Surgical technique of total hip arthroplasty: an experience from a tertiary level hospital in India

Background: Total hip arthroplasty (THA) is performed for patients with hip pain, which may arise due to a variety of conditions.Methods: An observational study of 20 hip joints presenting to the Department of Orthopedic Surgery, Indira Gandhi Medical College Shimla from December 2008 till December 2010 for THR was done. Laboratory and imaging investigations were performed as per the standard operating protocol of our center. Modified Harris Hip scoring was done for all included patients pre-operatively. A posterolateral approach with posterior dislocation of hip was used in all the patients. Pre-operative and intra-operative details were noted using a pretested semi-structured questionnaire. Data were analysed descriptively and tabulated to draw conclusions.Results: Both sides were operated with equal frequency, while one patient had a bilateral THA. Most common indication of surgery was osteoarthritis secondary to avascular necrosis head of femur (n=16). Pre-operative modified Harris hip score was poor in all 20 hip joints. Duration of surgery ranged from 110 minutes to 190 minutes, mean duration being 139 minutes. Average blood loss during the surgical procedures was about 532 ml with average drainage of about 230 ml. On an average medullary canal flare index of 3.97 was for all the patients. Morphological cortical index averaged at 3.11 and Dorr index at 3.54.Conclusions: Most systems for THA are modular which provide flexibility in dealing with intraoperative anatomical variations. Different types of femoral and acetabular implants are available for use which reflect the different philosophies regarding the techniques involved in THA. Further studies are required to support our findings

Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition

Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime.Comment: 97 pages, 3 figure

BOFFIN TTS:Few-Shot Speaker Adaptation by Bayesian Optimization

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a one-size-fits-all adaptation strategy, with convincing synthesis requiring a corpus-specific configuration of the hyper-parameters that control fine-tuning. By using Bayesian optimization to efficiently optimize these hyper-parameter values for a target speaker, we are able to perform adaptation with an average 30% improvement in speaker similarity over standard techniques. Results indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new speakers using less than ten minutes of audio, achieving the same naturalness as produced for the speakers used to train the base model

Mapping India's Energy Policy 2022

Carefully designed energy support measures—subsidies, public utilities' investments, and public finance institutions' lending—and government's energy revenues play a key role in India's transition to clean energy and reaching net-zero emissions by 2070. Looking at how the Government of India has supported different types of energy from FY 2014 to FY 2021, the study aims to improve transparency, create accountability, and encourage a responsible shift in support away from fossil fuels and toward clean energy.Mapping India's Energy Subsidies 2022 covers India's subsidies to fossil fuels, electricity transmission and distribution, renewable energy, and electric vehicles between fiscal year (FY) 2014 and FY 2021.We found that fossil fuels continue to receive far more subsidies than clean energy in India. This disparity became even more pronounced from FY 2020 to FY 2021, going from 7.3 times to 9 times the amount of subsidies to renewables

Mapping India's energy subsidies 2021: time for renewed support to clean energy.

Government support is more important than ever for the energy transition in the wake of COVID-19, as governments around the world take unprecedented measures to help stimulate economic recovery. Shifting government support from fossil to clean energy can ensure that every rupee of public money helps access, affordability, energy security and the shift to a low-carbon economy. This report examines how the Government of India has used subsidies to support different types of energy from FY 2014 until FY 2020, and draws on qualitative data to describe major shifts since the onset of COVID-19. In light of the government commitments to Aatmanirbhar Bharat ("self-reliant India"), it also includes two special thematic chapters. The first explores how subsidy policy can best promote solar photovoltaic (PV) manufacturing as part of the road to 450 GW of renewable energy by 2030. The second examines how investments by public sector undertakings (PSUs) - that is, enterprises where the government is the majority owner - are supporting clean energy. Our data, summarized in Figure ES1, cover all subsidies from production to consumption for coal, oil and gas, electricity transmission and distribution (T&D), renewable energy, and electric vehicles (EVs). Nuclear and hydropower are not included due to a lack of adequate data availability. The underlying data are available online and have been made easier to explore with an accompanying data portal