相关论文: Ensuring Robustness in Training Set Based Global 2…

We perform a number of inter-related cosmographic fits to the legacy05 and gold06 supernova datasets. We pay particular attention to the influence of both statistical and systematic uncertainties, and also to the extent to which the choice…

Different combinations of input parameters to filament identification algorithms, such as Disperse and FilFinder, produce numerous different output skeletons. The skeletons are a one pixel wide representation of the filamentary structure in…

We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for…

The singular value decomposition (SVD) is a crucial tool in machine learning and statistical data analysis. However, it is highly susceptible to outliers in the data matrix. Existing robust SVD algorithms often sacrifice speed for…

Discrepancies between measurements of decay modes with an underlying quark level transition $b\to s \ell^+\ell^-$ and standard model (SM) predictions have persisted for several years, particularly for the muon channels. The inadequacy of…

The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs. We extend the scope of certifiable robustness to problems with more general and structured…

The paper presents new metrics to quantify and test for (i) the equality of distributions and (ii) the independence between two high-dimensional random vectors. We show that the energy distance based on the usual Euclidean distance cannot…

Based on the Kolmogorov-Arnold Network (KAN), we present a novel emulator of the global 21 cm cosmology signal, $\texttt{21cmKAN}$, that provides extremely fast training speed while achieving nearly equivalent accuracy to the most accurate…

To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent and robust performance across runs. Our empirical analysis…

Control of systematic uncertainties in the use of Type Ia supernovae as standardized distance indicators can be achieved through contrasting subsets of observationally-characterized, like supernovae. Essentially, like supernovae at…

In order to precisely measure the cosmological parameters and answer the fundamental questions in cosmology, it is necessary to develop new, powerful cosmological probes, in addition to the proposed next-generation optical survey projects.…

The low statistical errors on cosmological parameters promised by future galaxy surveys will only be realised with the development of new, fast, analysis methods that reduce potential systematic problems to low levels. We present an…

The cosmic 21-cm line of hydrogen is expected to be measured in detail by the next generation of radio telescopes. The enormous dataset from future 21-cm surveys will revolutionize our understanding of early cosmic times. We present a…

This paper describes a new approach to the optimization of information extraction in multi-wavelength image cubes of cosmological fields. The objective is to create a framework for the automatic identification and tagging of sources…

The redshifted 21-cm signal of neutral Hydrogen is a promising probe into the period of evolution of our Universe when the first stars were formed (Cosmic Dawn), to the period where the entire Universe changed its state from being…

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES)…

Anomaly detection plays a critical role in modern data-driven applications, from identifying fraudulent transactions and safeguarding network infrastructure to monitoring sensor systems for irregular patterns. Traditional approaches, such…

The reliability of a learning model is key to the successful deployment of machine learning in various applications. However, it is difficult to describe the phenomenon due to the complicated nature of the problems in machine learning. It…

In recent years, cosmic shear has emerged as a powerful tool to study the statistical distribution of matter in our Universe. Apart from the standard two-point correlation functions, several alternative methods like peak count statistics…

Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100,000 galaxy-scale strong lenses, a…