Orca 2: Teaching Small Language Models How to Reason

Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs' reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. make Orca 2 weights publicly available at aka.ms/orca-lm to support research on the development, evaluation, and alignment of smaller LMsComment: Added url to model weights fixed typo in Author nam

Galaxy And Mass Assembly (GAMA): properties and evolution of red spiral galaxies

For abstract see article

CPIDM: A Clustering-Based Profound Iterating Deep Learning Model for HSI Segmentation

The existing work on unsupervised segmentation frequently does not present any statistical extent to estimating and equating procedures, gratifying a qualitative calculation. Furthermore, regardless of the datum that enormous research is dedicated to the advancement of a novel segmentation approach and upgrading the deep learning techniques, there is an absence of research comprehending the assessment of eminent conventional segmentation methodologies for HSI. In this paper, to moderately fill this gap, we propose a direct method that diminishes the issues to some extent with the deep learning methods in the arena of a HSI space and evaluate the proposed segmentation techniques based on the method of the clustering-based profound iterating deep learning model for HSI segmentation termed as CPIDM. The proposed model is an unsupervised HSI clustering technique centered on the density of pixels in the spectral interplanetary space and the distance concerning the pixels. Furthermore, CPIDM is a fully convolutional neural network. In general, fully convolutional nets remain spatially invariant preventing them from modeling position-reliant outlines. The proposed network maneuvers this by encompassing an innovative position inclined convolutional stratum. The anticipated unique edifice of deep unsupervised segmentation deciphers the delinquency of oversegmentation and nonlinearity of data due to noise and outliers. The spectrum efficacy is erudite and incidental from united feedback via deep hierarchy with pooling and convolutional strata; as a consequence, it formulates an affiliation among class dissemination and spectra along with three-dimensional features. Moreover, the anticipated deep learning model has revealed that it is conceivable to expressively accelerate the segmentation process without substantive quality loss due to the existence of noise and outliers. The proposed CPIDM approach outperforms many state-of-the-art segmentation approaches that include watershed transform and neuro-fuzzy approach as validated by the experimental consequences

Evaluation of visual pedagogy teaching method for improving oral hygiene practice in children with Autism: An interventional study

BACKGROUND: Evaluation of visual pedagogy teaching method for improving oral hygiene practice in children with Autism: An interventional study: Visual pedagogy is a relatively newer approach to improve dental care in autistic children. The present study aimed to evaluate visual pedagogy in the practice of oral hygiene in autistic children. MATERIALS AND METHODS: This interventional and prospective study was conducted in the Department of Paediatric and Preventive Dentistry. Required approval was obtained from Institutional Ethical Board. Written informed consent was obtained from parents/caregivers. The age range was 5–12 years which included 100 participants (40 males and 60 females). Improvement of oral hygiene was evaluated by recording the tooth brushing technique and ability to follow instructions as presented in the educational video shown on smartphones with Wi-Fi/mobile data. Inclusion criteria: (1) Accessibility and (2) Age range between 5 to 12 years. Exclusion criteria: (1) Non-cooperative children, (2) Children receiving medicines that influence oral health, and (3) Inability to follow-ups. Fones technique was used for brushing teeth in video recording demonstrating it in simple structured steps. Statistical analysis was performed using Chi-square and Independent t tests. RESULTS: Statistically significant improvement was observed in oral hygiene (plaque index) after training patients with visual pedagogy. CONCLUSION: In the present study, the use of visual pedagogy showed improvement in the oral hygiene scores of autistic children

A simple phenotypic classification for celiac disease

Background/AimsCeliac disease is a global health problem. The presentation of celiac disease has unfolded over years and it is now known that it can manifest at different ages, has varied presentations, and is prone to develop complications, if not managed properly. Although the Oslo definitions provide consensus on the various terminologies used in literature, there is no phenotypic classification providing a composite diagnosis for the disease.MethodsVarious variables identified for phenotypic classification included age at diagnosis, age at onset of symptoms, clinical presentation, family history and complications. These were applied to the existing registry of 1,664 patients at Dayanand Medical College and Hospital, Ludhiana, India. In addition, age was evaluated as below 15 and below 18 years. Cross tabulations were used for the verification of the classification using the existing data. Expert opinion was sought from both international and national experts of varying fields.ResultsAfter empirical verification, age at diagnosis was considered appropriate in between A1 (<18) and A2 (≥18). The disease presentation has been classified into 3 types–P1 (classical), P2 (non-classical) and P3 (asymptomatic). Complications were considered as absent (C0) or present (C1). A single phenotypic classification based on these 3 characteristics, namely age at the diagnosis, clinical presentation, and intestinal complications (APC classification) was derived.ConclusionsAPC classification (age at diagnosis, presentation, complications) is a simple disease explanatory classification for patients with celiac disease aimed at providing a composite diagnosis