Abstract

Our investigation centres on assessing the importance of a biased parameter (𝛼) in a multiplex Markov chain (MMC) model that is characterized by biased random walks in multiplex networks. We explore how varying complex network topologies affect the total multiplex imbalance as a function of biased parameter. Our primary finding is that the system demonstrates a gradual increase in total imbalance within both positive and negative regions of the biased parameter, with a consistent minimum value occurring at 𝛼 = −1. In contrast to the negative region, the total imbalance is consistently high when 𝛼 is significantly positive. We perform a detailed examination of four different network structures and establish three sets of multiplex networks. In each of these networks, the second layer consists of a Regular Random network, while the first layer is either a Barabási–Albert, Erdős-Rényi, or Watts Strogatz network, depending on the set. Our results demonstrate that the combination of Barabási–Albert and Random Regular Network exhibits the highest level of right saturation imbalance. Additionally, for left saturation imbalance, the Erdős–Rényi and Random Regular combination achieve a slightly higher value. We also observe that the total amount of imbalance at 𝛼 = −1 follows a decreasing trend as the size of the network of each layer increases. Furthermore, we are also able to illustrate that the second most significant eigenvalue of the supra-transition matrix exhibits a similar pattern in response to changes in the bias parameter, aligning with the overall system’s imbalance.

Abstract

We delve into the interplay between network’s symmetry and functioning for a generic class of dynamical systems. Primarily, we focus on a class of systems that characterize the spreading process, such as the spread of epidemics in complex networks, where the coupling configuration is nonlinear rather than diffusive. Through theoretical and numerical analysis, we establish a compelling connection between the symmetry of the graph and the trajectories followed by the dynamical processes for those nodes forming symmetry orbits and displaying identical eigenvector centrality. In particular, we are able to show that when the initial transitory states are removed, the symmetric group of nodes respond synchronously; nonetheless, they maintain a constant distance from each other and hence follow splay states. We have verified this phenomenon once more using two distinct kinds of networks. In one instance, every node takes part in nontrivial clusters. In the alternative scenario, we create symmetric orbits as per our target. The cluster nodes show splay states in both situations.

Abstract

Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studies while no such measure was proposed so far, how complex were the extreme events when compared to non-extreme chaos. We address here this question of complexity in extreme events and investigate if we can distinguish them from non-extreme chaotic signal. The normalized Shannon entropy in combination with disequlibrium is used for our study and it is able to distinguish between extreme chaos and non-extreme chaos and moreover, it depicts the transition points from periodic to extremes via Pomeau-Manneville intermittency and, from small amplitude to large amplitude chaos and its transition to extremes via interior crisis. We report a general trend of complexity against a system parameter that increases during a transition to extreme events, reaches a maximum, and then starts decreasing. We employ three models, a nonautonomous Liénard system, 2-dimensional Ikeda map and a 6-dimensional coupled Hindmarh-Rose system to validate our proposition

Abstract

This paper is a short intervention in locating the concerns about labour in the present phase of digital transformation within the longer history of technological change. It engages with some key analytical frameworks in technology studies and development economics to identify the continuities in the present digital revolution with previous moments of equivalent technological change. It also highlights some crucial differences with the current digital turn. Finally, the paper uses available data and information to deliberate on the possible impact of the ongoing digital transformation on the employment question and the nature of work in India.

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

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.

Abstract

This article asks what it is like for the rural poor to live with coal over time as mines expand and agriculture and forest-dependent ways of living inevitably become more restricted. Research on the expansion of open pit coal mining in India shows a widespread inability to appropriately compensate the rural poor for lost land and access to common property resources. Yet it is simultaneously clear that the growth of coal extraction over time ensures increased community dependence on mainly informal, coal-based livelihoods. What then happens over the long-term for people who live in and around the coalfields? Drawing on evidence from 2011 to 2022, this article explores longer term changes for mine-affected people next to major coal mines in Telangana state, India. Specifically, it examines a) the acquisition of forest and agricultural lands for the expanding mines, b) the operating mines and their environmental implications, but also improved job opportunities, and c) mine closure as an opportunity to rehabilitate the landscape and return the land for alternative community uses post mining. The results add to the understanding of the long-term changes that large-scale mining brings to rural communities, and the challenges to a just transition once coal mining inevitably comes to an end.

Abstract

Editorial note

Abstract

The role of completely positive–indivisibility (CP-indivisibility) and incompatibility as valuable resources for various information-theoretic tasks is widely acknowledged. This study delves into the intricate relationship between CP-divisibility and channel compatibility. Our investigation focuses on the behavior of incompatibility robustness of quantum channels for a pair of generic dynamical maps. We show that the incompatibility robustness of channels is monotonically nonincreasing for a pair of generic CP-divisible dynamical maps. Further, our explicit study of the behavior of incompatibility robustness with time for some specific dynamical maps reveals nonmonotonic behavior in the CP-indivisible regime. Additionally, we propose a measure of CP-indivisibility based on the incompatibility robustness of quantum channels. Our investigation provides valuable insights into the nature of quantum dynamical maps and their relevance in information-theoretic applications.

Abstract

The complex, glinty appearance of detailed normal-mapped surfaces at different scales requires expensive per-pixel Normal Distribution Function computations. Moreover, large light sources further compound this integration and increase the noise in the Monte Carlo renderer. Specialized rendering techniques that explicitly express the underlying normal distribution have been developed to improve performance for glinty surfaces controlled by a fixed material roughness. We present a new method that supports spatially varying roughness based on a neural histogram that computes per-pixel NDFs with arbitrary positions and sizes. Our representation is both memory and compute efficient. Additionally, we fully integrate direct illumination for all light directions in constant time. Our approach decouples roughness and normal distribution, allowing the live editing of the spatially varying roughness of complex normal-mapped objects. We demonstrate that our approach improves on previous work by achieving smaller footprints while offering GPU-friendly computation and compact representation.