Miracle-dz/UniFAR-FTUs huggingface dataset:
Insertion pre-Lie products and translation of rough paths based on multi-indices

We use the diagram-free approach to regularity structures introduced by Otto et. al. to build rough paths based on multi-indices. We identify the analogue of the insertion pre-Lie algebra of trees and use it to build the corresponding group of translations of rough paths. We make this identification precise by showing that the natural dictionary between trees and multi-indices is a pre-Lie morphism under insertion, which in turn yields a Hopf algebra morphism to the rooted tree Hopf algebra equipped with the extraction-contraction coproduct.
Interpretable 2D Vision Models for 3D Medical Images

Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success. This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. Our method employs attention pooling to learn to assign each slice an importance weight and, by that, obtain a weighted average of all 2D slices. These weights directly quantify the contribution of each slice to the contribution and thus make the model prediction inspectable. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods. Furthermore, we compare the in-built interpretability of our approach to HiResCam, a state-of-the-art retrospective interpretability approach.
Black Hole Polarimetry I. A Signature of Electromagnetic Energy Extraction

In 1977, Blandford and Znajek showed that the electromagnetic field surrounding a rotating black hole can harvest its spin energy and use it to power a collimated astrophysical jet, such as the one launched from the center of the elliptical galaxy M87. Today, interferometric observations with the Event Horizon Telescope (EHT) are delivering high-resolution, event-horizon-scale, polarimetric images of the supermassive black hole M87* at the jet launching point. These polarimetric images offer an unprecedented window into the electromagnetic field structure around a black hole. In this paper, we show that a simple polarimetric observable—the phase ∠β 2 of the second azimuthal Fourier mode of the linear polarization in a near-horizon image—depends on the sign of the electromagnetic energy flux and therefore provides a direct probe of black hole energy extraction. In Boyer–Lindquist coordinates, the Poynting flux for axisymmetric electromagnetic fields is proportional to the product B ϕ B r . The phase ∠β 2 likewise depends on the ratio B ϕ /B r , thereby enabling an observer to determine the direction of electromagnetic energy flow in the near-horizon environment experimentally. Data from the 2017 EHT observations of M87* are consistent with electromagnetic energy outflow. Currently envisioned multifrequency observations of M87* will achieve higher dynamic range and angular resolution, and hence deliver measurements of ∠β 2 closer to the event horizon as well as better constraints on Faraday rotation. Such observations will enable a definitive test for energy extraction from the black hole M87*.
On the ability of CNNs to extract color invariant intensity based features for image classification

Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training distribution is well-documented. Recent studies suggest that CNNs exhibit a bias toward texture instead of object shape in image classification tasks, and that background information may affect predictions. This paper investigates the ability of CNNs to adapt to different color distributions in an image while maintaining context and background. The results of our experiments on modified MNIST and FashionMNIST data demonstrate that changes in color can substantially affect classification accuracy. The paper explores the effects of various regularization techniques on generalization error across datasets and proposes a minor architectural modification utilizing the dropout regularization in a novel way that enhances model reliance on color-invariant intensity-based features for improved classification accuracy. Overall, this work contributes to ongoing efforts to understand the limitations and challenges of CNNs in image classification tasks and offers potential solutions to enhance their performance.
Outlier-free merging of homogeneous groups of pre-classified observations under contamination

ABSTRACT We study the problem of merging homogeneous groups of pre-classified observations from a robust perspective motivated by the anti-fraud analysis of international trade data. This problem may be seen as a clustering task which exploits preliminary information on the potential clusters, available in the form of group-wise linear regressions. Robustness is then needed because of the sensitivity of likelihood-based regression methods to deviations from the postulated model. Through simulations run under different contamination scenarios, we assess the impact of outliers both on group-wise regression fitting and on the quality of the final clusters. We also compare alternative robust methods that can be adopted to detect the outliers and thus to clean the data. One major conclusion of our study is that the use of robust procedures for preliminary outlier detection is generally recommended, except perhaps when contamination is weak and the identification of cluster labels is more important than the estimation of group-specific population parameters. We also apply the methodology to find homogeneous groups of transactions in one empirical example that illustrates our motivating anti-fraud framework.
Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptation

Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Although existing methods often show high performance on specific datasets, they lack the ability to generalize to unseen scenarios. Unsupervised Domain Adaptation (UDA) tries to adapt a model, pre-trained in a supervised manner on a source domain, to an unlabelled target domain. There are only a few works on UDA for gait recognition proposing solutions to limited scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of gait recognition models, caused by the bias in the target domain to viewing angle or walking direction. We then suggest a remedy to reduce this bias with a novel triplet selection strategy combined with curriculum learning. To this end, we present Gait Orientation-based method for Unsupervised Domain Adaptation (GOUDA). We provide extensive experiments on four widely-used gait datasets, CASIA-B, OU-MVLP, GREW, and Gait3D, and on three backbones, GaitSet, GaitPart, and GaitGL, justifying the view bias and showing the superiority of our proposed method over prior UDA works.
Generating Benchmarks for Factuality Evaluation of Language Models

Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing methods for factuality evaluation of LLM generation focus on facts sampled from the LM itself, and thus do not control the set of evaluated facts and might under-represent domain specific or rare facts. We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM’s propensity to generate true facts from the corpus vs. similar but incorrect statements. We use our framework to create three benchmarks: Wiki-FACTOR, News-FACTOR and Expert-FACTOR. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation, as measured by human annotators.
A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media

Over the last decade, there has been a vast increase in eating disorder diagnoses and eating disorder-attributed deaths, reaching their zenith during the Covid-19 pandemic. This immense growth derived in part from the stressors of the pandemic but also from increased exposure to social media, which is rife with content that promotes eating disorders. Such content can induce eating disorders in viewers. This study aimed to create a multimodal deep learning model capable of determining whether a given social media post promotes eating disorders based on a combination of visual and textual data. A labeled dataset of Tweets was collected from Twitter, upon which twelve deep learning models were trained and tested. Based on model performance, the most effective deep learning model was the multimodal fusion of the RoBERTa natural language processing model and the MaxViT image classification model, attaining accuracy and F1 scores of 95.9% and 0.959 respectively. The RoBERTa and MaxViT fusion model, deployed to classify an unlabeled dataset of posts from the social media sites Tumblr and Reddit, generated similar classifications as previous research studies that did not employ artificial intelligence, showing that artificial intelligence can develop insights congruent to those of researchers. Additionally, the model was used to conduct a time-series analysis of yet unseen Tweets from eight Twitter hashtags, uncovering that the relative abundance of pro-eating disorder content has decreased drastically. However, since approximately 2018, pro-eating disorder content has either stopped its decline or risen once more in ampleness.
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators

A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating graphs has theoretical challenges, such as isomorphic representations -- evaluating how well a generative model performs is difficult. Which model to choose depending on the application domain? We extensively study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space. Manifold-based metrics outperform kernel-based metrics in embedding space. We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings. It shows the superiority of GRAN over GraphRNN - further, our proposed adaptation of GraphRNN with a depth-first search ordering is effective for small-sized graphs. A guideline on good practices regarding dataset selection and node feature initialization is provided. Our work is accompanied by open-source code and reproducible experiments.
A Causal Framework to Unify Common Domain Generalization Approaches

Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years. A large number of approaches have been proposed. Different approaches are motivated from different perspectives, making it difficult to gain an overall understanding of the area. In this paper, we propose a causal framework for domain generalization and present an understanding of common DG approaches in the framework. Our work sheds new lights on the following questions: (1) What are the key ideas behind each DG method? (2) Why is it expected to improve generalization to new domains theoretically? (3) How are different DG methods related to each other and what are relative advantages and limitations? By providing a unified perspective on DG, we hope to help researchers better understand the underlying principles and develop more effective approaches for this critical problem in machine learning.
TOI 4201 b and TOI 5344 b: Discovery of Two Transiting Giant Planets around M-dwarf Stars and Revised Parameters for Three Others

We present the discovery from the TESS mission of two giant planets transiting M-dwarf stars: TOI 4201 b and TOI 5344 b. We also provide precise radial velocity measurements and updated system parameters for three other M dwarfs with transiting giant planets: TOI 519, TOI 3629, and TOI 3714. We measure planetary masses of 0.525 ± 0.064 M J, 0.243 ± 0.020 M J, 0.689 ± 0.030 M J, 2.57 ± 0.15 M J, and 0.412±0.040 M J for TOI 519 b, TOI 3629 b, TOI 3714 b, TOI 4201 b, and TOI 5344 b, respectively. The corresponding stellar masses are 0.372 ± 0.018 M ☉, 0.635 ± 0.032 M ☉, 0.522 ± 0.028 M ☉, 0.626 ± 0.033 M ☉, and 0.612 ± 0.034 M ☉. All five hosts have supersolar metallicities, providing further support for recent findings that, like for solar-type stars, close-in giant planets are preferentially found around metal-rich M-dwarf host stars. Finally, we describe a procedure for accounting for systematic errors in stellar evolution models when those models are included directly in fitting a transiting planet system.
Scale interaction during an extreme rain event over southeast India

The major rains and floods over southeast India (during October through to December 2015) are addressed in the context of atmospheric scale interactions in the frequency domain. Some of the salient observational features of this period include: (i) the major El Niño of 2015, (ii) the stretch of lower tropospheric easterlies from the region of the warm sea‐surface temperature anomalies westwards to the east coast of India, (iii) presence of shear flow instability, in the presence of convection, along a long stretch of the easterly wind belt from the eastern Pacific Ocean to the eastern Bay of Bengal, (iv) large conversions of horizontal shear vorticity to curvature vorticity along this stretch of easterly trades, where these rain‐producing storms were forming, (v) an active IntraSeasonal Oscillation (ISO) time‐scale oscillation in the wind field that alternated between cyclonic and anticyclonic phases over southeast India during this period of heavy rains, and (vi) an active quasi‐biweekly oscillation that provides alternating onshore and offshore winds during this same period. The ISO and the quasi‐biweekly components contribute to the enhancement of the moisture supply from the Bay of Bengal during the extreme rain events. The synoptic scale receives its energy largely from organized convection within these disturbances on horizontal scales of the order of 2500 km. Other aspects such as the role of the sea‐surface temperatures of the Bay of Bengal and the role of Gill's antisymmetric heat source of the El Niño are also examined in this study.
Return Link Optimized Resource Allocation for Satellite Communications in the Ku/Ka-Band

Broadband satellite networks play an important role in today’s worldwide telecommunication infrastructure, providing services to an increasing number of users. Therefore, an efficient management of the spectrum resources is required in order to meet the fast-growing service demand. To this purpose, this paper addresses the optimization of the return carrier frequency plan for a broadband network benefiting from adaptive return channel selection (ARCS). The optimization problem is formulated as a multiobjective instance aiming at minimizing the total bandwidth and the unused throughput by using integer linear programming techniques. So as to capture events in which multiple terminals experience fade simultaneously, the spatial correlation of the attenuation fields has been incorporated in the optimization process. Moreover, physical layer characteristics and a minimum guaranteed throughput per user have been included as optimization constraints. Hence, the final outcome of this paper is a general technique providing an optimized carrier allocation plan, i.e., the number of carriers required to cover a certain area and guarantee a given throughput to each user.
PARALLEL ALGORITHM FOR CALCULATING GENERAL EQUILIBRIUM IN MULTIREGION ECONOMIC GROWTH MODELS

We develop and analyze a parallel algorithm for computing a solution in a multiregion dynamic general equilibrium model. The algorithm is based on an iterative method of the Gauss–Seidel type and exploits a special block structure of the model. Calculation of prices and input-output ratios in production for different time steps is carried out in parallel. We implement the parallel algorithm using the OpenMP interface for systems with shared memory. The efficiency of the algorithm is studied with the numbers of cores varying in the full range from one to the number of time steps of the model.
Some New Thoughts on the Multipoint Method for Reactor Physics Applications

The multipoint approach proves to be an ecient method for the neutron kinetics of nuclear reactors and accelerator-drive systems. In the present work some features of multipoint kinetics are discussed with reference to standard discretization schemes, both in space and in energy. In the second part of the paper a physically-base approach to construct a multipoint model is presented. It is shown how the parameters of the model can be derived through a Monte Carlo simulation evaluating transfer probabilities and characteristic times. Some results are obtained for an idealized fast system to evidence the properties of the multipoint parameters. At last, a standard point kinetic model is reconstructed from the multipoint equations, showing the relationship between the classic integral parameters of a multiplying systems and the multipoint parameters. This allows to consider a multipoint model for inverse applications aiming at the experimental determination of integral parameters.
Far Ultraviolet Photometry of F and G Dwarfs in the Hyades, Coma Berenices, and NGC 2281 Open Clusters as Obtained by GALEX

A two-color diagram formed from GALEX FUV and Johnson BV photometry is presented for F and early G dwarfs in three open clusters: the Hyades, Coma Berenices, and NGC 2281. Denoted as (mFUV − B) versus B − V, the position of dwarf stars in such a diagram has been shown by previous work to vary with the level of chromospheric and coronal activity. The upper main sequences of the Hyades and Coma Ber clusters match quite well in this two-color diagram, consistent with their similar ages and metallicities, and effectively zero reddening. While the upper main sequence of NGC 2281 is also fairly well defined, there is some uncertainty in the reddening of this cluster, which hinders an attempt to compare it with the other two. The results of this work suggest that it may become feasible to define isochrones for Population I dwarf stars in a (mFUV − B) versus B − V diagram if a sufficient sample of stars of well-known ages can be used as calibrating sources. However, the utility of such a diagram for age-dating purposes will depend upon the additional availability of accurate reddening measurements.
The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered.
Cryogenic cave minerals recorded 1889 CE melt event in northeast Greenland

The investigation of cryogenic cave minerals (CCMs) has developed in recent decades to be a particularly valuable proxy for palaeo-permafrost reconstruction. Due to difficulties, however, in obtaining reliable chronologies with the so-called “fine” form of these minerals, such studies have thus far utilised the “coarse” form. In this study, we successfully investigate the northernmost-known deposit of fine-grained cryogenic cave minerals (CCMs), which are situated in Cove Cave (Greenlandic translation: Eqik Qaarusussuaq), a low-elevation permafrost cave in northeast Greenland (80° N). The Cove Cave CCMs display 15 a complex mineralogy that consists of fine-grained cryogenic cave carbonates (CCC fine ) as well as sulphate minerals (gypsum, eugsterite, mirabilite, and löweite). In comparison to CCC fine from the mid-latitudes, positive δ 13 C values (7.0 to 11.4 ‰) recorded in Cove Cave are similar. In contrast, Cove Cave CCC fine δ 18 O values are ca. 8 to 16 ‰ lower. Furthermore, despite previous CCC fine dating efforts being unsuccessful, here we demonstrate that precise dating is possible with both isochron-based 230 Th/U dating and 14 C dating if the dead carbon fraction is reliably known. 20 The dating result (65±17 a BP; 1885±17 CE) shows that the CCMs formed during the late Little Ice Age, a time interval characterised by cold temperatures and abundant permafrost in northeast Greenland, making water infiltration into Cove Cave dependent on water amount and latent heat. We relate the CCM formation to a combination of black carbon deposition and anomalously high temperatures, which occurred over a few days, in the summer of 1889 CE. Such extreme conditions led to widespread melting over large areas of the Greenland ice sheet. We propose that the anomalous (weather) conditions of 1889 CE 25 also affected northeast Greenland, where the enhanced melting of a local ice cap resulted in water entering the cave and rapidly freezing. While CCC fine and gypsum likely precipitated concurrently with freezing, the origin of the other sulphate minerals might not be purely cryogenic but could be linked to subsequent sublimation of this ice accumulation in the very dry cave environment.