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 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

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

Transportation of a suspended payload using quadrotor demands a controller to simultaneously track the desired path and stabilize the planar payload swing angles. Such a control objective is made challenging by the need to orchestrate the coupling between fully actuated dynamics (quadrotor attitude) and underactuated dynamics (quadrotor position and planar payload swings). State-of-the-art controllers cannot orchestrate such coupled dynamics in the presence of unmodeled and state-dependent terms, such as aerodynamic drags and rotor downwash. This article solves this control challenge by adaptively estimating the state-dependent uncertainty. Closed-loop stability for the coupled underactuated and fully actuated dynamics is established analytically. Extensive real-time experiments confirm significant improvements over the state-of-the-art under various scenarios, such as path tracking with suspended payload and anti-swing control against externally induced payload swings.

Abstract

LOCALITY and INTERFERENCE are two mechanisms which are attested to drive sentence comprehension. However, the relationship between them remains unclear—are they alternative explanations or do they operate independently? To answer this question, we test the hypothesis that in Hindi, interference effects (measured by semantic similarity and case markers) significantly predict locality effects (modelled using dependency length quantifying distance between syntactic heads and their dependents) within a sentence, while controlling for expectation-based measures and discourse givenness. Using data from the HindiUrdu Treebank corpus (HUTB), we validate the stated hypothesis. We demonstrate that sentences with longer dependency length consistently have semantically similar preverbal dependents, more case markers, greater syntactic surprisal, and violate intra-sentential givenness considerations. Overall, our findings point towards the conclusion that locality effects are reducible to broader memory interference effects rather than being distinct manifestations of locality in syntax. Finally, we discuss the implications of our findings for the theories of interference in comprehension.

Abstract

Emergency vehicles (EVs) perform a critical task of attending medical emergencies and delay in their operations can result in loss of lives to long term or permanent health implications. Therefore, it is very important to design strategies that can reduce the delay of EVs caused by slow moving traffic. Most of the existing work on this topic focuses on assignment and dispatch of EVs from different base stations to hospitals or finding the appropriate routes from dispatch location to hospital. However, these works ignore the effect of lane changes when EV is travelling on a stretch of a road. In this work, we focus on lane level dynamics for EV traversal and showcase that a pro-active picking of lanes can result in significant reductions in traversal time. In particular, we design a Reinforcement Learning (RL) model to compute the most optimal lane for an EV to travel at each timestep. We propose RLLS (Reinforcement Learning based Lane Search) algorithm for a general purposes EV trave

Abstract

In recent years, the rapid advancement of generative artificial intelligence (GenAI) has revolutionized the landscape of drug design, offering innovative solutions to potentially expedite the discovery of novel therapeutics. GenAI encompasses algorithms and models that autonomously create new data, including text, images, and molecules, often mirroring characteristics of existing datasets. This comprehensive review delves into the realm of GenAI for drug design, emphasizing recent advancements and methodologies that have propelled the field forward. Specifically, we focus on three prominent paradigms: transformers, diffusion models, and reinforcement learning algorithms, which have been exceptionally impactful in the last few years. By synthesizing insights from a myriad of studies and developments, we elucidate the potential of these approaches in accelerating the drug discovery process. Through a detailed analysis, we explore the current state and future directions of GenAI in the context of drug design, highlighting its transformative impact on pharmaceutical research and development.

Abstract

We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions—including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community.