Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Some Investigations on Surface Texturing on Monel 400 Using Photochemical Machining

This paper reports the creation of surface textures using Photochemical Machining Process (PCM) on Monel 400. Machining is carried out on Monel 400 sheets. The influence of the photolithography parameters and spinning speed on the photoresist films were investigated. The etching media used is HNO3:FeCl3:H2O = 30ml: 500g: per liter concentration. The effects of etching time and temperature on the etched topography pattern were studied. It has been observed that time has more influence on the depth of etch as compared to temperature. However temperature influence surface finish more.</jats:p

The Effect of the Rolling Direction, Temperature, and Etching Time on the Photochemical Machining of Monel 400 Microchannels

The present paper describes the effect of the rolling direction on the quality of microchannels manufactured using photochemical machining (PCM) of Monel 400. Experiments were carried out to fabricate microchannels along and across the rolling direction to investigate the effect of the grain orientation on microchannel etching. The input parameters considered were channel width and rolling direction, whereas the depth of etch was the response parameters. Different channels of widths of 60, 100, 150, 200, and 250 μm were etched. The effects of the etching time and temperature of the etchant solution on the undercut and depth of the microchannels were studied. For good quality microchannels, the effects of spinning time, spinning speed, exposure time, and photoresist film strength were also taken into consideration. Optimized values of the above were used for the experimentation. The results show that the depth of etch of the microchannel increases more along the rolling direction than across the rolling direction. The channel width and depth are significantly affected by the etching time and temperature. The proposed study reports an improvement in the quality of microchannels produced using PCM

Ewing′s sarcoma of maxilla: A rare case report

Ewing's sarcoma is uncommon malignancy of childhood, frequently involving the mandible. The occurrence in maxilla is rare. It is histopathologically characterized by sheets of round cells positive for CD99. Although the prognosis is poor but early diagnosis and long term follow up can improve the survival. This article presents a rare case of Ewing's sarcoma of maxilla in a 15 year old male patient showing excessive fibro-osseous response which is not a frequent presentation. A retrospective analysis of cases of Ewings sarcoma of maxilla published in the English litreture is reviewed. In our case, diagnosis was confirmed by immunohistochemistry where sheets of round tumor cells were positive for CD 99. Ewings sarcoma of maxilla is a rare and aggressive tumor. Hence early diagnosis, combined therapy and long term follow up is suggested in such cases

Offline handwritten signature verification using various Machine Learning Algorithms

In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of gaussian filtering technique, gray level co-occurrence matrix feature extraction technique, principle component analysis and kernel principal component analysis associated with various machine learning algorithms. The publicly available Kaggle offline handwritten signature dataset is used for training. This article compares the accuracy of the dataset on various machine learning algorithms. After training datasets the lowest accuracy achieved is 56.66% for Naive Bayes algorithm. The highest accuracy achieved is 82% for K-Nearest Neighbour (KNN) and 81.66% for Random Forest using principle components and kernel principle components of the dataset.</jats:p