Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

We introduce and study a d-dimensional generalization of graph Hamiltonian cycles. These are the Hamiltonian d-dimensional cycles in Knd (the complete simplicial d-complex over a vertex set of size n). Hamiltonian d-cycles are the simple d-cycles of a complete rank, or, equivalently, of size 1+(n−1d). The discussion is restricted to the fields F2 and Q. For d=2, we characterize the n's for which Hamiltonian 2-cycles exist. For d=3 it is shown that Hamiltonian 3-cycles exist for infinitely many n's. In general, it is shown that there always exist simple d-cycles of size (n−1d)−O(nd−3). All the above results are constructive. Our approach naturally extends to (and in fact, involves) d-fillings, generalizing the notion of T-joins in graphs. Given a (d−1)-cycle Zd−1∈Knd, F is its d-filling if ∂F=Zd−1. We call a d-filling Hamiltonian if it is acyclic and of a complete rank, or, equivalently, is of size (n−1d). If a Hamiltonian d-cycle Z over F2 contains a d-simplex σ, then Z∖σ is a Hamiltonian d-filling of ∂σ (a closely related fact is also true for cycles over Q). Thus, the two notions are closely related. Most of the above results about Hamiltonian d-cycles hold for Hamiltonian d-fillings as well. © 2021 Elsevier Inc.

World University root node

Department of Computer Science and Engineering

Fulltext

Journal of Combinatorial Theory. Series B

Hamiltonian and pseudo-Hamiltonian cycles and fillings in simplicial complexes

Quantities are financial, technological, physical and other measures that denote relevant properties of entities, such as revenue of companies, energy efficiency of cars or distance and brightness of stars and galaxies. Queries with filter conditions on quantities are an important building block for downstream analytics and pose challenges when the content of interest is spread across a huge number of web tables and other ad-hoc datasets. Search engines support quantity lookups, but largely fail on quantity filters. The QuTE system presented in this paper aims to overcome these problems. It comprises methods for automatically extracting entity-quantity facts from web tables, as well as methods for online query processing, with new techniques for query matching and answer ranking.

Department of Data Science

Proceedings of the ACM SIGMOD International Conference on Management of Data

QuTE: Answering Quantity Queries from Web Tables

The Midwestern “Corn-Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid- to late-summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid- or late-season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field-grown crops and this response will protect crops against growth and yield losses from late-season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high-resolution daily weather data, satellite-derived phenological metrics, field yield data, and ecosystem-scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre-primed crop species may provide improved resilience to future climate change. © 2021 The Authors. Food and Energy Security published by John Wiley & Sons Ltd.

Environmental Sciences and Sustainable Engineering Centre (ESSENCE)

Food and Energy Security

Drought imprints on crops can reduce yield loss: Nature's insights for food security

This chapter summarizes the recent advances in the electrochemical detection of toxic chemicals with the use of metal oxide-metal nanocomposites as the electrochemical sensing platform. The study of toxic chemicals was classified into the following five major groups: endocrine disrupting chemicals, pesticides and disinfectants, environmental pollutants, mycotoxins, and volatile organic compounds. Electrochemical (amperometric, voltametric, and impedimetric) sensor systems made up of metal oxide-metal nanocomposites are systematically reviewed. A metal oxide-metal nanocomposite as an entity provides augmented characteristics compared to the original constituents due to the synergism between the metal nanoparticles and the metal oxide derivatives. The resultant nanocomposites furnish an enriched active surface area, better adsorption ability, and improved electrocatalytic activity. As a result, the sensor systems not only engender subfemtomolar detection limits but also thrive in a widespread concentration range along with much better selectivity. Integration of nanocomposites with electroanalytical systems is extensively sought after to harvest various features, such as miniaturization, rugged instrumentation with no mechanical parts, portable on-site analysis, multiplex analysis, automation, tunable microprobes apt for in vivo analysis, etc., in the developed sensor systems. It is anticipated that this chapter will help electrochemists to deliberate innovative combinations of metal oxides and metal nanoparticles along with other advanced functional materials while constructing electrochemical sensor systems for on-site analysis of any necessary analyte. © 2021 Elsevier Inc. All rights reserved.

Department of Chemistry

Metal Oxides in Nanocomposite-Based Electrochemical Sensors for Toxic Chemicals

Metal oxide-metal nanocomposite-modified electrochemical sensors for toxic chemicals

During the blood stage of malaria pathogenesis, parasites invade healthy red blood cells (RBC) to multiply inside the host and evade the immune response. When attached to RBC, the parasite first has to align its apex with the membrane for a successful invasion. Since the parasite’s apex sits at the pointed end of an oval (egg-like) shape with a large local curvature, apical alignment is in general an energetically un-favorable process. Previously, using coarse-grained mesoscopic simulations, we have shown that optimal alignment time is achieved due to RBC membrane deformation and the stochastic nature of bond-based interactions between the parasite and RBC membrane (Hillringhaus et al., 2020). Here, we demonstrate that the parasite’s shape has a prominent effect on the alignment process. The alignment times of spherical parasites for intermediate and large bond off-rates (or weak membrane-parasite interactions) are found to be close to those of an egg-like shape. However, for small bond off-rates (or strong adhesion and large membrane deformations), the alignment time for a spherical shape increases drastically. Parasite shapes with large aspect ratios such as oblate and long prolate ellipsoids are found to exhibit very long alignment times in comparison to the egg-like shape. At a stiffened RBC, spherical parasite aligns faster than any other investigated shapes. This study shows that the original egg-like shape performs not worse for parasite alignment than other considered shapes, but is more robust with respect to different adhesion interactions and RBC membrane rigidities. © 2021, eLife Sciences Publications Ltd. All rights reserved.

Department of Physics

eLife

Effect of Malaria parasite shape on its alignment at erythrocyte membrane

Prestress in amorphous solids bears the memory of their formation and plays a profound role in their mechanical properties, from stiffening or softening elastic moduli to shifting frequencies of vibrational modes, as well as directing yielding and solidification in the nonlinear regime. Here we develop a set of mathematical tools to investigate elasticity of prestressed discrete networks, which disentangles the effects from disorder in configuration and disorder in prestress. Applying these methods to prestressed triangular lattices and a computational model of amorphous solids, we demonstrate the importance of prestress on elasticity and reveal a number of intriguing effects caused by prestress, including strong spatial heterogeneity in stress-response unique to prestressed solids, power-law distribution of minimal dipole stiffness and a new criterion to classify floppy modes.

arXiv preprint arXiv:2110.

Prestressed elasticity of amorphous solids

The current work demonstrates a novel multi-criteria decision-making (MCDM) technique to optimize the process parameters during the wire electric discharge machining of Inconel 718. A hybrid GRA-TOPSIS algorithm using entropy weights is developed to optimize wire electric discharge machining processes parameters for maximizing the cutting speed and simultaneously minimizing the surface roughness and flatness error. Experiments were conducted according to Taguchi L18 orthogonal array considering pulse on time, pulse off time, servo voltage, wire feed rate, and wire electrode types as the input parameters. The weights of each responses were calculated by entropy weights method. The different alternatives were ranked using the hybrid technique, and an optimum parameter combination is found that maximizes the process outcome. The analysis of variance (ANOVA) was performed to study the relative significance of process parameters on the performance index. The results were finally validated by performing confirmation tests. The entropy weighted GRA-TOPSIS method was observed to improve the overall process performance compared to conventional TOPSIS. Microstructural analysis, morphological studies, and EDS analysis were conducted to study the surface integrity of the machined surface under optimal parameter settings. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Department of Mechanical Engineering

Process Integration and Optimization for Sustainability

Multi-response Optimization of Wire EDM of Inconel 718 Using a Hybrid Entropy Weighted GRA-TOPSIS Method

Generative adversarial networks (GANs) are among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging task. This is often attributed to the lack of correlation between the training progress and the trajectory of the generator and discriminator losses and the need for the GAN's subjective evaluation. A recently proposed measure inspired by game theory - the duality gap, aims to bridge this gap. However, as we demonstrate, the duality gap's capability remains constrained due to limitations posed by its estimation process. This paper presents a theoretical understanding of this limitation and proposes a more dependable estimation process for the duality gap. At the crux of our approach is the idea that local perturbations can help agents in a zero-sum game escape non-Nash saddle points efficiently. Through exhaustive experimentation across GAN models and datasets, we establish the efficacy of our approach in capturing the GAN training progress with minimal increase to the computational complexity. Further, we show that our estimate, with its ability to identify model convergence/divergence, is a potential performance measure that can be used to tune the hyperparameters of a GAN. © 2021 IEEE.

Proceedings of the International Joint Conference on Neural Networks

On Duality Gap as a Measure for Monitoring GAN Training

The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier’s output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classification decision has to be explained. The proposed procedure finds the largest (high coverage) axis-aligned hyper-cuboid such that a high percentage of the instances in the hyper-cuboid have the same class label as the instance being explained (high precision). Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined. They are maximized using gradient-based optimizers. The quality of the approximations is rigorously analyzed theoretically and experimentally. Heuristics for simplifying the generated explanations for achieving better interpretability and a greedy selection algorithm that combines the local explanations for creating global explanations for the model covering a large part of the instance space are also proposed. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete). The wide-scale applicability of the framework is validated on a variety of synthetic and real-world datasets from different domains (tabular, text, and image). © 2021, IFIP International Federation for Information Processing.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

We define and discuss properties of the class of unbounded operators which attain minimum modulus. We establish a relationship between this class and the class of norm attaining bounded operators and compare the properties of both. Also we define absolutely minimum attaining operators (for not necessarily bounded) and characterize injective absolutely minimum attaining operators as those with compact generalized inverse. We give several consequences, one of those is that every such operator has a non trivial hyperinvariant subspace. © 2019, Forum D'Analystes, Chennai.