Department of Computer Science and Engineering

The GRAPES-3 experiment is designed to perform precision studies of gamma-ray sources in the TeV-PeV energy region. It consists of 400 plastic scintillator detectors spanning an effective area of 25000 m2 and a large area (560 m2) muon telescope which records ∼ 4 x 109 muons every day. With the recent installation of an improved triggerless data acquisition (DAQ) system, the information related to every muon is recorded with a timing resolution of 10 ns. The angular resolution and pointing accuracy of the upgraded muon telescope has been validated by characterizing the shadow of the moon among recorded muons. Here, the details of the analysis and results, as well as the simulation studies to account for the deflection of the particles in the Earth’s magnetic field will be presented.

Audio has become an increasingly crucial biometric modality due to its ability to provide an intuitive way for humans to interact with machines. It is currently being used for a range of applications including person authentication to banking to virtual assistants. Research has shown that these systems are also susceptible to spoofing and attacks. Therefore, protecting audio processing systems against fraudulent activities such as identity theft, financial fraud, and spreading misinformation, is of paramount importance. This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. The paper further highlights the importance of considering the ethical and privacy implications of audio spoofing detection systems. Lastly, the work aims to accentuate the need for building more robust and generalizable methods, the integration of automatic speaker verification and countermeasure systems, and better evaluation protocols.

In this paper, we present the first decremental fixed-parameter sensitivity oracles for a number of basic covering and packing problems on graphs. In particular, we obtain the first decremental sensitivity oracles for Vertex Planarization (delete k vertices to make the graph planar) and Cycle Packing (pack k vertex-disjoint cycles in the given graph). That is, we give a sensitivity oracle that preprocesses the given graph in time f(k, ℓ)nO(1) such that, when given a set of ℓ edge deletions, the data structure decides in time f(k, ℓ) whether the updated graph is a positive instance of the problem. These results are obtained as a corollary of our central result, which is the first decremental sensitivity oracle for Topological Minor Deletion (cover all topological minors in the input graph that belong to a specified set, using k vertices). Though our methodology closely follows the literature, we are able to produce the first explicit bounds on the preprocessing and query times for several problems. We also initiate the study of fixed-parameter sensitivity oracles with so-called structural parameterizations and give sufficient conditions for the existence of fixed-parameter sensitivity oracles where the parameter is just the treewidth of the graph. In contrast, all existing literature on this topic and the aforementioned results in this paper assume a bound on the solution size (a weaker parameter than treewidth for many problems). As corollaries, we obtain decremental sensitivity oracles for well-studied problems such as Vertex Cover and Dominating Set when only the treewidth of the input graph is bounded. A feature of our methodology behind these results is that we are able to obtain query times independent of treewidth.

The GRAPES-3 experiment located in Ooty consists of an array of 400 plastic scintillator detectors spread over an area of 25000m2 and a large area (560 m2) muon telescope. Every day, the array records about 3 million showers induced by the interaction of primary cosmic rays in the atmosphere. One of the primary objectives of the experiment is to measure the energy spectrum and composition of the cosmic rays in the TeV-PeV energy range. However, some of the detected showers have cores outside the array. This fraction increases with energy due to the higher lateral spread of shower particles at higher energies. Identifying these events is thus crucial for accurate measurement of the cosmic ray energy spectrum. This work will describe simple cut based as well as machine learning based strategies for identifying and excluding such events and their impact on the cosmic ray energy spectrum as measured by the Bayesian unfolding technique.

Generative adversarial networks (GANs) have shown remarkable success in the generation of data from natural data manifolds such as images. In several scenarios, it is desirable that generated data is well-clustered, especially when there is severe class imbalance. In this paper, we focus on the problem of clustering in the generated space of GANs and uncover its relationship with the characteristics of the latent space. We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and, (iii) imposition of the desired cluster priors on the latent space. We also identify the GAN models in the literature that partially satisfy these conditions and demonstrate the importance of all the components required, through ablative studies on multiple real-world image datasets. Additionally, we describe a procedure to construct a multimodal latent space which facilitates learning of cluster priors with sparse supervision. Codes for our implementation is available at https://github.com/NEMGAN/NEMGAN-P.

The GRAPES-3 experiment located in Ooty consists of a dense array of 400 plastic scintillator detectors spread over an area of 25,000 m2 and a large area (560 m2) tracking muon telescope. Everyday, the array records about 3 million showers in the energy range of 1 TeV - 10 PeV induced by the interaction of primary cosmic rays in the atmosphere. These showers are reconstructed in order to find several shower parameters such as shower core, size, and age. High-energy showers landing far away from the array often trigger the array and are found to have their reconstructed cores within the array even though their true cores lie outside, due to reconstruction of partial information. These showers contaminate and lead to an inaccurate measurement of energy spectrum and composition. Such showers are removed by applying quality cuts on various shower parameters, manually as well as with machine learning approach. This work describes the improvements achieved in removal of such contaminated showers with the help of machine learning.

Malnutrition among infants and young children is a pervasive public health concern, particularly in developing countries where resources are limited. Millions of children globally suffer from malnourishment and its complications. Despite the best efforts of governments and organizations, malnourishment persists and remains a leading cause of morbidity and mortality among children under five. Physical measurements, such as weight, height, middle-upper-arm-circumference (muac), and head circumference are commonly used to assess the nutritional status of children. However, this approach can be resource-intensive and challenging to carry out on a large scale. In this research, we are developing NutriAI, a low-cost solution that leverages small sample size classification approach to detect malnutrition by analyzing 2D images of the subjects in multiple poses. The proposed solution will not only reduce the workload of health workers but also provide a more efficient means of monitoring the nutritional status of children. On the dataset prepared as part of this research, the baseline results highlight that the modern deep learning approaches can facilitate malnutrition detection via anthropometric indicators in the presence of diversity with respect to age, gender, physical characteristics, and accessories including clothing.

The concept of sum labelling was introduced in 1990 by Harary. A graph is a sum graph if its vertices can be labelled by distinct positive integers in such a way that two vertices are connected by an edge if and only if the sum of their labels is the label of another vertex in the graph. It is easy to see that every sum graph has at least one isolated vertex, and every graph can be made a sum graph by adding at most n2 isolated vertices to it. The minimum number of isolated vertices that need to be added to a graph to make it a sum graph is called the sum number of the graph. The sum number of several prominent graph classes (e.g., cycles, trees, complete graphs) is already well known. We examine the effect of taking the disjoint union of graphs on the sum number. In particular, we provide a complete characterisation of the sum number of graphs of maximum degree two, since every such graph is the disjoint union of paths and cycles.