The Nexus of Data Science and Big Data: Accelerating Data-Driven Decision Making in the Digital Age

In the contemporary digital landscape, integrating data science techniques with big data is essential for organizations to leverage their data effectively. This research paper examines core data science methodologies, including data pre-processing, exploratory data analysis, predictive modeling, and optimization. Through an in-depth exploration of these methods, this article illustrates how they collectively enable tasks such as data cleaning, integration, insightful exploration, accurate predictions through machine learning, and streamlined analysis processes. Emphasizing the complementary relationship between data science and big data, the study underscores how this integration empowers organizations to make informed decisions, enhance operational efficiency, improve customer experiences, and foster innovation across diverse industries. These findings highlight the critical role of data-driven approaches in navigating the challenges of the digital age and shaping the future of global industries

Adaptive Coordination in Social Embodied Rearrangement

We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment. In Social Rearrangement, two robots coordinate to complete a long-horizon task, using onboard sensing and egocentric observations, and no privileged information about the environment. We study zero-shot coordination (ZSC) in this task, where an agent collaborates with a new partner, emulating a scenario where a robot collaborates with a new human partner. Prior ZSC approaches struggle to generalize in our complex and visually rich setting, and on further analysis, we find that they fail to generate diverse coordination behaviors at training time. To counter this, we propose Behavior Diversity Play (BDP), a novel ZSC approach that encourages diversity through a discriminability objective. Our results demonstrate that BDP learns adaptive agents that can tackle visual coordination, and zero-shot generalize to new partners in unseen environments, achieving 35% higher success and 32% higher efficiency compared to baselines

Imaging of Knee Joint Pathologies: A Comparative Study of Ultrasound and Magnetic Resonance Imaging

Background: Magnetic resonance imaging (MRI) has been accepted as the best non-invasive imaging modality for the evaluation of knee joint pathology but the advantages of ultrasound (US) over magnetic resonance imaging (MRI) are that the ultrasound is readily available, cheap and offers real-time imaging. Aim: To assess the accuracy of ultrasound in diagnosing knee joint pathologies using MRI as a reference. Materials And Methods: 50 patients were evaluated prospectively over a period of 1.5 years by USG followed by MRI of the affected knee. Accuracy of USG was calculated with MRI as reference. Results: In our study, the majority of patients were in age group 21-30 years. Perfect agreement was noted between ultrasound and MRI for detecting Baker’s cyst. Near perfect agreement was noted between ultrasound and MRI for detecting joint effusion, soft tissue edema and osteophytes. Substantial agreement was noted between ultrasound and MRI for Collateral ligaments tear and Meniscal injuries. Moderate agreement was noted between ultrasound and MRI for PCL tear. Fair agreement was noted between ultrasound and MRI for ACL tear. Conclusion: Knee USG has high accuracy in diagnosing pathologies like knee joint effusion, synovitis, popliteal/baker’s cysts, soft tissue edema/cellulitis, arthritic changes, collateral ligament and meniscal tears. Keywords: Knee joint pathologies, Ultrasound, MRI, Ligament

Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and motion, all while ensuring high simulation speed. (2) Human-in-the-loop infrastructure: enabling real human interaction with simulated robots via mouse/keyboard or a VR interface, facilitating evaluation of robot policies with human input. (3) Collaborative tasks: studying two collaborative tasks, Social Navigation and Social Rearrangement. Social Navigation investigates a robot's ability to locate and follow humanoid avatars in unseen environments, whereas Social Rearrangement addresses collaboration between a humanoid and robot while rearranging a scene. These contributions allow us to study end-to-end learned and heuristic baselines for human-robot collaboration in-depth, as well as evaluate them with humans in the loop. Our experiments demonstrate that learned robot policies lead to efficient task completion when collaborating with unseen humanoid agents and human partners that might exhibit behaviors that the robot has not seen before. Additionally, we observe emergent behaviors during collaborative task execution, such as the robot yielding space when obstructing a humanoid agent, thereby allowing the effective completion of the task by the humanoid agent. Furthermore, our experiments using the human-in-the-loop tool demonstrate that our automated evaluation with humanoids can provide an indication of the relative ordering of different policies when evaluated with real human collaborators. Habitat 3.0 unlocks interesting new features in simulators for Embodied AI, and we hope it paves the way for a new frontier of embodied human-AI interaction capabilities.Comment: Project page: http://aihabitat.org/habitat