Improvements & Evaluations on the MLCommons CloudMask Benchmark

In this paper, we report the performance benchmarking results of deep learning models on MLCommons' Science cloud-masking benchmark using a high-performance computing cluster at New York University (NYU): NYU Greene. MLCommons is a consortium that develops and maintains several scientific benchmarks that can benefit from developments in AI. We provide a description of the cloud-masking benchmark task, updated code, and the best model for this benchmark when using our selected hyperparameter settings. Our benchmarking results include the highest accuracy achieved on the NYU system as well as the average time taken for both training and inference on the benchmark across several runs/seeds. Our code can be found on GitHub. MLCommons team has been kept informed about our progress and may use the developed code for their future work.Comment: arXiv admin note: text overlap with arXiv:2401.0863

Student groups of complementary skills developing artificial intelligence solutions for natural sciences -- an authentic research education approach suitable for wide adoption

We report a methodology in which students gain experience in authentic research by developing artificial intelligence (AI) solutions for researchers in natural sciences. While creating education benefits for students, our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs. In order to accomplish this, we work with research laboratories that reveal/specify the needs they have, and then our student teams work on the discovery, design, and development of an AI solution for unique problems using a consulting-like arrangement. Our design addresses common barriers which appear in most existing authentic research education approaches and thus is suitable for wide adoption at various schools. To date, our group has been operating at New York University (NYU) for five consecutive semesters and has engaged more than seventy students, ranging from first-year college students to master's candidates, and worked on more than 15 projects with 14 collaborators

Chemical sensitivity of carbon nanofilms on diamond and quartz substrates

Carbon nanofilms are another nanomaterial promising for sensor applications. Herein, we are reporting a method to obtain chemically sensitive carbon nanofilms on diamond and quartz substrates using high temperature graphitization of diamond surface in vacuum and CVD deposition from methane. The conductance linearly decreases with the time of exposure to water vapor and it does not recover after the vapor is removed. In contrast, the conductance of the film decreases sharply in acetone vapor and it restores partially when the film is retracted from the vapor. Thus, the conductance of the carbon nanofilms on quartz is not electronically sensitive to water but sensitive to acetone. However, water vapor may work as an etchant for the carbon films on quartz reducing their thickness and lowering their conductance irreversibly

Stories as Technology: Past, Present, and Future

What is it about a good story that causes it to have life-changing effects on one person and not another? I wonder if future technologies will enable us to develop the type of truly deep and fine-grained understanding of stories as social, cognitive, and emotional technologies that might allow us to answer this question with a high-level of precision.</jats:p

Randomness in Science

Could we improve science by exploring new ways to inject randomness into the research process?</jats:p