Miracle-dz/UniFAR-FTUs huggingface dataset:
Shaken or Stirred: The Diffuse Interstellar Medium with Exceptionally High SiO Abundance

Interstellar shocks, a key element of stellar feedback processes, shape the structure of the interstellar medium (ISM) and are essential for the chemistry, thermodynamics, and kinematics of interstellar gas. Powerful, high-velocity shocks are driven by stellar winds, young supernova explosions, more evolved supernova remnants, cloud–cloud collisions, and protostellar outflows, whereas the existence and origin of much-lower-velocity shocks (≲10 km s−1) are not understood. Direct observational evidence for interstellar shocks in diffuse and translucent ISM environments has been especially lacking. We present the most sensitive survey to date of SiO—often considered an unambiguous tracer of interstellar shocks—in absorption, obtained with the Northern Extended Millimeter Array interferometer. We detect SiO in five of eight directions probing diffuse and translucent environments without ongoing star formation. Our results demonstrate that SiO formation in the diffuse ISM (i.e., in the absence of significant star formation and stellar feedback) is more widespread and effective than previously reported. The observed SiO line widths are all ≲4 km s−1, excluding high-velocity shocks as a formation mechanism. Yet, the SiO abundances we detect are mostly 1–2 orders of magnitude higher than those typically assumed in quiescent environments and are often accompanied by other molecular transitions whose column densities cannot be explained with UV-dominated chemical models. Our results challenge the traditional view of SiO production via stellar feedback sources and emphasize the need for observational constraints on the distribution of Si in the gas phase and grain mantles, which are crucial for understanding the physics of grain processing and the diffuse interstellar chemistry.
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions

We present the first $\varepsilon$-differentially private, computationally efficient algorithm that estimates the means of product distributions over $\{0,1\}^d$ accurately in total-variation distance, whilst attaining the optimal sample complexity to within polylogarithmic factors. The prior work had either solved this problem efficiently and optimally under weaker notions of privacy, or had solved it optimally while having exponential running times.
DINOv2: Learning Robust Visual Features without Supervision

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model (Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP (Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
Homomorphism-Distinguishing Closedness for Graphs of Bounded Tree-Width

Two graphs are homomorphism indistinguishable over a graph class $\mathcal{F}$, denoted by $G \equiv_{\mathcal{F}} H$, if $\operatorname{hom}(F,G) = \operatorname{hom}(F,H)$ for all $F \in \mathcal{F}$ where $\operatorname{hom}(F,G)$ denotes the number of homomorphisms from $F$ to $G$. A classical result of Lov\'{a}sz shows that isomorphism between graphs is equivalent to homomorphism indistinguishability over the class of all graphs. More recently, there has been a series of works giving natural algebraic and/or logical characterizations for homomorphism indistinguishability over certain restricted graph classes. A class of graphs $\mathcal{F}$ is homomorphism-distinguishing closed if, for every $F \notin \mathcal{F}$, there are graphs $G$ and $H$ such that $G \equiv_{\mathcal{F}} H$ and $\operatorname{hom}(F,G) \neq \operatorname{hom}(F,H)$. Roberson conjectured that every class closed under taking minors and disjoint unions is homomorphism-distinguishing closed which implies that every such class defines a distinct equivalence relation between graphs. In this note, we confirm this conjecture for the classes $\mathcal{T}_k$, $k \geq 1$, containing all graphs of tree-width at most $k$. As an application of this result, we also characterize which subgraph counts are detected by the $k$-dimensional Weisfeiler-Leman algorithm. This answers an open question from [Arvind et al., J. Comput. Syst. Sci., 2020].
A Comparison of Temperature Data from Automated and Manual Observing Networks in Georgia and Impacts of Siting Characteristics

AbstractThe objectives of this study were to compare average monthly and seasonal maximum and minimum temperatures of the Georgia Automated Environmental Monitoring Network (AEMN) to those of geographically close (i.e., paired) manual observations from U.S. Historical Climatology Network (USHCN) stations and Cooperative Observer Program (COOP) stations for the period 2002–13, and to evaluate the extent to which differences in siting characteristics of paired AEMN–USHCN stations contribute to the temperature differences. Correlations for monthly and seasonal maximum and minimum temperatures of paired AEMN–USHCN and AEMN–COOP stations were high and almost always significant, although the correlations for seasonal minimum temperatures were slightly lower than those of maximum temperatures, especially for summer. Monthly maximum and minimum temperatures and seasonal maximum temperatures of paired AEMN–USHCN and AEMN–COOP stations were significantly different in only a few instances, while seasonal minimum tem.
Reachability Analysis Using Hybrid Zonotopes and Functional Decomposition

This paper proposes methods for reachability analysis of nonlinear systems in both open loop and closed loop with advanced controllers. The methods combine hybrid zonotopes, a construct called a state-update set, functional decomposition, and special ordered set approximations to enable linear growth in reachable set memory complexity with time and linear scaling in computational complexity with the system dimension. Facilitating this combination are new identities for constructing nonconvex sets that contain nonlinear functions and for efficiently converting a collection of polytopes from vertex representation to hybrid zonotope representation. Benchmark numerical examples from the literature demonstrate the proposed methods and provide comparison to state-of-the-art techniques.
Lightning: An X-Ray to Submillimeter Galaxy SED-fitting Code with Physically Motivated Stellar, Dust, and AGN Models

We present an updated version of Lightning, a galaxy spectral energy distribution (SED) fitting code that can model X-ray to submillimeter observations. The models in Lightning include the options to contain contributions from stellar populations, dust attenuation and emission, and active galactic nuclei (AGNs). X-ray emission, when utilized, can be modeled as originating from stellar compact binary populations with the option to include emission from AGNs. We have also included a variety of algorithms to fit the models to observations and sample parameter posteriors; these include adaptive Markov Chain Monte Carlo (MCMC), affine-invariant MCMC, and Levenberg–Marquardt gradient-descent (MPFIT) algorithms. To demonstrate some of the capabilities of Lightning, we present several examples using a variety of observational data. These examples include (1) deriving the spatially resolved stellar properties of the nearby galaxy M81, (2) demonstrating how X-ray emission can provide constraints on the properties of the supermassive black hole of a distant AGN, (3) exploring how to rectify the attenuation effects of inclination on the derived the star formation rate of the edge-on galaxy NGC 4631, (4) comparing the performance of Lightning to similar Bayesian SED-fitting codes when deriving physical properties of the star-forming galaxy NGC 628, and (5) comparing the derived X-ray and UV-to-IR AGN properties from Lightning and CIGALE for a distant AGN. Lightning is an open-source application developed in IDL and is available at https://github.com/rafaeleufrasio/lightning.
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline

BACKGROUND AND OBJECTIVE The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds. METHODS The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original CCTAs, and classifies them using a fine-tuned Multi-Axis Vision Transformer architecture. With the aim of emulating the current clinical practice, the model is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension. Furthermore, it generates visually interpretable maps to assess the reliability of the predictions. RESULTS When run on a database of 1873 three-channel images of 253 patients collected at the Monzino Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for the two classification tasks, respectively. CONCLUSION According to our knowledge, this is the first model trained to assign CAD-RADS scores learning solely from patient scores and not requiring finer imaging annotation steps that are not part of the clinical routine.
Intrinsic thermal conductivity of ZrC from low to ultrahigh temperatures: A critical revisit

Current phonon transport theory based on ground-state calculations has been successful in predicting thermal conductivity at room and medium temperatures but may misrepresent behavior at high temperatures. In this work, we predict the thermal conductivity ($\kappa$) of ZrC including electronic and phonon contributions from 300 K to 3500 K, by including high-order phonon scattering, lattice expansion, temperature-dependent (TD) harmonic and anharmonic force constants, and inter-band phonon conduction by using first principles. For the phonon transport, we find that four-phonon scattering significantly reduces the phonon thermal conductivity ($\kappa_{ph}$), e.g., by $\sim$60% and $\sim$75% at 2500 K and 3500 K, respectively. After including four-phonon scattering and all other factors, $\kappa_{ph}$ shows a $\sim$T$^{-1.5}$ rather than $\sim$T$^{-1}$ dependence. The contribution from inter-band (Wigner) phonon conduction is small, even at ultra-high temperatures. The temperature dependence of anharmonic force constants decreases the phonon scattering cross-section at elevated temperatures and increases the $\kappa_{ph}$ significantly (by 52% at 3500 K). For the electronic thermal transport, we find that it is sensitive to and can be changed by 20% by the TD lattice constants. The Lorenz number varies from 1.6 to 3.3$\times$10$^{-8}$ W$\cdot\Omega\cdot$K$^{-2}$ at different temperatures. The theoretical prediction in the literature overpredicts $\kappa_{ph}$ (e.g., $\sim$28%) and underpredicts the $\kappa_{el}$ (e.g., $\sim$38%), resulting in an overall underprediction of $\kappa$ ($\sim$26% at 1500 K). The impacts of grain size and defects are found strong, and no reported experimental data has reached the intrinsic theoretical thermal conductivity of ZrC yet.
Estimate-Then-Optimize Versus Integrated-Estimation-Optimization: A Stochastic Dominance Perspective

In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature suggests the integration of the estimation and optimization processes, by selecting model parameters that lead to the best empirical objective performance. Such an integrated approach can be readily shown to outperform simple “estimate then optimize” when the model is misspecified. In this paper, we argue that when the model class is rich enough to cover the ground truth, the performance ordering between the two approaches is reversed for nonlinear problems in a strong sense. Simple “estimate then optimize” outperforms the integrated approach in terms of stochastic dominance of the asymptotic optimality gap, i,e, the mean, all other moments, and the entire asymptotic distribution of the optimality gap is always better. Analogous results also hold under constrained settings and when contextual features are available. We also provide experimental findings to support our theory.
The Strength of the Sheared Magnetic Field in the Galactic’s Circumnuclear Disk

Recent high-resolution 53 μm polarimetric observations from SOFIA/HAWC+ have revealed the inferred plane-of-the-sky magnetic field (B-field) orientation in the Galactic center’s circumnuclear disk (CND). The B-field is mostly aligned with the steamers of ionized material falling onto Sgr A* at large, differential velocities (shear). In such conditions, estimating the B-field strength with the “classical” Davis–Chandrasekhar–Fermi (DCF) method does not provide accurate results. We derive a “modified” DCF method by solving the ideal-MHD equations from first principles considering the effects of a large-scale, shear flow on the propagation of a fast magnetosonic wave. In the context of the DCF approximation, both the value of the shear and its Laplacian affect the inferred B-field strength. Using synthetic polarization data from MHD simulations for a medium dominated by shear flows, we find that the “classical” DCF determines B-field strengths only within >50% of the true value where the “modified” DCF results are improved significantly (∼3%–22%). Applying our “modified” DCF method to the CND revealed B-field strengths of 1–16 mG in the northern arm, 1–13 mG in the eastern arm, and 3–27 mG in the western arc at spatial scales ≲1 pc, with median values of 5.1 ± 0.8, 4.0 ± 1.2, and 8.5 ± 2.3 mG, respectively. The balance between turbulent gas energy (kinetic plus hydrostatic) and turbulent magnetic energy densities suggest that, along the magnetic-field-flow direction, magnetic effects become less dominant as the shear flow increases and weakens the B-field via magnetic convection. Our results indicate that the transition from magnetically to gravitationally dominated accretion of material onto Sgr A* starts at distances ∼1 pc.
Effect of droplet-induced fluid motion on the interphase synthesis of nanoparticles in a microfluidic reactor

Droplet-based interphase synthesis provides means to produce nanoparticles with low polydispersity by controlling mass transport through droplet dynamics. An experimentally validated model, based on coupled computational fluid dynamics with population balance equation is proposed. The model incorporates: (i) hydrodynamics and thermal-transport of droplet-laden flow, (ii) spatial variation of species concentration with interfacial mass transfer and (iii) a tertiary phase within droplets to capture evolution of particle size distribution (PSD). We conclude that, mixing both inside and outside the droplets controls the PSD (arithmetic mean diameter (D10), ~ 50 nm). Consequently, as spherical-droplet diameter increases from 71% to 96% of the channel diameter (812 {\mu}m), coefficient of variation (CV=standard deviation/mean diameter) of PSD decreases from 33% to 21.3%, implying less polydisperse nanoparticles, due to altered mixing. With non-spherical drops, CV is further reduced to only 11%, which is significantly lower compared to that of 40% in single-phase micro-reactor, suggesting benefit of former.
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments

Based on the predictive map theory of spatial learning in animals, this study delves into the dynamics of Successor Feature (SF) and Predecessor Feature (PF) algorithms within noisy environments. Utilizing Q-learning and Q($\lambda$) learning as benchmarks for comparative analysis, our investigation yielded unexpected outcomes. Contrary to prevailing expectations and previous literature where PF demonstrated superior performance, our findings reveal that in noisy environments, PF did not surpass SF. In a one-dimensional grid world, SF exhibited superior adaptability, maintaining robust performance across varying noise levels. This trend of diminishing performance with increasing noise was consistent across all examined algorithms, indicating a linear degradation pattern. The scenario shifted in a two-dimensional grid world, where the impact of noise on algorithm performance demonstrated a non-linear relationship, influenced by the $\lambda$ parameter of the eligibility trace. This complexity suggests that the interaction between noise and algorithm efficacy is tied to the environmental dimensionality and specific algorithmic parameters. Furthermore, this research contributes to the bridging discourse between computational neuroscience and reinforcement learning (RL), exploring the neurobiological parallels of SF and PF learning in spatial navigation. Despite the unforeseen performance trends, the findings enrich our comprehension of the strengths and weaknesses inherent in RL algorithms. This knowledge is pivotal for advancing applications in robotics, gaming AI, and autonomous vehicle navigation, underscoring the imperative for continued exploration into how RL algorithms process and learn from noisy inputs.
Analyzing the Interaction Between Down-Sampling and Selection

Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection. However, evaluating populations on large training sets can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use the lexicase parent selection algorithm. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.
Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain Knowledge

Combinatorial optimization problems (COPs) are an important research topic in various fields. In recent times, there have been many attempts to solve COPs using deep learning-based approaches. We propose a novel neural network model that solves COPs involving geometry based on self-attention and a new attention mechanism. The proposed model is designed such that the model efficiently learns point-to-point relationships in COPs involving geometry using self-attention in the encoder. We propose efficient input and output sequence ordering methods that reduce ambiguities such that the model learns the sequences more regularly and effectively. Geometric COPs involve geometric requirements that need to be satisfied. In the decoder, a new masking scheme using domain knowledge is proposed to provide a high penalty when the geometric requirement of the problem is not satisfied. The proposed neural net is a flexible framework that can be applied to various COPs involving geometry. We conduct experiments to demonstrate the effectiveness of the proposed model for three COPs involving geometry: Delaunay triangulation, convex hull, and the planar Traveling Salesman problem. Our experimental results show that the proposed model exhibits competitive performance in finding approximate solutions for solving these problems.
Video alignment using unsupervised learning of local and global features

In this paper, we tackle the problem of video alignment, the process of matching the frames of a pair of videos containing similar actions. The main challenge in video alignment is that accurate correspondence should be established despite the differences in the execution processes and appearances between the two videos. We introduce an unsupervised method for alignment that uses global and local features of the frames. In particular, we introduce effective features for each video frame using three machine vision tools: person detection, pose estimation, and VGG network. Then, the features are processed and combined to construct a multidimensional time series that represents the video. The resulting time series are used to align videos of the same actions using a novel version of dynamic time warping named Diagonalized Dynamic Time Warping(DDTW). The main advantage of our approach is that no training is required, which makes it applicable for any new type of action without any need to collect training samples for it. For evaluation, we considered video synchronization and phase classification tasks on the Penn action dataset. Also, for an effective evaluation of the video synchronization task, we present a new metric called Enclosed Area Error(EAE). The results show that our method outperforms previous state-of-the-art methods, such as TCC, and other self-supervised and weakly supervised methods.
The role of object-centric representations, guided attention, and external memory on generalizing visual relations

Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs have been developed in order to enable learning abstract relation from images. In this work, we systematically evaluate a series of DNNs that integrate mechanism such as slot attention, recurrently guided attention, and external memory, in the simplest possible visual reasoning task: deciding whether two objects are the same or different. We found that, although some models performed better than others in generalizing the same-different relation to specific types of images, no model was able to generalize this relation across the board. We conclude that abstract visual reasoning remains largely an unresolved challenge for DNNs.