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This study extends the use of symbolic computation in Matrix Structural Analysis (MSA) to plane (2D) trusses, building on previous work that focused on continuous beams. An open-source MATLAB program, hosted on GitHub, was developed to perform symbolic analysis of 2D trusses under point loads for any configuration. Using MATLAB's Symbolic Math Toolbox, the program derives analytical expressions for displacements, support reactions, and axial forces, providing a deeper understanding of truss behavior. The symbolic approach also supports efficient and scalable sensitivity analysis by directly computing partial derivatives of outputs with respect to input parameters, enhancing design exploration and optimization. This tool serves as a valuable resource for both engineering practice and education, offering clear insights into parameter relationships and enriching the understanding of structural mechanics. The accuracy of the symbolic results has been rigorously validated against two commercial finite element software programs and results from the literature, with full agreement, confirming the validity and generality of the methodology.

This study investigates the use of symbolic computation in Matrix Structural Analysis (MSA) for continuous beams, leveraging the MATLAB Symbolic Math Toolbox. By employing symbolic MSA, analytical expressions for displacements, support reactions, and internal forces are derived, offering deeper insights into structural behavior. This approach facilitates efficient and scalable sensitivity analysis, where partial derivatives of outputs concerning input parameters can be directly computed, enhancing design exploration. The development includes an open-source MATLAB program, hosted on GitHub, enabling symbolic analysis of continuous beams subjected to point and uniform loads. This approach is invaluable for both engineering practice and pedagogy, enriching the understanding of structural mechanics and aiding in education by illustrating clear parameter relationships. The program supports deriving influence lines and identifying maximum response values.

Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by capturing pixel intensity changes with exceptional dynamic range, minimal latency, and energy efficiency, setting them apart from conventional frame-based cameras. The distinctive capabilities of event cameras have ignited significant interest in the domain of event-based action recognition, recognizing their vast potential for advancement. However, the development in this field is currently slowed by the lack of comprehensive, large-scale datasets, which are critical for developing robust recognition frameworks. To bridge this gap, we introduces DailyDVS-200, a meticulously curated benchmark dataset tailored for the event-based action recognition community. DailyDVS-200 is extensive, covering 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. This dataset is designed to reflect a broad spectrum of action types, scene complexities, and data acquisition diversity. Each sequence in the dataset is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions. Moreover, DailyDVS-200 is structured to facilitate a wide range of research paths, offering a solid foundation for both validating existing approaches and inspiring novel methodologies. By setting a new benchmark in the field, we challenge the current limitations of neuromorphic data processing and invite a surge of new approaches in event-based action recognition techniques, which paves the way for future explorations in neuromorphic computing and beyond. The dataset and source code are available at https://github.com/QiWang233/DailyDVS-200., Comment: Accepted to ECCV 2024

While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black-box counterparts. In this work, we present a new framework named Language-guided Interpretable Action Recognition framework (LaIAR). LaIAR leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence, we redefine the problem of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model, we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR., Comment: Accepted by CVPR 2024

Flowcharts and mind maps, collectively known as flowmind, are vital in daily activities, with hand-drawn versions facilitating real-time collaboration. However, there's a growing need to digitize them for efficient processing. Automated conversion methods are essential to overcome manual conversion challenges. Existing sketch recognition methods face limitations in practical situations, being field-specific and lacking digital conversion steps. Our paper introduces the Flowmind2digital method and hdFlowmind dataset to address these challenges. Flowmind2digital, utilizing neural networks and keypoint detection, achieves a record 87.3% accuracy on our dataset, surpassing previous methods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowminds across 22 scenarios, outperforms existing datasets. Additionally, our experiments emphasize the importance of simple graphics, enhancing accuracy by 9.3%.

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on 8 parameters including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain confined concrete strain and the ultimate compressive strength of confined concrete fcc'. Three metaheuristic models are implemented including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error and their predicted results are validated against the experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale time-consuming experimental tests that make the process quick and economical., Comment: 28 Pages, 19 Figures

In this paper, we compare the scalar field dynamics in axion-like and power law potentials for both positive and negative values of the exponents. We find that, for positive exponents, both the potentials exhibit similar scalar field dynamics and it can be difficult to distinguish them at least at the background level. Even though the potentials are oscillatory in nature for positive exponents scaling solutions can be achieved for larger values of the exponent for which the dynamics can be different during early times. Because of the presence of this scaling nature there is a turnaround in the values of the scalar field equation of state as we increase the values of the exponent in both the potentials. This indicates the deviation from the oscillatory behaviour for the larger values of the exponent. For negative values of the exponent, the dynamics of the scalar field is distinguishable and axion-like potential can give rise to cosmologically viable tracker solutions unlike the power law potentials. For negative values of the exponent, axion-like potential can behave like a cosmological constant around its minima and the dark energy scale can be related to the potential scale. Due to the cosmological constant like behavior of the axion-like potential for negative exponent around its minima the late time dynamics can be similar to $\Lambda$CDM and we get similar observational constraint on the parameters for both $\Lambda$CDM and axion-like potential with negative exponent. So, while for positive exponents we may not distinguish the two potentials for negative exponents the dynamics of the scalar field is distinguishable., Comment: 16 pages, 3 tables and 13 figures. Observational data analysis added with some more clarifications. Conclusion unchanged. Accepted in PRD

Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant limitations. To address these challenges, in this paper, we propose a novel neural rendering system called UE4-NeRF, specifically designed for real-time rendering of large-scale scenes. We partitioned each large scene into different sub-NeRFs. In order to represent the partitioned independent scene, we initialize polygonal meshes by constructing multiple regular octahedra within the scene and the vertices of the polygonal faces are continuously optimized during the training process. Drawing inspiration from Level of Detail (LOD) techniques, we trained meshes of varying levels of detail for different observation levels. Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4), achieving real-time rendering of large-scale scenes at 4K resolution with a frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing in subsequent stages. Furthermore, through experiments, we have demonstrated that our method achieves rendering quality comparable to state-of-the-art approaches. Project page: https://jamchaos.github.io/UE4-NeRF/., Comment: Accepted by NeurIPS2023

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively., Comment: 34 Pages,25 Figures

Potholes are fatal and can cause severe damage to vehicles as well as can cause deadly accidents. In South Asian countries, pavement distresses are the primary cause due to poor subgrade conditions, lack of subsurface drainage, and excessive rainfalls. The present research compares the performance of three pre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet 18, and MobileNet. At first, pavement images are classified to find whether images contain potholes, i.e., Potholes or Normal. Secondly, pavements images are classi-fied into three categories, i.e., Small Pothole, Large Pothole, and Normal. Pavement images are taken from 3.5 feet (waist height) and 2 feet. MobileNet v2 has an accuracy of 98% for detecting a pothole. The classification of images taken at the height of 2 feet has an accuracy value of 87.33%, 88.67%, and 92% for classifying the large, small, and normal pavement, respectively. Similarly, the classification of the images taken from full of waist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%., Comment: 24 Pages, 26 Figures

From the last decade, researchers in the field of machine learning (ML) and assistive developmental robotics (ADR) have taken an interest in artificial empathy (AE) as a possible future paradigm for human-robot interaction (HRI). Humans learn empathy since birth, therefore, it is challenging to instill this sense in robots and intelligent machines. Nevertheless, by training over a vast amount of data and time, imitating empathy, to a certain extent, can be possible for robots. Training techniques for AE, along with findings from the field of empathetic AI research, are ever-evolving. The standard workflow for artificial empathy consists of three stages: 1) Emotion Recognition (ER) using the retrieved features from video or textual data, 2) analyzing the perceived emotion or degree of empathy to choose the best course of action, and 3) carrying out a response action. Recent studies that show AE being used with virtual agents or robots often include Deep Learning (DL) techniques. For instance, models like VGGFace are used to conduct ER. Semi-supervised models like Autoencoders generate the corresponding emotional states and behavioral responses. However, there has not been any study that presents an independent approach for evaluating AE, or the degree to which a reaction was empathetic. This paper aims to investigate and evaluate existing works for measuring and evaluating empathy, as well as the datasets that have been collected and used so far. Our goal is to highlight and facilitate the use of state-of-the-art methods in the area of AE by comparing their performance. This will aid researchers in the area of AE in selecting their approaches with precision.