| 1. |
- Chattopadhyay, Tanmoy, et al.
(författare)
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Hard X-Ray Polarization Catalog for a Five-year Sample of Gamma-Ray Bursts Using AstroSat CZT Imager
- 2022
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Ingår i: Astrophysical Journal. - : IOP Publishing Ltd. - 0004-637X .- 1538-4357. ; 936:1
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Tidskriftsartikel (refereegranskat)abstract
- The Cadmium Zinc Telluride Imager (CZTI) on board AstroSat has been regularly detecting gamma-ray bursts (GRBs) since its launch in 2015. Its sensitivity to polarization measurements at energies above 100 keV allows CZTI to attempt spectropolarimetric studies of GRBs. Here, we present the first catalog of GRB polarization measurements made by CZTI during its first five years of operation. This includes the time-integrated polarization measurements of the prompt emission of 20 GRBs in the energy range 100-600 keV. The sample includes the bright GRBs that were detected within an angle range of 0 degrees-60 degrees and 120 degrees-180 degrees where the instrument has useful polarization sensitivity and is less prone to systematics. We implement a few new modifications in the analysis to enhance the polarimetric sensitivity of the instrument. The majority of the GRBs in the sample are found to possess less/null polarization across the total bursts' duration in contrast to a small fraction of five GRBs that exhibit high polarization. The low polarization across the bursts might be due either to the burst being intrinsically weakly polarized or to a varying polarization angle within the burst even when it is highly polarized. In comparison to POLAR measurements, CZTI has detected a larger number of cases with high polarization. This may be a consequence of the higher energy window of CZTI observations, which results in the sampling of a shorter duration of burst emissions than POLAR, thereby probing emissions with less temporal variation in polarization properties.
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| 2. |
- Gupta, Ekta, et al.
(författare)
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Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images
- 2023
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Ingår i: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665488686 - 9781665488679
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Konferensbidrag (refereegranskat)abstract
- This work presents a novel label-efficient self-supervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training. The proposed approach uses the prior learning from the source DR image dataset to classify images drawn from the target datasets. The image representations learned from the unlabeled source domain dataset through contrastive learning are used to classify DR images from the target domain dataset. Moreover, the proposed approach requires a few labeled images to perform successfully on DR image classification tasks in cross-domain settings. The proposed work experiments with four publicly available datasets: EyePACS, APTOS 2019, MESSIDOR-I, and Fundus Images for self-supervised representation learning-based DR image classification in cross-domain settings. The proposed method achieves state-of-the-art results on binary and multi-classification of DR images, even in cross-domain settings. The proposed method outperforms the existing DR image binary and multi-class classification methods proposed in the literature. The proposed method is also validated qualitatively using class activation maps, revealing that the method can learn explainable image representations. The source code and trained models are published on GitHub11https://github.com/prakashchhipa/Learning-Self-Supervised-Representations-for-Label-Efficient-Cross-Domain-Knowledge-Transfer-on-DRF.
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| 3. |
- Gupta, Varun, et al.
(författare)
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Strategic Prototyping Technology Adoption in Startups: Framework, Challenges, and Opportunities
- 2022
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Ingår i: IT Professional: technology solutions for the enterprise. - 1520-9202. ; 24:3, s. 88-95
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Tidskriftsartikel (refereegranskat)abstract
- Startups use prototyping to develop and validate their products and business models. For startups, it is important to have a good knowledge of relevant adoption factors, to make decisions regarding which prototyping technology to adopt. In this article, we identify such factors based on our practical experiences of startups in various industries. We define a research framework, describe challenges affecting the factors, and present opportunities for startups to improve their adoption process.
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| 4. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 5. |
- Chattopadhyay, Tanmoy, et al.
(författare)
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Sub-MeV spectroscopy with AstroSat-CZT imager for gamma ray bursts
- 2021
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Ingår i: Journal of astrophysics and astronomy. - : Springer Nature. - 0250-6335 .- 0973-7758. ; 42:2
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Tidskriftsartikel (refereegranskat)abstract
- Cadmium-Zinc-Telluride Imager (CZTI) onboard AstroSat has been a prolific Gamma-Ray Burst (GRB) monitor. While the 2-pixel Compton scattered events (100-300 keV) are used to extract sensitive spectroscopic information, the inclusion of the low-gain pixels (similar to 20% of the detector plane) after careful calibration extends the energy range of Compton energy spectra to 600 keV. The new feature also allows single-pixel spectroscopy of the GRBs to the sub-MeV range which is otherwise limited to 150 keV. We also introduced a new noise rejection algorithm in the analysis ('Compton noise'). These new additions not only enhances the spectroscopic sensitivity of CZTI, but the sub-MeV spectroscopy will also allow proper characterization of the GRBs not detected by Fermi. This article describes the methodology of single, Compton event and veto spectroscopy in 100-900 keV combined for the GRBs detected in the first year of operation. CZTI in last five years has detected similar to 20 bright GRBs. The new methodologies, when applied on the spectral analysis for this large sample of GRBs, has the potential to improve the results significantly and help in better understanding the prompt emission mechanism.
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| 6. |
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| 7. |
- Chopra, Muskaan, et al.
(författare)
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Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery
- 2023
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Ingår i: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665488686 - 9781665488679
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Konferensbidrag (refereegranskat)abstract
- This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain ef-forts primarily focus on fully supervised learning approaches that rely entirely on human annotations. On the other hand, human annotations in remote sensing satellite imagery are always subject to limited quantity due to high costs and domain expertise, making transfer learning a viable alternative. The proposed approach investigates the knowledge transfer of self-supervised representations across the distinct source and target data distributions in depth in the remote sensing data domain. In this arrangement, self-supervised contrastive learning- based pretraining is performed on the source dataset, and downstream tasks are performed on the target datasets in a round-robin fashion. Experiments are conducted on three publicly avail-able datasets, UC Merced Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification tasks versus label efficiency. In self-supervised knowledge transfer, the pro-posed approach achieves state-of-the-art performance with label efficiency labels and outperforms a fully supervised setting. A more in-depth qualitative examination reveals consistent evidence for explainable representation learning. The source code and trained models are published on GitHub1.
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| 8. |
- Kumar, Harsh, et al.
(författare)
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The long-active afterglow of GRB 210204A : detection of the most delayed flares in a gamma-ray burst
- 2022
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 513:2, s. 2777-2793
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Tidskriftsartikel (refereegranskat)abstract
- We present results from extensive broadband follow-up of GRB 210204A over the period of 30 d. We detect optical flares in the afterglow at 7.6 x 10(5) s and 1.1 x 10(6) s after the burst: the most delayed flaring ever detected in a GRB afterglow. At the source redshift of 0.876, the rest-frame delay is 5.8 x 10(5) s (6.71 d). We investigate possible causes for this flaring and conclude that the most likely cause is a refreshed shock in the jet. The prompt emission of the GRB is within the range of typical long bursts: it shows three disjoint emission episodes, which all follow the typical GRB correlations. This suggests that GRB 210204A might not have any special properties that caused late-time flaring, and the lack of such detections for other afterglows might be resulting from the paucity of late-time observations. Systematic late-time follow-up of a larger sample of GRBs can shed more light on such afterglow behaviour. Further analysis of the GRB 210204A shows that the late-time bump in the light curve is highly unlikely due to underlying SNe at redshift (z) = 0.876 and is more likely due to the late-time flaring activity. The cause of this variability is not clearly quantifiable due to the lack of multiband data at late-time constraints by bad weather conditions. The flare of GRB 210204A is the latest flare detected to date.
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| 9. |
- Nguyen-Duc, Anh, et al.
(författare)
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Preface of the Workshop Organizers
- 2021
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - 2332-6441 .- 1090-705X. ; 2021-September, s. 200-201
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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