Evolutionary Dynamics and the Phase Structure of the Minority Game

We show that a simple evolutionary scheme, when applied to the minority game (MG), changes the phase structure of the game. In this scheme each agent evolves individually whenever his wealth reaches the specified bankruptcy level, in contrast to the evolutionary schemes used in the previous works. We show that evolution greatly suppresses herding behavior, and it leads to better overall performance of the agents. Similar to the standard non-evolutionary MG, the dependence of the standard deviation $\sigma$ on the number of agents $N$ and the memory length $m$ can be characterized by a universal curve. We suggest a Crowd-Anticrowd theory for understanding the effect of evolution in the MG.Comment: 4 pages and 3 figure

Simulation of the Oxygen Reduction Reaction (ORR) Inside the Cathode Catalyst Layer (CCL) of Proton Exchange Membrane Fuel Cells Using the Kinetic Monte Carlo Method

In this paper, a numerical model of the kinetic Monte Carlo (KMC) method has been developed to study the oxygen reduction reaction (ORR) that occurs inside the cathode catalyst layer (CCL). Firstly, a 3-D model of the CCL that consists of Pt and carbon spheres is built using the sphere packing method; secondly, an efficient procedure of the proton-oxygen reaction process is developed and simulated. In the proton-oxygen reaction process, all of the continuous movements of protons and oxygen are considered. The maximum reaction distance is determined to be 8 Å. The input pressures of protons and oxygen are represented by the number of spheres of the species. The value of the current density is calculated based on the amount of reaction during the interval time. Indications are that the results of the present model match reasonably well with the published results. A new way to apply the KMC method in the proton exchange membrane fuel cell (PEMFC) research field is developed in this paper

SGIDN-LCD: An Appearance-based Loop Closure Detection Algorithm using Superpixel Grids and Incremental Dynamic Nodes

Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint variance, and dynamic objects in scenes. This paper introduces an online based on Superpixel Grids (SGs) LCD approach, SGIDN-LCD, to find similarities between scenes via hand-crafted features extracted from SGs. Unlike traditional Bag-of-Words (BoW) models requiring pre-training, we propose an adaptive mechanism to group similar images called $\textbf{\textit{dynamic}}$ $\textbf{\textit{node}}$, which incremental adjusts the database in an online manner, allowing for efficient retrieval of previously viewed images. Experimental results demonstrate the SGIDN-LCD significantly improving LCD precision-recall and efficiency. Moreover, our proposed overall LCD method outperforms state-of-the-art approaches on multiple typical datasets

Fractional transport of bed-material load in sand-bed channels

Spring 1999.Includes bibliographic references (pages 186-193).This dissertation presents a new method for predicting fractional transport rates of bedmaterial load in sand-bed channels. The proposed method is developed based on the concept of the transport capacity fraction (TCF) approach. The bed-material concentration for a given size fraction is obtained by weighting the bed-material concentration, C1, with a transport capacity distribution function, Pci. The procedure and a detailed example problem showing the use of the proposed method are provided. Two transport capacity distribution functions are developed. The first function is in terms of relative fall velocity. This function is derived from the unit stream power theory and the concepts of the TCF approach and the bed material fraction (BMF) approach. The second function is in terms of relative diameter. It is derived from the Engelund and Hansen's transport relations and the concepts of the TCF approach and the BMF approach. The sheltering and exposure effects are considered in both functions. The coefficients in both functions were calibrated using 118 sets of flume and field data (891 data points) falling in sand sizes. The formulations using relative diameter is suggested for practical applications because of its simplicity (no need for relative fall velocity computations). For the computation of bed-material concentrations, the effect of size gradations on the transport of sediment mixtures is investigated in detail. First, a new relationship is proposed for predicting the median diameter, D50t, of bed-material load. This equation is developed based on the 118 sets of data used for the development of transport capacity distribution functions plus 280 sets of CSU flume data. Then, the effect of size gradation on the transport of sediment mixtures is demonstrated by the use of Engelund and Hansen's transport function and Yang's unit stream power function. To account for size gradation effects, the newly developed expression for the median diameter, D50t, is proposed for use as the representative size in bed-material load computations. For the existing bed-material load equations, an equivalent diameter, De, is proposed. This equivalent diameter, which is related to D50t, is incorporated into the Engelund and Hansen, Ackers and White, and Yang formulas for the computation of bed-material concentrations. The proposed method is compared with various existing fractional transport methods using 118 sets of measurements (891 data points) and verified using 48 sets of independent data (327 data points). Comparison and verification indicate that the proposed method provides better predictions for fractional bed-material concentrations and size fractions of sediment in transport

Unsupervised Domain Adaptation on Reading Comprehension

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202

PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes

In this paper, we consider the problems in the practical application of visual simultaneous localization and mapping (SLAM). With the popularization and application of the technology in wide scope, the practicability of SLAM system has become a new hot topic after the accuracy and robustness, e.g., how to keep the stability of the system and achieve accurate pose estimation in the low-texture and dynamic environment, and how to improve the universality and real-time performance of the system in the real scenes, etc. This paper proposes a real-time stereo indirect visual SLAM system, PLD-SLAM, which combines point and line features, and avoid the impact of dynamic objects in highly dynamic environments. We also present a novel global gray similarity (GGS) algorithm to achieve reasonable keyframe selection and efficient loop closure detection (LCD). Benefiting from the GGS, PLD-SLAM can realize real-time accurate pose estimation in most real scenes without pre-training and loading a huge feature dictionary model. To verify the performance of the proposed system, we compare it with existing state-of-the-art (SOTA) methods on the public datasets KITTI, EuRoC MAV, and the indoor stereo datasets provided by us, etc. The experiments show that the PLD-SLAM has better real-time performance while ensuring stability and accuracy in most scenarios. In addition, through the analysis of the experimental results of the GGS, we can find it has excellent performance in the keyframe selection and LCD