Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamical Equilibrium in the Molecular ISM in 28 Nearby Star-forming Galaxies

We compare the observed turbulent pressure in molecular gas, Pturb, to the required pressure for the interstellar gas to stay in equilibrium in the gravitational potential of a galaxy, PDE. To do this, we combine arcsecond resolution CO data from PHANGS-ALMA with multiwavelength data that trace the atomic gas, stellar structure, and star formation rate (SFR) for 28 nearby star-forming galaxies. We find that Pturb correlates with—but almost always exceeds—the estimated PDE on kiloparsec scales. This indicates that the molecular gas is overpressurized relative to the large-scale environment. We show that this overpressurization can be explained by the clumpy nature of molecular gas; a revised estimate of PDE on cloud scales, which accounts for molecular gas self-gravity, external gravity, and ambient pressure, agrees well with the observed Pturb in galaxy disks. We also find that molecular gas with cloud-scale in our sample is more likely to be self-gravitating, whereas gas at lower pressure it appears more influenced by ambient pressure and/or external gravity. Furthermore, we show that the ratio between Pturb and the observed SFR surface density, , is compatible with stellar feedback-driven momentum injection in most cases, while a subset of the regions may show evidence of turbulence driven by additional sources. The correlation between and kpc-scale PDE in galaxy disks is consistent with the expectation from self-regulated star formation models. Finally, we confirm the empirical correlation between molecular-to-atomic gas ratio and kpc-scale PDE reported in previous works.
Inexact Tensor Methods with Dynamic Accuracies

In this paper, we study inexact high-order Tensor Methods for solving convex optimization problems with composite objective. At every step of such methods, we use approximate solution of the auxiliary problem, defined by the bound for the residual in function value. We propose two dynamic strategies for choosing the inner accuracy: the first one is decreasing as $1/k^{p + 1}$, where $p \geq 1$ is the order of the method and $k$ is the iteration counter, and the second approach is using for the inner accuracy the last progress in the target objective. We show that inexact Tensor Methods with these strategies achieve the same global convergence rate as in the error-free case. For the second approach we also establish local superlinear rates (for $p \geq 2$), and propose the accelerated scheme. Lastly, we present computational results on a variety of machine learning problems for several methods and different accuracy policies.
Potential Themis-family Asteroid Contribution to the Jupiter-family Comet Population

Recent dynamical analyses suggest that some Jupiter family comets (JFCs) may originate in the main asteroid belt instead of the outer solar system. This possibility is particularly interesting given evidence that icy main-belt objects are known to be present in the Themis asteroid family. We report results from dynamical analyses specifically investigating the possibility that icy Themis family members could contribute to the observed population of JFCs. Numerical integrations show that such dynamical evolution is indeed possible via a combination of eccentricity excitation apparently driven by the nearby 2:1 mean-motion resonance with Jupiter, gravitational interactions with planets other than Jupiter, and the Yarkovsky effect. We estimate that, at any given time, there may be tens of objects from the Themis family on JFC-like orbits with the potential to mimic active JFCs from the outer solar system, although not all, or even any, may necessarily be observably active. We find that dynamically evolved Themis family objects on JFC-like orbits have semimajor axes between 3.15 and 3.40 au for the vast majority of their time on such orbits, consistent with the strong role that the 2:1 mean-motion resonance with Jupiter likely plays in their dynamical evolution. We conclude that a contribution from the Themis family to the active JFC population is plausible, although further work is needed to better characterize this contribution.
Optimal Mapper for OFDM With Index Modulation: A Spectro-Computational Analysis

In this work, we present an optimal mapper for OFDM with index modulation (OFDM-IM). By optimal we mean the mapper achieves the lowest possible asymptotic computational complexity (CC) when the spectral efficiency (SE) gain over OFDM maximizes. We propose the spectro-computational (SC) analysis to capture the trade-off between CC and SE and to demonstrate that an <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-subcarrier OFDM-IM mapper must run in exact <inline-formula> <tex-math notation="LaTeX">$\Theta (N)$ </tex-math></inline-formula> time complexity. We show that an OFDM-IM mapper running faster than such complexity cannot reach the maximal SE whereas one running slower nullifies the mapping throughput for arbitrarily large <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>. We demonstrate our theoretical findings by implementing an open-source library that supports all DSP steps to map/demap an <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-subcarrier complex frequency-domain OFDM-IM symbol. Our implementation supports different index selector algorithms and is the first to enable the SE maximization while preserving the same time and space asymptotic complexities of the classic OFDM mapper.
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor

In order to mitigate the high communication cost in distributed and federated learning, various vector compression schemes, such as quantization, sparsification and dithering, have become very popular. In designing a compression method, one aims to communicate as few bits as possible, which minimizes the cost per communication round, while at the same time attempting to impart as little distortion (variance) to the communicated messages as possible, which minimizes the adverse effect of the compression on the overall number of communication rounds. However, intuitively, these two goals are fundamentally in conflict: the more compression we allow, the more distorted the messages become. We formalize this intuition and prove an uncertainty principle for randomized compression operators, thus quantifying this limitation mathematically, and effectively providing asymptotically tight lower bounds on what might be achievable with communication compression. Motivated by these developments, we call for the search for the optimal compression operator. In an attempt to take a first step in this direction, we consider an unbiased compression method inspired by the Kashin representation of vectors, which we call Kashin compression (KC). In contrast to all previously proposed compression mechanisms, KC enjoys a dimension independent variance bound for which we derive an explicit formula even in the regime when only a few bits need to be communicate per each vector entry.
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

Bayesian reward learning from demonstrations enables rigorous safety and uncertainty analysis when performing imitation learning. However, Bayesian reward learning methods are typically computationally intractable for complex control problems. We propose Bayesian Reward Extrapolation (Bayesian REX), a highly efficient Bayesian reward learning algorithm that scales to high-dimensional imitation learning problems by pre-training a low-dimensional feature encoding via self-supervised tasks and then leveraging preferences over demonstrations to perform fast Bayesian inference. Bayesian REX can learn to play Atari games from demonstrations, without access to the game score and can generate 100,000 samples from the posterior over reward functions in only 5 minutes on a personal laptop. Bayesian REX also results in imitation learning performance that is competitive with or better than state-of-the-art methods that only learn point estimates of the reward function. Finally, Bayesian REX enables efficient high-confidence policy evaluation without having access to samples of the reward function. These high-confidence performance bounds can be used to rank the performance and risk of a variety of evaluation policies and provide a way to detect reward hacking behaviors.
Unsupervised Pre-Trained, Texture Aware and Lightweight Model for Deep Learning Based Iris Recognition Under Limited Annotated Data

In this paper, we present a texture aware lightweight deep learning framework for iris recognition. Our contributions are primarily three fold. Firstly, to address the dearth of labelled iris data, we propose a reconstruction loss guided unsupervised pre-training stage followed by supervised refinement. This drives the network weights to focus on discriminative iris texture patterns. Next, we propose several texture aware improvisations inside a Convolution Neural Net to better leverage iris textures. Finally, we show that our systematic training and architectural choices enable us to design an efficient framework with upto $ 100\times$ fewer parameters than contemporary deep learning baselines yet achieve better recognition performance for within and cross dataset evaluations.
Cosmological parameter analyses using transversal BAO data

We investigate observational constraints on cosmological parameters combining 15 measurements of the transversal BAO scale (obtained free of any fiducial cosmology) with Planck–CMB data to explore the parametric space of some cosmological models. We investigate how much Planck + transversal BAO data can constraint the minimum Lambda cold dark matter (ΛCDM) model, and extensions, including neutrinos mass scale Mν, and the possibility for a dynamical dark energy (DE) scenario. Assuming the ΛCDM cosmology, we find H0 = 69.23 ± 0.50 km s−1 Mpc−1, Mν < 0.11 eV, and rdrag = 147.59 ± 0.26 Mpc (the sound horizon at drag epoch) from Planck + transversal BAO data. When assuming a dynamical DE cosmology, we find that the inclusion of the BAO data can indeed break the degeneracy of the DE free parameters, improving the constraints on the full parameter space significantly. We note that the model is compatible with local measurements of H0 and there is no tension on H0 estimates in this dynamical DE context. Also, we discuss constraints and consequences from a joint analysis with the local H0 measurement from SH0ES. Finally, we perform a model-independent analysis for the deceleration parameter, q(z), using only information from transversal BAO data.
Improvement, generalization, and scheme conversion of Wilson-line operators on the lattice in the auxiliary field approach

Nonlocal quark bilinear operators connected by link paths are used for studying parton distribution functions (PDFs) and transverse momentum-dependent PDFs of hadrons using lattice QCD. The nonlocality makes it difficult to understand the renormalization and improvement of these operators using standard methods. In previous work, we showed that by introducing an auxiliary field on the lattice, one can understand an on-axis Wilson-line operator as the product of two local operators in an extended theory. In this paper, we provide details about the calculation in perturbation theory of the factor for conversion from our lattice-suitable renormalization scheme to the MS-bar scheme. Extending our work, we study Symanzik improvement of the extended theory to understand the pattern of discretization effects linear in the lattice spacing, $a$, which are present even if the lattice fermion action exactly preserves chiral symmetry. This provides a prospect for an eventual $O(a)$ improvement of lattice calculations of PDFs. We also generalize our approach to apply to Wilson lines along lattice diagonals and to piecewise-straight link paths.
Regional Inequality Simulations Based on Asset Exchange Models with Exchange Range and Local Support Bias

To gain insights into the regional inequality problem, we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics by setting spatial exchange range and adding bias to asset fraction probability in equivalent exchanges. Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, first, increasing the intra-regional economic circulation rate, and, second, narrowing down the exchange range (inter-regional economic zone). Avoiding overconcentration of assets due to repeated exchanges, however, requires, thirdly, adding local support bias (distribution norm). A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution, eventually approaching the normal distribution and further reducing the Gini coefficient. Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation

Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of either distribution shift or augmentation diversity. Inspired by these, we seek to quantify how data augmentation improves model generalization. To this end, we introduce interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two.
Optimal anytime regret with two experts

The multiplicative weights method is an algorithm for the problem of prediction with expert advice. It achieves the minimax regret asymptotically if the number of experts is large, and the time horizon is known in advance. Optimal algorithms are also known if there are exactly two or three experts, and the time horizon is known in advance. In the anytime setting, where the time horizon is not known in advance, algorithms can be obtained by the doubling trick, but they are not optimal, let alone practical. No minimax optimal algorithm was previously known in the anytime setting, regardless of the number of experts. We design the first minimax optimal algorithm for minimizing regret in the anytime setting. We consider the case of two experts, and prove that the optimal regret is $\gamma \sqrt{t} / 2$ at all time steps $t$, where $\gamma$ is a natural constant that arose 35 years ago in studying fundamental properties of Brownian motion. The algorithm is designed by considering a continuous analogue, which is solved using ideas from stochastic calculus.
Joint Power Control and LSFD for Wireless-Powered Cell-Free Massive MIMO

This paper considers wireless uplink information and downlink power transfer in cell-free massive multiple-input multiple-output systems. The single-antenna user equipments (UEs) utilize the energy harvested in the downlink to transmit uplink pilot and information signals to the multiple-antenna access points (APs). We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. We derive the average harvested energy by using a practical non-linear energy harvesting circuit model for both coherent and non-coherent transmission schemes. Furthermore, the uplink spectral efficiency (SE) is derived for all the considered methods and the max-min fairness problem is cast where the optimization variables are the AP and UE power control coefficients together with the large-scale fading decoding vectors. The objective is to maximize the minimum SE of the UEs’ under APs’ and UEs’ transmission power constraints. A novel alternating optimization algorithm with guaranteed convergence and improvement at each step is proposed to solve the highly-coupled non-convex problem.
Kernel and Rich Regimes in Overparametrized Models

A recent line of work studies overparametrized neural networks in the "kernel regime," i.e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution. This stands in contrast to other studies which demonstrate how gradient descent on overparametrized multilayer networks can induce rich implicit biases that are not RKHS norms. Building on an observation by Chizat and Bach, we show how the scale of the initialization controls the transition between the "kernel" (aka lazy) and "rich" (aka active) regimes and affects generalization properties in multilayer homogeneous models. We also highlight an interesting role for the width of a model in the case that the predictor is not identically zero at initialization. We provide a complete and detailed analysis for a family of simple depth-$D$ models that already exhibit an interesting and meaningful transition between the kernel and rich regimes, and we also demonstrate this transition empirically for more complex matrix factorization models and multilayer non-linear networks.
On the Reconstruction of Geodesic Subspaces of ℝN

We consider the topological and geometric reconstruction of a geodesic subspace of [Formula: see text] both from the Čech and Vietoris-Rips filtrations on a finite, Hausdorff-close, Euclidean sample. Our reconstruction technique leverages the intrinsic length metric induced by the geodesics on the subspace. We consider the distortion and convexity radius as our sampling parameters for the reconstruction problem. For a geodesic subspace with finite distortion and positive convexity radius, we guarantee a correct computation of its homotopy and homology groups from the sample. This technique provides alternative sampling conditions to the existing and commonly used conditions based on weak feature size and [Formula: see text]–reach, and performs better under certain types of perturbations of the geodesic subspace. For geodesic subspaces of [Formula: see text], we also devise an algorithm to output a homotopy equivalent geometric complex that has a very small Hausdorff distance to the unknown underlying space.
Control of magnetic interactions between surface adatoms via orbital repopulation

We propose a reversible mechanism for switching Heisenberg-type exchange interactions between deposited transition metal adatoms from ferromagnetic to antiferromagnetic. Using first-principles calculations, we show that this mechanism can be realized for cobalt atoms on the surface of black phosphorus by making use of electrically-controlled orbital repopulation, as recently demonstrated by scanning probe techniques [Nat. Commun. 9, 3904 (2018)]. We find that field-induced repopulation not only affects the spin state, but also causes considerable modification of exchange interaction between adatoms, including its sign. Our model analysis demonstrates that variable adatom-substrate hybridization is a key factor responsible for this modification. We perform quantum simulations of inelastic tunneling characteristics and discuss possible ways to verify the proposed mechanism experimentally.
Properties of giant molecular clouds in the strongly barred galaxy NGC 1300

Star formation activity depends on galactic-scale environments. To understand the variations in star formation activity, comparing the properties of giant molecular clouds (GMCs) among environments with different star formation efficiency (SFE) is necessary. We thus focus on a strongly barred galaxy to investigate the impact of the galactic environment on the GMC properties, because the SFE is clearly lower in bar regions than in arm regions. In this paper, we present the 12CO(1 − 0) observations towards the western bar, arm, and bar-end regions of the strongly barred galaxy NGC 1300 with ALMA 12-m array at a high angular resolution of ∼40 pc. We detected GMCs associated with the dark lanes not only in the arm and bar-end regions but also in the bar region, where massive star formation is not seen. Using the CPROPS algorithm, we identified and characterized 233 GMCs across the observed regions. Based on the Kolmogorov–Smirnov test, we find that there is virtually no significant variations in GMC properties (e.g. radius, velocity dispersion, molecular gas mass, and virial parameter) among the bar, arm, and bar-end region. These results suggest that systematic differences in the physical properties of the GMCs are not the cause for SFE differences with environments, and that there should be other mechanisms which control the SFE of the GMCs such as fast cloud–cloud collisions in NGC 1300.
Disk evolution of the M87’s nucleus observed in 2008

We report the discovery of year-scale X-ray variation in the nuclear region of the M87 by reanalyzing the 8 Chandra observations from 2007 to 2008. The X-ray spectra are fitted and decomposed into the disk and flaring components. This year-scale X-ray variability can be explained quite well by a simple clumpy accretion model. We conclude that the central super-massive black hole of the M87 was accreting a cloud of $\sim0.5$M$_\odot$ at that time.
Disjointness-preserving operators and isospectral Laplacians

All the known counterexamples to Kac' famous question "can one hear the shape of a drum", i.e., does isospectrality of two Laplacians on domains imply that the domains are congruent, consist of pairs of domains composed of copies of isometric building blocks arranged in different ways, such that the unitary operator intertwining the Laplacians acts as a sum of overlapping "local" isometries mapping the copies to each other. We prove and explore a complementary positive statement: if an operator intertwining two appropriate realisations of the Laplacian on a pair of domains preserves disjoint supports, then under additional assumptions on it generally far weaker than unitarity, the domains are congruent. We show this in particular for the Dirichlet, Neumann and Robin Laplacians on spaces of continuous functions and on $L^2$-spaces.
How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue — how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto nondominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SBSE problems, especially when the problem nature or decision maker’s preferences are explicitly/implicitly known. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.