Abstract

In this paper, we propose a network whose nodes are labeled by the composite numbers and two nodes are connected by an undirected link if they are relatively prime to each other. As the size of the network increases, the network will be connected whenever the largest possible node index n ≥ 49. To investigate how the nodes are connected, we analytically describe that the link density saturates to 6/π2 , whereas the average degree increases linearly with slope 6/π2 with the size of the network. To investigate how the neighbors of the nodes are connected to each other, we find the shortest path length will be at most 3 for 49 ≤ n ≤ 288 and it is at most 2 for n ≥ 289. We also derive an analytic expression for the local clustering coefficients of the nodes, which quantifies how close the neighbors of a node to form a triangle. We also provide an expression for the number of r-length labeled cycles, which indicates the existence of a cycle of length at most O(log n). Finally, we show that this graph sequence is actually a sequence of weakly pseudo-random graphs. We numerically verify our observed analytical results. As a possible application, we have observed less synchronizability (the ratio of the largest and smallest positive eigenvalue of the Laplacian matrix is high) as compared to Erdős–Rényi random network and Barabási–Albert network. This unusual observation is consistent with the prolonged transient behaviors of ecological and predator–prey networks which can easily avoid the global synchronization.

Abstract

This research explores the foundational frameworks and conceptual arguments shaping environmental litigation in the Supreme Court of India. Our central aim was to systematically analyze the legal language used to construct environmental arguments within the court. We sought to identify the recurring frameworks employed and understand how these frameworks integrate fundamental concepts like the right to life and freedom with environmental concerns.

Abstract

This article presents a metacognitive decision-making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting and confirming stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of “which-sensor-to-use,” “when-to-switch-sensor,” and “how-to-search.” Based on the information gathered by sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching, where the lower levels of search aim to localize a target (detection) and the highest level of a search exploit a target (confirmation). The performance of the MDM framework with two sensors having a low accuracy for detection and increased accuracy to confirm targets is evaluated through Monte Carlo simulations and compared with six decentralized multi-UAV search algorithms (three self-cognitive searches and three self and social-cognitive-based searches). The results indicate that the MDM framework can efficiently detect and confirm targets in an unknown environment.

Abstract

This article proposes the multiswarm cooperative information-driven search and divide and conquer mitigation control (MSCIDC) approach for faster detection and mitigation of forest fires by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of unmanned aerial vehicles (UAVs) flying together to search and quench the fire areas effectively. The multiswarm cooperative information-driven search uses a two-stage search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire locations. The search level is selected based on the thermal sensor information about the potential fire area. The dynamic nature of swarms acquired from global regulative repulsion and merging between swarms reduces the detection and mitigation time compared to the existing methods. The local attraction among the swarm members helps the nondetector members reach the fire location faster, and divide-and-conquer mitigation control ensures a nonoverlapping fire sector allocation for all members quenching the fire. The performance of the MSCIDC has been compared with different multi- UAV methods using a simulated pine forest environment. The Monte-Carlo simulation results indicate that the MSCIDC reduces the average forest area burnt by 65% and mission time by 60% compared to the best case of the multi- UAV approaches, guaranteeing a faster and more successful mission.

Abstract

Developing a reinforcement learning (RL) framework that works satisfactorily in deterministic and stochastic environments is challenging. To address this problem, a twin agent RL framework is proposed in this work, wherein we amalgamate both stochastic and deterministic agents’ actions in a multiagent framework that works with a feedback mechanism that actively monitors the output. The proposed algorithm uses twin actor networks of different agents, corresponding to deterministic and stochastic agents, and an action selection critic network is used to choose the best action from both agents. Here, the algorithm blends the outcomes of two reinforcement learning (RL) agents, a stochastic agent and a deterministic agent, namely, Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3), respectively. We assess the effectiveness of the proposed algorithm by applying it to two case studies: (i) monoclonal antibody (mAb) production and (ii) production of propylene glycol (PG). The studies are conducted in the presence of parametric uncertainties, measurement noise, and nominal conditions. It is observed that for case study 1, the root-mean-square error (RMSE) value for the proposed algorithm is reduced by 40.9% when compared with TD3 and 27.57% when compared with PPO for the simulations. Similarly, for case study 2, the RMSE for the proposed algorithm is reduced by 8.87% when compared with TD3 and 5.8% with PPO. Based on extensive simulations, it is found that the proposed twin agent algorithm has faster convergence and better set-point tracking when compared to the agents operated individually.

Abstract

Graph neural networks (GNNs) are increasingly being used on sensitive graph-structured data, necessitating techniques for handling unlearning requests on the trained models, particularly node unlearning. However, unlearning nodes on GNNs is challenging due to the interdependence between the nodes in a graph. We compare MEGU, a state-of-the-art graph unlearning method, and SCRUB, a general unlearning method for classification, to investigate the efficacy of graph unlearning methods over traditional unlearning methods. Surprisingly, we find that SCRUB performs comparably or better than MEGU on random node removal and on removing an adversarial node injection attack. Our results suggest that 1) graph unlearning studies should incorporate general unlearning methods like SCRUB as baselines, and 2) there is a need for more rigorous behavioral evaluations that reveal the differential advantages of proposed graph unlearning methods. Our work, therefore, motivates future research into more comprehensive evaluations for assessing the true utility of graph unlearning algorithms.

Abstract

Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-toend data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems.

Abstract

We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accurate, effective and efficient retrieval, particularly for less prevalent findings. CheXtriv outperforms state-of-the-art global and local approaches by to in retrieval accuracy and to in ranking quality.

Abstract

Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., image time series (TS) based analysis. Image TS represents the evolution of anatomy over time, but different anatomies may have different structural characteristics and temporal paths. Therefore, separating the time-dependent path difference and time-independent basic anatomy/shape changes is important when comparing two image TS to understand the causes of the observed differences better. A method to untangle and quantify the path and shape difference between the TS is presented in this paper. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric can separate and quantify the path and shape differences between TS.

Abstract

This paper presents the architecture of a two-wheeler mobile sensing platform aimed at collecting compre-hensive two-wheeler riding data. Two-wheelers, prevalent in many countries for their agility and efficiency lack extensive study in autonomous vehicle research. Recognizing this gap, our work introduces a scalable and modular platform equipped with multiple sensors and high-performance embedded systems. The platform captures essential data points such as GPS, acceleration, gyroscope, speed, and 360-degree image and depth data, crucial for developing deep learning models for autonomous navigation and accident prevention. We detail the hardware architecture, consisting of an Nvidia Jetson TX2 NX platform, Raspberry Pi 4 Model B, and various sensors, all tailored to operate efficiently on a two-wheeler. The software methodology is designed to synchronize and process the data effectively, hence allowing us to generate accurate and thorough datasets. Our results demonstrate the platform's potential in improving two-wheeler safety and contributing to the advancement of autonomous vehicle technologies.