5 research outputs found
On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment
Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving
literature that hampers the release of fully-autonomous vehicles today. Several
simulators have been in iteration after their inception to mitigate the problem
of complex scenarios with multiple agents in Autonomous Driving. One such
simulator--SMARTS, discusses the importance of cooperative multi-agent
learning. For this problem, we discuss two approaches--MAPPO and MADDPG, which
are based on-policy and off-policy RL approaches. We compare our results with
the state-of-the-art results for this challenge and discuss the potential areas
of improvement while discussing the explainability of these approaches in
conjunction with waypoints in the SMARTS environment.Comment: 6 pages, 5 figure
Estimating Time to Clear Pendency of Cases in High Courts in India using Linear Regression
Indian Judiciary is suffering from burden of millions of cases that are lying
pending in its courts at all the levels. The High Court National Judicial Data
Grid (HC-NJDG) indexes all the cases pending in the high courts and publishes
the data publicly. In this paper, we analyze the data that we have collected
from the HC-NJDG portal on 229 randomly chosen days between August 31, 2017 to
March 22, 2020, including these dates. Thus, the data analyzed in the paper
spans a period of more than two and a half years. We show that: 1) the pending
cases in most of the high courts is increasing linearly with time. 2) the case
load on judges in various high courts is very unevenly distributed, making
judges of some high courts hundred times more loaded than others. 3) for some
high courts it may take even a hundred years to clear the pendency cases if
proper measures are not taken.
We also suggest some policy changes that may help clear the pendency within a
fixed time of either five or fifteen years. Finally, we find that the rate of
institution of cases in high courts can be easily handled by the current
sanctioned strength. However, extra judges are needed only to clear earlier
backlogs.Comment: 12 pages, 9 figures, JURISIN 2022. arXiv admin note: text overlap
with arXiv:2307.1061
Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model
Demographics, Social determinants of health, and family history documented in
the unstructured text within the electronic health records are increasingly
being studied to understand how this information can be utilized with the
structured data to improve healthcare outcomes. After the GPT models were
released, many studies have applied GPT models to extract this information from
the narrative clinical notes. Different from the existing work, our research
focuses on investigating the zero-shot learning on extracting this information
together by providing minimum information to the GPT model. We utilize
de-identified real-world clinical notes annotated for demographics, various
social determinants, and family history information. Given that the GPT model
might provide text different from the text in the original data, we explore two
sets of evaluation metrics, including the traditional NER evaluation metrics
and semantic similarity evaluation metrics, to completely understand the
performance. Our results show that the GPT-3.5 method achieved an average of
0.975 F1 on demographics extraction, 0.615 F1 on social determinants
extraction, and 0.722 F1 on family history extraction. We believe these results
can be further improved through model fine-tuning or few-shots learning.
Through the case studies, we also identified the limitations of the GPT models,
which need to be addressed in future research.Comment: 5 pages, 4 figure
Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images
An entity\u27s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models-Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)-detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley