Miracle-dz/UniFAR-FTUs huggingface dataset:
The Similar Seven: A Set of Very Alike Exoplanets to Test Correlations between System Parameters and Atmospheric Properties

Studies of exoplanetary atmospheres have found no definite correlations between observed high-altitude aerosols and other system parameters. This could be, in part, because of the lack of homogeneous exoplanet samples for which specific parameters can be isolated and inspected. Here, we present a set of seven exoplanets with very similar system parameters. We analyze existing photometric time series, Gaia parallax, and high-resolution spectroscopic data to produce a new set of homogeneous stellar, planetary, and orbital parameters for these systems. With this, we confirm that most measured parameters for all systems are very similar, except for the host stars’ metallicities and possibly high-energy irradiation levels, which require UV and X-ray observations to constrain. From the sample, WASP-6b, WASP-96b, and WASP-110b have observed transmission spectra that we use to estimate their aerosol coverage levels using the Na i doublet 5892.9 Å. We find a tentative correlation between the metallicity of the host stars and the planetary aerosol levels. The trend we find with stellar metallicity can be tested by observing transmission spectra of the remaining planets in the sample. Based on our prediction, WASP-25b and WASP-55b should have higher levels of aerosols than WASP-124b and HATS-29b. Finally, we highlight how targeted surveys of alike planets similar to the ones presented here might prove key for identifying driving factors for atmospheric properties of exoplanets in the future and could be used as a sample selection criterion for future observations with, e.g., JWST, ARIEL, and the next generation of ground-based telescopes.
Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations

Generative language models have improved drastically, and can now produce realistic text outputs that are difficult to distinguish from human-written content. For malicious actors, these language models bring the promise of automating the creation of convincing and misleading text for use in influence operations. This report assesses how language models might change influence operations in the future, and what steps can be taken to mitigate this threat. We lay out possible changes to the actors, behaviors, and content of online influence operations, and provide a framework for stages of the language model-to-influence operations pipeline that mitigations could target (model construction, model access, content dissemination, and belief formation). While no reasonable mitigation can be expected to fully prevent the threat of AI-enabled influence operations, a combination of multiple mitigations may make an important difference.
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains

This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label correlations. Specifically, their effectiveness has been proven not only when considering a single domain but also when taking into account multiple domains. However, the topology of the used graph is not optimal as it is pre-defined heuristically. In addition, consecutive Graph Convolutional Network (GCN) aggregations tend to destroy the feature similarity. To overcome these issues, an architecture for learning the graph connectivity in an end-to-end fashion is introduced. This is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme. Numerous experiments are reported on well-known single-domain and multi-domain benchmarks. The results demonstrate that our approach achieves competitive results in terms of mean Average Precision (mAP) and model size as compared to the state-of-the-art. The code will be made publicly available.
An Analysis of Quantile Temporal-Difference Learning

We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Unlike classical TD learning, which can be analysed with standard stochastic approximation tools, QTD updates do not approximate contraction mappings, are highly non-linear, and may have multiple fixed points. The core result of this paper is a proof of convergence to the fixed points of a related family of dynamic programming procedures with probability 1, putting QTD on firm theoretical footing. The proof establishes connections between QTD and non-linear differential inclusions through stochastic approximation theory and non-smooth analysis.
Does progress on ImageNet transfer to real-world datasets?

Does progress on ImageNet transfer to real-world datasets? We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57% - 83%) on six practical image classification datasets. In particular, we study datasets collected with the goal of solving real-world tasks (e.g., classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models. On multiple datasets, models with higher ImageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not. We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.
Solving the semidefinite relaxation of QUBOs in matrix multiplication time, and faster with a quantum computer

Recent works on quantum algorithms for solving semidefinite optimization (SDO) problems have leveraged a quantum-mechanical interpretation of positive semidefinite matrices to develop methods that obtain quantum speedups with respect to the dimension $n$ and number of constraints $m$. While their dependence on other parameters suggests no overall speedup over classical methodologies, some quantum SDO solvers provide speedups in the low-precision regime. We exploit this fact to our advantage, and present an iterative refinement scheme for the Hamiltonian Updates algorithm of Brand\~ao et al. (Quantum 6, 625 (2022)) to exponentially improve the dependence of their algorithm on precision. As a result, we obtain a classical algorithm to solve the semidefinite relaxation of Quadratic Unconstrained Binary Optimization problems (QUBOs) in matrix multiplication time. Provided access to a quantum read/classical write random access memory (QRAM), a quantum implementation of our algorithm exhibits a worst case running time of $\mathcal{O} \left(ns + n^{1.5} \cdot \text{polylog} \left(n, \| C \|_F, \frac{1}{\epsilon} \right) \right)$.
A particle method for 1‐D compressible fluid flow

This paper proposes a novel particle scheme that provides convergent approximations of a weak solution of the Navier‐Stokes equations for the 1‐D flow of a viscous compressible fluid. Moreover, it is shown that all differential inequalities that hold for the fluid model are preserved by the particle method: mass is conserved, mechanical energy is decaying, and a modified mechanical energy functional is also decaying. The proposed particle method can be used both as a numerical method and as a method of proving existence of solutions for compressible fluid models.
Associated Prime Divisors in the Sense of Krull

In a recent paper by Douglas Underwood [8] several definitions of “associated prime divisors” were discussed and shown to be unique. In this note we produce a fifth type, which is due to W. Krull, and is found in his classical paper [2] and further discussed by B. Banaschewski in [1]. Historically this characterization considerably predates the other four definitions. Throughout this note, R denotes a commutative ring with unity, and all ideals and elements are assumed to be in such a ring. We shall let upper case letters, most frequently the beginning of the alphabet, denote ideals and lower case letters, elements of R. On the whole, our terminology will be that of [9]. We do, however, take exception with [9] in two instances, viz.
Studying Logging Practice in Machine Learning-based Applications

Logging is a common practice in traditional software development. Several research works have been done to investigate the different characteristics of logging practices in traditional software systems (e.g., Android applications, JAVA applications, C/C++ applications). Nowadays, we are witnessing more and more development of Machine Learning-based applications (ML-based applications). Today, there are many popular libraries that facilitate and contribute to the development of such applications, among which we can mention: Pytorch, Tensorflow, Theano, MXNet, Scikit-Learn, Caffe, and Keras. Despite the popularity of ML, we don't have a clear understanding of logging practices in ML applications. In this paper, we aim to fill this knowledge gap and help ML practitioners understand the characteristics of logging in ML-based applications. In particular, we conduct an empirical study on 110 open-source ML-based applications. Through a quantitative analysis, we find that logging practice in ML-based applications is less pervasive than in traditional applications including Android, JAVA, and C/C++ applications. Furthermore, the majority of logging statements in ML-based applications are in info and warn levels, compared to traditional applications where info is the majority of logging statement in C/C++ application and debug, error levels constitute the majority of logging statement in Android application. We also perform a quantitative and qualitative analysis of a random sample of logging statements to understand where ML developers put most of logging statements and examine why and how they are using logging. These analyses led to the following observations: (i) ML developers put most of the logging statements in model training, and in non-ML components. (ii) Data and model management appear to be the main reason behind the introduction of logging statements in ML-based applications.
Inhomogeneous Enrichment of Radioactive Nuclei in the Galaxy: Deposition of Live 53Mn, 60Fe, 182Hf, and 244Pu into Deep-sea Archives. Surfing the Wave?

While modeling the galactic chemical evolution (GCE) of stable elements provides insights to the formation history of the Galaxy and the relative contributions of nucleosynthesis sites, modeling the evolution of short-lived radioisotopes (SLRs) can provide supplementary timing information on recent nucleosynthesis. To study the evolution of SLRs, we need to understand their spatial distribution. Using a three-dimensional GCE model, we investigated the evolution of four SLRs: 53Mn, 60Fe, 182Hf, and 244Pu with the aim of explaining detections of recent (within the last ≈1–20 Myr) deposition of live 53Mn, 60Fe, and 244Pu of extrasolar origin into deep-sea reservoirs. We find that core-collapse supernovae are the dominant propagation mechanism of SLRs in the Galaxy. This results in the simultaneous arrival of these four SLRs on Earth, although they could have been produced in different astrophysical sites, which can explain why live extrasolar 53Mn, 60Fe, and 244Pu are found within the same, or similar, layers of deep-sea sediments. We predict that 182Hf should also be found in such sediments at similar depths.
Kinetic transverse dispersion relation for relativistic magnetized electron-positron plasmas with maxwell-juttner velocity distribution functions

We use a kinetic treatment to study the linear transverse dispersion relation for a magnetized isotropic relativistic electron-positron plasma with finite relativistic temperature. The explicit linear dispersion relation for electromagnetic waves propagating along a constant background magnetic field is presented, including an analytical continuation to the whole complex frequency plane for the case of Maxwell-Juttner velocity distribution functions. This dispersion relation is studied numerically for various temperatures. For left-handed solutions, the system presents two branches, the electromagnetic ordinary mode and the Alfven mode. In the low frequency regime, the Alfven branch has two dispersive zones, the normal zone (where ∂ω/∂k > 0) and an anomalous zone (where ∂ω/∂k < 0). We find that in the anomalous zone of the Alfven branch, the electromagnetic waves are damped, and there is a maximum wave number for which the Alfven branch is suppressed. We also study the dependence of the Alfven velocity and effective plasma frequency with the temperature. We complemented the analytical and numerical approaches with relativistic full particle simulations, which consistently agree with the analytical results.
The Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters. XXIV. Differences in Internal Kinematics of Multiple Stellar Populations

Our understanding of the kinematic properties of multiple stellar populations (mPOPs) in Galactic globular clusters (GCs) is still limited compared to what we know about their chemical and photometric characteristics. Such limitation arises from the lack of a comprehensive observational investigation of this topic. Here we present the first homogeneous kinematic analysis of mPOPs in 56 GCs based on high-precision proper motions computed with Hubble Space Telescope data. We focused on red-giant-branch stars, for which the mPOP tagging is clearer, and measured the velocity dispersion of stars belonging to first (1G) and second generations (2G). We find that 1G stars are generally kinematically isotropic even at the half-light radius, whereas 2G stars are isotropic at the center and become radially anisotropic before the half-light radius. The radial anisotropy is induced by a lower tangential velocity dispersion of 2G stars with respect to the 1G population, while the radial component of the motion is comparable. We also show possible evidence that the kinematic properties of mPOPs are affected by the Galactic tidal field, corroborating previous observational and theoretical results suggesting a relation between the strength of the external tidal field and some properties of mPOPs. Although limited to the GCs’ central regions, our analysis leads to new insights into the mPOP phenomenon, and provides the motivation for future observational studies of the internal kinematics of mPOPs.
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient

Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient. In recent years, it has been shown that in the convex setting it is possible to avoid linesearch altogether, and to allow the stepsize to adapt based on a local smoothness estimate without any backtracks or evaluations of the function value. In this work we propose an adaptive proximal gradient method, adaPG, that uses novel estimates of the local smoothness modulus which leads to less conservative stepsize updates and that can additionally cope with nonsmooth terms. This idea is extended to the primal-dual setting where an adaptive three term primal-dual algorithm, adaPD, is proposed which can be viewed as an extension of the PDHG method. Moreover, in this setting the ``essentially'' fully adaptive variant adaPD$^+$ is proposed that avoids evaluating the linear operator norm by invoking a backtracking procedure, that, remarkably, does not require extra gradient evaluations. Numerical simulations demonstrate the effectiveness of the proposed algorithms compared to the state of the art.
Non-Fermi liquid behavior in a simple model of Fermi arcs and pseudogap

In this paper, we consider a perturbed version of a very simple and exactly solvable model that supports Fermi arcs and pseudogap in its ground state and excitation spectrum, which includes Hubbard-like interactions in both momentum and real spaces. We find that the combined effects give rise to non-Fermi liquid behavior in the electron self-energy. This points to a novel mechanism that leads to non-Fermi liquid behavior, which is of strong current interest in the context of strongly correlated metals, that often become superconductors. Comparison will be made with phenomenology of high- temperature cuprate superconductors.
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.
Conservation properties of a leapfrog finite-difference time-domain method for the Schrödinger equation

We study the probability and energy conservation properties of a leap-frog finite-difference time-domain (FDTD) method for solving the Schr\"odinger equation. We propose expressions for the total numerical probability and energy contained in a region, and for the flux of probability current and power through its boundary. We show that the proposed expressions satisfy the conservation of probability and energy under suitable conditions. We demonstrate their connection to the Courant-Friedrichs-Lewy condition for stability. We argue that these findings can be used for developing a modular framework for stability analysis in advanced algorithms based on FDTD for solving the Schr\"odinger equation.
A Systematic Study of Ia-CSM Supernovae from the ZTF Bright Transient Survey

Among the supernovae (SNe) that show strong interaction with a circumstellar medium (CSM), there is a rare subclass of Type Ia supernovae, SNe Ia-CSM, which show strong narrow hydrogen emission lines much like SNe IIn but on top of a diluted Type Ia spectrum. The only previous systematic study of this class identified 16 SNe Ia-CSM, eight historic and eight from the Palomar Transient Factory (PTF). Now using the successor survey to PTF, the Zwicky Transient Facility (ZTF), we have classified 12 additional SNe Ia-CSM through the systematic Bright Transient Survey (BTS). Consistent with previous studies, we find these SNe to have slowly evolving optical light curves with peak absolute magnitudes between −19.1 and −21, spectra having weak Hβ and large Balmer decrements of ∼7. Out of the 10 SNe from our sample observed by NEOWISE, nine have 3σ detections, with some SNe showing a reduction in the red wing of Hα, indicative of newly formed dust. We do not find our SN Ia-CSM sample to have a significantly different distribution of equivalent widths of He i λ5876 than SNe IIn as observed in Silverman et al. The hosts tend to be late-type galaxies with recent star formation. We derive a rate estimate of 29−21+27 Gpc−3 yr−1 for SNe Ia-CSM, which is ∼0.02%–0.2% of the SN Ia rate. We also identify six ambiguous SNe IIn/Ia-CSM in the BTS sample and including them gives an upper limit rate of 0.07%–0.8%. This work nearly doubles the sample of well-studied Ia-CSM objects in Silverman et al., increasing the total number to 28.
Post-processing output from ensembles with and without parametrised convection, to create accurate, blended, high-fidelity rainfall forecasts

Funding information The authors gratefully acknowledge financial support from the European Union Connecting Europe Facility (CEF) – Telecommunication Sector Programme for the MISTRAL – Meteo Italian SupercompuTing poRtAL project (Grant Agreement No. INEA/CEF/ICT/A2017/1567101). Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address, one can try to use output from convection-parametrising (global) ensembles, post-processed to forecast at point-scale, or convection-resolving limited area ensembles. The newmethodology described here combines both. We apply “ecPointrainfall” post-processing to the ECMWF global ensemble. Alongside we use 2.2km COSMO LAM ensemble output (centred on Italy), and also post-process that, using a scaleselective neighbourhood approach to compensate for insufficient members and to preserve consistently forecast local details. The two resulting scale-compatible components then undergo lead-time-weighted blending, to create the final probabilistic 6h rainfall forecasts. Product creation for forecasters, in this way, constituted the “Italy Flash Flood use case”within the EU-fundedMISTRAL project; real-time delivery of open access products is ongoing. One year of verification shows that, of the five components (2 x raw, 2 x post-processed and blended), ecPoint is the most skilful. The post-processed COSMO ensemble adds most value to summer convective events in the evening, when the globalmodel has an underprediction bias.
Nucleon electric dipole moment from the 
θ
 term with lattice chiral fermions

We calculate the nucleon electric dipole moment (EDM) from the $\theta$ term with overlap fermions on three domain wall lattices with different sea pion masses at lattice spacing 0.11 fm. Due to the chiral symmetry conserved by the overlap fermions, we have well defined topological charge and chiral limit for the EDM. Thus, the chiral extrapolation can be carried out reliably at nonzero lattice spacings. We use three to four different partially quenched valence pion masses for each sea pion mass and find that the EDM dependence on the valence and sea pion masses behaves oppositely, which can be described by partially quenched chiral perturbation theory. With the help of the cluster decomposition error reduction (CDER) technique, we determine the neutron and proton EDM at the physical pion mass to be $d_{n}=-0.00148\left(14\right)\left(31\right)\bar\theta$ e$\cdot$fm and $d_{p}=0.0038\left(11\right)\left(8\right)\bar\theta$ e$\cdot$fm. This work is a clear demonstration of the advantages of using chiral fermions in the nucleon EDM calculation and paves the road to future precise studies of the strong $CP$ violation effects.
The nature of discrete second-order self-similarity

A new treatment of second-order self-similarity and asymptotic self-similarity for stationary discrete time series is given, based on the fixed points of a renormalisation operator with normalisation factors which are not assumed to be power laws. A complete classification of fixed points is provided, consisting of the fractional noise and one other class. A convenient variance time function approach to process characterisation is used to exhibit large explicit families of processes asymptotic to particular fixed points. A natural, general definition of discrete long-range dependence is provided and contrasted with common alternatives. The closely related discrete form of regular variation is defined, its main properties given, and its connection to discrete self-similarity explained. Folkloric results on long-range dependence are proved or disproved rigorously.