arXiv subjects

Varun Viswanath

Publications and source records attributed to Varun Viswanath.

5 recordsLinked to original sources

arXiv2025

Automated Assertion Generation and Regression Testing for Machine Learning Notebooks

Jupyter Notebooks have become the de-facto choice for data scientists and machine learning (ML) engineers for prototyping and experimenting with ML pipelines, offering a rich interactive interface for code, data, and visualization in one place. However, notebooks provide limited support for testing. As a result, during continuous development, many silent (non-crashing) regressions often go unnoticed, making notebooks unreliable and results hard to reproduce. We introduce NBTestGen, the first automated assertion generation approach for ML notebooks. NBTestGen generates regression-based assertions that check properties of data processing, model building, and model evaluation steps in a typical ML notebook. To support integration of such assertions in notebooks, we introduce the first regression testing framework (called NBTest) that can be used as a Jupyter plugin and allows developers to write cell-scoped assertions in notebooks. Such assertions are 1) cell-scoped: they are linked to specific notebook cells and execute only after those cells are executed, 2) non-intrusive: they do not block notebook execution (in a Jupyter session), so that development can continue when they fail, and 3) they integrate with pytest and CI pipelines, allowing developers to easily do regression testing of their notebooks. We evaluate NBTestGen on a corpus of 585 notebooks from the popular Kaggle platform. NBTestGen generates a total of 21542 assertions (36.82 on average per notebook). The generated assertions kill 72.21% of ML-specific mutations, while maintaining a high passrate of 100.00%. We also show that NBTestGen can detect 69.68% of historical regressions in 531 older versions of Kaggle notebooks. A popular ML library, SHAP, integrated NBTest into their CI. Further, we perform a user study with 17 ML developers that shows that such users find NBTest highly intuitive and useful.

cs.SE↗

arXiv2024

Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features

This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine Learning algorithms and Conv2D Long Short-Term Memory model based Deep Learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 $R^2$ Score, 0.18 MAE, 0.10 RMSE with Conv2D Long Short-Term Memory model, showcasing the power transform technique's innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and Deep Learning techniques for solar energy prediction.

cs.LG↗

arXiv2023

SpecTracle: Wearable Facial Motion Tracking from Unobtrusive Peripheral Cameras

Facial motion tracking in head-mounted displays (HMD) has the potential to enable immersive "face-to-face" interaction in a virtual environment. However, current works on facial tracking are not suitable for unobtrusive augmented reality (AR) glasses or do not have the ability to track arbitrary facial movements. In this work, we demonstrate a novel system called SpecTracle that tracks a user's facial motions using two wide-angle cameras mounted right next to the visor of a Hololens. Avoiding the usage of cameras extended in front of the face, our system greatly improves the feasibility to integrate full-face tracking into a low-profile form factor. We also demonstrate that a neural network-based model processing the wide-angle cameras can run in real-time at 24 frames per second (fps) on a mobile GPU and track independent facial movement for different parts of the face with a user-independent model. Using a short personalized calibration, the system improves its tracking performance by 42.3% compared to the user-independent model.

cs.HC↗

arXiv2021

Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior

The SARS-CoV-2 pandemic has so far unfolded diversely across the fifty United States of America, reflected both in different time progressions of infection "waves" and in magnitudes of local infection rates. Despite a marked diversity of presentations, most U.S. states experienced their single greatest surge in daily new cases during the transition from Fall 2020 to Winter 2021. Popular media also cite additional similarities between states -- often despite disparities in governmental policies, reported mask-wearing compliance rates, and vaccination percentages. Here, we identify a set of robust, low-dimensional clusters that 1) summarize the timings and relative heights of four historical COVID-19 "wave opportunities" accessible to all 50 U.S. states, 2) correlate with geographical and intervention patterns associated with those groups of states they encompass, and 3) predict aspects of the "fifth wave" of new infections in the late Summer of 2021. In particular, we argue that clustering elucidates a negative relationship between vaccination rates and subsequent case-load variabilities within state groups. We advance the hypothesis that vaccination acts as a ``seat belt," in effect constraining the likely range of new-case upticks, even in the context of the Summer 2021, variant-driven surge.

physics.soc-ph↗

arXiv2021

Smartphone Camera Oximetry in an Induced Hypoxemia Study

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO$_2$) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health, as well as improve physicians' ability to remotely diagnose and treat respiratory conditions. In this work, we take a step towards this goal by performing the first clinical development validation on a smartphone-based SpO$_2$ sensing system using a varied fraction of inspired oxygen (FiO$_2$) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO$_2$ values (70%-100%) for the first time. This contrasts with previous studies, which evaluated performance on a far smaller range (85%-100%). We build a deep learning model using this data to demonstrate accurate reporting of SpO$_2$ level with an overall MAE=5.00% SpO$_2$ and identifying positive cases of low SpO$_2$<90% with 81% sensitivity and 79% specificity. We ground our analysis with a summary of recent literature in smartphone-based SpO2 monitoring, and we provide the data from the FiO$_2$ study in open-source format, so that others may build on this work.

cs.LG↗