ISI Digital Commons (Indian Statistical Institute )
Not a member yet
7571 research outputs found
Sort by
Fixed-width confidence interval of log odds ratio in correlated setup for crossover design
No full textThis article deals with fixed-width confidence intervals of log odds ratio for correlated setup in crossover design. Related asymptotics are obtained. Procedures are evaluated by simulations, followed by a data study
Fuzzy Deep Learning for the Diagnosis of Alzheimer\u27s Disease: Approaches and Challenges
No full textAlzheimer\u27s disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis
Gathering Over Heterogeneous Meeting Nodes
No full textWe consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection, the proposed algorithm solves the problem within a finite time period. The algorithm runs in O(dn) moves and O(dn) epochs, where d is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and n is the total number of robots in the system
Iterative roots of multifunctions
No full textSome easily verifiable sufficient conditions for the nonexistence of iterative roots for multifunctions on arbitrary nonempty sets are presented. Typically if the graph of the multifunction has a distinguished point with a relatively large number of paths leading to it then such a multifunction does not admit any iterative root. These results can be applied to single-valued maps by considering their pullbacks as multifunctions. This is illustrated by showing the nonexistence of iterative roots of some specified orders for certain complex polynomials
Modeling and identifiability of non-homogenous Poisson process cure rate model
No full textThe promotion time cure models or bounded cumulative hazards model (BCH) was proposed as an alternative to the mixture cure models. In the present paper, this model is modified to provide a class of cure rate models based on a non-homogeneous Poisson process (NHPP). The properties of this class are studied. Also, when censored observations are present, distinguishing censored individuals from the cured group lead to identifiability issues in the members of this class. These identifiability issues are investigated and finally few members of this class are provided. Simulation results using an example of the NHPP cure rate model with exponentiated intensity and exponential baseline is supplemented. The application of the model is illustrated using E1684 real data from a study that included 284 patients from the Eastern Cooperative Oncology Group (ECOG) phase III clinical trial
Monogenity of iterates of polynomials
No full textIn this article, we study the monogenity of a tower of number fields defined by the iterates of a stable polynomial. We give a necessary condition for the monogenity of the number fields defined by the iterates of a stable polynomial. When the stable polynomial is of certain type, we also give a sufficient condition for the monogenity of the fields defined by each of its iterate. As a consequence, we obtain an infinite 3-tower of monogenic number fields. Moreover, we construct an infinite family of stable polynomials such that each of its iterate is non-monogenic
NDOrder: Exploring a novel decoding order for scene text recognition
No full textText recognition in scene images is still considered as a challenging task for the computer vision and pattern recognition community. For text images affected by multiple adverse factors, such as occlusion (due to obstacles) and poor quality (due to blur and low resolution), the performance of the state-of-the-art scene text recognition methods degrades. The key reason is that the existing encoder–decoder framework follows fixed left-to-right decoding order, which lacks sufficient contextual information. In this paper, we present a novel decoding order where good-quality characters can first be decoded followed by low-quality characters, which preserves the contextual information irrespective of the aforementioned difficult scenarios. Our method, named NDOrder, extracts visual features with a ViT encoder and then decodes with the Random Order Generation module (ROG) for learning to decode with random decoding orders and the Vision-Content-Position module (VCP) for exploiting the connections among visual information, content and position. In addition, a new dataset named OLQT (Occluded and Low-Quality Text) is created by manually collecting text images that suffer from occlusion or low-quality from several standard text recognition datasets. The dataset is now available at https://github.com/djzhong1/OLQT. Experiments on OLQT and public scene text recognition benchmarks show that the proposed method achieves state-of-the-art performance
NeuroDAVIS: A neural network model for data visualization
No full textThe task of dimensionality reduction and visualization of high-dimensional datasets remains a challenging problem since long. Modern high-throughput technologies produce large datasets having multiple views with relatively new data types. Visualization of these datasets require efficient algorithms that can uncover hidden patterns in the data without affecting the local and global structures, and bring out the inherent non-linearity within the data. To this end, however, very few such methodologies exist, which can realize this task. In this work, we have introduced a novel unsupervised deep neural network model, called NeuroDAVIS, for data visualization. NeuroDAVIS is capable of extracting important features from the data, without assuming any data distribution, visualize effectively in low dimension, and preserve both local and global structures simultaneously. It has been shown theoretically that neighbourhood relationship of the data in high dimension remains preserved in lower dimension. The performance of NeuroDAVIS has been evaluated on a wide variety of synthetic and real high-dimensional datasets including numeric, textual, image and biological data. NeuroDAVIS has been highly competitive against t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Principal Component Analysis (PCA) and Random Projection (RP) with respect to visualization quality, preservation of data size, shape, and both local and global structure. It has outperformed Fast interpolation-based t-SNE (Fit-SNE), a variant of t-SNE, for most of the datasets. For the biological datasets, besides t-SNE, UMAP, Fit-SNE, PCA and RP, NeuroDAVIS has also performed well as compared to other state-of-the-art algorithms, like Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) and the siamese neural network-based method, called IVIS. Downstream classification and clustering analyses have also revealed favourable results for NeuroDAVIS-generated embeddings
On the feasibility of E2E verifiable online voting – A case study from Durga Puja trial
No full textIndia is the largest democracy by population and has one of the largest deployments of e-voting in the world for national elections. However, the e-voting machines used in India are not end-to-end (E2E) verifiable. The inability to verify the tallying integrity of an election by the public leaves the outcome open to disputes. E2E verifiable e-voting systems are commonly regarded as the most promising solution to address this problem, but they had not been implemented or trialed in India. It was unclear whether such systems would be usable and practical to the Indian people. Previous works such as Helios require a set of tallying authorities (TAs) to perform the decryption and tallying operations, but finding and managing TAs can prove difficult. This paper presents a TA-free E2E verifiable online voting system based on the DRE-ip protocol. In collaboration with the local authority of New Town, Kolkata, India, we conducted an online voting trial as part of the 2022 Durga Puja festival celebration, during which residents of New Town were invited to use mobile phones to vote for their favorite pujas (festival decorations) in an E2E verifiable manner. 543 participants attended the Durga Puja trial and 95 of them provided feedback by filling in an anonymous survey after voting. Based on the voter feedback, participants generally found the system easy to use. This was the first time that an E2E online voting system had been built and tested in India, suggesting its feasibility for non-statutory voting scenarios
On the Span of ℓ Distance Coloring of Infinite Hexagonal Grid
No full textFor a graph G(V, E) and ℓ ∈ N, an ℓ distance coloring is a coloring f : V → {1, 2, . . ., t} of V with t colors such that ∀u, v ∈ V, u ̸= v, f(u) ̸= f(v) when d(u, v) ≤ ℓ. Here d(u, v) is the distance between u and v and is equal to the minimum number of edges that connect u and v in G. The span of ℓ distance coloring of G, χℓ(G), is the minimum t among all ℓ distance coloring of G. A class of channel assignment problem in cellular network can be formulated as a distance graph coloring problem in regular grid graphs. The cellular network is often modelled as an infinite hexagonal grid H, and hence determining χℓ(H) has relevance from practical point of view. Jacko and Jendrol [Discussiones Mathematicae Graph Theory, 2005] determined the exact value of χℓ(H) for any odd ℓ and for even ℓ ≥ 8, it is conjectured that χℓ(H) = - 3 8 (ℓ + 4 3 )2 - where [x] is an integer, x ∈ R and x− 1 2 \u3c [x] ≤ x+ 1 2 . For ℓ = 8, the conjecture has been proved by Ghosh and Koley [22nd Italian Conference on Theoretical Computer Science, 2021]. In this paper, we prove the conjecture for any even ℓ ≥ 10