A study on livelihood activities followed by the male rural youths in flood affected Dhemaji district of Assam state of India

The study was conducted at flood affected Dhemaji district of Assam where the flood is a major concern for the livelihood of rural male youth. The paper focused on existing livelihood activities opted by of rural male youths, their reason for choosing the activities and problems encountered in doing it. To date, no study has looked specifically at all the three aspects. The present study together investigated all the three elements. The study selected 200 rural male youths from the district as respondents following multistage random sampling. The survey found the mean age of the respondents as 28.83 years. The study found that the respondents opted 17 types of livelihood activities. Most (25.50%) of the interviewees chose sali rice cultivation, and they (100.00%) cited the availability of suitable land as a reason. Under vegetable cultivation, 96.07% respondents mentioned scope for round the year production and income generation as reasons. The respondents opted piggery(100.00%), poultry (100.00%), weaving (100.00%), dairy (92.85%), goatery (91.66%) and fishery (90.00%) mentioned high market demand as a reason. Regarding the problem, all respondents (100.00%) opting sali rice cultivation and vegetable cultivation reported flood, flood-induced sand and insufficient irrigation as problems. The high price of improved livestock breed was an issue for respondents (100.00%) opting dairy, poultry, and piggery. In fishery, 80.00% respondents mentioned nonavailability of quality fingerlings as a problem. The paper urges the policy makers, researchers and development organisations for utilising the findings to select appropriate interventions for providing livelihood security to male rural youths of Dhemaji district of Assam State, India

Anaesthetic Management of a Down’s Syndrome Patient Acyanotic Congenital Cardiac Anomalies and Hypothyroidism: A Case Report

Down Syndrome (DS) is a common chromosomal disorder that is associated with multiple anomalies in different organ systems. Cardiac anomalies are frequently observed in individuals with DS, and as patients with Congenital Heart Disease (CHD) are at an increased risk of developing complications related to anaesthesia during the perioperative and postoperative periods, it is crucial to pay attention to the anaesthetic management of DS patients undergoing corrective surgery for cardiac anomalies. Complete Atrioventricular Septal Defects (CAVSD) are the most prevalent cardiac defects in DS patients, followed by isolated ventricular septal defects, atrial septal defects, patent ductus arteriosus, and tetralogy of Fallot. This case report focuses on the anaesthetic management of a one-year-old female with DS who was diagnosed with a combination of Atrioventricular Septal Defects (AVSDs) and patent ductus arteriosus. Due to the concurrent diagnosis of hypothyroidism, the case required a thorough preanaesthetic examination and meticulous attention to perioperative anaesthetic management, considering factors such as airway difficulty, cervical spine instability, ligament laxity, and susceptibility to infections. The patient underwent cardiac surgery following a standard anaesthetic protocol, and the procedure was well tolerated. After the surgery, she was transferred to the Intensive Care Unit (ICU). The postoperative period was uneventful, and the patient was discharged on the eighth postoperative day

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc