Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection

We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy. Dynamic Tiling starts with non-overlapping tiles for initial detections and utilizes dynamic overlapping rates along with a tile minimizer. This dual approach effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead by reducing the number of forward passes through the object detection model. Adaptable to a variety of operational environments, our method negates the need for laborious recalibration. Additionally, our large-small filtering mechanism boosts the detection quality across a range of object sizes. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods, setting new benchmarks for efficiency and accuracy

Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

An implementation of smoothed particle hydrodynamic methods for fluids problems

In this article, we present a numerical method that is Smoothed Particle Hydrodynamic (SPH) method. In the SPH method for the Navier - Stokes equations the most widespread method to solve for pressure and mass conservation is the weakly compressible assumption (WCSPH). This article presents two important benchmark problems to validate the algorithm of SPH method. The two benchmark problems chosen are the Lid - driven cavity problem and Poiseuille flow problem at very low Reynolds numbers. The SPH results are also in good agreement with the analytical solution

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASS

Investigation of bond performance of reinforced fly ash-based Geopolymer concrete using experiments and numerical analysis

This study evaluates the bond performance of reinforced fly ash-based geopolymer concrete by using experiments and numerical analysis. Three types of mixture proportions along with two types of reinforcement diameter, (d12, ribbed bar) and (d14, smooth bar) mm, were selected for experimental work. The bond behaviour of reinforced geopolymer concrete is determined using the pullout test, and Finite Element Analysis (FEA). The test data indicated that the bond strength of reinforced fly ash-based geopolymer concrete increases with the increase in compressive strength. The concrete cover to diameter ratio (c/db) increases from 4.86 to 5.75 and the bond strength of all three groups of samples also increases. Besides, the bond stress-slip curves obtained by the ABAQUS software closely match the results from experimental works. Furthermore, the parametric analyses show that when the compressive strength of geopolymer concreteincreases, the bond strength of reinforced fly ash-based geopolymer concrete increases. These results are consistent with the test data

MULTI-PIXEL PHOTON COUNTER FOR OPERATING A TABLETOP COSMIC RAY DETECTOR UNDER LOOSELY CONTROLLED CONDITIONS

The multi-pixel photon counter (MPPC) has recently emerged as a great type of silicon photomultiplier to replace or compensate for conventional vacuum-based photomultiplier tubes. An MPPC provides many advantageous features, such as high electrical gain, outstanding photon detection efficiency, fast timing response, immunity to magnetic fields, low-voltage operation, compactness, portability, and cost-effectiveness. This article examines the electrical and optical characteristics of an MPPC under loosely controlled environmental conditions. We also report a measurement of the light yield captured by the MPPC when a cosmic ray passes through the plastic scintillator, demonstrating that such a setup is suitable as a simple, cost-effective tabletop cosmic ray detector for educational and research purposes

Phlogacanthus cornutus: chemical profiles and antioxidant effects

Phlogacanthus cornutus is a rare species and the chemical profiles and the bioactivities of this plant are unknown. In present study, the chemical components of the acetone extract as well as the antioxidant activity of acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus were firstly reported. A total of 33 constituents were identify in the acetone extract of this plant using Gas Chromatography/Mass Spectrometry assay, in which trans-cinnamic acid (21.26%), neophytadiene (6.36%), linolenic acid (5.86%), dihydroagathic acid (5.71%), n-hexadecanoic acid (5.53%), phytol (4.14%) and cis-cinnamic acid (3.23%) were the major compounds. The acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus showed DPPH radical scavenging activity with IC50 value of 234.31, 185.95, 758.65 and 458.52 µg/mL respectively

F2SD: A dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.Comment: Accepted at ICMV 202

Child stunting is associated with child, maternal, and environmental factors in Vietnam

Child stunting in Vietnam has reduced substantially since the turn of the century but has remained relatively high for several years. We analysed data on children 6–59 months (n = 85,932) from the Vietnam Nutritional Surveillance System, a nationally representative cross‐sectional survey. Multivariable Poisson regression models were used to estimate relative risk (RR) of stunting, stratified by child age and ecological region. Covariates at the child, maternal, household, and environmental levels were included based on available data and the World Health Organization conceptual framework on child stunting. Among children 6–23 months, the strongest associations with child stunting were child age in years (RR: 2.49; 95% CI [2.26, 2.73]), maternal height < 145 cm compared with ≥150 cm (RR: 2.04; 95% CI [1.85, 2.26]), living in the Northeast compared with the Southeast (RR: 2.01; 95% CI [1.69, 2.39]), no maternal education compared with a graduate education (RR: 1.77; 95% CI, [1.44, 2.16]), and birthweight < 2,500 g (RR: 1.75; 95% CI [1.55, 1.98]). For children 24–59 months, the strongest associations with child stunting were no maternal education compared with a graduate education (RR: 2.07; 95% CI [1.79, 2.40]), living in the Northeast compared with the Southeast (RR: 1.94; 95% CI [1.74, 2.16]), and maternal height < 145 cm compared with ≥150 cm (RR: 1.81; 95% CI [1.69, 1.94]). Targeted approaches that address the strongest stunting determinants among vulnerable populations are needed and discussed. Multifaceted approaches outside the health sector are also needed to reduce inequalities in socioeconomic status.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151838/1/mcn12826.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151838/2/mcn12826_am.pd