Starry Night Panorama with Advanced Feature Extraction and Star Stitching

Panoramic photography involves merging multiple photos of the same scene, each with overlapping views, to create a detailed image. When combining astrophotography with panoramic landscapes, challenges arise from image noise and subject motion. To address this, incorporating spatially variant registration steps in the panorama process can merge several shorter exposures into a final image with reduced noise and without motion artifacts. This method tackles two main issues in creating night sky panoramas: low signal-to-noise ratio (SNR) and motion blur.Initially, the images are divided into land and sky segments. Then, potential star locations are identified from a star image. Extracting features from night images is complex, and the Scale-Invariant Feature Transform (SIFT) algorithm is chosen for its robustness to rotation, scale changes, and noise. In astrophotography panoramas, more features need extraction, and SIFT performs well compared to other methods.Next, matching star features between images with common points allows combining two short exposures. A seamless blending technique removes visible seams between merged images. Compensating for star motion involves warping images using local transformations for smooth alignment. Finally, the combined exposures are stitched into a panorama using a spherical projection method

Neglected obstetric haemorrhage leading to acute kidney injury

Pregnancy related acute kidney injury takes substantial share of acute kidney injury (AKI) in India, with obstetrical haemorrhage having high morbidity and mortality. A young female had neglected obstetric haemorrhage (unrecognized intrauterine and massive intraperitoneal bleeding post caesarean, due to uterine trauma and atony) and dangerous intra-abdominal hypertension with exsanguination eventually leading to shock, multifactorial AKI, metabolic acidosis, and hyperkalemia. Intensive and aggressive management with subtotal hysterectomy, inotropes, fluid management, mechanical ventilation, tracheostomy, and hemodialysis changed the outcome. Despite odds against, neglected obstetric haemorrhage with complicated AKI, was managed successfully by emergency hysterectomy, aggressive intervention for AKI with intensive fluid, ventilatory management and daily hemodialysis. Timely identification and aggressive management of this condition and complications is pivotal in preventing complications, morbidity, and maternal mortality.

Human or Neural Translation

Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain

Open source quality control tool for translation memory using artificial intelligence

La mémoire de traduction (MT) joue un rôle décisif lors de la traduction et constitue une base de données idéale pour la plupart des professionnels de la langue. Cependant, une MT est très sujète au bruit et, en outre, il n’y a pas de source spécifique. Des efforts importants ont été déployés pour nettoyer des MT, en particulier pour former un meilleur système de traduction automatique. Dans cette thèse, nous essayons également de nettoyer la MT mais avec un objectif plus large : maintenir sa qualité globale et la rendre suffisament robuste pour un usage interne dans les institutions. Nous proposons un processus en deux étapes : d’abord nettoyer une MT institutionnelle (presque propre), c’est-à-dire éliminer le bruit, puis détecter les textes traduits à partir de systèmes neuronaux de traduction. Pour la tâche d’élimination du bruit, nous proposons une architecture impliquant cinq approches basées sur l’heuristique, l’ingénierie fonctionnelle et l’apprentissage profond. Nous évaluons cette tâche à la fois par annotation manuelle et traduction automatique (TA). Nous signalons un gain notable de +1,08 score BLEU par rapport à un système de nettoyage état de l’art. Nous proposons également un outil Web qui annote automatiquement les traductions incorrectes, y compris mal alignées, pour les institutions afin de maintenir une MT sans erreur. Les modèles neuronaux profonds ont considérablement amélioré les systèmes MT, et ces systèmes traduisent une immense quantité de texte chaque jour. Le matériel traduit par de tels systèmes finissent par peuplet les MT, et le stockage de ces unités de traduction dans TM n’est pas idéal. Nous proposons un module de détection sous deux conditions: une tâche bilingue et une monolingue (pour ce dernier cas, le classificateur ne regarde que la traduction, pas la phrase originale). Nous rapportons une précision moyenne d’environ 85 % en domaine et 75 % hors domaine dans le cas bilingue et 81 % en domaine et 63 % hors domaine pour le cas monolingue en utilisant des classificateurs d’apprentissage profond.Translation Memory (TM) plays a decisive role during translation and is the go-to database for most language professionals. However, they are highly prone to noise, and additionally, there is no one specific source. There have been many significant efforts in cleaning the TM, especially for training a better Machine Translation system. In this thesis, we also try to clean the TM but with a broader goal of maintaining its overall quality and making it robust for internal use in institutions. We propose a two-step process, first clean an almost clean TM, i.e. noise removal and then detect texts translated from neural machine translation systems. For the noise removal task, we propose an architecture involving five approaches based on heuristics, feature engineering, and deep-learning and evaluate this task by both manual annotation and Machine Translation (MT). We report a notable gain of +1.08 BLEU score over a state-of-the-art, off-the-shelf TM cleaning system. We also propose a web-based tool “OSTI: An Open-Source Translation-memory Instrument” that automatically annotates the incorrect translations (including misaligned) for the institutions to maintain an error-free TM. Deep neural models tremendously improved MT systems, and these systems are translating an immense amount of text every day. The automatically translated text finds a way to TM, and storing these translation units in TM is not ideal. We propose a detection module under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report a mean accuracy of around 85% in-domain and 75% out-of-domain for bilingual and 81% in-domain and 63% out-of-domain from monolingual tasks using deep-learning classifiers

Development of Predisposition,Injury,Response,Organ failure model for predicting acute kidney injury in acute on chronic liver failure.

Background and Aim There is limited data on predictors of acute kidney injury(AKI) in ACLF. We developed a PIRO model (Predisposition, Injury, Response, Organ failure) for predicting AKI in a multi-centric cohort of ACLF patients. Patients and Methods Data of 2360 patients from APASL-ACLF Research Consortium (AARC) was analysed. Multivariate logistic regression model (PIRO score) was developed from a derivation cohort (n=1363) which was validated in another prospective multicentric cohort of ACLF patients (n=997) Results Factors significant for P component were serum creatinine[(≥2mg/dl)OR 4.52, 95% CI (3.67-5.30)], bilirubin [(/dL,OR 1) versus (12-30 mg/dL,OR 1.45, 95% 1.1-2.63) versus (≥30 mg/dL,OR 2.6, 95% CI 1.3-5.2)], serum potassium [(/LOR-1)versus (3-4.9 mmol/L,OR 2.7, 95% CI 1.05-1.97) versus (≥5 mmol/L,OR 4.34, 95% CI 1.67-11.3)] and blood urea (OR 3.73, 95% CI 2.5-5.5); for I component nephrotoxic medications (OR-9.86, 95% CI 3.2-30.8); for R component,Systemic Inflammatory Response Syndrome,(OR-2.14, 95% CI 1.4-3.3); for O component, Circulatory failure (OR-3.5, 95% CI 2.2-5.5). The PIRO score predicted AKI with C-index of 0.95 and 0.96 in the derivation and validation cohort.The increasing PIRO score was also associated with mortality (p \u3c 0.001) in both the derivation and validation cohorts. Conclusions The PIRO model identifies and stratifies ACLF patients at risk of developing AKI. It reliably predicts mortality in these patients, underscoring the prognostic significance of AKI in patients with ACLF