Miracle-dz/UniFAR-FTUs huggingface dataset:
GOALS-JWST: Unveiling Dusty Compact Sources in the Merging Galaxy IIZw096

We have used the Mid-InfraRed Instrument (MIRI) on the James Webb Space Telescope (JWST) to obtain the first spatially resolved, mid-infrared images of IIZw096, a merging luminous infrared galaxy (LIRG) at z = 0.036. Previous observations with the Spitzer Space Telescope suggested that the vast majority of the total IR luminosity (L IR) of the system originated from a small region outside of the two merging nuclei. New observations with JWST/MIRI now allow an accurate measurement of the location and luminosity density of the source that is responsible for the bulk of the IR emission. We estimate that 40%–70% of the IR bolometric luminosity, or 3–5 × 1011 L ⊙, arises from a source no larger than 175 pc in radius, suggesting a luminosity density of at least 3–5 × 1012 L ⊙ kpc−2. In addition, we detect 11 other star-forming sources, five of which were previously unknown. The MIRI F1500W/F560W colors of most of these sources, including the source responsible for the bulk of the far-IR emission, are much redder than the nuclei of local LIRGs. These observations reveal the power of JWST to disentangle the complex regions at the hearts of merging, dusty galaxies.
A Robust Instrumental Variable Method Accounting for Treatment Switching in Open-Label Randomized Controlled Trials

In a randomized controlled trial, treatment switching (also called contamination or crossover) occurs when a patient initially assigned to one treatment arm changes to another arm during the course of follow-up. Overlooking treatment switching might substantially bias the evaluation of treatment efficacy or safety. To account for treatment switching, instrumental variable (IV) methods by leveraging the initial randomized assignment as an IV serve as natural adjustment methods because they allow dependent treatment switching possibly due to underlying prognoses. However, the ``exclusion restriction'' assumption for IV methods, which requires the initial randomization to have no direct effect on the outcome, remains questionable, especially for open-label trials. We propose a robust instrumental variable estimator circumventing such a caveat. We derive large-sample properties of our proposed estimator, along with inferential tools. We conduct extensive simulations to examine the finite performance of our estimator and its associated inferential tools. An R package ``ivsacim'' implementing all proposed methods is freely available on R CRAN. We apply the estimator to evaluate the treatment effect of Nucleoside Reverse Transcriptase Inhibitors (NRTIs) on a safety outcome in the Optimized Treatment That Includes or Omits NRTIs trial.
Two-Tailed Averaging: Anytime, Adaptive, Once-in-a-While Optimal Weight Averaging for Better Generalization

Tail Averaging improves on Polyak averaging's non-asymptotic behaviour by excluding a number of leading iterates of stochastic optimization from its calculations. In practice, with a finite number of optimization steps and a learning rate that cannot be annealed to zero, Tail Averaging can get much closer to a local minimum point of the training loss than either the individual iterates or the Polyak average. However, the number of leading iterates to ignore is an important hyperparameter, and starting averaging too early or too late leads to inefficient use of resources or suboptimal solutions. Our work focusses on improving generalization, which makes setting this hyperparameter even more difficult, especially in the presence of other hyperparameters and overfitting. Furthermore, before averaging starts, the loss is only weakly informative of the final performance, which makes early stopping unreliable. To alleviate these problems, we propose an anytime variant of Tail Averaging intended for improving generalization not pure optimization, that has no hyperparameters and approximates the optimal tail at all optimization steps. Our algorithm is based on two running averages with adaptive lengths bounded in terms of the optimal tail length, one of which achieves approximate optimality with some regularity. Requiring only the additional storage for two sets of weights and periodic evaluation of the loss, the proposed Two-Tailed Averaging algorithm is a practical and widely applicable method for improving generalization.
Descriptor Distillation: a Teacher-Student-Regularized Framework for Learning Local Descriptors

Learning a fast and discriminative patch descriptor is a challenging topic in computer vision. Recently, many existing works focus on training various descriptor learning networks by minimizing a triplet loss (or its variants), which is expected to decrease the distance between each positive pair and increase the distance between each negative pair. However, such an expectation has to be lowered due to the non-perfect convergence of network optimizer to a local solution. Addressing this problem and the open computational speed problem, we propose a Descriptor Distillation framework for local descriptor learning, called DesDis, where a student model gains knowledge from a pre-trained teacher model, and it is further enhanced via a designed teacher-student regularizer. This teacher-student regularizer is to constrain the difference between the positive (also negative) pair similarity from the teacher model and that from the student model, and we theoretically prove that a more effective student model could be trained by minimizing a weighted combination of the triplet loss and this regularizer, than its teacher which is trained by minimizing the triplet loss singly. Under the proposed DesDis, many existing descriptor networks could be embedded as the teacher model, and accordingly, both equal-weight and light-weight student models could be derived, which outperform their teacher in either accuracy or speed. Experimental results on 3 public datasets demonstrate that the equal-weight student models, derived from the proposed DesDis framework by utilizing three typical descriptor learning networks as teacher models, could achieve significantly better performances than their teachers and several other comparative methods. In addition, the derived light-weight models could achieve 8 times or even faster speeds than the comparative methods under similar patch verification performances
Dwarf galaxies with central cores in modified Newtonian dynamics (MOND) gravity

Some dwarf galaxies are within the Mondian regime at all radii, i.e., the gravitational acceleration provided by the observed baryons is always below the threshold of $g_\dag\simeq 1.2\times 10^{-10}\,{\rm m\,s^{-2}}$. These dwarf galaxies often show cores, in the sense that assuming Newton's gravity to explain their rotation curves, the total density profile $\rho(r)$ presents a central plateau or {\em core} ($d\log \rho/d\log r\rightarrow 0$ when $r\rightarrow 0$). Here we show that under MOND gravity, the existence of this core implies a baryon content whose density $g_{\rm bar}$ must decrease toward the center of the gravitational potential ($g_{\rm bar}\rightarrow 0$ when $r\rightarrow 0$). Such drop of baryons toward the central region is neither observed nor appears in numerical simulations of galaxy formation following MOND gravity. We analyze the problem posed for MOND as well as possible workarounds.
Random Graph Matching at Otter’s Threshold via Counting Chandeliers

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erdős–Rényi graphs G(n,q) whose edges are correlated through a latent vertex correspondence, we show that this algorithm correctly matches all but a vanishing fraction of the vertices with high probability, provided that nq→∞ and the edge correlation coefficient ρ satisfies ρ2>α ≈ 0.338, where α is Otter’s tree-counting constant. Moreover, this almost exact matching can be made exact under an extra condition that is information-theoretically necessary. This is the first polynomial-time graph matching algorithm that succeeds at an explicit constant correlation and applies to both sparse and dense graphs. In comparison, previous methods either require ρ=1−o(1) or are restricted to sparse graphs. The crux of the algorithm is a carefully curated family of rooted trees called chandeliers, which allows effective extraction of the graph correlation from the counts of the same tree while suppressing the undesirable correlation between those of different trees.
Scalable adaptive algorithms for next-generation multiphase simulations

—The accuracy of multiphysics simulations is strongly contingent up on the ﬁnest resolution of mesh used to resolve the interface. However, the increased resolution comes at a cost of inverting a larger matrix size. In this work, we propose algorithmic advances that aims to reduce the computational cost without compromising on the physics by selectively detecting the key regions of interest (droplets/ﬁlaments) that requires signiﬁcantly higher resolution. The overall framework uses an adaptive octree–based mesh generator, which is integrated with PETSc’s linear algebra solver. We demonstrate the scaling of the framework up to 114,688 processes on TACC Frontera. Finally we deploy the framework to simulate primary jet atomization on an equivalent 35 trillion grid points – 64 × greater than the state–of–the–art simulations.
Extreme singular values of inhomogeneous sparse random rectangular matrices

We develop a unified approach to bounding the largest and smallest singular values of an inhomogeneous random rectangular matrix, based on the non-backtracking operator and the Ihara-Bass formula for general Hermitian matrices with a bipartite block structure. Our main results are probabilistic upper (respectively, lower) bounds for the largest (respectively, smallest) singular values of a large rectangular random matrix $X$. These bounds are given in terms of the maximal and minimal $\ell_2$-norms of the rows and columns of the variance profile of $X$. The proofs involve finding probabilistic upper bounds on the spectral radius of an associated non-backtracking matrix $B$. The two-sided bounds can be applied to the centered adjacency matrix of sparse inhomogeneous Erd\H{o}s-R\'{e}nyi bipartite graphs for a wide range of sparsity. In particular, for Erd\H{o}s-R\'{e}nyi bipartite graphs $\mathcal G(n,m,p)$ with $p=\omega(\log n)/n$, and $m/n\to y \in (0,1)$, our sharp bounds imply that there are no outliers outside the support of the Mar\v{c}enko-Pastur law almost surely. This result extends the Bai-Yin theorem to sparse rectangular random matrices.
Phase-controlled asymmetric optomechanical entanglement against defect losses

Jing-Xue Liu,1 Ya-Feng Jiao,1, 2, ∗ Ying Li,1 Xun-Wei Xu,1 Qiong-Yi He,3, 4 and Hui Jing1, 5, † Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha 410081, China Laboratory of Chemical Biology & Traditional Chinese Medicine Research, Ministry of Education College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha 410081, China State Key Laboratory for Mesoscopic Physics, School of Physics, Frontiers Science Center for Nano-optoelectronics, & Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, China Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China Synergetic Innovation Academy for Quantum Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China (Dated: September 27, 2022)
Robust Causality and False Attribution in Data-Driven Earth Science Discoveries

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific and stakeholder challenges and data availability combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. Given that natural experiments, controlled trials, interventions, and counterfactual examinations are often impractical, information-theoretic methods have been developed and are being continually refined in the earth sciences. Here we show that transfer entropy-based causal graphs, which have recently become popular in the earth sciences with high-profile discoveries, can be spurious even when augmented with statistical significance. We develop a subsample-based ensemble approach for robust causality analysis. Simulated data, and observations in climate and ecohydrology, suggest the robustness and consistency of this approach.
Explainable Reinforcement Learning via Model Transforms

Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures. This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepancies that may arise between the behavior of an agent and the behavior that is anticipated by an observer. Most recent approaches have relied either on domain knowledge that may not always be available, on an analysis of the agent's policy, or on an analysis of specific elements of the underlying environment, typically modeled as a Markov Decision Process (MDP). Our key claim is that even if the underlying model is not fully known (e.g., the transition probabilities have not been accurately learned) or is not maintained by the agent (i.e., when using model-free methods), the model can nevertheless be exploited to automatically generate explanations. For this purpose, we suggest using formal MDP abstractions and transforms, previously used in the literature for expediting the search for optimal policies, to automatically produce explanations. Since such transforms are typically based on a symbolic representation of the environment, they can provide meaningful explanations for gaps between the anticipated and actual agent behavior. We formally define the explainability problem, suggest a class of transforms that can be used for explaining emergent behaviors, and suggest methods that enable efficient search for an explanation. We demonstrate the approach on a set of standard benchmarks.
Limit behavior of a class of Cantor-integers

In this paper, we study a class of Cantor-integers { C n } n ≥ 1 with the base conversion function f : { 0 , . , m } → { 0 , . , p } being strictly increasing and satisfying f (0) = 0 and f ( m ) = p . Firstly we provide an algorithm to compute the superior and inferior of the sequence (cid:8) C n n α (cid:9) n ≥ 1 where α = log p +1 m +1 , and obtain the exact values of the superior and inferior when f is a class of quadratic function. Secondly we show that the sequence (cid:8) C n n α (cid:9) n ≥ 1 is dense in the close interval with the endpoints being its inferior and superior respectively. As a consequence, (i) we get the upper and lower pointwise density 1 /α -density of the self-similar measure supported on C at 0, where C is the Cantor set induced by Cantor-integers. (ii) the sequence { C n n α } n ≥ 1 does not have cumulative distribution function but have logarithmic distribution functions (given by a speciﬁc Lebesgue integral). Lastly we obtain the Mellin-Perron formula for the summation function of Cantor-integers. In addition, we investigate some analytic properties of the limit function induced by Cantor-integers.
A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion

Cyrillic and Traditional Mongolian are the two main members of the Mongolian writing system. The Cyrillic-Traditional Mongolian Bidirectional Conversion (CTMBC) task includes two conversion processes, including Cyrillic Mongolian to Traditional Mongolian (C2T) and Traditional Mongolian to Cyrillic Mongolian conversions (T2C). Previous researchers adopted the traditional joint sequence model, since the CTMBC task is a natural Sequence-to-Sequence (Seq2Seq) modeling problem. Recent studies have shown that Recurrent Neural Network (RNN) and Self-attention (or Transformer) based encoder-decoder models have shown significant improvement in machine translation tasks between some major languages, such as Mandarin, English, French, etc. However, an open problem remains as to whether the CTMBC quality can be improved by utilizing the RNN and Transformer models. To answer this question, this paper investigates the utility of these two powerful techniques for CTMBC task combined with agglutinative characteristics of Mongolian language. We build the encoder-decoder based CTMBC model based on RNN and Transformer respectively and compare the different network configurations deeply. The experimental results show that both RNN and Transformer models outperform the traditional joint sequence model, where the Transformer achieves the best performance. Compared with the joint sequence baseline, the word error rate (WER) of the Transformer for C2T and T2C decreased by 5.72\% and 5.06\% respectively.
Asymptotics of singular values for quantum derivatives

We obtain Weyl type asymptotics for the quantised derivative ¯ df of a function f from the homgeneous Sobolev space ˙ W 1 d ( R d ) on R d . The asymptotic coeﬃcient k∇ f k L d ( R d ) is equivalent to the norm of ¯ df in the principal ideal L d, ∞ , thus, providing a non-asymptotic, uniform bound on the spec- trum of ¯ df. Our methods are based on the C ∗ -algebraic notion of the principal symbol mapping on R d , as developed recently by the last two authors and collaborators.
Extension of HOPS out to 500 pc (eHOPS). I. Identification and Modeling of Protostars in the Aquila Molecular Clouds

We present a Spitzer/Herschel focused survey of the Aquila molecular clouds (d ∼ 436 pc) as part of the eHOPS (extension of the Herschel orion protostar survey, or HOPS, Out to 500 ParSecs) census of nearby protostars. For every source detected in the Herschel/PACS bands, the eHOPS-Aquila catalog contains 1–850 μm SEDs assembled from the Two Micron All Sky Survey, Spitzer, Herschel, the Wide-field Infrared Survey Explorer, and James Clerk Maxwell Telescope/SCUBA-2 data. Using a newly developed set of criteria, we classify objects by their SEDs as protostars, pre-main-sequence stars with disks, and galaxies. A total of 172 protostars are found in Aquila, tightly concentrated in the molecular filaments that thread the clouds. Of these, 71 (42%) are Class 0 protostars, 54 (31%) are Class I protostars, 43 (25%) are flat-spectrum protostars, and four (2%) are Class II sources. Ten of the Class 0 protostars are young PACS bright red sources similar to those discovered in Orion. We compare the SEDs to a grid of radiative transfer models to constrain the luminosities, envelope densities, and envelope masses of the protostars. A comparison of the eHOPS-Aquila to the HOPS protostars in Orion finds that the protostellar luminosity functions in the two star-forming regions are statistically indistinguishable, the bolometric temperatures/envelope masses of eHOPS-Aquila protostars are shifted to cooler temperatures/higher masses, and the eHOPS-Aquila protostars do not show the decline in luminosity with evolution found in Orion. We briefly discuss whether these differences are due to biases between the samples, diverging star formation histories, or the influence of environment on protostellar evolution.
Correlating LHCb Bs0 → μ+μ− results with the ATLAS-CMS multijet supersymmetry search

We show that the No-Scale Flipped SU(5) construction is transparently consistent with recent LHCb results for decays, due primarily to suppression from the rather small value of tanβ ∼ 20 that is globally enforced across the model space. This fact should be interpreted in conjunction with the demonstrated evasion of mass limits from the ATLAS and CMS SUSY searches and the more important potential explanation of small observed excesses in the multijet data. The No-Scale Flipped SU(5) benchmark model that best fits these excesses has a gaugino mass scale of M1/2 = 708 GeV, which drives masses for the bino-dominated LSP GeV, light stop GeV, gluino GeV, and heavy squark GeV. The corresponding total prediction for the rare B-decay of suggests that the SUSY contribution may indeed be much smaller than that expected from the Standard Model in this framework, fitting quite comfortably within the very tightly constrained region remaining viable after the most recent LHCb measurements.
Hierarchical Cyclic Pursuit: Algebraic Curves Containing the Laplacian Spectra

This article addresses the problem of multiagent communication in networks with a regular directed ring structure. These can be viewed as hierarchical extensions of the classical cyclic pursuit topology. We show that the spectra of the corresponding Laplacian matrices allow exact localization on the complex plane. Furthermore, we derive a general form of the characteristic polynomial of such matrices, analyze the algebraic curves its roots belong to, and propose a way to obtain their closed-form equations. In combination with frequency-domain consensus criteria for high-order single-input single-output linear agents, these curves enable one to analyze the feasibility of consensus in networks with a varying number of agents.
Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models

Deep learning models have achieved impressive prediction performance but often sacrifice interpretability and speed, critical considerations in high-stakes domains and compute-limited settings. In contrast, generalized additive models (GAMs) can maintain interpretability and speed but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. This work aims to bridge this gap by using pre-trained neural language models to extract embeddings from each input before aggregating them and learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, dramatically speed up inference, and generalize well to novel inputs (e.g. unseen ngrams in text). Across a variety of natural-languageprocessing datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability or speed. All code is made available on Github.1
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapa