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Pratik Joshi

Publications and source records attributed to Pratik Joshi.

9 recordsLinked to original sources
arXiv2026

Numerical Model Simulation of the Carruthers GCI Images

The Carruthers Geocorona Observatory, launched in September 2025, is NASA's first mission devoted to investigating the fundamental nature of Earth's exosphere from its distant vantage in halo orbit around the Earth-Sun Lagrange (L1) point. Its primary payload, the GeoCoronal Imager, consists of two coaligned photometric imagers that measure the radiance of ultraviolet emission at 121.6 nm (Lyman-$\alpha$, or Ly-$\alpha$) from exospheric hydrogen atoms simultaneously at wide and narrow fields of view. In order to validate the calibration and hydrogen density retrieval algorithms used in the Carruthers data processing pipeline, we developed a comprehensive numerical simulator to produce realistic images similar to those collected by the actual imagers on orbit. This paper details the algorithms used to simulate the exospheric emissions, background scene components, and instrument measurement model necessary to produce synthetic raw images.

astro-ph.IM↗
arXiv2026

Carruthers Data Processing Pipeline: Photon Background Removal

The Carruthers Geocorona Observatory, launched in September 2025, is NASA's first mission devoted to investigating the fundamental nature of Earth's exosphere from its distant vantage in halo orbit around the Earth-Sun Lagrange 1 (L1) point. Its primary payload, the GeoCoronal Imager, consists of two coaligned photometric imagers that measure ultraviolet Lyman-alpha emission radiance from exospheric hydrogen simultaneously at wide- and narrow- fields of view. These observations will map the exosphere's global spatial structure and observe its temporal variability in response to geomagnetic storms. However, a critical step in that analysis is isolating the in-band exospheric hydrogen Lyman-alpha signal from any other source of photons, including in-band InterPlanetary Hydrogen photon background and out-of-band photon backgrounds, which are emitted from Earth's limb. This paper details the algorithms used to retrieve and remove photon backgrounds from the GCI science images acquired on-orbit. Finally, the science data processing pipeline that transforms instrument-effect corrected images (L1B science data product) into absolutely-calibrated exospheric H measurements in physical units (L1C science data product) is detailed. Evaluation of algorithm performance based on a realistic pre-flight case study using synthetic data demonstrates that these photon background removal algorithms achieve high accuracy, leaving a residual systematic bias in isolated exospheric Lyman-alpha of only 3% under beginning-of-life conditions.

astro-ph.IM↗
arXiv2026

Carruthers Data Processing Pipeline: Radiometric Responsivity Calibration

The Carruthers Geocorona Observatory is NASA's first mission dedicated to investigating the fundamental nature of Earth's exosphere and its dynamic response to space weather. Its primary payload, the GeoCoronal Imager, consists of two co-aligned broadband photometric imagers that support simultaneous, common-volume sensing of ultraviolet emission at 121.6 nm (Lyman-alpha) by exospheric hydrogen atoms. Accurate exospheric parameter retrieval from these images requires accurate knowledge of the instrument's optical responsivity, which enables conversion of measured exospheric signal rates into the scientifically relevant quantity of emission radiance. The Carruthers mission achieves absolute sensitivity calibration through the acquisition of photometric measurements of stars and subsequent inversion of the observed fluxes to retrieve the wavelength-dependent responsivity across the passband for each imaging configuration. The retrieval algorithm performance is enhanced by its incorporation of an objective, algorithm-driven ranking criterion to systematically select target calibration stars from a stellar spectral library. The end-to-end workflow, from the stellar selection criterion to the passband inversion, is validated using synthetically generated stellar measurements. These validation tests establish that our optical responsivity retrieval approach has high recovery fidelity, achieving error rates of <5% at the Lyman-alpha wavelength for all primary science imaging modes.

astro-ph.IM↗
arXiv2026

Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio

Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have spurred significant interest across various modalities, the audio-visual setting remains underexplored. Furthermore, although foundational multimodal models like SAM-Audio encapsulate rich static priors, our empirical analysis reveals that these representations struggle in incremental settings. This work bridges this gap by integrating SAM-Audio's audio-visual priors into the CIL setting. Specifically, we leverage its dense audio and visual representations and employ a novel guided attention strategy where the audio features contextually guide the visual representations. To further mitigate catastrophic forgetting, we introduce dual-level distillation objectives at both the feature and logit levels. Extensive evaluations on audio-visual CIL benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods.

cs.CV↗
arXiv2024

Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning

Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- a few harmful data mixed in the fine-tuning dataset can break the LLMs's safety alignment. While several defenses have been proposed, our evaluation shows that existing defenses fail \textit{when some specific training hyper-parameters are chosen} -- a large learning rate or a large number of training epochs in the fine-tuning stage can easily invalidate the defense. To this end, we propose Antidote, a post-fine-tuning stage solution, which remains \textbf{\textit{agnostic to the training hyper-parameters in the fine-tuning stage}}. Antidote relies on the philosophy that by removing the harmful parameters, the harmful model can be recovered from the harmful behaviors, regardless of how those harmful parameters are formed in the fine-tuning stage. With this philosophy, we introduce a one-shot pruning stage after harmful fine-tuning to remove the harmful weights that are responsible for the generation of harmful content. Despite its embarrassing simplicity, empirical results show that Antidote can reduce harmful score while maintaining accuracy on downstream tasks. Code is available at https://github.com/git-disl/Antidote.

cs.AI↗
arXiv2024

CodeGemma: Open Code Models Based on Gemma

This paper introduces CodeGemma, a collection of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model variants. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, and match code capabilities of other open models. CodeGemma 2B is a state-of-the-art code completion model designed for fast code infilling and open-ended generation in latency-sensitive settings.

cs.CL↗
arXiv2021

The State and Fate of Linguistic Diversity and Inclusion in the NLP World

Language technologies contribute to promoting multilingualism and linguistic diversity around the world. However, only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications. In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time. Our quantitative investigation underlines the disparity between languages, especially in terms of their resources, and calls into question the "language agnostic" status of current models and systems. Through this paper, we attempt to convince the ACL community to prioritise the resolution of the predicaments highlighted here, so that no language is left behind.

cs.CL↗
arXiv2020

TaxiNLI: Taking a Ride up the NLU Hill

Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TAXINLI, a new dataset, that has 10k examples from the MNLI dataset (Williams et al., 2018) with these taxonomic labels. Through various experiments on TAXINLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies - a large jump over the previous models - some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.

cs.AI↗
arXiv2019

Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities

In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so, we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what they have achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.

cs.CL↗