arXiv subjects

Nihar Thakkar

Publications and source records attributed to Nihar Thakkar.

3 recordsLinked to original sources

arXiv2026

Machine Learning-Based State Estimation for an Actual Transmission System Using Field PMU Data

Time-synchronized state estimation (SE) plays a critical role in ensuring real-time situational awareness in modern power systems. However, achieving full system observability using phasor measurement units (PMUs) is often impractical due to cost and deployment constraints. Moreover, SE operation at PMU timescales imposes stringent requirements on latency, robustness, and reliability that are difficult to satisfy using conventional iterative hybrid SE techniques under incomplete observability by PMUs. This paper evaluates the feasibility of deploying deep neural networks for PMU-timescale, time-synchronized SE in real-world PMU-unobservable transmission systems using actual data from a US power utility. Key contributions include a systematic assessment of estimation accuracy, scalability, and computational performance under realistic operating conditions.

eess.SY↗

arXiv2023

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct and a definitive answer. However, in real-world applications, users often ask questions that don't have a definitive answer. Incorrectly answering such questions certainly hampers a system's reliability and trustworthiness. Can SOTA models accurately identify such questions and provide a reasonable response? To investigate the above question, we introduce QnotA, a dataset consisting of five different categories of questions that don't have definitive answers. Furthermore, for each QnotA instance, we also provide a corresponding QA instance i.e. an alternate question that ''can be'' answered. With this data, we formulate three evaluation tasks that test a system's ability to 'identify', 'distinguish', and 'justify' QnotA questions. Through comprehensive experiments, we show that even SOTA models including GPT-3 and Flan T5 do not fare well on these tasks and lack considerably behind the human performance baseline. We conduct a thorough analysis which further leads to several interesting findings. Overall, we believe our work and findings will encourage and facilitate further research in this important area and help develop more robust models.

cs.CL↗

arXiv2020

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.

cs.CY↗