Abstract

Autocatalytic systems called hypercycles are very often incorporated in "origin of life" models. We investigate the dynamics of certain related models called bimolecular autocatalytic systems. In particular, we consider the dynamics corresponding to the relative populations in these networks, and show that it can be analyzed using well-chosen autonomous polynomial dynamical systems. Moreover, we use results from reaction network theory to prove persistence and permanence of several families of bimolecular autocatalytic systems called autocatalytic recombination systems. Keywords: Autocatalytic recombination networks; Origin of life; Permanence; Persistence. Copyright © 2022 Elsevier Inc. All rights reserved.

Abstract

Quadrotors are becoming more and more essen- tial for applications such as payload delivery, inspection and search-and-rescue. Such operations pose considerable control challenges, especially when various (a priori unbounded) state- dependent unknown dynamics arises from payload variations, aerodynamic effects and from reaction forces while operating close to the ground or in a confined space. However, existing adaptive control strategies for quadrotors cannot handle un- known state-dependent uncertainties. We address such unsolved control challenge in this work via a novel adaptive method for artificial time delay control, where unknown dynamics is robustly compensated by using input and state measurements collected at immediate past time instant (i.e., artificially delayed). Closed-loop stability is established via Lyapunov theory. The effectiveness of this controller is validated using experimental results

Abstract

Quadrotors are increasingly used nowadays as a mode of aerial transport during relief operations, payload delivery etc. However, in addition to higher inertia, large size payload may interfere with prop wash (displaced mass of air by the propeller), leading to unstable behaviour. On the other hand, using different quadrotors according to payload size is not feasible either economically or operationally. Therefore, in this work, we have developed a unique scissor-mechanism based reconfigurable quadrotor which can adapt to various sizes of payloads. A robust controller is also proposed to negotiate the parametric variations stemming from the reconfigurations in the system. The effectiveness of the proposed mechanism is studied extensively via comparative experiments.

Abstract

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic con- straints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that ad- dress kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dy- namics and compared to a standard double-stage approach

Abstract

The high maneuverability of fixed-wing un- manned aerial vehicles (UAVs) exposes these systems to several dynamical and parametric uncertainties, severely affecting the fidelity of modeling and causing limited guid- ance autonomy. This article shows enhanced autonomy via adaptation mechanisms embedded in the guidance law: a vector-field method is proposed that does not require a priori knowledge of the UAV course time constant, cou- pling effects, and wind amplitude/direction. Stability and performance are assessed using the Lyapunov theory. The method is tested on software-in-the loop and hardware-in- the-loop UAV platforms, showing that the proposed guid- ance law outperforms state-of-the-art guidance controllers and standard vector-field approaches in the presence of significant uncertainty. Index Terms—Adaptive guidance, adaptive sliding-mode control, fixed-wing unmanned aerial vehicles (UAV), un- known dynamics, vector field (VF).

Abstract

It is our pleasure to welcome you to the 8th Annual Conference on Cognitive Science (ACCS8) held online and organized by the Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham (Amrita University), Kerala India from January 20-22, 2022. Cognitive Science is an interdisciplinary domain that challenges researchers and scientists to examine cognitive processes and understand how the mind works, functions, and behaves. Given the interdisciplinary nature of cognitive science, The Government of India and the Department of Science and Technology had formed the Association for Cognitive Science (ACS) in 2013. In the last seven years, the ACS has been organizing an Annual Conference of Cognitive Science (ACCS) at some of the top universities in India including IIT Kanpur, IIT Gandhinagar, IIT Guwahati, IISc Bangalore, BITS Pilani Goa and University of Hyderabad. In the 2022 event organized by Amrita Vishwa Vidyapeetham, ACCS8 had 700 participants of which several of them were researchers or PhD students. The 30 oral and 61 poster presentations allowed 3 tracks of talks and 3 tracks of poster jam sessions. 2022’s ACCS has been graced by a wonderful array of 6 keynote speakers from various regions across the world. Prof. Nandini Chatterjee Singh from UNESCO MGIEP (India) talked on Game-based courses for learning – results of an online digital game-based course to build social and emotional skills in adolescents. Prof. Claudia Gandini Wheeler-Kingshott of University College London (UK) addressed on Principles of quantitative magnetic resonance imaging and relevance to clinical applications. Prof. Kenji Doya of Okinawa Institute of Science and Technology (Japan) spoke on Neural Circuits for Mental Simulation. Prof. Egidio D’Angelo of University Pavia (Italy) addressed Bottom-up and top-down strategies for multiscale brain modelling. Prof. Ned Block of New York University (USA) spoke on an empirical argument that perception is non-conceptual. Prof. Bhavani Rao R of Amrita University (India) addressed on “Humane” Computer Interaction – The role of HCI in facilitating social and vocational empowerment.

Abstract

In this work, we describe an end-to-end deep learning architecture for Heart Murmur Detection from Phonocardiogram(PCG) recordings as part of The George B. Moody PhysioNet Challenge 2022. Our team, “Team IIITH” received a weighted accuracy score of 0.708 (ranked 19th out of 40 teams) and Challenge cost of 13264 (ranked 22nd out of 39 teams) on the official hidden test set. In our approach, the PCG recordings are first downsampled to 1000 Hz before being passed through a Butterworth’s low and high pass filter to remove baseline wanders and high-frequency noise present in the recordings. The PCG recordings are then broken down into 10-second segments and normalized to bring all trainable samples to the same size. To extract embeddings more efficiently, we built a custom 1-dimensional Residual Network (ResNet) where the 10-second inputs are passed through variable-sized kernel ResNets in parallel, before being concatenated and passed through the next ResNet layer to account for different length dependencies across the PCG signal. The output of this custom ResNet is then fed to a 2-layer feed-forward network for final classification. Cross-Entropy Loss with class weights was employed to account for class imbalance. Our approach obtained a 5-fold Cross-Validation weighted accuracy score of 0.71 and challenge cost score of 12067 on the training set.

Abstract

Neuroimaging techniques such as fMRI record brain activation while participants experience a stimulus. The concreteness of concepts defines how well our brain is able to imagine them. We hypothesise that brain activation would be distinctly different when participants view stimuli corresponding to concrete words versus abstract words. Specifically, we expect primary sensory areas to engage more in the former. To study this, we used a multi-view dataset with each concept being displayed as a picture, as a word in a sentence and also as a wordcloud [1]. We investigate how well a machine learning-based decoder trained on concrete concepts can decode both concrete and abstract concepts and vice versa for the model trained on abstract concepts. We find that a model trained on stimuli related to concrete concepts yields better accuracy than the model trained on abstract concepts. We also explore the contribution of voxels from different brain regions in this decoding process. Analysis of the contribution of different brain networks reveals exciting cognitive insights.

Abstract

We consider replication-based distributed storage systems in which each node stores the same quantum of data and each data bit stored has the same replication factor across the nodes. Such systems are referred to as balanced distributed databases. When existing nodes leave or new nodes are added to this system, the balanced nature of the database is lost, either due to the reduction in the replication factor, or the non-uniformity of the storage at the nodes. This triggers a rebalancing algorithm, that exchanges data between the nodes so that the balance of the database is reinstated. The goal is then to design rebalancing schemes with minimal communication load. In a recent work by Krishnan et al., coded transmissions were used to rebalance a carefully designed distributed database from a node removal or addition. These coded rebalancing schemes have optimal communication load, however, require the file-size to be at least exponential in the system parameters. In this work, we consider a cyclic balanced database (where data is cyclically placed in the system nodes) and present coded rebalancing schemes for node removal and addition in such a database. These databases (and the associated rebalancing schemes) require the file-size to be only cubic in the number of nodes in the system. We bound the advantage of our node removal rebalancing scheme over the uncoded scheme, and show that our scheme has a smaller communication load. In the node addition scenario, the rebalancing scheme presented is a simple uncoded scheme, which we show has optimal load.

Abstract

In this study we carried out a comprehensive analysis of SARS-CoV2 mutations and their spread in India over the past two years of the pandemic (27th Jan’ 2020 – 8th Mar’ 2022). The analysis covers four important timelines, viz., the early phase, followed by the first, second and third waves of the pandemic in the country. Phylogenetic analysis of the isolates indicated multiple independent entries of coronavirus in the country, while principal component analysis identified few state-specific clusters. Genetic analysis of isolates during the first year revealed that though lockdown helped in controlling the spread of the virus, region-specific set of shared mutations were developed during the early phase due to local community transmissions. We thus report the evolution of state-specific subclades, namely, I/GJ-20A (Gujarat), I/MH-2 (Maharashtra), I/Tel-A-20B, I/Tel-B-20B (Telangana), and I/AP-20A (Andhra Pradesh) that explain the demographic variation in the impact of COVID-19 across states. In the second year of the pandemic, India faced an aggressive second wave while the third wave was quite mild in terms of severity. Here we also discuss the prevalence and impact of different lineages and Variants of Concerns/Interests, viz., Delta, Kappa, Omicron, etc. observed during this period. From the genetic analysis of mutation spectra of Indian isolates, the insights gained in its transmission, geographic distribution, containment, and impact are discussed.