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We present FLINT (learning-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach to estimate flow fields for 2D+time and 3D+time scientific ensemble data. FLINT can flexibly handle different types of…
The Physics Analysis eXpert (PAX) is an open source toolkit for high energy physics analysis. The C++ class collection provided by PAX is deployed in a number of analyses with complex event topologies at Tevatron and LHC. In this article,…
We present a spectroscopic catalog of 67082 M dwarfs from the LAMOST pilot survey. For each spectrum of the catalog, spectral subtype, radial velocity, equivalent width of H${\alpha}$, a number of prominent molecular band indices and the…
SDAMS is the ensemble of database + software packages aimed to the archiving, quick-look analysis, off-line analysis, network accessibility and plotting of the SPOrt produced data. Many of the aspects related to data archiving, analysis and…
The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual…
We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting…
A new set of software applications and libraries for use in the archival and analysis of pulsar astronomical data is introduced. Known collectively as the PSRCHIVE scheme, the code was developed in parallel with a new data storage format…
We present the design of SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) a new 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. SCALES enables low-resolution (R~50)…
JWST's exquisite data have opened the doors to new possibilities in detecting broad classes of astronomical objects, but also to new challenges in classifying those objects. In this work, we introduce SESHAT, the Stellar Evolutionary Stage…
Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction. Despite their capabilities, LLMs often generate outputs that deviate from predefined schemas, significantly…
We present the Swift X-ray Cluster Survey (SWXCS) catalog obtained using archival data from the X-ray telescope (XRT) on board the Swift satellite acquired from 2005 to 2012, extending the first release of the SWXCS. The catalog provides…
Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for…
This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems. As its name indicates, the package exploits spectrum slicing, a…
AOSAT is a python package for the analysis of single-conjugate adaptive optics (SCAO) simulation results. Python is widely used in the astronomical community these days, and AOSAT may be used stand-alone, integrated into a simulation…
Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors. A critical aspect of such tissue profiling is quantifying the spatial relationship relationships among cells at…
Libraries of stellar spectra are fundamental tools for the study of stellar populations and both empirical and synthetic libraries have been used for this purpose. In this paper, a new library of high resolution synthetic spectra is…
Technological advances in high performance computing and maturing physical models allow scientists to simulate weather and climate evolutions with an increasing accuracy. While this improved accuracy allows us to explore complex dynamical…
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be used for approximating high-dimensional functions and, in particular, for surrogate-model-based uncertainty quantification. It is lightweight,…
Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings. Knowledge distillation (KD) offers a viable solution by…
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource…