DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for HMER

The task of recognising Handwritten Mathematical Expressions (HMER) is crucial in the fields of digital education and scholarly research. However, it is difficult to accurately determine the length and complex spatial relationships among symbols in handwritten mathematical expressions. In this study, we present a novel encoder-decoder architecture (DenseBAM-GI) for HMER, where the encoder has a Bottleneck Attention Module (BAM) to improve feature representation and the decoder has a Gated Input-GRU (GI-GRU) unit with an extra gate to make decoding long and complex expressions easier. The proposed model is an efficient and lightweight architecture with performance equivalent to state-of-the-art models in terms of Expression Recognition Rate (exprate). It also performs better in terms of top 1, 2, and 3 error accuracy across the CROHME 2014, 2016, and 2019 datasets. DenseBAM-GI achieves the best exprate among all models on the CROHME 2019 dataset. Importantly, these successes are accomplished with a drop in the complexity of the calculation and a reduction in the need for GPU memory

FAM: fast adaptive federated meta-learning

In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model, which can then be personalized locally on individual clients. Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. Nonetheless, learning suffers when data distributions diverge. There is a need to learn a global model that can be adapted using client's specific information to create personalized models on clients is required. MRI data suffers from this problem, wherein, one, due to data acquisition challenges, local data at a site is sufficient for training an accurate model and two, there is a restriction of data sharing due to privacy concerns and three, there is a need for personalization of a learnt shared global model on account of domain shift across client sites. The global model is sparse and captures the common features in the MRI. This skeleton network is grown on each client to train a personalized model by learning additional client-specific parameters from local data. Experimental results show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the sparse parameter set to be communicated during federated learning drastically reduced communication overhead, which makes the scheme viable for networks with limited resources.Comment: 13 Pages, 1 figur

Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment

In Federated Learning, model training is performed across multiple computing devices, where only parameters are shared with a common central server without exchanging their data instances. This strategy assumes abundance of resources on individual clients and utilizes these resources to build a richer model as user's models. However, when the assumption of the abundance of resources is violated, learning may not be possible as some nodes may not be able to participate in the process. In this paper, we propose a sparse form of federated learning that performs well in a Resource Constrained Environment. Our goal is to make learning possible, regardless of a node's space, computing, or bandwidth scarcity. The method is based on the observation that model size viz a viz available resources defines resource scarcity, which entails that reduction of the number of parameters without affecting accuracy is key to model training in a resource-constrained environment. In this work, the Lottery Ticket Hypothesis approach is utilized to progressively sparsify models to encourage nodes with resource scarcity to participate in collaborative training. We validate Equitable-FL on the $MNIST$, $F-MNIST$, and $CIFAR-10$ benchmark datasets, as well as the $Brain-MRI$ data and the $PlantVillage$ datasets. Further, we examine the effect of sparsity on performance, model size compaction, and speed-up for training. Results obtained from experiments performed for training convolutional neural networks validate the efficacy of Equitable-FL in heterogeneous resource-constrained learning environment.Comment: 12 pages, 7 figure

Detection of mullerian duct anomalies: diagnostic utility of two dimensional ultrasonography as compared to magnetic resonance imaging

Background: Mullerian duct anomalies (MDAs) are a fascinating group of disorders that have varied clinical presentation from being asymptomatic to primary amenorrhea to inability to reproduce. Correct diagnosis of the condition plays a crucial role in management. Imaging plays a pivotal role in making correct diagnosis. This study aims to find the prevalence of MDAs amongst study population and their relation with infertility and also compares diagnostic utility of pelvic ultrasound with MRI.Methods: A randomized diagnostic test evaluation study was conducted in the Department of Radiodiagnosis and Imaging of a tertiary care teaching hospital over a period of 2 years. The patient first underwent pelvic 2D USG in multiple planes using curvilinear probe of 3MHz to 5 MHz. frequency and then MRI.Results: Most common MDA in total study sample and in primary infertility group is arcuate uterus while in recurrent abortions group it is unicornuate uterus. Out of total study sample of 75 patients 2D USG detected 18 cases of MDA while MRI detected 22 cases of MDA. So, 2D USG failed to detect 04 cases of MDA in total study population bringing overall sensitivity of 2D USG as 81.8%, specificity of 100%, PPV of 100%, NPV of 93.4% and accuracy of 94.6%.Conclusions: 2D USG has a few limitations but in view of relatively simple imaging procedure, ease of availability and cost effectiveness it should be utilized as an initial imaging modality in patients with suspicion of MDAs

Comparative assessment of severity and prognosis of acute pancreatitis through APACHE II and HAPS predictor models

Background: Acute pancreatitis is one of the leading causes of hospitalization amongst all gastrointestinal disorders with high burden of morbidity and mortality. Predicting the progression of AP in terms of course and outcome to determine suitable management strategy and level of care is challenging. A number of predictor models are developed to predict the severity of acute pancreatitis but they vary in their definitions of severity. HAPS have been proposed as a simple scoring tool for assessment of severity and prognosis of acute pancreatitis. Thus, the aim of present study was to investigate the usefulness of HAPS predictor model against APACHE II model.Methods: Current investigation was a hospital based prospective study conducted on 80 proven cases of acute pancreatitis at K. K. hospital, Uttar Pradesh. The serum amylase and lipase levels of all enrolled patients, were tested and measured at admission, and at 48 and 72 hours post admission. The pancreatitis-specific clinical investigations like; HAPS, APACHE II were calculated and assessed statistically in terms of sensitivity, specificity, positive and negative predictive values and accuracy.Results: The findings of present investigation revealed that amongst the two scoring systems, APACHE II was superior predictor model in terms of sensitivity and specificity for various outcomes like severe acute pancreatitis, hospital stay >7 days and in-hospital mortality. However, HAPS exhibited high specificity for all the outcomes.Conclusions: HAPS can be recommended as a useful tool for early evaluation of acute pancreatitis in patients specifically in primary care settings of developing countries like India

Transcerebellar diameter: an effective tool in predicting gestational age in normal and IUGR pregnancy

Background: Gestational age is the common term used during pregnancy to describe how far advanced is the pregnancy. In the second and third trimesters, estimation of gestational age is accomplished by measuring the biparietal diameter, head circumference, abdominal circumference, and femur length. The transverse cerebellar diameter (TCD) may serve as a reliable predictor of gestational age (GA) of the fetus and a standard against which aberrations in other fetal parameters can be compared.Methods: The study was conducted in the tertiary care teaching hospital from July 2016 to March 2017. 200 pregnant women of gestational age 15-40 weeks of pregnancy referred from Dept of Obs and Gynae for antenatal scan comprised our study sample.Results: Age of women ranged from 18 to 43 years with maximum number of patients aged 26-30 years. Maximum cases with clinical suspicion for IUGR were in gestational age >36-40 weeks (50%). Evaluation of difference in actual and estimated gestational age between normal and actual gestational age showed that for normal pregnancy as well as in IUGR pregnancies mean difference between estimated and actual gestational age was minimum in TCD followed by other established parameters.Conclusions: TCD being a stable parameter irrespective of growth status of fetus, provides a basis for its usefulness as a ratio to predict IUGR and other perainatal outcomes as used in several studies. Thus, despite not being a direct marker for IUGR it can serve as a surrogate marker for detection of IUGR and another adverse perinatal outcome

Infrared (8-12 um) Dome Materials: Current Status

The 8-12 um range of infrared radiation being very significant for various electrooptic applications, various materials present themselves as candidates for use as dome (window) materialsin this range. This paper discusses various thermal, mechanical and optical properties of thesematerials. Further, trends in the development of these materials are also presented

Electrophoretic Studies of Biologically Important Mixed Metal – Ascorbic Acid –Nitrilotriacetate Complexes

Quantitative indication of a complex formation comes from the estimation of the stability or formation constants characterizing the equilibria corresponding to the successive addition of ligands. The binary equilibria of metal (II) / (III)–ascorbic acid and also mixed equilibria metal (II) / (III)–ascorbic acid–NTA have been studied using ionophoretic technique. The stability constants of metal–ascorbic acid binary complexes are found to be 103.77, 102.47,102.27 and that of metal–ascorbic acid–NTA mixed complexes have been found to be 106.05, 105.93, 105.75, for Fe(III), Cu(II) and Co(II) complexes, respectively at 25 °C and ionic strength Ic = 0.1 mol dm–3 (HClO4). (doi: 10.5562/cca1778

Optimization of Tine Spacing of Seed Drill for Dual Banding of Fertilizer

Dual banding of fertilizer is one of the most effective techniques for plants, which is achieved with a combination of tines mounted on seed drill. Optimization of spacing between tines is essential for band placement of fertilizer and ease of operation of machine. The experiments were conducted in soil bin to optimize the spacings between tines using response surface methodology (RSM). The lateral, vertical and longitudinal spacings between tines were 50–100, 25–75 and 250–300 mm, respectively. Analysis of variance and Pearson's correlation analysis showed that tines spacings significantly influenced the draft, soil disturbance area, specific draft and seeding depth. The optimum lateral, vertical and longitudinal spacings between dual tines were found to be 50, 53 and 250 mm; 50, 51 and 279 mm; and 50, 45 and 283 mm for soil compaction levels of 400, 600 and 800 kPa, respectively. The RSM successfully optimized the spacing between dual tines and predicted the soil-tool interaction parameters with an error of 0.12 to 5%. Dual banding of fertilizer can be accomplished by mounting this dual tine system to an existing seed drill at optimal spacing. It will aid in the performance of seed drill by reducing soil disturbance and power requirement

Comparison of predictive values of Mannheim peritonitis index, acute physiology and chronic health evaluation-II and Portsmouth-POSSUM scoring systems for prognosis of mortality in patients with perforation peritonitis

Background: Perforation peritonitis has emerged as one of the very common cause of surgical emergencies, particularly in developing countries like India. If left untreated for long due to improper prognosis or late diagnosis, perforation peritonitis may prove potentially fatal with a high mortality and morbidity rate. Scoring systems like APACHE-II (acute physiology and chronic health evaluation), p-POSSUM (Portsmouth-POSSUM) and MPI (Mannheim peritonitis index) may serve as simple, critical, and efficient prognostic tools in predicting the mortality in patients with perforation peritonitis. Thus, the aim of the current investigation was to examine the usefulness and accuracy of these scoring systems for predicting the mortality rate in perforation peritonitis.Methods: Current study was a prospective observational comparative study conducted at department of general surgery, KK Hospital, Lucknow. Detailed clinical and lab investigations of the participating patients were done and their demographic details were documented. Using history, clinical examination and lab values p-POSSUM, APACHE-II and MPI scores were calculated. Scores of each scoring system were statistically analyzed in prognosticating the mortality rate.Results: Mean age of the participating patients was 41.24±19.32 years. Abdominal pain and vomiting were observed as the most common symptoms in majority of patients.  No mortality was observed in patients with ≤20 MPI score, ≤20 APACH-II scores and ≤55 p-POSSUM score. Whereas mortality rate was observed to be 21.53% in patients with >20 MPI score, 82% in >20 APACH-II scores and 78% in >55 p-POSSUM score.Conclusions: APACHE II and p-POSSUM scores had a higher sensitivity and specificity in comparison to MPI for predicting the mortality in perforation peritonitis