Abstract

In this paper, we study the role of nodes and edges in a complex network in dictating the robustness of a community structure towards structural perturbations. Specifically, we attempt to identify all vital nodes, which, when removed, would lead to a large change in the underlying community structure of the network. This problem is critical because the community structure of a network allows us to explore deep underlying insights into how the function and topology of the network affect each other. Moreover, it even provides a way to condense large net- works into smaller modules where each community acts as a meta node and aids in more straightforward network analysis. If the community structure were to be compromised by either accidental or intentional per- turbations to the network, that would make such analysis difficult. Since identifying such vital nodes is computationally intractable, we propose a suite of heuristics that allow to find solutions close to the optimality. To show the effectiveness of our approach, we first test these heuristics on small networks and then move to more extensive networks to show that we achieve similar results. Further analysis reveals that the pro- posed approaches are useful to analyze the vulnerability of communities in networks irrespective of their size and scale. Additionally, we show the performance through an extrinsic evaluation framework – we employ two tasks, i.e., link prediction and information diffusion, and show that the effect of our algorithms on these tasks is higher than the other baselines.

Abstract

Movement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music‑induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.

Abstract

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and dataintensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.

Abstract

In addition to the three long-standing anomalies seen in the muon g − 2, RK(∗), and RD(∗) observables by various independent experiments, the CDF collaboration has found another significant one in the W boson mass. These anomalies might be intertwined at a fundamental level and have a single new physics explanation. In this paper, we investigate the possibility of whether these anomalies can be explained in a model of a weak-singlet S1 and a weak-triplet S3 scalar leptoquarks. We find that with an appropriate flavour ansatz and mass mixing between the S1 and S3 leptoquarks through a Higgs portal, the model can explain the four anomalies simultaneously while being consistent with potential spoilsport observables like R νν K(∗) .

Abstract

Quadrotors have become extremely popular in various application scenarios which include disaster response, maintenance etc. A crucial challenge in terms of control design surfaces when a quadrotor has to operate under space constraints (e.g. pipeline inspection from inside, payload delivery at a precise location) under parametric perturbations and environmental disturbances (e.g. wind, gust). To deal with such scenarios, a suitable controller should simultaneously ensure tracking accuracy within predefined bounds to avoid constraint violation and tackle uncertainties. However, the state-of-the-art designs are either inapplicable for quadrotor system which is under-actuated in nature, or are unable to tackle system parametric uncertainties while being under full state (i.e. position, attitude angle, linear velocity and angular velocity) constraints. To address such issues in literature, a robust controller is introduced in …

Abstract

We embark on a hitherto unreported problem of an autonomous robot (self-driving car) navigating in dynamic scenes in a manner that reduces its localization error and eventual cumulative drift or Absolute Trajectory Error, which is pronounced in such dynamic scenes. With the hugely popular Velodyne-16 3D LIDAR as the main sensing modality, and the accurate LIDAR-based Localization and Mapping algorithm, LOAM, as the state estimation framework, we show that in the absence of a navigation policy, drift rapidly accumulates in the presence of moving objects. To overcome this, we learn actions that lead to drift-minimized navigation through a suitable set of reward and penalty functions. We use Proximal Policy Optimization, a class of Deep Reinforcement Learning methods, to learn the actions that result in drift-minimized trajectories. We show by extensive comparisons on a variety of synthetic, yet photo-realistic scenes made available through the CARLA Simulator the superior performance of the proposed framework vis-a-vis methods that do not adopt such policies.

Abstract

Table-top Rearrangement and Planning is a challenging problem that relies heavily on an excellent perception stack. The perception stack involves observing and registering the 3D scene on the table, detecting what objects are on the table, and how to manipulate them. Consequently, it greatly influences the system’s task-planning and motion-planning stacks that follow. We present a comprehensive overview and discuss different challenges associated with the perception module. This work is a result of our extensive involvement in the ICRA 2022 Open Cloud Robot Table Organization Challenge, in which we currently stand first on the leaderboard (as of 24th of April 2022, the final week of the challenge).

Abstract

Agricultural industry is one of the most vital industries that has a major contribution to the economy due to its share in the Gross Domestic Product (GDP) and as a source of employment. The past few decades have seen immense change in the operation of agricultural sector with the introduction of precision farming in conjunction with Internet of Things (IoT). The application of such advancements is highly based on exchange of messages between various devices in the farming. This paper aims to study the security scenarios applicable in husbandry through the analysis of possible attacks and threats. The testbeds available for agriculture based on IoT have been studied. An architecture for smart farming is proposed which is independent of the underlying technologies that may be used and the requirements of security have been laid out based on the proposed architecture. A literature survey of security protocols …

Abstract

Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (COVID) - 19 from Chest X-Ray ( CXR) images.However, incorporating explainability in these so- lutions remains relatively less explored. We present a hi- erarchical classification approach for separating normal, non- COVID pneumonia (NCP ) and COVID cases using CXR im- ages. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (MARL ) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to sepa- rate pneumonia cases from normal cases while the second stage uses the MARL architecture to discriminate between COVID and NCP cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accu- racy respectively over three large, public datasets for nor- mal vs. NCP vs. COVID classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that MARL deems the peripheral regions of the lungs to be more important in the case of COVID cases while central regions are seen as more important in NCP cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.

Abstract

Locomotion in Virtual Reality (VR) acts as a motion tracking unit for simulating user movements based on the Degree-of-Freedom (DOF) of the application. For effective locomotion, VR practitioners may have to transform their hardware from 3-DOF to 6-DOF. In this context, we conducted a literature review on different motion tracking methods employed in the Head-Mounted-Devices (HMD) to understand such hardware transformation to conduct locomotion in VR. Our observations led us to formulate a taxonomy of the tracking methods for locomotion in VR based on system design. Our study also captures different metrics that VR practitioners use to evaluate the hardware based on the context, performance, and significance for conducting locomotion.