evaluation of learned mappings that respect human perception limits and maximize user comfort by enforcing priors and combining strategies from unpaired image translation and disentanglement. We demonstrate qualitatively and quantitatively that our AudioViewer model maintains important audio features in the generated video and that generated videos of faces and numbers are well suited for visualizing high-dimensional audio features since they can easily be parsed by humans to match and distinguish between sounds, words, and speakers.
Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning

In this work, we study the protection that fuzzy commitments offer when they are applied to facial images, processed by the state of the art deep learning facial recognition systems. We show that while these systems are capable of producing great accuracy, they produce templates of too little entropy. As a result, we present a reconstruction attack that takes a protected template, and reconstructs a facial image. The reconstructed facial images greatly resemble the original ones. In the simplest attack scenario, more than 78% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FAR). Even in the"hardest"settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better under-standing of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website -taldatech.github. io/soft-intro-vae-web.
Physics and astrophysics of ultra-high energy cosmic rays: recent results from the Pierre Auger Observatory

Ultra-high-energy cosmic rays (UHECRs) are the highest energy messengers in the universe, with energies up to 10 eV. Studies of astrophysical particles (nuclei, electrons, neutrinos and photons) at their highest observed energies have implications for fundamental physics as well as astrophysics. The primary particles interact in the atmosphere (or in the Earth) and generate extensive air showers. Analysis of those showers enables one not only to estimate the energy, direction and most probable mass of the primary cosmic particles, but also to obtain information about the properties of their hadronic interactions at energies more than one order of magnitude above that accessible with the current highest energy human-made accelerator. The Pierre Auger Observatory, located in the province of Mendoza, Argentina, is the largest cosmic ray experiment ever built. The observatory was designed as a hybrid detector covering an area of 3000 km and has been taking data for almost twenty years. In this paper, a selection of the latest results is presented: the cosmic ray energy spectrum, studies of hadronic physics, searches for a directional anisotropy and studies of mass composition (including the photon and neutrino searches). Finally, the current upgrade (“AugerPrime”) of the observatory, which is mostly aimed at improving the sensitivity to the particle type and mass of ultra-high energy cosmic rays, is described.
Semi-annual, annual and Universal Time variations in the magnetosphere and in geomagnetic activity: 4. Polar Cap motions and origins of the Universal Time effect

We use theam,an, asand theaσgeomagnetic indices to the explore a previously overlooked factor in magnetospheric electrodynamics, namely the inductive effect of diurnal motions of the Earth’s magnetic poles toward and away from the Sun caused by Earth’s rotation. Because the offset of the (eccentric dipole) geomagnetic pole from the rotational axis is roughly twice as large in the southern hemisphere compared to the northern, the effects there are predicted to be roughly twice the amplitude of those in the northern hemisphere. Hemispheric differences have previously been discussed in terms of polar ionospheric conductivities generated by solar photoionization, effects which we allow for by looking at the dipole tilt effect on the time-of-year variations of the indices. The electric field induced in a geocentric frame is shown to also be a significant factor and gives a modulation of the voltage applied by the solar wind flow in the southern hemisphere that is typically a ±30% diurnal modulation for disturbed intervals rising to ±76% in quiet times. For the northern hemisphere these are 15% and 38% modulations. Motion away from/towards the Sun reduces/enhances the directly-driven ionospheric voltages and reduces/enhances the magnetic energy stored in the tail and we estimate that approximately 10% of the effect appears in directly driven ionospheric voltages and 90% in changes of the rate of energy storage or release in the near-Earth tail. The hemispheric asymmetry in the geomagnetic pole offsets from the rotational axis is shown to be the dominant factor in driving Universal Time (UT) variations and hemispheric differences in geomagnetic activity. Combined with the effect of solar wind dynamic pressure and dipole tilt on the pressure balance in the near-Earth tail, the effect provides an excellent explanation of how the observed Russell-McPherron pattern with time-of-yearFandUTin the driving power input into the magnetosphere is converted into the equinoctialF-UTpattern in average geomagnetic activity (after correction is made for dipole tilt effects on ionospheric conductivity), added to a pronouncedUTvariation with minimum at 02–10 UT. In addition, we show that the predicted and observedUTvariations in average geomagnetic activity has implications for the occurrence of the largest events that also show the nettUTvariation.
Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks

Understanding fluid phase behavior in high pressure and high temperature conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems. The study of vapor-liquid equilibrium (VLE) curves also forms an integral part of the design and modeling of the control processes in chemical and oil gas industries. The main objective of this study was to develop data-driven models to predict VLE of Type III binary mixtures involving long-chained n-alkanes and nitrogen. Two data-driven models have been proposed in this study, each of which was competent in estimating VLE for the binary systems of C10/N2 and C12/N2, at pressures ranging up to 50 – 60 MPa. Both the models showed better performance (less average absolute percentage error) in predicting equilibrium pressure of the binary mixtures as compared to the VLE modeled using PengRobinson equation of state (PR-EOS). The data-driven models were also able to correctly trace the change in curvature of the vapor phase composition, close to the mixture critical point, at high pressures and temperatures – a feature of the n-alkane/nitrogen system VLE which the PR-EOS model fails to capture.
Appearance-Invariant 6-DoF Visual Localization using Generative Adversarial Networks

We propose a novel visual localization network when outside environment has changed such as different illumination, weather and season. The visual localization network is composed of a feature extraction network and pose regression network. The feature extraction network is made up of an encoder network based on the Generative Adversarial Network CycleGAN, which can capture intrinsic appearance-invariant feature maps from unpaired samples of different weathers and seasons. With such an invariant feature, we use a 6-DoF pose regression network to tackle long-term visual localization in the presence of outdoor illumination, weather and season changes. A variety of challenging datasets for place recognition and localization are used to prove our visual localization network, and the results show that our method outperforms state-of-the-art methods in the scenarios with various environment changes.