The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models

Machine Reading Comprehension (MRC) is a challenging task in the field of Natural Language Processing (NLP), where a machine is required to read a given text passage and answer a set of questions based on it. This paper provides an overview of recent advances in MRC and highlights some of the key challenges and future directions of this research area. It also evaluates the performance of several baseline models on the dataset, evaluates the challenges that the dataset poses for existing MRC models, and introduces the DistilBERT model to improve the accuracy of the answer extraction process. The supervised paradigm for training machine reading and comprehension models represents a practical path forward for creating comprehensive natural language understanding systems. To enhance the DistilBERT basic model's functionality, we have experimented with a variety of question heads that differ in the number of layers, activation function, and general structure. DistilBERT is a model for question-resolution tasks that is successful and delivers state-of-the-art performance while requiring less computational resources than large models like BERT, according to the presented technique. We could enhance the model's functionality and obtain a better understanding of how the model functions by investigating other question head architectures. These findings could serve as a foundation for future study on how to make question-and-answer systems and other tasks connected to the processing of natural languages. &nbsp

Liquisolid Technique for Enhancement of Dissolution Properties of Lornoxicam

and Explotab, polyethylene glycol 400 and propylene glycol were employed as carrier, coating material, disintegrant and non volatile solvent respectively for preparing LS compacts. Evaluation: The in vitro release pattern of LS compacts and directly compressed tablets were studied using USP-II apparatus. The prepared LS compacts were evaluated for their flow properties such as bulk density, tapped density, angle of repose, Carr's compressibility index and Hausner's ratio. The interaction between drug and excipients in prepared LS compacts were studied IR spectroscopy. The drug release rates of LS compacts were distinctly higher as compared to directly compressed tablets, which show significant benefit of LS in increasing wetting properties and surface area of drug available for dissolution. The LS-1 of LS powder system showed acceptable flowability, Carr's compressibility index and Hausner's ratio. The DSC and XRD studies conforms the no significant interaction between the drug and excipients used in LS compacts. Conclusion: From this study it concludes that the LS technique is a promising alternative for improvement of dissolution property of water-insoluble drugs

Comparative evaluation of sulcus/ vestibular depth by intraoral examination and on master cast/denture in completely edentulous cases

Context: Residual ridge resorption(RRR) affect the patient’s satisfaction with complete denture by decreasing the sulcus / vestibular depth, Aims: The aim of this study is to find the correlation between difference in sulcus / vestibular depth by intraoral examination and on master cast/ old denture with patient’s satisfaction from complete denture. Settings and Design: The study included old denture wearer and complete denture prosthesis made by different operators in the college. Methods and Material: The vestibular sulcus depths at 7 different sites in edentulous maxilla and 12 different sites in edentulous mandible  and respective flange height in maxillary and mandibular denture were measured. Prevalidated questionnaire was used to measure the satisfaction of patients with denture. Statistical analysis used:  Pearson correlation was done to find the correlation between satisfaction score and difference in sulcus / vestibular depth. Results: The difference in sulcus depth in maxilla with maxillary cast and mandible with mandibular master cast/ denture showed medium to large negative correlation with p value 0.01. The increase in difference will lead to decrease in satisfaction score of patients.&nbsp

Explainable Pathfinding for Inscrutable Planners with Inductive Logic Programming

The complexity of the solutions that artificial intelligence can learn to solve problems currently surpasses its ability to explain these solutions. In many domains, explainable solutions are a necessary condition while optimality is not. Therefore, we seek to constrain solutions to the space of solutions that can be explained to a human. To do this, we build on inductive logic programming (ILP) techniques that allow us to define robust background knowledge and inductive biases. By combining ILP with a given inscrutable planner, we are able to construct an explainable graph representing solutions to all states in the state space. This graph can then be summarized using a variety of methods such as hierarchical representations and simple if/else rules. We test our approach on Towers of Hanoi and discuss future work for applications to the Rubik’s cube

ALLURE: A Multi-Modal Guided Environment for Helping Children Learn to Solve a Rubik’s Cube with Automatic Solving and Interactive Explanations

Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, an educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills. ALLURE can both find and explain its own strategies for solving the Rubik’s cube as well as build on user-provided strategies. Collaboration between AI and user happens using visual and natural language modalities

Comparative Evaluation of Sulcus/ Vestibular Depth by Intraoral Examination and on Master Cast/denture in Completely Edentulous Cases

Context: Residual ridge resorption(RRR) affect the patient's satisfaction with complete denture by decreasing the sulcus / vestibular depth, Aims: The aim of this study is to find the correlation between difference in sulcus / vestibular depth by intraoral examination and on master cast/ old denture with patient's satisfaction from complete denture. Settings and Design: The study included old denture wearer and complete denture prosthesis made by different operators in the college. Methods and Material: The vestibular sulcus depths at 7 different sites in edentulous maxilla and 12 different sites in edentulous mandible  and respective flange height in maxillary and mandibular denture were measured. Prevalidated questionnaire was used to measure the satisfaction of patients with denture. Statistical analysis used:  Pearson correlation was done to find the correlation between satisfaction score and difference in sulcus / vestibular depth. Results: The difference in sulcus depth in maxilla with maxillary cast and mandible with mandibular master cast/ denture showed medium to large negative correlation with p value 0.01. The increase in difference will lead to decrease in satisfaction score of patients.&nbsp

India's First Robotic Eye for Time-domain Astrophysics: The GROWTH-India Telescope

We present the design and performance of the GROWTH-India telescope, a 0.7 m robotic telescope dedicated to time-domain astronomy. The telescope is equipped with a 4k back-illuminated camera that gives a 0.degrees 82 field of view and a sensitivity of m (g ') similar to 20.5 in 5 minute exposures. Custom software handles observatory operations: attaining high on-sky observing efficiencies (greater than or similar to 80%) and allowing rapid response to targets of opportunity. The data processing pipelines are capable of performing point-spread function photometry as well as image subtraction for transient searches. We also present an overview of the GROWTH-India telescope's contributions to the studies of gamma-ray bursts, the electromagnetic counterparts to gravitational wave sources, supernovae, novae, and solar system objects

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