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2,980 results on '"Rangnekar A"'

1. COVID-19-Associated Liver Injury

Author: Gildea DT, Woo SM, O'Connor CE, and Rangnekar AS
Subjects: covid-19, cholangiopathy, dili, hepatitis, transplant, liver, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract: Daniel T Gildea,1 Stephanie M Woo,2 Corinne E O’Connor,3 Amol S Rangnekar4 1Department of Internal Medicine, MedStar Georgetown University Hospital, Washington, DC, USA; 2Department of Gastroenterology, MedStar Georgetown University Hospital, Washington, DC, USA; 3Georgetown University School of Medicine, Washington, DC, USA; 4MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital, Washington, DC, USACorrespondence: Daniel T Gildea, Tel +1 302-985-7777, Email daniel.gildea@medstar.netAbstract: This review analyzes data regarding liver injury associated with COVID-19 infection. We discuss reported effects on the liver from both COVID-19 and COVID-19 treatment as well as pathophysiology, review the potential role of drug-induced liver injury as an etiology of COVID-19-associated liver injury, and touch on other reports of significant outcomes including COVID-19 cholangiopathy and autoimmune hepatitis. Finally, we review the implications of COVID-19 infection in liver transplant recipients.Keywords: COVID-19, cholangiopathy, DILI, hepatitis, transplant, liver
Published: 2023

2. Transformer-based segmentation of adnexal lesions and ovarian implants in CT images

Author: Rangnekar, Aneesh, Boehm, Kevin M., Aherne, Emily A., Nikolovski, Ines, Gangai, Natalie, Liu, Ying, Zamarin, Dimitry, Roche, Kara L., Shah, Sohrab P., Lakhman, Yulia, and Veeraraghavan, Harini
Subjects: Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Two self-supervised pretrained transformer-based segmentation models (SMIT and Swin UNETR) fine-tuned on a dataset of ovarian cancer CT images provided reasonably accurate delineations of the tumors in an independent test dataset. Tumors in the adnexa were segmented more accurately by both transformers (SMIT and Swin UNETR) than the omental implants. AI-assisted labeling performed on 72 out of 245 omental implants resulted in smaller manual editing effort of 39.55 mm compared to full manual correction of partial labels of 106.49 mm and resulted in overall improved accuracy performance. Both SMIT and Swin UNETR did not generate any false detection of omental metastases in the urinary bladder and relatively few false detections in the small bowel, with 2.16 cc on average for SMIT and 7.37 cc for Swin UNETR respectively.
Published: 2024

3. Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences

Author: Jiang, Jue, Rangnekar, Aneesh, and Veeraraghavan, Harini
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses the curated task dataset for both pretraining the networks and fine-tuning them. Availability of large, diverse, and uncurated public medical image sets provides the opportunity to apply SSL in the "wild" and potentially extract features robust to imaging variations. However, the benefit of wild- vs self-pretraining has not been studied for medical image analysis. In this paper, we compare robustness of wild versus self-pretrained transformer (vision transformer [ViT] and hierarchical shifted window [Swin]) models to computed tomography (CT) imaging differences for non-small cell lung cancer (NSCLC) segmentation. Wild-pretrained Swin models outperformed self-pretrained Swin for the various imaging acquisitions. ViT resulted in similar accuracy for both wild- and self-pretrained models. Masked image prediction pretext task that forces networks to learn the local structure resulted in higher accuracy compared to contrastive task that models global image information. Wild-pretrained models resulted in higher feature reuse at the lower level layers and feature differentiation close to output layer after fine-tuning. Hence, we conclude: Wild-pretrained networks were more robust to analyzed CT imaging differences for lung tumor segmentation than self-pretrained methods. Swin architecture benefited from such pretraining more than ViT.
Published: 2024

4. Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment

Author: Gomez, Jorge Tapias, Rangnekar, Aneesh, Williams, Hannah, Thompson, Hannah, Garcia-Aguilar, Julio, Smith, Joshua Jesse, and Veeraraghavan, Harini
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Endoscopic images are used at various stages of rectal cancer treatment starting from cancer screening, diagnosis, during treatment to assess response and toxicity from treatments such as colitis, and at follow up to detect new tumor or local regrowth (LR). However, subjective assessment is highly variable and can underestimate the degree of response in some patients, subjecting them to unnecessary surgery, or overestimate response that places patients at risk of disease spread. Advances in deep learning has shown the ability to produce consistent and objective response assessment for endoscopic images. However, methods for detecting cancers, regrowth, and monitoring response during the entire course of patient treatment and follow-up are lacking. This is because, automated diagnosis and rectal cancer response assessment requires methods that are robust to inherent imaging illumination variations and confounding conditions (blood, scope, blurring) present in endoscopy images as well as changes to the normal lumen and tumor during treatment. Hence, a hierarchical shifted window (Swin) transformer was trained to distinguish rectal cancer from normal lumen using endoscopy images. Swin as well as two convolutional (ResNet-50, WideResNet-50), and vision transformer (ViT) models were trained and evaluated on follow-up longitudinal images to detect LR on private dataset as well as on out-of-distribution (OOD) public colonoscopy datasets to detect pre/non-cancerous polyps. Color shifts were applied using optimal transport to simulate distribution shifts. Swin and ResNet models were similarly accurate in the in-distribution dataset. Swin was more accurate than other methods (follow-up: 0.84, OOD: 0.83) even when subject to color shifts (follow-up: 0.83, OOD: 0.87), indicating capability to provide robust performance for longitudinal cancer assessment.
Published: 2024

5. Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets

Author: Rangnekar, Aneesh, Nadkarni, Nishant, Jiang, Jue, and Veeraraghavan, Harini
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. However, reliable performance on ID dataset does not guarantee robust generalization on out-of-distribution (OOD) datasets. Importantly, once deployed for clinical use, it is impractical to have `ground truth' delineations to assess ongoing performance drifts, especially when images fall into OOD category due to different imaging protocols. Hence, we introduced a comprehensive set of computationally fast metrics to evaluate the performance of multiple foundation models (Swin UNETR, SimMIM, iBOT, SMIT) trained with self-supervised learning (SSL). SSL pretraining was selected as this approach is applicable for large, diverse, and unlabeled image sets. All models were fine-tuned on identical datasets for lung tumor segmentation from computed tomography (CT) scans. SimMIM, iBOT, and SMIT used identical architecture, pretraining, and fine-tuning datasets to assess performance variations with the choice of pretext tasks used in SSL. Evaluation was performed on two public lung cancer datasets (LRAD: n = 140, 5Rater: n = 21) with different image acquisitions and tumor stage compared to training data (n = 317 public resource with stage III-IV lung cancers) and a public non-cancer dataset containing volumetric CT scans of patients with pulmonary embolism (n = 120). All models produced similarly accurate tumor segmentation on the lung cancer testing datasets. SMIT produced a highest F1-score (LRAD: 0.60, 5Rater: 0.64) and lowest entropy (LRAD: 0.06, 5Rater: 0.12), indicating higher tumor detection rate and confident segmentations. In the OOD dataset, SMIT misdetected least number of tumors, indicated by median volume occupancy of 5.67 cc compared to second best method SimMIM of 9.97 cc.
Published: 2024

6. Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network

Author: Williams, Hannah, Thompson, Hannah M., Lee, Christina, Rangnekar, Aneesh, Gomez, Jorge T., Widmar, Maria, Wei, Iris H., Pappou, Emmanouil P., Nash, Garrett M., Weiser, Martin R., Paty, Philip B., Smith, J. Joshua, Veeraraghavan, Harini, and Garcia-Aguilar, Julio
Published: 2024
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7. Harnessing the Power of Immunotherapy in Liver Transplantation for HCC

Author: Alhaqqan, Dalal, Khiew, Yii Chun, Dadashzadeh, Maryam, He, Aiwu Ruth, Thomas, Arul, and Rangnekar, Amol
Published: 2024
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8. Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis

Author: Jiang, Jue, Rangnekar, Aneesh, Choi, Chloe Min Seo, and Veeraraghavan, Harini
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Pretraining vision transformers (ViT) with attention guided masked image modeling (MIM) has shown to increase downstream accuracy for natural image analysis. Hierarchical shifted window (Swin) transformer, often used in medical image analysis cannot use attention guided masking as it lacks an explicit [CLS] token, needed for computing attention maps for selective masking. We thus enhanced Swin with semantic class attention. We developed a co-distilled Swin transformer that combines a noisy momentum updated teacher to guide selective masking for MIM. Our approach called \textsc{s}e\textsc{m}antic \textsc{a}ttention guided co-distillation with noisy teacher \textsc{r}egularized Swin \textsc{T}rans\textsc{F}ormer (SMARTFormer) was applied for analyzing 3D computed tomography datasets with lung nodules and malignant lung cancers (LC). We also analyzed the impact of semantic attention and noisy teacher on pretraining and downstream accuracy. SMARTFormer classified lesions (malignant from benign) with a high accuracy of 0.895 of 1000 nodules, predicted LC treatment response with accuracy of 0.74, and achieved high accuracies even in limited data regimes. Pretraining with semantic attention and noisy teacher improved ability to distinguish semantically meaningful structures such as organs in a unsupervised clustering task and localize abnormal structures like tumors. Code, models will be made available through GitHub upon paper acceptance., Comment: Paper is under review at TMI
Published: 2023

9. Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Álvarez-López, S., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonnand, R., Booker, P., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chalathadka-Subrahmanya, S., Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, I. P., Chang, R. -J., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Curyło, M., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhurandhar, S., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Du, H., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C. R., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franke, A., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Gais, J., Galaudage, S., Gallardo, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodwin-Jones, A. W., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupte, N., Gurav, R., Gurs, J., Gustafson, E. K., Gutierrez, N., Guzman, F., Haba, D., Haegel, L., Hain, G., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Hohmann, J. N., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hong, Q., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Hyland, J., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Iosif, P., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, D., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jarov, S., Jasal, P., Jaume, R., Javed, W., Jenner, K., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Karydas, T., Kas-danouche, Y. A., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katsuren, J. K., Katzman, W., Kaur, T., Kawabe, K., Kéfélian, F., Keitel, D., Kelley-Derzon, J., Kemper, S. A., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khanam, T., Khazanov, E. A., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, P., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Koliadko, N. D., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. 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J., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schmitz, S. J., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Sheela, A., Shen, B., Shepard, K. G., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. 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D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tonelli, M., Toriyama, A., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, H. T., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yang, F. W., Yang, K. Z., Yang, L. -C., Yang, Y. -C., Yang, Yang, Yang, Yi, Yap, M. J., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yeung, T. Y., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Yanqi, Zhang, Ya, Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level., Comment: 24 pages, 5 figures
Published: 2023

10. Preference for teamwork, personal interaction and communities of practice: does co-worker support matter?

Author: Dutta, Anjali and Rangnekar, Santosh
Published: 2024
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11. Tumor suppressor Par-4 activates autophagy-dependent ferroptosis

Author: Subburayan, Karthikeyan, Thayyullathil, Faisal, Pallichankandy, Siraj, Cheratta, Anees Rahman, Alakkal, Ameer, Sultana, Mehar, Drou, Nizar, Arshad, Muhammad, Palanikumar, L., Magzoub, Mazin, Rangnekar, Vivek M., and Galadari, Sehamuddin
Published: 2024
Full Text: View/download PDF

12. Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine

Author: Jose, Anmi, Kulkarni, Pallavi, Thilakan, Jaya, Munisamy, Murali, Malhotra, Anvita Gupta, Singh, Jitendra, Kumar, Ashok, Rangnekar, Vivek M., Arya, Neha, and Rao, Mahadev
Published: 2024
Full Text: View/download PDF

13. USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution

Author: Rangnekar, Vikrant, Upadhyay, Uddeshya, Akata, Zeynep, and Banerjee, Biplab
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation and labeling makes it challenging to achieve accurate results. We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the most informative samples for labeling, reducing the overall annotation cost while improving performance. Despite its potential, active learning has not been widely explored in high-dimensional computer vision regression tasks like super-resolution. We address this research gap and propose a new framework called USIM-DAL that leverages the statistical properties of colour images to learn informative priors using probabilistic deep neural networks that model the heteroscedastic predictive distribution allowing uncertainty quantification. Moreover, the aleatoric uncertainty from the network serves as a proxy for error that is used for active learning. Our experiments on a wide variety of datasets spanning applications in natural images (visual genome, BSD100), medical imaging (histopathology slides), and remote sensing (satellite images) demonstrate the efficacy of the newly proposed USIM-DAL and superiority over several dense regression active learning methods., Comment: Accepted at UAI 2023
Published: 2023

14. Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Alléné, C., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badaracco, F., Badger, C., Bae, S., Bae, Y., Bagnasco, S., Bai, Y., Baier, J. G., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Caneva, G., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. -H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y-K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Diab, R., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D. -F., Gao, D., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Ge, G. -G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godoy, F. G., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Goncharov, B., González, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimm, S. J., Grote, H., Grunewald, S., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gupta, S. K., Gurs, J., Gustafson, R., Gutierrez, N., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. -J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, T-C., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hough, J., Hourihane, S., Howell, D., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B-H., Hsieh, H-F., Hsiung, C., Huang, H-Y., Huang, P., Huang, Y-C., Huang, Y. -J., Huang, Y., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Hyland, J., Iandolo, G. A., Ide, S., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iosif, P., Irwin, J., Gupta, Ish, Isi, M., Ito, K., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janquart, J., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jarov, S., Jaume, R., Jenkins, A. C., Jenner, K., Jeon, C., Jia, W., Jiang, J., Jin, H. -B., Johns, G. R., Johnston, R., Johny, N., Jones, A. W., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jung, K., Jung, P., Junker, J., Juste, V., Kaihotsu, K., Kajita, T., Kakizaki, M., Kalaghatgi, C., Kalogera, V., Kamai, B., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kao, Y., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, P., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., King, B., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Kobayashi, Y., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kovalam, M., Koyama, N., Kozak, D. B., Kozakai, C., Kranzhoff, L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuromiya, Y., Kuroyanagi, S., Kuwahara, S., Kwak, K., Lacaille, G., Lagabbe, P., Laghi, D., Lalande, E., Lalleman, M., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, E., Lee, H. M., Lee, K., Lee, R., Legred, I. N., Lehmann, J., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Letendre, N., Levesque, C., Levin, Y., Leviton, J. N., Leyde, K., Li, A. K. Y., Li, B., Li, K. L., Li, P., Li, T. G. F., Li, X., Lin, C-Y., Lin, E. T., Lin, F-K., Lin, F-L., Lin, H. L., Lin, L. C. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, G. C., Liu, J., Liu, X., Llamas, F., Lo, R. K. L., Lo, T., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Lousto, C. O., Lovelace, G., Lowry, M. J., Lucaccioni, J. F., Lück, H., Lumaca, D., Lundgren, A. P., Lung, Y., Luo, L. -W., Lussier, A. W., Lynam, J. E., Ma'arif, M., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, S., Majorana, E., Makarem, C. N., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B. R., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Mark, Z., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V. A., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Masserot, A., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P. K., McCormick, S., McCuller, L., McGhee, G. I., McGinn, J., McGuire, S. C., McIsaac, C., McIver, J., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Miller, A., Miller, A. L., Miller, B., Millhouse, M., Mills, J. C., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Miravet-Tenés, M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitchell, A. L., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyo, K., Miyoki, S., Mo, Geoffrey, Modafferi, L. M., Moguel, E., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moragues, J., Moraru, D., Morawski, F., More, A., More, S., Moreno, C., Moreno, G., Mori, Y., Morisaki, S., Morisue, N., Moriwaki, Y., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Muusse, S., Nadji, S. L., Nagano, K., Nagar, A., Nagar, T., Nakamura, K., Nakano, H., Nakano, M., Nakayama, Y., Napolano, V., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neubauer, P., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Quynh, L. Nguyen, Ni, J., Ni, W. -T., Nichols, S. A., Nieradka, G., Nishimoto, T., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Notte, J., Novak, J., Nozaki, S., Nurbek, G., Nuttall, L. K., Obayashi, Y., Oberling, J., O'Brien, B. D., O'Dell, J., Oelker, E., Oertel, M., Ogaki, W., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohashi, T., Ohkawa, M., Ohme, F., Ohta, H., Okutani, Y., Oliveri, R., Olivetto, C., Oohara, K., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, E., Oshino, S., Ossokine, S., Osthelder, C., Otabe, S., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Pagano, R., Pagliaroli, G., Pai, A., Pai, S. A., Pal, S., Palamos, J. R., Palashov, O., Palomba, C., Pan, K. -C., Panda, P. K., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. 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K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Quartey, N., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, D., Rana, J., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reinhard, M., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reyes, J., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Richards, D., Richardson, J. W., Richardson, L., Riles, K., Rinaldi, S., Robertson, C., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rodriguez, S., Rolland, L., Rollins, J. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects., Comment: 28 pages, 11 figures
Published: 2023

15. Approach to Carbapenemase Detection in Klebsiella pneumoniae in Routine Diagnostic Laboratories

Author: Rangnekar Aseem, Shalini Shenoy, Suchitra Shenoy Mala, Shrikala Baliga, and Agarwal Ashish
Subjects: carbapenems, extended spectrum beta-lactamases, metallo-β-lactamase, modified hodge test, Medicine
Abstract: Introduction: Resistance to Carbapenems in Klebsiella may be due to Carbapenem hydrolysing enzymes. Accurate detection of carbapenemase must be done for patient treatment and epidemiological purposes. Aim: To detect carbapenemase production by performing Modified Hodge Test (MHT), Combined Disk Test (CDT) for Metallo-b-Lactamases (MBL) and PCR for blaKPC gene, to evaluate the performance of MHT using MacConkey Agar (MCA) and to access the value of MHT for carbapenemase detection. Material and Methods: Using a prospective laboratory study design, 153 Extended Spectrum Beta-Lactamases (ESBL) producing Klebsiella pneumoniae from clinical samples of patients admitted in the Kasturba Medical College were collected from January 2014 to December 2015. Isolates resistant to carbapenems by disk diffusion were subjected to MHT on MCA and Mueller Hinton agar (MHA). All isolates were tested for (MBL) production by Imipenem and Imipenem-EDTA CDT and subjected to PCR for the presence of blaKPC gene. Results: Out of 153 isolates, 54 were resistant to one of the carbapenems. Among these, 13 were positive for MHT on MHA, while 23 were positive by MHT on MCA. Number of MBL producers was 23 (42.5%), while blaKPC was detected in 2 out of the 54 isolates. Conclusion: Though detection of drug resistance gene remains the method of choice, it can be performed only in centers with adequate resources. Hence, for most laboratories in resource poor countries, the MHT performed on MCA with concomitant CDT for MBL detection seem to be a better option for detection of Carbapenem resistance.
Published: 2016
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16. Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Baiotti, L., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. 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Subjects: General Relativity and Quantum Cosmology
Abstract: The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages., Comment: 27 pages, 3 figures
Published: 2023
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17. Search for subsolar-mass black hole binaries in the second part of Advanced LIGO's and Advanced Virgo's third observing run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Alléné, C., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badaracco, F., Badger, C., Bae, S., Bae, Y., Bagnasco, S., Bai, Y., Baier, J. G., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Caneva, G., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. -H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y-K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Diab, R., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D. -F., Gao, D., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Ge, G. -G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godoy, F. G., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Goncharov, B., González, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimm, S. J., Grote, H., Grunewald, S., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gupta, S. K., Gurs, J., Gustafson, R., Gutierrez, N., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. -J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. 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K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Quartey, N., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, D., Rana, J., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reinhard, M., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reyes, J., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Richards, D., Richardson, J. W., Richardson, L., Riles, K., Rinaldi, S., Robertson, C., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rodriguez, S., Rolland, L., Rollins, J. G., Romanelli, M., Romano, R., Romel, C. L., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Rowlinson, S. J., Roy, Santosh, Roy, Soumen, Royzman, A., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Ryan, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Saha, S., Saito, Y., Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Samajdar, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sanuy, A., Saravanan, T. R., Sarin, N., Sasli, A., Sassolas, B., Satari, H., Sathyaprakash, B. S., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Sequino, V., Sergeev, A., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A., Sharma, P., Shawhan, P., Shcheblanov, N. S., Sheela, A., Sheridan, E., Shikano, Y., Shikauchi, M., Shimizu, H., Shimode, K., Shinkai, H., Shishido, T., Shoda, A., Shoemaker, D. H., Shoemaker, D. M., ShyamSundar, S., Sieniawska, M., Sigg, D., Silenzi, L., Singer, L. P., Singh, D., Singh, M. K., Singh, N., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Soldateschi, J., Somala, S. N., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevenson, S., Stops, D. J., Strain, K. A., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sugimoto, R., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Suzuki, Toshikazu, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takano, S., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamanini, N., Tanaka, K., Tanaka, Taiki, Tanaka, Takahiro, Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Taranto, C., Taruya, A., Tasson, J. D., Tenorio, R., Terhune, J. E. S., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, M., Thomas, P., Thomas, S., Thompson, D., Thompson, E. E., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomura, T., Tonelli, M., Torres-Forné, A., Torrie, C. I., Melo, I. Tosta e, Tournefier, E., Töyrä, D., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsai, D., Tsang, K. W., Tsang, T., Tsao, J-S., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsuna, D., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Tuyenbayev, D., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Ueda, A., Uehara, T., Ueno, K., Ueshima, G., Unnikrishnan, C. S., Urban, A. L., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. 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T., White, D. D., Whiting, B. F., Whittle, C., Wilk, O. S., Wilken, D., Williams, C. E., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wojtowicz, I. A., Wong, D., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Yadav, N., Yamada, T., Yamamoto, H., Yamamoto, K., Yamamoto, T., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, K. Z., Yang, L., Yang, Y. -C., Yang, Y., Yang, Yang, Yap, M. J., Yeeles, D. W., Yeh, S. -W., Yelikar, A. B., Yokoyama, J., Yokozawa, T., Yoo, J., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. -P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zheng, Y., Zhou, R., Zhu, X. J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., Zweizig, J., Shandera, S., and Jeong, D.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We describe a search for gravitational waves from compact binaries with at least one component with mass 0.2 $M_\odot$ -- $1.0 M_\odot$ and mass ratio $q \geq 0.1$ in Advanced LIGO and Advanced Virgo data collected between 1 November 2019, 15:00 UTC and 27 March 2020, 17:00 UTC. No signals were detected. The most significant candidate has a false alarm rate of 0.2 $\mathrm{yr}^{-1}$. We estimate the sensitivity of our search over the entirety of Advanced LIGO's and Advanced Virgo's third observing run, and present the most stringent limits to date on the merger rate of binary black holes with at least one subsolar-mass component. We use the upper limits to constrain two fiducial scenarios that could produce subsolar-mass black holes: primordial black holes (PBH) and a model of dissipative dark matter. The PBH model uses recent prescriptions for the merger rate of PBH binaries that include a rate suppression factor to effectively account for PBH early binary disruptions. If the PBHs are monochromatically distributed, we can exclude a dark matter fraction in PBHs $f_\mathrm{PBH} \gtrsim 0.6$ (at 90% confidence) in the probed subsolar-mass range. However, if we allow for broad PBH mass distributions we are unable to rule out $f_\mathrm{PBH} = 1$. For the dissipative model, where the dark matter has chemistry that allows a small fraction to cool and collapse into black holes, we find an upper bound $f_{\mathrm{DBH}} < 10^{-5}$ on the fraction of atomic dark matter collapsed into black holes., Comment: https://dcc.ligo.org/P2200139
Published: 2022
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18. BIM-Based Generative Design Processes for Targeted Value Design of Low-Carbon Buildings

Author: Bhilare, Saurav, Rangnekar, Salonee, Khatri, Diya, Yogeeswaran, Kantheepan, Chen, Qian, Ji, Wenying, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Desjardins, Serge, editor, and Poitras, Gérard J., editor
Published: 2024
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19. 3D Swin Transformer for Partial Medical Auto Segmentation

Author: Rangnekar, Aneesh, Jiang, Jue, Veeraraghavan, Harini, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ma, Jun, editor, and Wang, Bo, editor
Published: 2024
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20. Co-worker Support for Human Resource Flexibility and Resilience: A Literature Review

Author: Dutta, Anjali, Rangnekar, Santosh, Sushil, Series Editor, Chroust, Gerhard, Editorial Board Member, Connell, Julia, Editorial Board Member, Evans, Stuart, Editorial Board Member, Fujiwara, Takao, Editorial Board Member, C. Jackson OBE, Mike, Editorial Board Member, Jain, Rashmi, Editorial Board Member, Palanisamy, Ramaraj, Editorial Board Member, A. Stohr, Edward, Editorial Board Member, Rani, Neelam, editor, and Joshi, Rohit, editor
Published: 2024
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21. Measuring Individual Readiness for Change: The Role of Planfulness and Learning Through Experimentation

Author: Pahwa, Mohit, Rangnekar, Santosh, Sushil, Series Editor, Chroust, Gerhard, Editorial Board Member, Connell, Julia, Editorial Board Member, Evans, Stuart, Editorial Board Member, Fujiwara, Takao, Editorial Board Member, C. Jackson OBE, Mike, Editorial Board Member, Jain, Rashmi, Editorial Board Member, Palanisamy, Ramaraj, Editorial Board Member, A. Stohr, Edward, Editorial Board Member, Rani, Neelam, editor, and Joshi, Rohit, editor
Published: 2024
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22. Clarity Inside, Curious Outside: Exploring the Linkages Among Vividness of Future Self, Perseverance, and Joyful Exploration

Author: Khindri, Aakash, Rangnekar, Santosh, Sushil, Series Editor, Chroust, Gerhard, Editorial Board Member, Connell, Julia, Editorial Board Member, Evans, Stuart, Editorial Board Member, Fujiwara, Takao, Editorial Board Member, C. Jackson OBE, Mike, Editorial Board Member, Jain, Rashmi, Editorial Board Member, Palanisamy, Ramaraj, Editorial Board Member, A. Stohr, Edward, Editorial Board Member, Rani, Neelam, editor, and Joshi, Rohit, editor
Published: 2024
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23. The Resilience Trio: Self-kindness, Humanity, and Courage in Coping with Isolation

Author: Khurana, Rahul, Rangnekar, Santosh, Sushil, Series Editor, Chroust, Gerhard, Editorial Board Member, Connell, Julia, Editorial Board Member, Evans, Stuart, Editorial Board Member, Fujiwara, Takao, Editorial Board Member, C. Jackson OBE, Mike, Editorial Board Member, Jain, Rashmi, Editorial Board Member, Palanisamy, Ramaraj, Editorial Board Member, A. Stohr, Edward, Editorial Board Member, Rani, Neelam, editor, and Joshi, Rohit, editor
Published: 2024
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24. SpecAL: Towards Active Learning for Semantic Segmentation of Hyperspectral Imagery

Author: Rangnekar, Aneesh, Ientilucci, Emmett, Kanan, Christopher, Hoffman, Matthew, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Blasch, Erik, editor, Darema, Frederica, editor, and Aved, Alex, editor
Published: 2024
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25. Cleavage rate of In vitro matured oocytes fertilized with conventional semen in buffaloes

Author: Pitroda, M.H., Khillare, K.P., Amle, M.B., Meshram, M.D., Mali, A.B., Rangnekar, M.N., and Zawar, S.
Published: 2024
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26. Semantic Segmentation with Active Semi-Supervised Representation Learning

Author: Rangnekar, Aneesh, Kanan, Christopher, and Hoffman, Matthew
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Obtaining human per-pixel labels for semantic segmentation is incredibly laborious, often making labeled dataset construction prohibitively expensive. Here, we endeavor to overcome this problem with a novel algorithm that combines semi-supervised and active learning, resulting in the ability to train an effective semantic segmentation algorithm with significantly lesser labeled data. To do this, we extend the prior state-of-the-art S4AL algorithm by replacing its mean teacher approach for semi-supervised learning with a self-training approach that improves learning with noisy labels. We further boost the neural network's ability to query useful data by adding a contrastive learning head, which leads to better understanding of the objects in the scene, and hence, better queries for active learning. We evaluate our method on CamVid and CityScapes datasets, the de-facto standards for active learning for semantic segmentation. We achieve more than 95% of the network's performance on CamVid and CityScapes datasets, utilizing only 12.1% and 15.1% of the labeled data, respectively. We also benchmark our method across existing stand-alone semi-supervised learning methods on the CityScapes dataset and achieve superior performance without any bells or whistles., Comment: To appear in the British Machine Vision Conference (BMVC-2022)
Published: 2022

27. Model-based cross-correlation search for gravitational waves from the low-mass X-ray binary Scorpius X-1 in LIGO O3 data

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Alléné, C., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badaracco, F., Badger, C., Bae, S., Bae, Y., Bagnasco, S., Bai, Y., Baier, J. G., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Caneva, G., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. -H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y-K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. 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M., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Sequino, V., Sergeev, A., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A., Sharma, P., Shawhan, P., Shcheblanov, N. S., Sheela, A., Sheridan, E., Shikano, Y., Shikauchi, M., Shimizu, H., Shimode, K., Shinkai, H., Shishido, T., Shoda, A., Shoemaker, D. H., Shoemaker, D. M., ShyamSundar, S., Sieniawska, M., Sigg, D., Silenzi, L., Singer, L. P., Singh, D., Singh, M. K., Singh, N., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Soldateschi, J., Somala, S. N., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stops, D. J., Strain, K. A., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sugimoto, R., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Suzuki, Toshikazu, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takano, S., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamanini, N., Tanaka, K., Tanaka, Taiki, Tanaka, Takahiro, Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Taranto, C., Taruya, A., Tasson, J. D., Tenorio, R., Terhune, J. E. S., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, M., Thomas, P., Thomas, S., Thompson, D., Thompson, E. E., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomura, T., Tonelli, M., Torres-Forné, A., Torrie, C. I., Melo, I. Tosta e, Tournefier, E., Töyrä, D., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsai, D., Tsang, K. W., Tsang, T., Tsao, J-S., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsuna, D., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Tuyenbayev, D., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Ueda, A., Uehara, T., Ueno, K., Ueshima, G., Unnikrishnan, C. S., Urban, A. L., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., Van de Walle, A., van Dongen, J., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Venugopalan, G., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vinet, J. -Y., Virtuoso, A., Vitale, S., Vocca, H., von Reis, E. R. G., von Wrangel, J. S. A., Vorvick, C., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, J., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Watchi, J., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wilk, O. S., Wilken, D., Williams, C. E., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wojtowicz, I. A., Wong, D., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Yadav, N., Yamada, T., Yamamoto, H., Yamamoto, K., Yamamoto, T., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, K. Z., Yang, L., Yang, Y. -C., Yang, Y., Yang, Yang, Yap, M. J., Yeeles, D. W., Yeh, S. -W., Yelikar, A. B., Yokoyama, J., Yokozawa, T., Yoo, J., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. -P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zheng, Y., Zhou, R., Zhu, X. J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO, Advanced Virgo and KAGRA. This is a semicoherent search which uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25Hz to 1600Hz, as well as ranges in orbital speed, frequency and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100Hz and 200Hz, correspond to an amplitude h0 of about 1e-25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4e-26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically-marginalized upper limits are close to the predicted amplitude from about 70Hz to 100Hz; the limits assuming the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40Hz to 200Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500Hz or more., Comment: 19 pages, Open Access Journal PDF
Published: 2022
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28. ASO Visual Abstract: Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network

Author: Williams, Hannah, Thompson, Hannah M., Lee, Christina, Rangnekar, Aneesh, Gomez, Jorge T., Widmar, Maria, Wei, Iris H., Pappou, Emmanouil P., Nash, Garrett M., Weiser, Martin R., Paty, Philip B., Smith, J. Joshua, Veeraraghavan, Harini, and Garcia-Aguilar, Julio
Published: 2024
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29. Solution-processable polytriazoles from spirocyclic monomers for membrane-based hydrocarbon separations

Author: Bruno, Nicholas C., Mathias, Ronita, Lee, Young Joo, Zhu, Guanghui, Ahn, Yun-Ho, Rangnekar, Neel D., Johnson, J. R., Hoy, Scott, Bechis, Irene, Tarzia, Andrew, Jelfs, Kim E., McCool, Benjamin A., Lively, Ryan, and Finn, M. G.
Published: 2023
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30. Semantic Segmentation with Active Semi-Supervised Learning

Author: Rangnekar, Aneesh, Kanan, Christopher, and Hoffman, Matthew
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Using deep learning, we now have the ability to create exceptionally good semantic segmentation systems; however, collecting the prerequisite pixel-wise annotations for training images remains expensive and time-consuming. Therefore, it would be ideal to minimize the number of human annotations needed when creating a new dataset. Here, we address this problem by proposing a novel algorithm that combines active learning and semi-supervised learning. Active learning is an approach for identifying the best unlabeled samples to annotate. While there has been work on active learning for segmentation, most methods require annotating all pixel objects in each image, rather than only the most informative regions. We argue that this is inefficient. Instead, our active learning approach aims to minimize the number of annotations per-image. Our method is enriched with semi-supervised learning, where we use pseudo labels generated with a teacher-student framework to identify image regions that help disambiguate confused classes. We also integrate mechanisms that enable better performance on imbalanced label distributions, which have not been studied previously for active learning in semantic segmentation. In experiments on the CamVid and CityScapes datasets, our method obtains over 95% of the network's performance on the full-training set using less than 17% of the training data, whereas the previous state of the art required 40% of the training data., Comment: To appear in the Winter Conference on Applications of Computer Vision (WACV-2023)
Published: 2022

31. Co-worker support and community of practice: the role of concern for team member and preference for teamwork

Author: Dutta, Anjali and Rangnekar, Santosh
Published: 2023
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32. Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients

Author: Reinders, Floris C.J., Savenije, Mark H.F., de Ridder, Mischa, Maspero, Matteo, Doornaert, Patricia A.H., Terhaard, Chris H.J., Raaijmakers, Cornelis P.J., Zakeri, Kaveh, Lee, Nancy Y., Aliotta, Eric, Rangnekar, Aneesh, Veeraraghavan, Harini, and Philippens, Marielle E.P.
Published: 2024
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33. Trends of drug resistance in M. tuberculosis in a reference laboratory in Central India: Forging ahead towards TB elimination

Author: Desikan, Prabha, Panwalkar, Nikita, Rangnekar, Aseem, Khan, Zeba, Punde, Ram Prakash, Sharma, Arun Kumar, Kushwaha, Ragini, Ponnuraja, Chinnaiyan, and Anand, Sridhar
Published: 2024
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34. Can Adaptability Improve Openness to People's Ideas? The Moderating Role of Work Experience

Author: Khindri, Aakash and Rangnekar, Santosh
Published: 2023
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35. Do Mindful Employees Fit Their Organization Well? A Mediating Model Through Temperance

Author: Khurana, Rahul and Rangnekar, Santosh
Published: 2023
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36. Afraid of Learning? Towards the Learning Through Communities of Practice Instigated by Individuals' Preference for Teamwork and Concern for Team Member

Author: Dutta, Anjali and Rangnekar, Santosh
Published: 2023
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37. Revitalization of Human Resource Management in the Era of Artificial Intelligence

Author: Rangnekar, Seva, Rangnekar, Samarth, Ger, Aparna, Kacprzyk, Janusz, Series Editor, Singh, Pradeep Kumar, editor, Wierzchoń, Sławomir T., editor, Pawłowski, Wiesław, editor, Kar, Arpan Kumar, editor, and Kumar, Yugal, editor
Published: 2023
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38. O blood type is not associated with worse coagulopathy or outcome in exsanguinating trauma

Author: Gwin, J. Cole, Rangnekar, Niyati, Murray, Glenn P., Byerly, Saskya, Fleming, Andrew M., Easterday, Thomas S., Kerwin, Andrew J., and Howley, Isaac W.
Published: 2024
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39. Promoting concern for team members in Indian enterprises through personal interaction: mediating through a preference for teamwork and co-worker support

Author: Dutta, Anjali and Rangnekar, Santosh
Published: 2023
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40. Data-driven predictions of complex organic mixture permeation in polymer membranes

Author: Lee, Young Joo, Chen, Lihua, Nistane, Janhavi, Jang, Hye Youn, Weber, Dylan J., Scott, Joseph K., Rangnekar, Neel D., Marshall, Bennett D., Li, Wenjun, Johnson, J. R., Bruno, Nicholas C., Finn, M. G., Ramprasad, Rampi, and Lively, Ryan P.
Published: 2023
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41. Initial experience with 3D laparoscopic choledochal cyst (CDC) excision and hepatico-duodenostomy (HD) in 21 children

Author: Parelkar, Sandesh V., Makhija, Deepa P., Sanghvi, Beejal V., Gupta, Rahul K., Mudkhedkar, Kedar P., Shah, Rujuta S., Kumar, Abhijit, Rangnekar, Aditi, and Shah, Nehal
Published: 2023
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42. Can We Picture Equity? Critically Examining Cross-Cultural Short-Term Project Collaborations

Author: Clarke-De Reza, Sara, Coppens, Andrew D., Gopal, Shakuntala Devi, Honwad, Sameer, Niphadkar, Madhura, and Rangnekar, Shraddha
Abstract: This paper explores equity challenges common to short-term cross-cultural research partnerships. We focus on a project-based activity in which U.S. undergraduate students and college faculty taught middle-school students in Goa, India how to make podcasts about complex environmental problems. Project team members conducted a collaborative auto-ethnography focused on questions of power, leadership, collaboration, and equity, and examined exit interview photo elicitation data to identify the core challenges of ethical and equitable short-term cross-cultural research and programming. Our use of photographs as conversation prompts helped to highlight contradictions and asymmetries along axes of power, cultural imperialism, knower-knowledge, age, race/ethnicity, social class, and gender. We reflect on possibilities for educational research that rejects a "voluntourism" model and moves, if imperfectly, toward more equitable international collaborations.
Published: 2022
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43. USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution.

Author: Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, and Biplab Banerjee
Published: 2023

44. 3D Swin Transformer for Partial Medical Auto Segmentation.

Author: Aneesh Rangnekar, Jue Jiang, and Harini Veeraraghavan
Published: 2023
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45. Semantic Segmentation with Active Semi-Supervised Learning.

Author: Aneesh Rangnekar, Christopher Kanan, and Matthew J. Hoffman 0001
Published: 2023
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46. DDDAS-Based Remote Sensing

Author: Hoffman, Matthew J., Rangnekar, Aneesh, Mulhollan, Zachary, Vodacek, Anthony, Darema, Frederica, editor, Blasch, Erik P., editor, Ravela, Sai, editor, and Aved, Alex J., editor
Published: 2023
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47. Development and Validation of a Noninvasive Model for the Detection of High-Risk Varices in Patients with Unresectable HCC

Author: Parikh, Neehar D., Jones, Patricia, Salgia, Reena, Bhan, Irun, Grinspan, Lauren T., Jou, Janice H., Zhou, Kali, Jalal, Prasun, Roccaro, Giorgio, Rangnekar, Amol S., Benhammou, Jihane N., Pillai, Anjana, Mehta, Neil, Wedd, Joel, Yang, Ju Dong, Kim, Amy K., Duarte-Rojo, Andres, Oloruntoba, Omobonike O., Tevar, Amit, Au, Jennifer S., Blain, Yamile, Rao, Sanjana, Catalano, Onofrio A., Lewis, Sara, Mendiratta-Lala, Mishal, King, Kevin, Sachdev, Lekha, Lee, Edward W., Bruno, Jill, Kamel, Ihab, Tolosa, Celestina, Kao, Karissa, Badawi, Tarek, Przybyszewski, Eric M., Quirk, Lisa, Nathani, Piyush, Haydel, Brandy, Leven, Emily, Wong, Nicole, Albertian, Robert, Chen, Ariana, Aloor, Fuad Z., Mohamed, Islam B., Elkheshen, Ahmed, Marvil, Charles, Issac, Gerard, Clinton, Joseph W., Woo, Stephanie M., Yum, Jung, Rieger, Erin, Hutchison, Alan L., Turner, Don A., Alsudaney, Manaf, Hernandez, Perla, Xu, Ziyi, Khalid, Abdullah, Barrick, Bethany, Wang, Bo, Tapper, Elliot B., Hao, Wei, and Singal, Amit G.
Published: 2024
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48. A Randomized Placebo-Controlled Trial on Polyherbal Formulation as an Add-on to Standard-of-Care in Mild-to-Moderate Covid-19: Efficacy and Safety Approaches

Author: Patankar, Suresh B., primary, Gorde, Anupama, additional, Joshi, Kalpana, additional, Suryawanshi, Kishor, additional, Soni, Pravin, additional, Shah, Tejas, additional, Patankar, Sagar, additional, Jha, Diwakar, additional, Raje, Rajesh, additional, and Rangnekar, Hrishikesh, additional
Published: 2023
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49. Calibrated Vehicle Paint Signatures for Simulating Hyperspectral Imagery

Author: Mulhollan, Zachary, Rangnekar, Aneesh, Bauch, Timothy, Hoffman, Matthew J., and Vodacek, Anthony
Subjects: Electrical Engineering and Systems Science - Image and Video Processing
Abstract: We investigate a procedure for rapidly adding calibrated vehicle visible-near infrared (VNIR) paint signatures to an existing hyperspectral simulator - The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model - to create more diversity in simulated urban scenes. The DIRSIG model can produce synthetic hyperspectral imagery with user-specified geometry, atmospheric conditions, and ground target spectra. To render an object pixel's spectral signature, DIRSIG uses a large database of reflectance curves for the corresponding object material and a bidirectional reflectance model to introduce s due to orientation and surface structure. However, this database contains only a few spectral curves for vehicle paints and generates new paint signatures by combining these curves internally. In this paper we demonstrate a method to rapidly generate multiple paint spectra, flying a drone carrying a pushbroom hyperspectral camera to image a university parking lot. We then process the images to convert them from the digital count space to spectral reflectance without the need of calibration panels in the scene, and port the paint signatures into DIRSIG for successful integration into the newly rendered sets of synthetic VNIR hyperspectral scenes., Comment: 10 pages with supplementary material, 7 figures, submitted to CVPR PBVS 2020 Conference, Dataset uploaded to https://github.com/mulhollanz/Hyperspectral_Vehicle_Paint_Signatures_Dataset.git
Published: 2020

50. AeroRIT: A New Scene for Hyperspectral Image Analysis

Author: Rangnekar, Aneesh, Mokashi, Nilay, Ientilucci, Emmett, Kanan, Christopher, and Hoffman, Matthew J.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various sub-categories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the performance of three popular architectures - SegNet, U-Net, and Res-U-Net, for scene understanding and object identification via the task of dense semantic segmentation to establish a benchmark for the scene. To further strengthen the network, we add squeeze and excitation blocks for better channel interactions and use self-supervised learning for better encoder initialization. Aerial hyperspectral image analysis has been restricted to small datasets with limited train/test splits capabilities and we believe that AeroRIT will help advance the research in the field with a more complex object distribution to perform well on. The full dataset, with flight lines in radiance and reflectance domain, is available for download at https://github.com/aneesh3108/AeroRIT. This dataset is the first step towards developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks like vehicle tracking and occlusion handling., Comment: To appear in IEEE TGRS
Published: 2019
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