Miracle-dz/UniFAR-FTUs huggingface dataset:
The Opposition Effect and Negative Polarization of Structural Analogs for Planetary Regoliths

To better understand the negative polarization and brightness opposition effects observed on airless celestial bodies, we carried out simultaneous photometric and polarimetric measurements of laboratory samples that simulate the structure of planetary regoliths. Computer modeling of shadow-hiding and coherent backscatter in regolith-like media are also presented. The laboratory investigations were carried out with a photometer/polarimeter at phase angles covering 0.2°–4° and wavelengths of 0.63 and 0.45 μm. We studied samples that characterize a variety of microscopic structures and albedos. A particle-size dependence of the negative branch of polarization for powdered dielectric surfaces was found. Colored samples such as a powder Fe2O3 exhibit a very prominent wavelength dependence of the photometric and polarimetric opposition phenomena. Metallic powders usually exhibit a wide branch of the negative polarization independent of the size of particles. For fine dielectric powders, both opposition phenomena become more prominent when the samples were compressed. Our computer modeling based on ray tracing in particulate media shows that shadow-hiding affects the negative polarization only in combination with the coherent backscatter enhancement. Modeling reveals that scattering orders higher than second contribute to negative polarization even in dark particulate surfaces. Our model qualitatively reproduces the effects of varying sample-compression that we observed in the laboratory. Our experimental and computer modeling studies mutually confirm that the degree of polarization for highly reflective dielectric surfaces depends not only on phase angle but also on surface tilt. Even at exactly zero phase the degree of polarization for tilted surfaces can be nonzero. A tilt of the surface normal to the scattering plane gives a parallel shift of the negative polarization branch to large values of |P|. The tilt in the perpendicular plane gives the same shift in the direction of positive polarization. At exactly zero phase angle, a celestial body of irregular shape can exhibit nonzero polarization even in integral polarimetric observations.
The structure of brightest cluster members. II - Mergers

Surface photometry of 342 bright elliptical galaxies in 103 clusters is analyzed for evidence of mergers. Structural differences between brightest cluster members (BCMs) and normal ellipticals can be summarized as having enlarged characteristic radii and shallow profile slopes (beta greater than -1.7). Profile morphology criteria for the elliptical types gE, D, and cD are outlined. Comparison of observations with numerical simulations of mergers strongly suggests a past history of dynamical growth for BCMs. Weak correlations of global cluster properties to BCMs supports the hypothesis proposed by Merritt (1984) that mergers are important in early subgroups before virialization and formation of a cluster identity. 65 references.
Identification of the shape of the inclusion having essentially bounded conductivity

Consider an isotropic conductor of electric current which occupies a domain Ω C R(n > 2) with conductivity coefficient 7 = 1 + /ιχζ>, where D is an open subset of Ω with Lipschitz boundary. We assume that Ω \ D is connected and that h € L°°(D) satisfies 1 + h(x) > C for almost all χ 6 D with positive constant C. Denote by Λ7(/) the flux across 0Ω induced by a electric potential / on 0Ω. We assume that D and h are unknown. This paper concerns the unique determination of D by means of Λ7(/) for infinitely many /. We prove that one can determine D without any regularity assumption on h in D.
Ultraviolet light-assisted domain inversion in magnesium-doped lithium niobate crystals

The influence of ultraviolet (UV) light (wavelengths λ=334 and 305nm) on the ferroelectric domain inversion of lithium niobate crystals doped with different amounts of magnesium ranging from 0to7.5mol% is investigated. Illumination at λ=334nm leads to a coercive field reduction of up to 50%, but only in samples doped with a magnesium concentration above the so-called optical damage threshold. For λ=305nm the effective coercive field is reduced significantly in all samples. Different behavior of the coercive field reduction at both wavelengths indicates the presence of two mechanisms. To explain the effect occurring for 305nm illumination a model is presented in which an UV-induced photoconductivity alters the electric-field distribution through the crystal thickness. Utilizing an UV-interference pattern and a suitable homogeneous external electrical field, periodically poled lithium niobate with a period length of 55μm is produced.
Convective cores and carbon ignition in intermediate-mass stars

It is found that in intermediate-mass stars, convective overshooting during central He burning is responsible for the overgrowth of the convective core. As a consequence, the upper mass limit for stars developing a degenerate C-O core is strongly reduced. It is shown that, when such an effect is taken into account, some previous disagreements between theory and observation are spontaneously solved. Finally, the implications convective overshooting has on supernovae triggered by electron capture are discussed briefly. 13 references.
Density profiles and correlations of harmonically trapped ultracold fermions via complex Langevin

Standard lattice formulations of non-relativistic Fermi gases with two spin components suffer from a sign problem in the cases of repulsive contact interactions and attractive contact interactions with spin imbalance. We discuss the nature of this sign problem and the applicability of stochastic quantisation with complex Langevin evolution in both cases. For repulsive interactions, we find that the results converge, using adaptive step size scaling and a Gaussian regulator to modify the lattice action. We present results on density profiles and correlations of a harmonically trapped system in one spatial dimension.
Coronal Densities, Temperatures, and Abundances during the 2019 Total Solar Eclipse: The Role of Multiwavelength Observations in Coronal Plasma Characterization

The Airborne Infrared Spectrometer (AIR-Spec) offers an unprecedented opportunity to explore the near-infrared (NIR) wavelength range. It has been flown at two total solar eclipses, in 2017 and 2019. The wavelength range of the much-improved instrument on the second flight (2019 July 2) was shifted to cover two density-sensitive lines from S xi. In this paper we study detailed diagnostics for temperature, electron density, and elemental abundances by comparing results from AIR-Spec slit positions above the east and west limbs with those from Hinode/EIS, the PolarCam detector, and SDO/AIA. We find very good agreement in the electron densities obtained from the EIS EUV line ratios, those from the NIR S xi ratio, and those obtained from the polarized brightness PolarCam measurements. Electron densities ranged from log N e [cm−3] = 8.4 near the limb to 7.2 at R 0 = 1.3. EIS spectra indicate that the temperature distribution above the west limb is near isothermal at around 1.3 MK, while that on the east has an additional higher-T component. The AIR-Spec radiances in Si x and S xi, as well as the AIA data in the 171, 193, and 211 Å bands, are consistent with the EIS results. EIS and AIR-Spec data indicate that the sulfur abundance (relative to silicon) is photospheric in both regions, confirming our previous results of the 2017 eclipse. The AIA data also indicate that the absolute iron abundance is photospheric. Our analysis confirms the importance of the diagnostic potential of the NIR wavelength range and that this important wavelength range can be used reliably and independently to determine coronal plasma parameters.
Worldline instantons for the momentum spectrum of Schwinger pair production in spacetime dependent fields

We show how to use the worldline-instanton formalism to calculate the momentum spectrum of the electron-positron pairs produced by an electric field that depends on both space and time. Using the LSZ reduction formula with a worldline representation for the propagator in a spacetime field, we make use of the saddle-point method to obtain a semiclassical approximation of the pair-production spectrum. In order to check the final result, we integrate the spectrum and compare with the results obtained using a previous instanton method for the imaginary part of the effective action.
Robust Meta-Representation Learning via Global Label Inference and Classification

Few-shot learning (FSL) is a central problem in meta-learning, where learners must efficiently learn from few labeled examples. Within FSL, feature pre-training has become a popular strategy to significantly improve generalization performance. However, the contribution of pre-training to generalization performance is often overlooked and understudied, with limited theoretical understanding. Further, pre-training requires a consistent set of global labels shared across training tasks, which may be unavailable in practice. In this work, we address the above issues by first showing the connection between pre-training and meta-learning. We discuss why pre-training yields more robust meta-representation and connect the theoretical analysis to existing works and empirical results. Second, we introduce Meta Label Learning (MeLa), a novel meta-learning algorithm that learns task relations by inferring global labels across tasks. This allows us to exploit pre-training for FSL even when global labels are unavailable or ill-defined. Lastly, we introduce an augmented pre-training procedure that further improves the learned meta-representation. Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific.
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement

Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Enhancement, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual enhancement tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github. io/ReVISE.
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning

Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization. While some approaches have been adopted to refine representations via auxiliary self-supervised losses while simultaneously learning decision policies, learning compositional representations from hand-designed and context-independent self-supervised losses (multi-view) still adapts relatively slowly to the real world, which contains many non-IID subspaces requiring rapid distribution shift in both time and spatial attention patterns at varying levels of abstraction. In contrast, supervised language model cascades have shown the flexibility to adapt to many diverse manifolds, and hints of self-learning needed for autonomous task transfer. However, to date, transfer methods for language models like few-shot learning and fine-tuning still require human supervision and transfer learning using self-learning methods has been underexplored. We propose a self-supervised loss policy called contrastive distillation which manifests latent variables with high mutual information with both source and target tasks from weights to tokens. We show how this outperforms common methods of transfer learning and suggests a useful design axis of trading off compute for generalizability for online transfer. Contrastive distillation is improved through sampling from memory and suggests a simple algorithm for more efficiently sampling negative examples for contrastive losses than random sampling.
Channel Simulation: Finite Blocklengths and Broadcast Channels

We study channel simulation under common randomness-assistance in the finite-blocklength regime and identify the smooth channel max-information as a linear program one-shot converse on the minimal simulation cost for fixed error tolerance. We show that this one-shot converse can be achieved exactly using no-signaling assisted codes, and approximately achieved using common randomness-assisted codes. Our one-shot converse thus takes on an analogous role to the celebrated meta-converse in the complementary problem of channel coding, and find tight relations between these two bounds. We asymptotically expand our bounds on the simulation cost for discrete memoryless channels, leading to the second-order as well as the moderate deviation rate expansion, which can be expressed in terms of the channel capacity and channel dispersion known from noisy channel coding. Our techniques extend to discrete memoryless broadcast channels. In stark contrast to the elusive broadcast channel capacity problem, we show that the reverse problem of broadcast channel simulation under common randomness-assistance allows for an efficiently computable single-letter characterization of the asymptotic rate region in terms of the broadcast channel's multi-partite mutual information. Finally, we present a Blahut-Arimoto type algorithm to compute the rate region efficiently.
Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction

We present a multi-scale predictive coding model for future video frames prediction. Drawing inspiration on the ``Predictive Coding"theories in cognitive science, it is updated by a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels. However, traditional predictive coding models only predict what is happening hierarchically rather than predicting the future. To address the problem, our model employs a multi-scale approach (Coarse to Fine), where the higher level neurons generate coarser predictions (lower resolution), while the lower level generate finer predictions (higher resolution). In terms of network architecture, we directly incorporate the encoder-decoder network within the LSTM module and share the final encoded high-level semantic information across different network levels. This enables comprehensive interaction between the current input and the historical states of LSTM compared with the traditional Encoder-LSTM-Decoder architecture, thus learning more believable temporal and spatial dependencies. Furthermore, to tackle the instability in adversarial training and mitigate the accumulation of prediction errors in long-term prediction, we propose several improvements to the training strategy. Our approach achieves good performance on datasets such as KTH, Moving MNIST and Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN.
Potential Singularity of the Axisymmetric Euler Equations with Cα Initial Vorticity for A Large Range of α. Part II: the N-Dimensional Case

In Part II of this sequence to our previous paper for the 3-dimensional Euler equations \cite{zhang2022potential}, we investigate potential singularity of the $n$-diemnsional axisymmetric Euler equations with $C^\alpha$ initial vorticity for a large range of $\alpha$. We use the adaptive mesh method to solve the $n$-dimensional axisymmetric Euler equations and use the scaling analysis and dynamic rescaling method to examine the potential blow-up and capture its self-similar profile. Our study shows that the $n$-dimensional axisymmetric Euler equations with our initial data develop finite-time blow-up when the H\"{o}lder exponent $\alpha<\alpha^*$, and this upper bound $\alpha^*$ can asymptotically approach $1-\frac{2}{n}$. Moreover, we introduce a stretching parameter $\delta$ along the $z$-direction. Based on a few assumptions inspired by our numerical experiments, we obtain $\alpha^*=1-\frac{2}{n}$ by studying the limiting case of $\delta \rightarrow 0$. For the general case, we propose a relatively simple one-dimensional model and numerically verify its approximation to the $n$-dimensional Euler equations. This one-dimensional model sheds useful light to our understanding of the blowup mechanism for the $n$-dimensional Euler equations. As shown in \cite{zhang2022potential}, the scaling behavior and regularity properties of our initial data are quite different from those of the initial data considered by Elgindi in \cite{elgindi2021finite}.
Local Policy Improvement for Recommender Systems

Recommender systems predict what items a user will interact with next, based on their past interactions. The problem is often approached through supervised learning, but recent advancements have shifted towards policy optimization of rewards (e.g., user engagement). One challenge with the latter is policy mismatch: we are only able to train a new policy given data collected from a previously-deployed policy. The conventional way to address this problem is through importance sampling correction, but this comes with practical limitations. We suggest an alternative approach of local policy improvement without off-policy correction. Our method computes and optimizes a lower bound of expected reward of the target policy, which is easy to estimate from data and does not involve density ratios (such as those appearing in importance sampling correction). This local policy improvement paradigm is ideal for recommender systems, as previous policies are typically of decent quality and policies are updated frequently. We provide empirical evidence and practical recipes for applying our technique in a sequential recommendation setting.
Uniqueness for the anisotropic fractional conductivity equation

In this paper we study an inverse problem for fractional anisotropic conductivity. Our nonlocal operator is based on the well-developed theory of nonlocal vector calculus, and differs substantially from other generalizations of the classical anisotropic conductivity operator obtained spectrally. We show that the anisotropic conductivity matrix can be recovered uniquely from fractional Dirichlet-to-Neumann data up to a natural gauge. Our analysis makes use of techniques recently developed for the study of the isotropic fractional elasticity equation, and generalizes them to the case of non-separable, anisotropic conductivities. The motivation for our study stems from its relation to the classical anisotropic Calder\'on problem, which at the time of writing is one of the main open problems in the field.
Radio and γ-ray Evidence for the Supernova Origin of High-velocity Cloud Complex M

Using λ21 cm galactic neutral hydrogen data from the HI4PI survey and 0.75–30 MeV γ-ray emission from the Imaging Compton Telescope, we have searched for the origin event that accelerated high-velocity cloud Complex M. Radio plots of l − b, l − v, and b − v show a cavity centered at (l, b) ∼ (150°, 50.°) and extending about ±33°. The best view of the cavity is at a velocity of −25 km s−1, which shows a circular cross section on the back (receding) face. Complex M, at −85 km s−1, is on the front (approaching) face. The γ-ray emission reveals several minima, the largest centered at (l, b) ∼(150°, 50.°) and coincident with the position and extent of the cavity seen in the radio data. Using the known distance to Complex M and assuming that the cavity is spherical, we can bootstrap the distance to the original, explosive source of the cavity D = 307 pc, calculate the radius of the cavity R = 166 pc, and approximate the expansion velocity V E ≈ 40 km s−1 of the cavity. The total energy of the expanding cavity is 3.0 ± 1.0 × 1050 erg, well within the range of a single supernova. These results indicate that this explosion took place about four million years ago. As the blast wave from this supernova propagated outwards, it began to sweep up interstellar gas and carved out the Local Chimney, a low-density extension of the Local Bubble that reaches into the galactic halo.
Buckling of 2D-FG Cylindrical Shells under Combined External Pressure and Axial Compression

This paper presents the stability of two-dimensional functionally graded (2D-FG) cylindrical shells subjected to combined external pressure and axial compression loads, based on classical shell theory. The material properties of functionally graded cylindrical shell are graded in two directional (radial and axial) and determined by the rule of mixture. The Euler’s equation is employed to derive the stability equations, which are solved by GDQ method to obtain the critical mechanical buckling loads of the 2D-FG cylindrical shells. The effects of shell geometry, the mechanical properties distribution in radial and axial direction on the critical buckling load are studied and compared with a cylindrical shell made of 1D-FGM. The numerical results reveal that the 2D-FGM has a significant effect on the critical buckling load. AMS subject classifications: 74K25, 74G60
Fiber sum formulas for 4-manifolds, topological modular forms and $6d\ \mathcal{N}=(1,0)$ theories

A relation between four manifolds and topological modular form (TMF) from the six dimensional approach was found by Gukov et al. We exhibit fiber sum formulas for smooth spin four manifolds associated to compactifications of free and interacting $6d\ (1,0)$ SCFTs using the link to the TMF. We find that even the free theories have nontrivial fiber sum formulas.
Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach

Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results.