Does the Provision of Healthcare Vary with Race? Evidence from Health Shocks to Patients Far From Home

A vast literature acknowledges that minority groups, particularly African-Americans, receive less, and lower-quality treatment than Caucasians in U.S. health facilities. It remains an open question as to how much of this disparity is a result of poverty, and how much, a result of more overt discrimination. Former empirical studies are far from conclusive given the endogeneity of hospital quality, as minorities are overrepresented in areas served by poor health facilities. To remedy this endogeneity issue, we observe visitors to the state of Florida, as well as travelers within Florida. When an individual experiences a health shock far from home, her hospital assignment becomes random. By contrasting treatment intensity, and patient outcomes of minority visitors with the total population, we find that residence plays a substantial role in the provision of healthcare. Our results indicate that though African-Americans as minority group receive less treatment and experience higher mortality rates, these disparities disappear for African-American visitors

Using microRNAs (miRNAs) as therapeutic agents in cancer: targeting the link between the downregulation of miRNAs and oncogenesis

With the worldwide prevalence of cancer, this disease has constituted a major public health problem for many years without great success in the development of treatment. This is due in part to the large genetic heterogeneity that exists, even between cancers of the same class. The current standard of care focuses on targeting shared elements such as rapid cell division, yet treatments like chemotherapy have tremendous cytotoxic and off-target adverse effects. Recently, a different link has been discovered that is shared by cancers of diverse types: the disruption in the regulation and expression of microRNAs (miRNAs). miRNAs, produced endogenously from discrete genes that exist for them, have been shown to be master regulators of diverse processes, including growth and division. They serve to downregulate or silence the expression of certain genes at the post-transcriptional level, with each miRNA regulating multiple target messenger RNAs. This provides an important therapeutic potential because the correction in the expression of any one miRNA, especially if it has tumor-suppressive functions, has the ability to inhibit cancer formation, growth, and metastasis by multiple related pathways. The mechanisms by which the expression of miRNA is altered in cancer are reciprocal; that is, the processes that lead to oncogenesis alter the production of miRNA and altered production of miRNA can lead to increased tumorigenicity. In particular, it has been found that miRNA is globally downregulated in most cancers. The downregulation of miRNA can arise incidentally because of chromosomal abnormalities that occur with cancer, or miRNA can be directly repressed through transcriptional control, epigenetic modification, or disruption in the biogenic production process. When miRNAs are downregulated, the condition can contribute to the main hallmarks of cancer: uncontrolled growth, angiogenesis, metastasis, and evasion of anti-growth and apoptotic signals. A therapeutic strategy that has been investigated in numerous preclinical studies is miRNA replacement therapy, and this treatment has been shown to be extremely efficacious with a wide range of candidates and targets in diverse cancer systems. However, these advances have only led to two clinical trials, partly because of the lack of efficacious delivery methods. Nevertheless, with the rapid advances being made in gene therapy to address these shortcomings, a translation of these benefits from the bench-to-bedside is almost certain

Broadening AI Ethics Narratives: An Indic Art View

Incorporating interdisciplinary perspectives is seen as an essential step towards enhancing artificial intelligence (AI) ethics. In this regard, the field of arts is perceived to play a key role in elucidating diverse historical and cultural narratives, serving as a bridge across research communities. Most of the works that examine the interplay between the field of arts and AI ethics concern digital artworks, largely exploring the potential of computational tools in being able to surface biases in AI systems. In this paper, we investigate a complementary direction--that of uncovering the unique socio-cultural perspectives embedded in human-made art, which in turn, can be valuable in expanding the horizon of AI ethics. Through qualitative interviews of sixteen artists, art scholars, and researchers of diverse Indian art forms like music, sculpture, painting, floor drawings, dance, etc., we explore how {\it non-Western} ethical abstractions, methods of learning, and participatory practices observed in Indian arts, one of the most ancient yet perpetual and influential art traditions, can inform the FAccT community. Insights from our study suggest (1) the need for incorporating holistic perspectives (that are informed both by data-driven observations and prior beliefs encapsulating the structural models of the world) in designing ethical AI algorithms, (2) the need for integrating multimodal data formats for design, development, and evaluation of ethical AI systems, (3) the need for viewing AI ethics as a dynamic, cumulative, shared process rather than as a self contained framework to facilitate adaptability without annihilation of values, (4) the need for consistent life-long learning to enhance AI accountability, and (5) the need for identifying ethical commonalities across cultures and infusing the same into AI system design, so as to enhance applicability across geographies

SACSoN: Scalable Autonomous Data Collection for Social Navigation

Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effective social navigation behaviors directly from data. However, collecting navigation data in human-occupied environments may require teleoperation or continuous monitoring, making the process prohibitively expensive to scale. In this paper, we present a scalable data collection system for vision-based navigation, SACSoN, that can autonomously navigate around pedestrians in challenging real-world environments while encouraging rich interactions. SACSoN uses visual observations to observe and react to humans in its vicinity. It couples this visual understanding with continual learning and an autonomous collision recovery system that limits the involvement of a human operator, allowing for better dataset scaling. We use a this system to collect the SACSoN dataset, the largest-of-its-kind visual navigation dataset of autonomous robots operating in human-occupied spaces, spanning over 75 hours and 4000 rich interactions with humans. Our experiments show that collecting data with a novel objective that encourages interactions, leads to significant improvements in downstream tasks such as inferring pedestrian dynamics and learning socially compliant navigation behaviors. We make videos of our autonomous data collection system and the SACSoN dataset publicly available on our project page.Comment: 9 pages, 12 figures, 4 table

GNM: A General Navigation Model to Drive Any Robot

Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could train more powerful navigation models. In this paper, we study how a general goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots, and enable broad generalization across environments and embodiments. We analyze the necessary design decisions for effective data sharing across robots, including the use of temporal context and standardized action spaces, and demonstrate that an omnipolicy trained from heterogeneous datasets outperforms policies trained on any single dataset. We curate 60 hours of navigation trajectories from 6 distinct robots, and deploy the trained GNM on a range of new robots, including an underactuated quadrotor. We find that training on diverse data leads to robustness against degradation in sensing and actuation. Using a pre-trained navigation model with broad generalization capabilities can bootstrap applications on novel robots going forward, and we hope that the GNM represents a step in that direction. For more information on the datasets, code, and videos, please check out http://sites.google.com/view/drive-any-robot

Analysis of market & business strategies of key players in UK professional services sector For Tata Consultancy Services MBA 2012

This project is part of the Nottingham University Business School Management Project and TCS’s Global Internship Program which provides an opportunity to work on challenging projects on information technology. The key areas include: improving operational efficiency and productivity; promoting business agility; simplification and transformation; managing enterprise risk and compliance; and enabling the understanding of markets and customers. The project from Tata Consultancy Services (TCS) is to analyze six companies for their High Tech services vertical in the context of the UK / Europe market; generate a business intelligence report identifying areas in the companies analyzed, where TCS with its consulting and IT expertise could provide services. The emerging service-dominant logic to marketing is based on the resource based view of the firm; the literature review is an attempt to understand the process of relationship building in professional business-to-business services. The report will look at companies from the core competency of the firm as proposed by Barney (1991) and support this with a review of literature on the service-dominant logic to marketing and relationship marketing. The companies analyzed were KPMG Europe LLP; G4S plc; Rentokil Initial plc; Panasonic Europe Limited; Cisco International Limited and Bunge London Limited. An industry analysis was carried out for each of the companies using the Porters Five Forces and Pestle frameworks. This was supported with a review of the strategy and a core competency view of the companies to identify areas where TCS could make value addition proposals. The report has discussion for the first three companies i.e. KPMG Europe LLP, G4S plc and Rentokil Initial plc. A full detailed report for each of these companies is included in annexures 2 to 7

ViNT: A Foundation Model for Visual Navigation

General-purpose pre-trained models ("foundation models") have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets with weak supervision, consuming much more training data than is available for any individual downstream application. In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation. ViNT is trained with a general goal-reaching objective that can be used with any navigation dataset, and employs a flexible Transformer-based architecture to learn navigational affordances and enable efficient adaptation to a variety of downstream navigational tasks. ViNT is trained on a number of existing navigation datasets, comprising hundreds of hours of robotic navigation from a variety of different robotic platforms, and exhibits positive transfer, outperforming specialist models trained on singular datasets. ViNT can be augmented with diffusion-based subgoal proposals to explore novel environments, and can solve kilometer-scale navigation problems when equipped with long-range heuristics. ViNT can also be adapted to novel task specifications with a technique inspired by prompt-tuning, where the goal encoder is replaced by an encoding of another task modality (e.g., GPS waypoints or routing commands) embedded into the same space of goal tokens. This flexibility and ability to accommodate a variety of downstream problem domains establishes ViNT as an effective foundation model for mobile robotics. For videos, code, and model checkpoints, see our project page at https://visualnav-transformer.github.io.Comment: Accepted for oral presentation at CoRL 202

Google Search Trends to Assess Public Interest in and Concern About Vuity for Treating Presbyopia

PURPOSE: To assess public awareness, interest, and concerns regarding Vuity (1.25% pilocarpine hydrochloride ophthalmic solution), an eye drop for the treatment of presbyopia, based on Google Trends. METHODS: We used Google Trends that provides a relative search volume for queried terms, to evaluate searches for Vuity from June 30, 2021, to June 30, 2022, in the United States. The data for this study were downloaded on June 30, 2022. Main outcome measures were changes in relative search volumes for the terms Vuity, Eye drops for reading, Eye drops for near vision, Presbyopia, Pilocarpine, and related popular search terms, such as Vuity side effects, and Vuity retinal detachment . RESULTS: Since the approval of Vuity on October 29, 2021, notable increases in the relative search volumes occurred for Vuity in October 2021, December 2021, and from March to April 2022, which coincided with its approval, availability, and subsequent direct-to-consumer advertising based on positive results of clinical trials. The direct-to-consumer advertising had the greatest impact on the search volume for Vuity. Specific interests included Vuity cost, where to buy it, and its side effects. Retinal detachment was the most highly searched Vuity side effect. Geographic variation was evident, with the relative search volumes highest for Vuity in Wyoming, followed by North Dakota. CONCLUSION: Google Trends is a useful tool for monitoring increases in public interest in Vuity for presbyopia. Public concerns regarding side effects warrant further real-world investigations of the causal relationship between Vuity and retinal detachment

A Comparative Study of Responses to Retina Questions from Either Experts, Expert-Edited Large Language Models, or Expert-Edited Large Language Models Alone

OBJECTIVE: To assess the quality, empathy, and safety of expert edited large language model (LLM), human expert created, and LLM responses to common retina patient questions. DESIGN: Randomized, masked multicenter study. PARTICIPANTS: Twenty-one common retina patient questions were randomly assigned among 13 retina specialists. METHODS: Each expert created a response (Expert) and then edited a LLM (ChatGPT-4)-generated response to that question (Expert + artificial intelligence [AI]), timing themselves for both tasks. Five LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, and Bard) also generated responses to each question. The original question along with anonymized and randomized Expert + AI, Expert, and LLM responses were evaluated by the other experts who did not write an expert response to the question. Evaluators judged quality and empathy (very poor, poor, acceptable, good, or very good) along with safety metrics (incorrect information, likelihood to cause harm, extent of harm, and missing content). MAIN OUTCOME: Mean quality and empathy score, proportion of responses with incorrect information, likelihood to cause harm, extent of harm, and missing content for each response type. RESULTS: There were 4008 total grades collected (2608 for quality and empathy; 1400 for safety metrics), with significant differences in both quality and empathy ( CONCLUSIONS: In this randomized, masked, multicenter study, LLM responses were comparable with experts in terms of quality, empathy, and safety metrics, warranting further exploration of their potential benefits in clinical settings. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of the article