Abstract

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

Abstract

Abstract This document contains the details of the authors' submission to the proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection Subtask A (monolingual) and B. Detection of machine-generated text is becoming an increasingly important task, with the advent of large language models (LLMs). In this paper, we lay out how using weighted averages of RoBERTa layers lets us capture information about text that is relevant to machine-generated text detection

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We present a hypergraph coloring based approach to pliable index coding (PICOD). We represent the given PICOD problem using a hypergraph consisting of m messages as vertices and the request-sets of the n clients as hyperedges. A conflict- free coloring of a hypergraph is an assignment of colors to its vertices so that each hyperedge contains a uniquely colored vertex. We show that various parameters arising out of conflict- free colorings (and some new variants) of the PICOD hypergraph result in new upper bounds for the optimal PICOD length. Using these new upper bounds, we show the existence of single-request PICOD schemes with length O(log2 Γ), where Γ is the maximum number of hyperedges overlapping with any hyperedge. For the t-request PICOD scenario, we show the existence of PICOD schemes of length max(O(log Γ log m), O(t log m)), under some mild conditions on the graph parameters. These results improve upon earlier work in general. We also show that our achievable lengths in the t-request case are asymptotically optimal, up to a multiplicative factor of log t. Our existence results are accompanied by randomized constructive algorithms, which have complexity polynomial in the parameters of the PICOD problem, in expectation or with high probability. Index Terms—Index Coding, Pliabl

Abstract

In the natural course of spoken language, individuals often engage in thinking and self-correction during speech production. These instances of interruption or correction are commonly referred to as disfluencies. When preparing data for subsequent downstream NLP tasks, these linguistic elements can be systematically removed, or handled as required, to enhance data quality. In this study, we present a comprehensive research on disfluencies in Indian languages. Our approach involves not only annotating real-world conversation transcripts but also conducting a detailed analysis of linguistic nuances inherent to Indian languages that are necessary to consider during annotation. Additionally, we introduce a robust algorithm for the synthetic generation of disfluent data. This algorithm aims to facilitate more effective model training for the identification of disfluencies in real-world conversations, thereby contributing to the advancement of disfluency research in Indian languages

Abstract

India's education sector faces a significant challenge due to its linguistic diversity, hindering inclusivity. The dominance of English on the internet underscores the critical need for translating educational content into Indian languages to enhance accessibility. Although Speech-to-Speech Machine Translation (SSMT) technologies exist, their deficiency in reproducing intonation results in monotonous translations, diminishing audience engagement and interest in the content. To address this issue, this paper demonstrates an SSMT application with a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech to give a more engaging experience. The SSMT pipeline also has components like a stress classifier that captures the stress in the source speech and allows it to be utilized during speech generation. The application takes in a speech file and gives a translated speech file with stress transferred from the source.

Abstract

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Abstract

The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits accessibility, emphasizing the crucial need for translation into Indian languages. Despite existing Speech-to-Speech Machine Translation (SSMT) technologies, the lack of intonation in these systems gives monotonous translations, leading to a loss of audience interest and disengagement from the content. To address this, our paper introduces a dataset with stress annotations in Indian English and also a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech. This dataset is used for training a stress detection model, which is then used in the SSMT system for detecting stress in the source speech and transferring it into the target language speech. The TTS architecture is based on FastPitch and can modify the variances based on stressed words given. We present an Indian English-to-Hindi SSMT system that can transfer stress and aim to enhance the overall quality and engagement of educational content.

Abstract

Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.

Abstract

Accurate multi-organ segmentation in abdominal CT is essential for diagnosis, treatment planning, and disease monitoring. However, it suffers from inaccurate segmentation due to intricate spatial relationships and varying organ shapes. Existing deep learning methods often struggle with imbalanced classes, size bias, and ambiguous boundaries, hindering segmentation accuracy. To address these challenges, this paper proposes Guided-nnUNet, a two-stage segmentation framework that decomposes abdominal multi-organ segmentation into organ localization and then localization-guided fine segmentation. In the first stage, a ResNet-50 model generates a low-dimensional localization map guiding organ locations. This spatial guidance is then fed into the second stage, where a 3D U-Net with dynamic affine feature-map transform performs the fine-grained segmentation by integrating spatial context from the localization map. Our evaluation on the publicly available AMOS and BTCV datasets demonstrates the effectiveness of the model. Guided-nnUNet achieves an average improvement of 7% and 9% on the AMOS and BTCV datasets, respectively, compared to the baseline nnUNet model. Additionally, our model outperforms the state-of-the-art MedNeXt by 3.6% and 5.3% on the AMOS and BTCV datasets, respectively. These results suggest that our two-stage solution offers a promising approach for accurate abdominal organ segmentation, particularly for overcoming the challenges associated with complex organ structures.

Abstract

We present a novel solution for accurate segmentation of pneumothorax from chest radiographs utilizing free-text radiology reports. Our solution employs text-guided attention to leverage the findings in the report to initially produce a low-dimensional region-localization map. These prior region maps are integrated at multiple scales in an encoder-decoder segmentation framework via dynamic affine feature map transform (DAFT). Extensive experiments on a public dataset CANDID-PTX, show that the integration of free-text reports significantly reduces the false positive predictions, while the DAFT-based fusion of localization maps improves the positive cases. Our method achieves a DSC of 0.60 for positive and 0.052 FPR for positive and negative cases, respectively, and 0.70 to 0.85 DSC for medium and large pneumothoraces.