SafeStart Medical: An Innovative HIT Solution to Never Events (WSPE)

SafeStart Medical is a health information technology company offering a healthcare system, or electronic health record, that reduces dangerous handoff and communication errors, reduces delays and cancellations in the operating room, and empowers the patient as an active member of their care team. SafeStart is an automated and digitized revenue cycle management tool that improves perioperative patient safety, quality of care, and patient throughput. They specifically aim to prevent the occurrence of Never Events, preventable and serious medical errors that occur at an alarming rate. As an intern at SafeStart, I performed research on clinical topics such as Never Events and COVID-19 in order to create thoughtful content for healthcare professionals and patients to post on social media platforms. I helped SafeStart establish themselves as thought leaders in the patient safety space.https://digitalcommons.imsa.edu/intern_reports_2020/1007/thumbnail.jp

Exploring Retinal Projection to the Medial Amygdala: Laterality, Sex, and Cell Types

In the mouse brain, there are 59 retinorecipient regions, including the medial amygdala (MeA). The MeA is a sensory integrating region where social information is processed, especially those related to sexual selection, aggression, and pup retrieval. The neurons that link visual input to the brain are known as retinal ganglion cells (RGCs). There are ~47 mouse RGC subtypes, each with their own light stimulus sensitivities and activity patterns. As the MeA is notorious as the sexually dimorphic social behavioral hub, it is worthwhile beginning to explore sex differences or laterality differences in this retinorecipient area. Using anterograde, intravitreal injections in the retinal terminal densities at the MeA from cable length, fill volume, tortuosity, and branch points, differences between the left MeA and the right MeA in male and female mice were explored. To identify which RGC subtypes are expressed in the MeA, we used functional and morphological analysis after retroviral tracing. These findings determined a broad overrepresentation of direction-selective and orientation-selective subtypes. Determining the functional specificity of RGCs projecting to the MeA may provide insight on the role of visual input in these medial amygdala-related behaviors

Human-to-Robot Imitation in the Wild

We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human data. We propose tackling this problem via an efficient one-shot robot learning algorithm, centered around learning from a third-person perspective. We call our method WHIRL: In-the-Wild Human Imitating Robot Learning. WHIRL extracts a prior over the intent of the human demonstrator, using it to initialize our agent's policy. We introduce an efficient real-world policy learning scheme that improves using interactions. Our key contributions are a simple sampling-based policy optimization approach, a novel objective function for aligning human and robot videos as well as an exploration method to boost sample efficiency. We show one-shot generalization and success in real-world settings, including 20 different manipulation tasks in the wild. Videos and talk at https://human2robot.github.ioComment: Published at RSS 2022. Demos at https://human2robot.github.i

Machining properties of Melia dubia wood

 In this paper result of working quality of Melia dubia was reported after testing them under six major wood working operations namely – planning, sanding, turning, shaping, boring and mortising based on Indian Standard IS 8292. The wood performed extremely well under planning. In shaping, the performance was good enough. Though all the other operations yielded poor results, the composite rating factor which is an overall performance indicator was 35 % more than that of Tectona grandis. The ease of working is only 93 % compared to teak. The working quality index which was based on the composite rating factor and ease of working worked out to 107 taking Tectona grandis as 100 mainly because of the high performance under planning and shaping and good performance under sanding

LLM Augmented LLMs: Expanding Capabilities through Composition

Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their monolithic structure, it is challenging and expensive to augment them or impart new skills. On the other hand, due to their adaptation abilities, several new instances of these models are being trained towards new domains and tasks. In this work, we study the problem of efficient and practical composition of existing foundation models with more specific models to enable newer capabilities. To this end, we propose CALM -- Composition to Augment Language Models -- which introduces cross-attention between models to compose their representations and enable new capabilities. Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using' existing LLMs along with a few additional parameters and data, (ii) Existing model weights are kept intact, and hence preserves existing capabilities, and (iii) Applies to diverse domains and settings. We illustrate that augmenting PaLM2-S with a smaller model trained on low-resource languages results in an absolute improvement of up to 13\% on tasks like translation into English and arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is augmented with a code-specific model, we see a relative improvement of 40\% over the base model for code generation and explanation tasks -- on-par with fully fine-tuned counterparts.Comment: 17 pages, 2 figures, 8 table