The POLARIS dataset is built from a decade of polarimetric observations (2014–2024) conducted with the SPHERE instrument on the Very Large Telescope (VLT). Specifically, it includes all public polarized light observations obtained using the IRDIS instrument, retrieved from the ESO Science Archive. These raw observations were uniformly preprocessed using a modified version of the IRDAP pipeline to generate high-quality Polarimetric Differential Imaging (PDI) products.

The dataset consists of three main components:

1. 96 labeled PDI-postprocessed polarimetric images (1024 × 1024 pixels), each annotated as either a target (with circumstellar disk structures) or a reference (with no detectable disk structures). This subset is approximately 3.18 GB in size.

2. 813 unlabeled PDI-postprocessed polarimetric images, each derived from sequences of preprocessed exposures in total intensity light ( 2014-2023) . These samples are also annotated with vegetation indices and land-use metadata. This component occupies approximately GB. The PDI-postprocessed polarimetric images for 2024 will be updated soon with new version. There will be total 921 unlabeld polarized data.

3. 206 RDI preprocessed exposure sequences used for downstream imputation, each corresponding to a labeled reference and composed of the original preprocessed exposures in total intensity light. The data is organized by year, with each archive file named according to its corresponding year. Each sequence contains 4n images (where n is the number of exposure cycles), with a resolution of 1024 × 1024 pixels per frame. This component totals approximately 38 GB (2014-2024).

4. All preprocessed exposure sequences, spanning 2014–2024, consist of $4n$ images per sequence (where $n$ is the number of exposure cycles), with each frame at a resolution of $1024 \times 1024$ pixels. The data are annotated with vegetation indices and land-use metadata. Due to its large volume (exceeding 300 GB), it is hosted via the following permanent Dropbox link for convenient access: https://www.dropbox.com/scl/fo/5bgfwb7d5gozo6k6pl5rc/AGJkkHLPGUIVlDRXssSAZpQ?rlkey=j0vm2xkbl0imgfvbcuba8e6lr&st=6ij3n0wz&dl=0

All files are provided in standard .fits format, following astronomical data conventions. The labeled PDI images support supervised learning tasks such as classification or domain adaptation, while the exposure sequences and unlabeled samples enable studies in imputation, denoising, self-supervised learning, or contrastive representation learning. The dataset will continue to expand as additional SPHERE observations are released to the public.

High Performance Computing Market will grow from USD 60.03 Billion to USD 115.09 Billion by 2034, showing an impressive CAGR of 7.5%

The global High-performance Liquid Chromatography market size is expected to reach USD 8.32 billion by 2032, according to a new study by Polaris Market Research.

Dataset Card for Thinking Draft Faithfulness Evaluation

This dataset accompanies the paper "Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models".

  Dataset Description

The Faithful Thinking Draft dataset is designed to evaluate how faithfully language models follow their own thinking drafts. It contains benchmarks for two key aspects of reasoning faithfulness:

Intra-Draft Faithfulness: Tests how consistently models follow their own reasoning steps when… See the full description on the dataset page: https://huggingface.co/datasets/polaris-73/faithful-thinking-draft.

The bias and uncertainty of calculated decay heat from spent nuclear fuel (SNF) are essential for code validation. Also, predicting these quantities is crucial for deriving decay heat safety margins, influencing the design and safety of facilities at the back end of the nuclear fuel cycle. This paper aims to analyze the calculated spent nuclear fuel decay heat biases, uncertainties, and correlations. The calculations are based on the Polaris and ORIGEN codes of the SCALE code system. Stochastically propagated uncertainties of inputs and nuclear data into calculated decay heats are compared. Uncertainty propagation using the former code is straightforward. In contrast, the counterpart of ORIGEN necessitated the pre-generation of perturbed nuclear cross-section libraries using TRITON, followed by coincident perturbations in the ORIGEN calculations. The decay heat uncertainties and correlations have shown that the observed validation biases are insignificant for both Polaris and ORIGEN. Also, similarities are noted between the calculated decay heat uncertainties and correlations of both codes. The fuel assembly burnup and cooling time significantly influence uncertainties and correlations, equivalently expressed in both Polaris and ORIGEN models. The analyzed decay heat data are highly correlated, particularly the fuel assemblies having either similar burnup or similar cooling time. The correlations were used in predicting the validation bias using machine learning models (ML). The predictive performance was analyzed for machine learning models weighting highly correlated benchmarks. The application of random forest models has resulted in promising variance reductions and predicted biases significantly similar to the validation ones. The machine learning results were verified using the MOCABA algorithm (a general Monte Carlo-Bayes procedure). The bias predictive performance of the Bayesian approach is examined on the same validation data. The study highlights the potential of neighborhood-based models, using correlations, in predicting the bias of spent nuclear fuel decay heat calculations and identifying influential and highly similar benchmarks.

3D Printing High Performance Plastic Market is set to expand significantly, with a projected ascent to $1108.93 million by 2032, underpinned by a consistent 24.25% CAGR over the forthcoming decade

Global cloud performance management was valued at USD 1.38 billion in 2021 and is expected to grow at a CAGR of 17.5% during the forecast period.

The High Performance Pigments Market register at a CAGR of 4.8% & reach USD 8.96 Billion by 2032. It is categorized as Source, Applicastion And Country.

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The global High Performance Composites Market is poised to reach USD 159.69 billion by 2034, growing at a CAGR of 9.4% from 2024 to 2034.

The global cloud performance management market size is expected to reach USD 5.51 billion by 2030, according to a new study by Polaris Market Research.

High-Performance Polyamides Market is estimated to grow at 5.5% CAGR to surpass USD 3,674.45 million by 2034.

The global high-performance thermoplastics market size is estimated to reach USD 65.6 billion by 2026 according to a new report by Polaris Market Research.

The global high-performance thermoplastics market size was valued at USD 36.5 billion in 2018 and is anticipated to grow at a CAGR of 7.7% from 2019 to 2026.

High-Performance Pigments Market is forecasted to grow at a rate of 4.6% in terms of value, from USD 5.14 billion in 2019 to reach USD 7.40 billion by 2027.

The global High-performance Liquid Chromatography Market was valued at USD 5.16 billion in 2023 and is expected to grow at a CAGR of 5.5% during the forecast period

The global High Performance Composites Market is expected to rise USD 158.69 billion by 2034 And anticipated to grow at a CAGR of 9.4%.

Global High-Performance Computing Market in terms of revenue is poised to reach USD 127.99 billion, growing at a CAGR of 9.7% during the forecast period by 2032.