Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Mass and Redshift Dependencies of the Cluster Pressure Profile with Stacks on Thermal Sunyaev–Zel’dovich Maps

We provide novel constraints on the parameters defining the universal pressure profile (UPP) within clusters of galaxies, and explore their dependencies on cluster mass and redshift, from measurements of Sunyaev–Zel’dovich (SZ) Compton y-profiles. We employ both Planck 2015 MILCA and Atacama Cosmology Telescope (ACT) Data Release 4 y-maps over a common ∼2100 deg2 footprint. We combine existing cluster catalogs, based on Kilo Degree Survey, Sloan Digital Sky Survey, and Dark Energy Spectroscopic Instrument Legacy Imaging Surveys observations, for a total of 23,820 clusters, spanning the mass range 1014.0 M ⊙ < M 500 < 1015.1 M ⊙ and the redshift range 0.02 < z < 0.98. We split the clusters into three independent bins in mass and redshift; for each combination, we detect the stacked SZ cluster signal and extract the mean y angular profile. The latter is predicted theoretically by adopting a halo model framework, and a Markov Chain Monte Carlo approach is employed to estimate the UPP parameters, the hydrostatic mass bias b h, and possible cluster miscentering effects. We constrain [P 0, c 500, α, β] to [5.9, 2.0, 1.8, 4.9] with Planck and to [3.8, 1.3, 1.0, 4.4] with ACT, using the full cluster sample, in agreement with previous findings. We do not find any compelling evidence for residual mass or redshift dependencies, thus expanding the validity of the cluster pressure profile over much larger M 500 and z ranges; this is the first time that the model has been tested on such a large (complete and representative) cluster sample. Finally, we obtain loose constraints on the hydrostatic mass bias in the range 0.2–0.3, again in broad agreement with previous works.
On Comparing Fair Classifiers under Data Bias

In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum&Stangl, 2019). We empirically study the effect of varying data biases on the accuracy and fairness of fair classifiers. Through extensive experiments on both synthetic and real-world datasets (e.g., Adult, German Credit, Bank Marketing, COMPAS), we empirically audit pre-, in-, and post-processing fair classifiers from standard fairness toolkits for their fairness and accuracy by injecting varying amounts of under-representation and label bias in their training data (but not the test data). Our main observations are: 1. The fairness and accuracy of many standard fair classifiers degrade severely as the bias injected in their training data increases, 2. A simple logistic regression model trained on the right data can often outperform, in both accuracy and fairness, most fair classifiers trained on biased training data, and 3. A few, simple fairness techniques (e.g., reweighing, exponentiated gradients) seem to offer stable accuracy and fairness guarantees even when their training data is injected with under-representation and label bias. Our experiments also show how to integrate a measure of data bias risk in the existing fairness dashboards for real-world deployments.
Direct imaging discovery of a super-Jovian around the young Sun-like star AF Leporis

(abridged) Expanding the sample of directly imaged companions to nearby, young stars that are amenable to detailed astrometric and spectroscopic studies is critical for the continued development and validation of theories of their evolution and atmospheric processes. The recent release of the {\it Gaia} astrometric catalogue allows us to efficiently search for these elusive companions by targeting those stars that exhibit the astrometric reflex motion induced by an orbiting companion. The nearby (27 pc), young (24 Myr) star AF Leporis (AF Lep) was targeted because of its astrometric acceleration, consistent with a wide-orbit planetary companion detectable with high-contrast imaging. We used the SPHERE instrument on the VLT to search for faint substellar companions in the immediate vicinity of AF Lep. We used observations of a nearby star interleaved with those of AF Lep to efficiently subtract the residual point spread function. This provided sensitivity to faint planetary-mass companions within 1 arcsec ($\sim$30 au) of the star. We detected the companion AF Lep b at a separation of 339 mas (9 au), within the inner edge of its unresolved debris disk. The measured $K$-band contrast and the age of the star yield a model-dependent mass of 4 and 6 $M_{\rm Jup}$, consistent with the mass derived from an orbital fit of $4. 3_{-1.2}^{+2.9}$ $M_{\rm Jup}$. The near-infrared SED of the planet is consistent with an object at the L--T spectral type transition, but under-luminous with respect to field-gravity objects. AF Lep b joins a growing number of substellar companions imaged around stars in the young $\beta$ Pic moving group. With a mass of between 3 and 7 $M_{\rm Jup}$, it occupies a gap in this isochronal sequence between the hotter, more massive companions like PZ~Tel~B and $\beta$~Pic~b, and the cooler 51~Eri~b, which is sufficiently cool for methane to form within its photosphere.
GRANDMA and HXMT Observations of GRB 221009A: The Standard Luminosity Afterglow of a Hyperluminous Gamma-Ray Burst—In Gedenken an David Alexander Kann

Object GRB 221009A is the brightest gamma-ray burst (GRB) detected in more than 50 yr of study. In this paper, we present observations in the X-ray and optical domains obtained by the GRANDMA Collaboration and the Insight Collaboration. We study the optical afterglow with empirical fitting using the GRANDMA+HXMT-LE data sets augmented with data from the literature up to 60 days. We then model numerically using a Bayesian approach, and we find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky Way dust column and moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT data set, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multiwavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the augmented data set. We find that the consideration of a jet structure (Gaussian or power law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions. Placed in the global context of GRB optical afterglows, we find that the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known sample despite its extreme energetics and the peculiar afterglow evolution.
When Can We Track Significant Preference Shifts in Dueling Bandits?

The $K$-armed dueling bandits problem, where the feedback is in the form of noisy pairwise preferences, has been widely studied due its applications in information retrieval, recommendation systems, etc. Motivated by concerns that user preferences/tastes can evolve over time, we consider the problem of dueling bandits with distribution shifts. Specifically, we study the recent notion of significant shifts (Suk and Kpotufe, 2022), and ask whether one can design an adaptive algorithm for the dueling problem with $O(\sqrt{K\tilde{L}T})$ dynamic regret, where $\tilde{L}$ is the (unknown) number of significant shifts in preferences. We show that the answer to this question depends on the properties of underlying preference distributions. Firstly, we give an impossibility result that rules out any algorithm with $O(\sqrt{K\tilde{L}T})$ dynamic regret under the well-studied Condorcet and SST classes of preference distributions. Secondly, we show that $\text{SST} \cap \text{STI}$ is the largest amongst popular classes of preference distributions where it is possible to design such an algorithm. Overall, our results provides an almost complete resolution of the above question for the hierarchy of distribution classes.
Extinction of Taurus, Orion, Perseus, and California Molecular Clouds Based on the LAMOST, 2MASS, and Gaia Surveys. I. 3D Extinction and Structure

<jats:p>The 3D extinction and structure are studied for the Taurus, Orion, Perseus, and California molecular clouds based on the LAMOST spectroscopy. Stellar color excess is calculated with the intrinsic color index derived from the atmospheric parameters in the LAMOST DR8 catalog and the observed color index in the Gaia EDR3 and the 2MASS PSC. In combination with the distance from the Gaia EDR3 parallax, the 3D dust extinction maps are retrieved in the color excesses <jats:inline-formula> <jats:tex-math> <? CDATA ${E}_{{{\rm{G}}}_{\mathrm{BP}},{{\rm{G}}}_{\mathrm{RP}}}$? > </jats:tex-math> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">G</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>BP</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">G</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>RP</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" xlink:href="apjacbbc7ieqn1.gif" xlink:type="simple" /> </jats:inline-formula> and <jats:inline-formula> <jats:tex-math> <? CDATA ${E}_{{\rm{J}},{{\rm{K}}}_{{\rm{S}}}}$? > </jats:tex-math> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">J</mml:mi> <mml:mo>,</mml:mo> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">S</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" xlink:href="apjacbbc7ieqn2.gif" xlink:type="simple" /> </jats:inline-formula> with an uncertainty of ∼0.03 mag and ∼0.07 mag respectively. The extinction maps successfully separate the clouds that overlap in the sky area and manifest the structure of the individual cloud. Meanwhile, a bow-like structure is found with a distance range from 175 to 250 pc, half of which is a part of the Per-Tau Shell in similar coordinates and distance while the other half is not. Three low-extinction rings are additionally discovered and briefly discussed. </jats:p>
Ordered Memory Baselines

Natural language semantics can be modeled using the phrase-structured model, which can be represented using a tree-type architecture. As a result, recent advances in natural language processing have been made utilising recursive neural networks using memory models that allow them to infer tree-type representations of the input sentence sequence. These new tree models have allowed for improvements in sentiment analysis and semantic recognition. Here we review the Ordered Memory model proposed by Shen et al. (2019) at the NeurIPS 2019 conference, and try to either create baselines that can perform better or create simpler models that can perform equally as well. We found that the Ordered Memory model performs on par with the state-of-the-art models used in tree-type modelling, and performs better than simplified baselines that require fewer parameters.
Density jump as a function of magnetic field strength for parallel collisionless shocks with anisotropic upstream pressure

The properties of collisionless shocks are frequently assessed in the magnetohydrodynamics (MHD) model. Yet, in a collisionless plasma, an ambient magnetic field can sustain a stable anisotropy in the upstream or the downstream, resulting in a departure from the MHD predicted behavior. We present a model allowing to derive the downstream anisotropy, hence the shock density jump, in terms of the upstream quantities. For simplicity, the case of a parallel shock in pair plasma is considered. Contrary to previous works where the upstream was assumed isotropic, here the upstream anisotropy A = T⊥/T∥ is a free parameter. The strong sonic shock regime is formally identical to the isotropic upstream case. Yet, for intermediate sonic Mach numbers, a variety of behaviors appear as a result of the anisotropy of the upstream.
SubTuning: Efficient Finetuning for Multi-Task Learning

Finetuning a pretrained model has become a standard approach for training neural networks on novel tasks, resulting in fast convergence and improved performance. In this work, we study an alternative ﬁnetuning method, where instead of ﬁnetuning all the weights of the network, we only train a carefully chosen subset of layers, keeping the rest of the weights frozen at their initial (pretrained) values. We demonstrate that subset ﬁnetuning (or SubTuning) often achieves accuracy comparable to full ﬁnetuning of the model, and even surpasses the performance of full ﬁnetun-ing when training data is scarce. Therefore, Sub-Tuning allows deploying new tasks at minimal computational cost, while enjoying the beneﬁts of ﬁnetuning the entire model. This yields a simple and effective method for multi-task learning, where different tasks do not interfere with one another, and yet share most of the resources at inference time. We demonstrate the efﬁciency of SubTuning across multiple tasks, using different network architectures and pretraining methods.
Variational Mixture of HyperGenerators for Learning Distributions Over Functions

Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with inference tasks, such as missing data imputation, or directly cannot tackle them. In this work, we propose a novel deep generative model, named VAMoH. VAMoH combines the capabilities of modeling continuous functions using INRs and the inference capabilities of Variational Autoencoders (VAEs). In addition, VAMoH relies on a normalizing flow to define the prior, and a mixture of hypernetworks to parametrize the data log-likelihood. This gives VAMoH a high expressive capability and interpretability. Through experiments on a diverse range of data types, such as images, voxels, and climate data, we show that VAMoH can effectively learn rich distributions over continuous functions. Furthermore, it can perform inference-related tasks, such as conditional super-resolution generation and in-painting, as well or better than previous approaches, while being less computationally demanding.
A novel approach to generate datasets with XAI ground truth to evaluate image models

With the increased usage of artificial intelligence (AI), it is imperative to understand how these models work internally. These needs have led to the development of a new field called eXplainable artificial intelligence (XAI). This field consists of on a set of techniques that allows us to theoretically determine the cause of the AI decisions. One main issue of XAI is how to verify the works on this field, taking into consideration the lack of ground truth (GT). In this study, we propose a new method to generate datasets with GT. We conducted a set of experiments that compared our GT with real model explanations and obtained excellent results confirming that our proposed method is correct.
Implication of cosmological upper bound on the validity of golden ratio neutrino mixings under radiative corrections

We study the implication of the most recent cosmological upper bound on the sum of three neutrino masses, on the validity of the golden ratio (GR) neutrino mixings defined at high energy seesaw scale, considering the possibility for generating low energy values of neutrino oscillation parameters through radiative corrections in the minimal supersymmetric standard model (MSSM). The present study is consistent with the most stringent and latest Planck data on cosmological upper bound, $\sum |m_{i}|<0.12$ eV. For the radiative generation of sin$\theta_{13}$ from an exact form of golden ratio (GR) neutrino mixing matrix defined at high seesaw energy scale, we take opposite CP parity mass eigenvalues ($m_{1},-m_{2},m_{3}$) with a non-zero real value of $m_{3}$, and a larger value of $\tan\beta>60$ in order to include large effects of radiative corrections in the calculation. The present analysis including the CP violating Dirac phase and SUSY threshold corrections, shows the validity of golden ratio neutrino mixings defined at high seesaw energy scale in the normal hierarchical (NH) model. The numerical analysis with the variations of four parameters viz. $M_{R}$, $m_{s}$, $\tan\beta$ and $\bar{\eta_{b}}$, shows that the best result for the validity is obtained at $M_{R}=10^{15}$ GeV, $m_{s}=1$ TeV, $\tan\beta=68$ and $\bar{\eta_{b}}=0.01$. However, the analysis based on inverted hierarchical (IH) model does not conform with this latest Planck data on cosmological bound but it still conforms with earlier Planck cosmological upper bound $\sum |m_{i}|<0.23$ eV, thus indicating possible preference of NH over IH models.
Relative equilibria of point vortices on the hyperbolic sphere

Relative equilibria of point vortices on a hyperbolic sphere with constant negative curvature is formulated and considered. Known symmetries for the vortex motion on the hyperbolic sphere are expounded by setting up a geometrical formulation of dynamics. In particular, the configuration of rings of vortices is investigated in detail.
Powerful Primitives in the Bounded Quantum Storage Model

The bounded quantum storage model aims to achieve security against computationally unbounded adversaries that are restricted only with respect to their quantum memories. In this work, we provide information-theoretic secure constructions in this model for the following powerful primitives: (1) CCA1-secure symmetric key encryption, message authentication codes, and one-time programs. These schemes require no quantum memory for the honest user, while they can be made secure against adversaries with arbitrarily large memories by increasing the transmission length sufficiently. (2) CCA1-secure asymmetric key encryption, encryption tokens, signatures, signature tokens, and program broadcast. These schemes are secure against adversaries with roughly $e^{\sqrt{m}}$ quantum memory where $m$ is the quantum memory required for the honest user. All of the constructions additionally satisfy notions of disappearing and unclonable security.
Is Distance Matrix Enough for Geometric Deep Learning?

Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into Message Passing Neural Networks (referred to as Vanilla DisGNN) is a straightforward way to learn the geometry, it has been demonstrated that Vanilla DisGNN is geometrically incomplete. In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families. Our counterexamples show the inherent limitation of Vanilla DisGNN to capture symmetric geometric structures. We then propose $k$-DisGNNs, which can effectively exploit the rich geometry contained in the distance matrix. We demonstrate the high expressive power of $k$-DisGNNs from three perspectives: 1. They can learn high-order geometric information that cannot be captured by Vanilla DisGNN. 2. They can unify some existing well-designed geometric models. 3. They are universal function approximators from geometric graphs to scalars (when $k\geq 2$) and vectors (when $k\geq 3$). Most importantly, we establish a connection between geometric deep learning (GDL) and traditional graph representation learning (GRL), showing that those highly expressive GNN models originally designed for GRL can also be applied to GDL with impressive performance, and that existing complicated, equivariant models are not the only solution. Experiments verify our theory. Our $k$-DisGNNs achieve many new state-of-the-art results on MD17.
Embedding Space Approach to Lorentzian CFT Amplitudes and Causal Spherical Functions

Conformal Field Theory in a Minkowski setting is discussed in an embedding space approach, paying special attention to causality constraints for four-point amplitudes. The physics of dilatation and Lorentz boost is emphasized in specifying the non-compact Maximal Abelian subgroup (MASG) of $SO(d,2)$. Reduction of a Conformal Field Theory (CFT) four-point amplitudes as functions of cross ratios is shown to be equivalent to enforcing $H$ bi-invariance, i.e., $F(hgh')=F(g)$, with $g\in SO(d,2)$ and $H$ an appropriate subgroup. Causality is imposed by introducing appropriate semigroups. Causal zonal spherical functions are constructed, making contact with Minkowski conformal blocks introduced previously.
Geometric Barriers for Stable and Online Algorithms for Discrepancy Minimization

For many computational problems involving randomness, intricate geometric features of the solution space have been used to rigorously rule out powerful classes of algorithms. This is often accomplished through the lens of the multi Overlap Gap Property ($m$-OGP), a rigorous barrier against algorithms exhibiting input stability. In this paper, we focus on the algorithmic tractability of two models: (i) discrepancy minimization, and (ii) the symmetric binary perceptron (\texttt{SBP}), a random constraint satisfaction problem as well as a toy model of a single-layer neural network. Our first focus is on the limits of online algorithms. By establishing and leveraging a novel geometrical barrier, we obtain sharp hardness guarantees against online algorithms for both the \texttt{SBP} and discrepancy minimization. Our results match the best known algorithmic guarantees, up to constant factors. Our second focus is on efficiently finding a constant discrepancy solution, given a random matrix $\mathcal{M}\in\mathbb{R}^{M\times n}$. In a smooth setting, where the entries of $\mathcal{M}$ are i.i. d. standard normal, we establish the presence of $m$-OGP for $n=\Theta(M\log M)$. Consequently, we rule out the class of stable algorithms at this value. These results give the first rigorous evidence towards a conjecture of Altschuler and Niles-Weed~\cite[Conjecture~1]{altschuler2021discrepancy}. Our methods use the intricate geometry of the solution space to prove tight hardness results for online algorithms. The barrier we establish is a novel variant of the $m$-OGP. Furthermore, it regards $m$-tuples of solutions with respect to correlated instances, with growing values of $m$, $m=\omega(1)$. Importantly, our results rule out online algorithms succeeding even with an exponentially small probability.
Hubble Tension: The Evidence of New Physics

The ΛCDM model provides a good fit to most astronomical observations but harbors large areas of phenomenology and ignorance. With the improvements in the precision and number of observations, discrepancies between key cosmological parameters of this model have emerged. Among them, the most notable tension is the 4σ to 6σ deviation between the Hubble constant (H0) estimations measured by the local distance ladder and the cosmic microwave background (CMB) measurement. In this review, we revisit the H0 tension based on the latest research and sort out evidence from solutions to this tension that might imply new physics beyond the ΛCDM model. The evidence leans more towards modifying the late-time universe.