Miracle-dz/UniFAR-FTUs huggingface dataset:
Accretion Flow Morphology in Numerical Simulations of Black Holes from the ngEHT Model Library: The Impact of Radiation Physics

In the past few years, the Event Horizon Telescope (EHT) has provided the first-ever event horizon-scale images of the supermassive black holes (BHs) M87* and Sagittarius A* (Sgr A*). The next-generation EHT project is an extension of the EHT array that promises larger angular resolution and higher sensitivity to the dim, extended flux around the central ring-like structure, possibly connecting the accretion flow and the jet. The ngEHT Analysis Challenges aim to understand the science extractability from synthetic images and movies to inform the ngEHT array design and analysis algorithm development. In this work, we compare the accretion flow structure and dynamics in numerical fluid simulations that specifically target M87* and Sgr A*, and were used to construct the source models in the challenge set. We consider (1) a steady-state axisymmetric radiatively inefficient accretion flow model with a time-dependent shearing hotspot, (2) two time-dependent single fluid general relativistic magnetohydrodynamic (GRMHD) simulations from the H-AMR code, (3) a two-temperature GRMHD simulation from the BHAC code, and (4) a two-temperature radiative GRMHD simulation from the KORAL code. We find that the different models exhibit remarkably similar temporal and spatial properties, except for the electron temperature, since radiative losses substantially cool down electrons near the BH and the jet sheath, signaling the importance of radiative cooling even for slowly accreting BHs such as M87*. We restrict ourselves to standard torus accretion flows, and leave larger explorations of alternate accretion models to future work.
Location-Aware Self-Supervised Transformers

Pixel-level labels are particularly expensive to acquire. Hence, pretraining is a critical step to improve models on a task like semantic segmentation. However, prominent algorithms for pretraining neural networks use image-level objectives, e.g. image classification, image-text alignment a la CLIP, or self-supervised contrastive learning. These objectives do not model spatial information, which might be sub-optimal when finetuning on downstream tasks with spatial reasoning. In this work, we pretrain network with a location-aware (LOCA) self-supervised method which fosters the emergence of strong dense features. Specifically, we use both a patch-level clustering scheme to mine dense pseudo-labels and a relative location prediction task to encourage learning about object parts and their spatial arrangements. Our experiments show that LOCA pretraining leads to representations that transfer competitively to challenging and diverse semantic segmentation datasets.
Effective-medium description of dense clusters of plasmonic nanoparticles with spatial dispersion

We study the electromagnetic behaviour of dense, spherical clusters made of hundreds of plasmonic nanoparticules under illumination by a plane wave. Using high-precision T-matrix numerical calculations, we compute the multipolar response of clusters up to 80 nm in radius and up to 44\% in particle volume fraction. We then investigate whether it is possible to obtain an effective medium description for the clusters, taking into account weak spatial dispersion in a fully consistent way. We find that the average scattered field as well as the average inner field of the spherical cluster can be accurately reproduced by applying an extended Mie theory to an equivalent homogeneous sphere characterized by three effective parameters: an electric permittivity $\varepsilon_{\mathrm{eff}}$ and a magnetic permeability $\mu_{\mathrm{eff}}$, associated to transverse modes, and a wavevector $k_\mathrm{L}$, associated to a longitudinal mode in the sphere. Our results show that artificial magnetism arises from interparticle couplings in the dense cluster, despite inclusions not displaying any individual magnetic dipole. We also find that, although largely overlooked in the literature on metamaterials, the presence of the longitudinal mode is essential to accurately reproduce the fields of the cluster, on par with the role of artificial magnetism. Our study therefore proves that, even for high concentration in inclusions, it is possible empirically to treat a cluster of plasmonic particles as a sphere made of a spatially-dispersive homogeneous medium. This offers a practical solution facilitating the computation of electromagnetic responses of such dense random media in diverse configurations of interest for the design of metamaterials and metasurfaces.
PHANGS–JWST First Results: The 21 μm Compact Source Population

We use PHANGS–James Webb Space Telescope (JWST) data to identify and classify 1271 compact 21 μm sources in four nearby galaxies using MIRI F2100W data. We identify sources using a dendrogram-based algorithm, and we measure the background-subtracted flux densities for JWST bands from 2 to 21 μm. Using the spectral energy distribution (SED) in JWST and HST bands plus ALMA and MUSE/VLT observations, we classify the sources by eye. Then we use this classification to define regions in color–color space and so establish a quantitative framework for classifying sources. We identify 1085 sources as belonging to the ISM of the target galaxies with the remainder being dusty stars or background galaxies. These 21 μm sources are strongly spatially associated with H ii regions (>92% of sources), while 74% of the sources are coincident with a stellar association defined in the HST data. Using SED fitting, we find that the stellar masses of the 21 μm sources span a range of 102–104 M ⊙ with mass-weighted ages down to 2 Myr. There is a tight correlation between attenuation-corrected Hα and 21 μm luminosity for L ν,F2100W > 1019 W Hz−1. Young embedded source candidates selected at 21 μm are found below this threshold and have M ⋆ < 103 M ⊙.
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models

We consider the problem of reconstructing the signal and the hidden variables from observations coming from a multi-layer network with rotationally invariant weight matrices. The multi-layer structure models inference from deep generative priors, and the rotational invariance imposed on the weights generalizes the i.i. d. Gaussian assumption by allowing for a complex correlation structure, which is typical in applications. In this work, we present a new class of approximate message passing (AMP) algorithms and give a state evolution recursion which precisely characterizes their performance in the large system limit. In contrast with the existing multi-layer VAMP (ML-VAMP) approach, our proposed AMP – dubbed multilayer rotationally invariant generalized AMP (ML-RI-GAMP) – provides a natural generalization beyond Gaussian designs, in the sense that it recovers the existing Gaussian AMP as a special case. Furthermore, ML-RI-GAMP exhibits a significantly lower complexity than ML-VAMP, as the computationally intensive singular value decomposition is replaced by an estimation of the moments of the design matrices. Finally, our numerical results show that this complexity gain comes at little to no cost in the performance of the algorithm.
Simulations of collisional effects in an inner-shell solid-density Mg X-ray laser

Inner-shell Kα X-ray lasers have been created by pumping gaseous, solid, and liquid targets with the intense X-ray output of free-electron lasers (FELs). For gaseous targets lasing relies on the creation of K-shell core holes on a time-scale short compared with filling via Auger decay. In the case of solid and liquid density systems, collisional effects will also be important, affecting not only populations but also line-widths, both of which impact the degree of overall gain, and its duration. However, to date, such collisional effects have not been extensively studied. We present here initial simulations using the CCFLY code of inner-shell lasing in solid-density Mg, where we self-consistently treat the effects of the incoming FEL radiation and the atomic kinetics of the Mg system, including radiative, Auger and collisional effects. We find that the combination of collisional population of the lower states of the lasing transitions and broadening of the lines precludes lasing on all but the Kα of the initially cold system. Even assuming instantaneous turning on of the FEL pump, we find the duration of the gain in the solid system to be sub-femtosecond. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight

This paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not trustworthy in the dark. The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time. To compensate for the lack of labeled dark videos, we introduce a pseudo-supervised learning scheme, which utilizes easy to obtain unlabeled and task-irrelevant dark videos to improve an activity recognizer in low light. As the lower color contrast results in visual information loss, we further propose to incorporate the complementary activity information within audio, which is invariant to illumination. Since the usefulness of audio and visual features differs depending on the amount of illumination, we introduce our `darkness-adaptive' audio-visual recognizer. Experiments on EPIC-Kitchens, Kinetics-Sound, and Charades demonstrate our proposals are superior to image enhancement, domain adaptation and alternative audio-visual fusion methods, and can even improve robustness to local darkness caused by occlusions. Project page: https://xiaobai1217.github. io/Day2Dark/
ObjectMatch: Robust Registration using Canonical Object Correspondences

We present ObjectMatch11https://cangumeli.github. io/ObjectMatch/, a semantic and object-centric camera pose estimator for RGB-D SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to predict such correspondences on a per-pixel level, which we then combine in our energy formulation with state-of-the-art keypoint matching solved with a joint Gauss-Newton optimization. In a pairwise setting, our method improves registration recall of state-of-the-art feature matching, including from 24% to 45% in pairs with 10% or less inter-frame overlap. In registering RGB-D sequences, our method outperforms cutting-edge SLAM baselines in challenging, low-frame-rate scenarios, achieving more than 35% reduction in trajectory error in multiple scenes.
Tunable Crossed Andreev Reflection and Elastic Cotunneling in Hybrid Nanowires

A short superconducting segment can couple attached quantum dots via elastic co-tunneling (ECT) and crossed Andreev reflection (CAR). Such coupled quantum dots can host Majorana bound states provided that the ratio between CAR and ECT can be controlled. Metallic superconductors have so far been shown to mediate such tunneling phenomena, albeit with limited tunability. Here we show that Andreev bound states formed in semiconductor-superconductor heterostructures can mediate CAR and ECT over mesoscopic length scales. Andreev bound states possess both an electron and a hole component, giving rise to an intricate interference phenomenon that allows us to tune the ratio between CAR and ECT deterministically. We further show that the combination of intrinsic spin-orbit coupling in InSb nanowires and an applied magnetic field provides another efficient knob to tune the ratio between ECT and CAR and optimize the amount of coupling between neighboring quantum dots.
Dielectric properties of GaAs AlGaAs multiple quantum well waveguides

GaAs/AlGaAs multiple quantum well (MQW) waveguides are investigated using the grating coupling technique. Large polarization dependent effects of the two-dimensional excitons are seen in the TE/TM dispersion relation of single mode slab waveguides. By treating the MQW as a layered composite dielectric, we deduce the oscillator strengths of the heavy and light hole excitons, and the background dielectric constant of the MQW without the excitonic contribution, for fields polarized parallel and perpendicular to the MQW layers.
A simple extension of Azadkia $\&$ Chatterjee's rank correlation to a vector of endogenous variables

We propose a direct and natural extension of Azadkia&Chatterjee's rank correlation $T$ introduced in [4] to a set of $q \geq 1$ endogenous variables. The approach builds upon converting the original vector-valued problem into a univariate problem and then applying the rank correlation $T$ to it. The novel measure $T^q$ then quantifies the scale-invariant extent of functional dependence of an endogenous vector ${\bf Y} = (Y_1,\dots,Y_q)$ on a number of exogenous variables ${\bf X} = (X_1,\dots,X_p)$, $p\geq1$, characterizes independence of ${\bf X}$ and ${\bf Y}$ as well as perfect dependence of ${\bf Y}$ on ${\bf X}$ and hence fulfills all the desired characteristics of a measure of predictability. Aiming at maximum interpretability, we provide various general invariance and continuity conditions for $T^q$ as well as novel ordering results for conditional distributions, revealing new insights into the nature of $T$. Building upon the graph-based estimator for $T$ in [4], we present a non-parametric estimator for $T^q$ that is strongly consistent in full generality, i.e., without any distributional assumptions. Based on this estimator we develop a model-free and dependence-based feature ranking and forward feature selection of multiple-outcome data, and establish tools for identifying networks between random variables. Real case studies illustrate the main aspects of the developed methodology.
Debiased Machine Learning Inequality of Opportunity in Europe∗

Inequality of Opportunity (IOp) is considered an unfair source of inequality, an obstacle for economic growth and a determinant of preferences for redistribution. IOp can be estimated in two steps: (i) fitted values are estimated by predicting an outcome given some circumstances out of the control of the individual, (ii) the inequality in the distribution of the fitted values is measured by some inequality index. Using machine learners in the prediction step allows to consider a rich set of circumstances but it leads to biases in the estimation of IOp. We propose to use debiased estimators based on the Gini coefficient and the Mean Logarithmic Deviation (MLD). Further, we measure the effect of each circumstance on IOp and provide valid standard errors. To stress the usefulness of inference, we provide a test to compare IOp in two populations and a group test to check joint significance of a group of circumstances. We use the debiased estimators to measure IOp in 29 European countries. Romania and Bulgaria are the countries with highest IOp. Southern countries tend to have high levels of IOp while Nordic countries have low IOp. Debiased estimators are more robust to the choice of the machine learner in the first step. Mother’s education and father’s occupation are key circumstances to explain inequality. JEL Classification: C13, C14, C21, D31, D63
High temperature spin-triplet topological superconductivity in K2Cr3As3

Spin-triplet superconductors are novel materials capable of harboring Majorana bound states that can be used in topological quantum computing. However, such bulk materials are still rare. Here we review the results that established K2Cr3As3 as a spin-triplet superconductor with transition temperature Tc as high as 6.5 K. We focus on the multiple-phases feature, and its exquisite distance to a ferromagnetic quantum critical point which is likely responsible for the high Tc . We touch on the topological aspects of the superconducting state, and suggest that it is a new route to the technical implementation using a topological spin-triplet superconductor at the highest temperature ever.
Why do investors buy shares of actively managed equity mutual funds? Considering the Correct Reference Portfolio from an Uninformed Investor's Perspective 1, 2

We use the Grossman \&Stiglitz (1980) framework to build a reference portfolio for uninformed investors and employ this portfolio to assess the performance of actively managed equity mutual funds. We propose an empirical methodology to construct this reference portfolio using the information on prices and supply. We show that mutual funds provide, on average, an insignificant alpha of 23 basis points per year when considering this portfolio as a reference. With the stock market index as a proxy for the market portfolio, the average fund alpha is negative and highly significant, --128 basis points per year. The results are robust when considering various subsets of funds based on their characteristics and their degree of selectivity. In line with rational expectations equilibrium models considering asymmetrically informed investors and partially revealing equilibrium prices, our study supports that active management adds value for uniformed investors.
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images

Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g., a pre-trained VGG, to obtain a latent representation. However, due to domain shift, pre-trained models from the natural image domain might not apply to other image domains, such as medical imaging. Notably, in medical imaging, evaluating the perceptual similarity is exclusively performed by specialists trained extensively in diverse medical fields. Thus, medical imaging remains devoid of task-specific, objective perceptual measures. This work answers the question: Is it necessary to rely on supervised learning to obtain an effective representation that could measure perceptual similarity, or is self-supervision sufficient? To understand whether recent contrastive self-supervised representation (CSR) may come to the rescue, we start with natural images and systematically evaluate CSR as a metric across numerous contemporary architectures and tasks and compare them with existing methods. We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings. We also demonstrate that CSR can significantly improve image quality in two image synthesis tasks. Finally, our extensive results suggest that perceptuality is an emergent property of CSR, which can be adapted to many image domains without requiring annotations.
EFFECT OF TIME AND INTENSITY OF TAPPING ON THE GUM YIELDS OF ACACIA SEYAL VAR. SEYAL IN SOUTH KORDOFAN, SUDAN

ABSTRACT A. seyal var. seyal (talh*) is an important multipurpose tree of the agro-silvo-pastoral systems in semi-arid South Kordofan. The species produces gum which is collected from natural exudation by the rural people. However, little information is available about the tapping possibility of A. seyal var. seyal. Therefore, a two-factor experiment was carried out at Umfakarin forest reserve in 2001/02. The first factor was time of tapping, which was tested in three levels (1st Nov., 15th Nov. and 1st Dec.). The second factor was tapping intensity, which was tested in four levels (2, 4, 6 and 8 branches). The results of the present study revealed the possibility of talh gum production by tapping A. seyal var. seyal. The tapping date had a significant effect on the amount of gum production. Trees in this experiment produced the highest gum yield when they were tapped on the 1st of November. However, the tapping intensity did not show a clear effect on the gum yield. Therefore, a tapping intensity of from two to four branches is recommended in order to minimize the likelihood of damaging the A. seyal var. seyal tapped trees.
The Optimality of Blocking Designs in Equally and Unequally Allocated Randomized Experiments with General Response

We consider the performance of the difference-in-means estimator in a two-arm randomized experiment under common experimental endpoints such as continuous (regression), incidence, proportion and survival. We examine performance under both equal and unequal allocation to treatment groups and we consider both the Neyman randomization model and the population model. We show that in the Neyman model, where the only source of randomness is the treatment manipulation, there is no free lunch: complete randomization is minimax for the estimator's mean squared error. In the population model, where each subject experiences response noise with zero mean, the optimal design is the deterministic perfect-balance allocation. However, this allocation is generally NP-hard to compute and moreover, depends on unknown response parameters. When considering the tail criterion of Kapelner et al. (2021), we show the optimal design is less random than complete randomization and more random than the deterministic perfect-balance allocation. We prove that Fisher's blocking design provides the asymptotically optimal degree of experimental randomness. Theoretical results are supported by simulations in all considered experimental settings.
Evince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features

—With the rapid advancement in synthetic speech generation technologies, great interest in differentiating spoof speech from the natural speech is emerging in the research community. The identiﬁcation of these synthetic signals is a difﬁcult task not only for the cutting-edge classiﬁcation models but also for humans themselves. To prevent potential adverse effects, it becomes crucial to detect spoof signals. From a forensics perspective, it is also important to predict the algorithm which generated them to identify the forger. This needs an understanding of the underlying attributes of spoof signals which serve as a signature for the synthesizer. This study emphasizes the segments of speech signals critical in identifying their authenticity by utilizing the Vocal Tract System( VTS ) and Voice Source( VS ) features. Inthis paper, we propose a system that detects spoof signals as well as identiﬁes the corresponding speech-generating algorithm. We achieve 99.58% in algorithm classiﬁcation accuracy. From experiments, we found that a VS feature-based system gives more attention to the transition of phonemes, while, a VTS feature-based system gives more attention to stationary segments of speech signals. We perform model fusion techniques on the VS-based and VTS-based systems to exploit the complementary information to develop a robust classiﬁer. Upon analyzing the confusion plots we found that WaveRNN is poorly classiﬁed depicting more naturalness. On the other hand, we identiﬁed that synthesizer like Waveform Concatenation, and Neural Source Filter is classiﬁed with the highest accuracy. Practical implications of this work can aid researchers from both forensics (leverage artifacts) and the speech communities (mitigate artifacts).