Miracle-dz/UniFAR-FTUs huggingface dataset:
A Three-dimensional Map of the Milky Way Using 66,000 Mira Variable Stars

We study the three-dimensional structure of the Milky Way using 65,981 Mira variable stars discovered by the Optical Gravitational Lensing Experiment survey. The spatial distribution of the Mira stars is analyzed with a model containing three barred components that include the X-shaped boxy component in the Galactic center (GC) and an axisymmetric disk. We take into account the distance uncertainties by implementing the Bayesian hierarchical inference method. The distance to the GC is R 0 = 7.66 ± 0.01(stat.) ± 0.39(sys.) kpc, while the inclination of the major axis of the bulge to the Sun–GC line of sight is θ = 20. °2 ± 0. °6(stat.) ± 0. °7(sys.). We present, for the first time, a detailed three-dimensional map of the Milky Way composed of young and intermediate-age stellar populations. Our analysis provides independent evidence for both the X-shaped bulge component and the flaring disk (being plausibly warped). We provide the complete data set of properties of Miras that were used for calculations in this work. The table includes mean brightness and amplitudes in nine photometric bands (covering a range of wavelengths from 0.5 to 12 μm), photometric chemical type, estimated extinction, and calculated distance with its uncertainty for each Mira variable. The median distance accuracy to a Mira star is at the level of 6.6%.
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models

In NLP, reusing pre-trained models instead of training from scratch has gained popularity; however, NLP models are mostly black boxes, very large, and often require significant resources. To ease, models trained with large corpora are made available, and developers reuse them for different problems. In contrast, developers mostly build their models from scratch for traditional DL-related problems. By doing so, they have control over the choice of algorithms, data processing, model structure, tuning hyperparameters, etc. Whereas in NLP, due to the reuse of the pre-trained models, NLP developers are limited to little to no control over such design decisions. They either apply tuning or transfer learning on pre-trained models to meet their requirements. Also, NLP models and their corresponding datasets are significantly larger than the traditional DL models and require heavy computation. Such reasons often lead to bugs in the system while reusing the pre-trained models. While bugs in traditional DL software have been intensively studied, the nature of extensive reuse and black-box structure motivates us to understand the different types of bugs that occur while reusing NLP models? What are the root causes of those bugs? How do these bugs affect the system? To answer these questions, We studied the bugs reported while reusing the 11 popular NLP models. We mined 9,214 issues from GitHub repositories and identified 984 bugs. We created a taxonomy with bug types, root causes, and impacts. Our observations led to several findings, including limited access to model internals resulting in a lack of robustness, lack of input validation leading to the propagation of algorithmic and data bias, and high-resource consumption causing more crashes, etc. Our observations suggest several bug patterns, which would greatly facilitate further efforts in reducing bugs in pre-trained models and code reuse.
Stochastic gravitational waves from postinflationary structure formation

Following inflation, the Universe may pass through an early matter-dominated phase supported by the oscillating inflaton condensate. Initially small fluctuations in the condensate grow gravitationally on subhorizon scales and can collapse to form nonlinear ``inflaton halos''. Their formation and subsequent tidal interactions will source gravitational waves, resulting in a stochastic background in the present Universe. We extend N-body simulations that model the growth and interaction of collapsed structures to compute the resulting gravitational wave emission. The spectrum of this radiation is well-matched by semi-analytical estimates based on the collapse of inflaton halos and their tidal evolution. We use this semi-analytic formalism to infer the spectrum for scenarios where the early matter-dominated phase gives way to a thermalized universe at temperatures as low as $100\,\mathrm{MeV}$ and we discuss the possible experimental opportunities created by this signal in inflationary models in which thermalization takes place long after inflation has completed.
Interpreting Deep Learning by Establishing a Rigorous Corresponding Relationship with Renormalization Group

In this paper, we focus on the interpretability of deep neural network. Our work is motivated by the renormalization group (RG) in statistical mechanics. RG plays the role of a bridge connecting microscopical properties and macroscopic properties, the coarse graining procedure of it is quite similar with the calculation between layers in the forward propagation of the neural network algorithm. From this point of view we establish a rigorous corresponding relationship between the deep neural network (DNN) and RG. Concretely, we consider the most general fully connected network structure and real space RG of one dimensional Ising model. We prove that when the parameters of neural network achieve their optimal value, the limit of coupling constant of the output of neural network equals to the ﬁxed point of the coupling constant in RG of one dimensional Ising model. This conclusion shows that the training process of neural network is equivalent to RG and therefore the network extract macroscopic feature from the input data just like RG.
CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models

This paper introduces transformer-based language models to the literature measuring corporate culture from text documents. We compile a unique data set of employee reviews that were labeled by human evaluators with respect to the information the reviews reveal about the firms’ corporate culture. Using this data set, we fine-tune state-of-the-art transformer-based language models to perform the same classification task. In out-of-sample predictions, our language models classify 17 to 30 percentage points more of employee reviews in line with human evaluators than traditional approaches of text classification. We make our models publicly available.
M ULTI - SCALE GIGAPIXEL MICROSCOPY USING A MULTI - CAMERA ARRAY MICROSCOPE

This article experimentally examines different conﬁgurations of a novel multi-camera array mi- croscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of “micro-cameras” to jointly image across a large ﬁeld-of-view at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts signiﬁcantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM conﬁgurations for different use cases. First, we demonstrate a conﬁguration that simultaneously images and estimates the 3D object depth across a 100 x 135 mm 2 ﬁeld-of-view (FOV) at approxi- mately 20 µ m resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM conﬁguration that records video across a continuous 83 x 123 mm 2 FOV with two-fold increased resolution (0.48 GP
Feature Selection with Distance Correlation

Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k. a. feature selection -- is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on Distance Correlation (DisCo), and demonstrate its effectiveness on the tasks of boosted top- and $W$-tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters.
Theoretical models of conformational transitions and ion conduction in voltage-dependent ion channels : Bioferroelectricity and superionic conduction

Theoretical studies of the structure and function of ion channels are reviewed, with emphasis on the underlying physical phenomena. Nerve and muscle membranes exhibit behavior interpretable as ferroelectric: Studies show that conformational transitions in voltage-dependent ion channels can be understood in terms of transitions from a ferroelectric state to a superionically conducting state. The ferroelectric-superionic transition hypothesis is supported by observations of voltage-gated ion conduction, surface charge, hysteresis, pyroelectricity, piezoelectricity, transition temperatures and Curie-Weiss behavior in these channels. Macroscopically, the opening of the sodium (Na+) channel appears to involve a moving phase boundary traveling from the outer to the inner surface of the axonal membrane. Molecular biology and electrophysiology have provided a partial picture of the microscopic structure of the Na+ channel, demonstrating a pattern of charged residues that suggests a “voltage sensor” role for the f.
Experimental Observations of the Topology of Convolutional Neural Network Activations

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture resulting in high-dimensional, difficult to interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers and discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight as to how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.
Differentially Private Adaptive Optimization with Delayed Preconditioners

Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information (e.g., public data) to boost the effectiveness of adaptive optimization. In this work, we explore techniques to estimate and efficiently adapt to gradient geometry in private adaptive optimization without auxiliary data. Motivated by the observation that adaptive methods can tolerate stale preconditioners, we propose differentially private adaptive training with delayed preconditioners (DP^2), a simple method that constructs delayed but less noisy preconditioners to better realize the benefits of adaptivity. Theoretically, we provide convergence guarantees for our method for both convex and non-convex problems, and analyze trade-offs between delay and privacy noise reduction. Empirically, we explore DP^2 across several real-world datasets, demonstrating that it can improve convergence speed by as much as 4x relative to non-adaptive baselines and match the performance of state-of-the-art optimization methods that require auxiliary data.
On the inviscid limit of the singular stochastic complex Ginzburg-Landau equation at statistical equilibrium

We study the limiting behavior of the two-dimensional singular stochastic stochastic cubic nonlinear complex Ginzburg-Landau with Gibbs measure initial data. We show that in the appropriate small viscosity and small noise regimes, the limiting dynamics is given by the deterministic cubic nonlinear Schr¨odinger equation at Gibbs equilibrium. In order to obtain this convergence, our approach combines both heat and Schr¨odinger analysis, within the framework of the Fourier restriction norm method of Bourgain (1993)
Calculation of hysteresis losses in hard superconductors carrying ac: isolated conductors and edges of thin sheets

Two methods of calculating hysteresis losses in hard superconductors are described. The London model is assumed in which the critical current density is taken independent of magnetic field. Losses in isolated wires of different cross section are considered but it is found that losses for solid wires vary by at most a factor of 3 for different shaped wires of the same current-carrying capacity. The loss at saturation current is usually 0·4-0·6 Ic2μ0/π. Losses at the edges of thin sheets are also calculated and a fourth-power dependence on current (for low current) is found. Three systems are examined: a slit parallel to the current in a wide sheet (Lc similar, equals μ0j2g2π3 F4/24), one pair of the edges of two wide strips set back-to-back and carrying antiparallel currents (Lc similar, equals μ0πj2s2 F4/6) and a long thin wall parallel to the current flow on a wide sheet (Lc similar, equals μ0π3j2a2 F4/3). Lc is the loss per cycle per unit length, F is the current peak as a fraction of saturation current, g the width of the slit, s the spacing of strips, a the height of the asperity and j the critical current density per unit width. All in MKS units.
Are the Heliosphere, Very Local Interstellar Medium, and Local Cavity in Pressure Balance with Galactic Gravity?

The Voyager spacecraft are providing the first in situ measurements of physical properties in the outer heliosphere beyond the heliopause. These data, together with data from the IBEX and Hubble Space Telescope and physical models consistent with these data, now provide critical measurements of pressures in the heliosphere and surrounding interstellar medium. Using these data, we assemble the first comprehensive survey of total pressures inside and outside of the heliopause, in the interstellar gas surrounding the heliosphere, and in the surrounding Local Cavity to determine whether the total pressures in each region are in balance with each other and with the gravitational pressure exerted by the galaxy. We intercompare total pressures in each region that include thermal, nonthermal, plasma, ram, and magnetic pressure components. An important result is the role of dynamic (ram) pressure. Total pressure balance at the heliopause can only be maintained with a substantial contribution of dynamic pressure from the inside. Also, total pressure balance between the outer heliosphere and pristine very local interstellar medium (VLISM) and between the pristine VLISM and the Local Cavity requires large dynamic pressure contributions.
On consistent regularities of control and value functions

In this paper we deal with nonsmooth optimal control problems in the case when the control is allowed to be a discontinuous function. We analyse smoothness assumptions on an adjoint process in deterministic and stochastic cases. Possibilities of steep generalized space-gradients of the adjoint function imply the necessity of an approximation of the Hamiltonian. The key question of such an approximation is a relationship between the control and the value function. Under quite general assumptions it is proved that the performance measure for the original process is determined by the control function with possible discontinuities.
Distributed Model Predictive Covariance Steering

—This paper proposes Distributed Model Predictive Covariance Steering (DMPCS), a novel method for safe multi-robot control under uncertainty. The scope of our approach is to blend covariance steering theory, distributed optimization and model predictive control (MPC) into a single methodology that is safe, scalable and decentralized. Initially, we pose a problem formulation that uses the Wasserstein distance to steer the state distributions of a multi-robot team to desired targets, and probabilistic constraints to ensure safety. We then transform this problem into a ﬁnite-dimensional optimization one by utilizing a disturbance feedback policy parametrization for covariance steering and a tractable approximation of the safety constraints. To solve the latter problem, we derive a decentralized consensus-based algorithm using the Alternating Direction Method of Multipliers (ADMM). This method is then extended to a receding horizon form, which yields the proposed DMPCS algorithm. Simulation experiments on large-scale prob- lems with up to hundreds of robots successfully demonstrate the effectiveness and scalability of DMPCS. Its superior capability in achieving safety is also highlighted through a comparison against a standard stochastic MPC approach. A video with all simulation experiments is available here.
Transfer Learning for High-Dimensional Quantile Regression via Convolution Smoothing

This paper studies the high-dimensional quantile regression problem under the transfer learning framework, where possibly related source datasets are available to make improvements on the estimation or prediction based solely on the target data. In the oracle case with known transferable sources, a smoothed two-step transfer learning algorithm based on convolution smoothing is proposed and the L1/L2 estimation error bounds of the corresponding estimator are also established. To avoid including non-informative sources, we propose to select the transferable sources adaptively and establish its selection consistency under regular conditions. Monte Carlo simulations as well as an empirical analysis of gene expression data demonstrate the effectiveness of the proposed procedure.
Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling

We consider the constrained sampling problem where the goal is to sample from a target distribution π(x) ∝ e−f(x) when x is constrained to lie on a convex body C ⊂ R. Motivated by penalty methods from continuous optimization, we propose and study penaltybased Langevin Dynamics (PLD) and penalty-based Hamiltonian Monte Carlo (PHMC) methods for constrained sampling that convert the constrained sampling problem into an unconstrained sampling problem by introducing a penalty function for constraint violations. When f is smooth and gradients of f are available, we show Õ(d/ε) iteration complexity for PLD to sample the target up to an ε-error where the error is measured in terms of the total variation distance and Õ(·) hides some logarithmic factors. For PHMC, we improve this result to Õ( √ d/ε) when the Hessian of f is Lipschitz and the boundary of C is sufficiently smooth. To our knowledge, these are the first convergence rate results for Hamiltonian Monte Carlo methods in the constrained sampling setting that can handle non-convex choices of f and can provide guarantees with the best dimension dependency among existing methods for constrained sampling when the gradients are deterministically available. We then consider the setting where only unbiased stochastic estimates of the gradients of f are available, motivated by applications to large-scale Bayesian learning problems. We propose PSGLD and PSGHMC methods that are variants of PLD and PHMC that can handle stochastic gradients and that are scaleable to large datasets without requiring Metropolis-Hasting correction steps. For PSGLD and PSGHMC, when f is strongly convex and smooth, we obtain an iteration complexity of Õ(d/ε) and Õ(d √ d/ε) respectively in the 2-Wasserstein distance. For the more general case, when f is smooth and f can be non-convex, we also provide finite-time performance bounds and iteration complexity results. Finally, we illustrate the performance of our algorithms on Bayesian LASSO regression and Bayesian constrained deep learning problems.
– A Modular and Multi-Modal Mapping Framework

Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-modality is a stepping stone towards achieving robustness in challenging environments and interoperability of heterogeneous multi-robot systems with varying sensor setups. With <monospace>maplab 2.0</monospace>, we provide a versatile open-source platform that facilitates developing, testing, and integrating new modules and features into a fully-fledged SLAM system. Through extensive experiments, we show that <monospace>maplab 2.0</monospace>’s accuracy is comparable to the state-of-the-art on the HILTI 2021 benchmark. Additionally, we showcase the flexibility of our system with three use cases: i) large-scale (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>10 <inline-formula><tex-math notation="LaTeX">$\text{km}$</tex-math></inline-formula>) multi-robot multi-session (23 missions) mapping, ii) integration of non-visual landmarks, and iii) incorporating a semantic object-based loop closure module into the mapping framework.
Weak approximation of nonlinear filtering for multiscale McKean-Vlasov stochastic systems

The work concerns the nonlinear filtering problem for a class of multiscale McKean-Vlasov stochastic systems. First of all, by a Poisson equation we prove that the solution of the slow part for a multiscale system weakly converges to the solution of the average equation. Then we define nonlinear filtering of the origin multiscale system and the average equation, and again through the same Poisson equation show the weak approximation between nonlinear filtering of the slow part for the origin multiscale system and that of the average equation.