3D GIS Modeling of Soft Geo-Objects: Taking Rainfall, Overland Flow, and Soil Erosion as an Example

In physics, objects can be divided into rigid and soft objects according to the object deformation capacity. Similarly, geo-object can also be classified into rigid geo-objects (e.g., building, urban) and soft geo-objects (e.g., mudflow, water, soil erosion). There are three types of approaches for 3D GIS modeling, i.e., surface-based, volume-based, and hybrids in terms of geometry. These approaches are suitable for representing rigid geo-objects, but they are not suitable to simulate the intrinsic properties of the soft geo-object, i.e., dynamics and deformation. And so far there are few GIS modeling methods for simulation of soft geo-objects. GIS flow elements (FEs) and GIS soft voxels (SVs) were proposed for 3D modeling of soft geo-objects. GIS flow elements can realistically represent the dynamics and stochastics of soft geo-objects, while GIS soft voxels simulate deformation of soft geo-objects. The authors discuss the implementation and computer programming of GIS flow elements and GIS soft voxels in this study. GIS FE and SV have been successfully applied in a case study toward the simulation of the process of rainfall, overland flow, and soil erosion. A software system has been designed and developed, which has the functions of data management, model computation, and 3D simulation

Neural Network Approximation for Pessimistic Offline Reinforcement Learning

Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-making scenarios, yet its theoretical guarantees are still under development. Existing works on offline RL theory primarily emphasize a few trivial settings, such as linear MDP or general function approximation with strong assumptions and independent data, which lack guidance for practical use. The coupling of deep learning and Bellman residuals makes this problem challenging, in addition to the difficulty of data dependence. In this paper, we establish a non-asymptotic estimation error of pessimistic offline RL using general neural network approximation with $\mathcal{C}$-mixing data regarding the structure of networks, the dimension of datasets, and the concentrability of data coverage, under mild assumptions. Our result shows that the estimation error consists of two parts: the first converges to zero at a desired rate on the sample size with partially controllable concentrability, and the second becomes negligible if the residual constraint is tight. This result demonstrates the explicit efficiency of deep adversarial offline RL frameworks. We utilize the empirical process tool for $\mathcal{C}$-mixing sequences and the neural network approximation theory for the H\"{o}lder class to achieve this. We also develop methods to bound the Bellman estimation error caused by function approximation with empirical Bellman constraint perturbations. Additionally, we present a result that lessens the curse of dimensionality using data with low intrinsic dimensionality and function classes with low complexity. Our estimation provides valuable insights into the development of deep offline RL and guidance for algorithm model design.Comment: Full version of the paper accepted to the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024

Indoor Particulate Matter Transfer in CNC Machining Workshop and The Influence of Ventilation Strategies—A Case Study

Particulate matter in Computer Numerical Control (CNC) machining workshop is harmful to workers’ health. This paper studies particulate matter transfer and the performance of various ventilation strategies in a CNC machining workshop. To obtain the boundary condition of the particle field, instruments were installed to obtain the particle size attenuation characteristics and source strength, respectively. The results show that the 99% cumulative mass concentration of particles is distributed within 1.5 μm, and the release rate of particles from the full enclosure. Next, the indoor flow field and particle field were simulated by numerical simulation with the measured boundary conditions. The working area’s age of air, particle concentration, and ventilation efficiency were compared between four displacement ventilation methods and one mixed ventilation method. The results show that the working area’s mean particle concentration and ventilation efficiency under longitudinal displacement ventilation is better than other methods. At the same time, the mean age of air is slightly worse. In addition, mixed ventilation can obtain lower mean age of air, but the particle concentration is higher in the working area. The bilateral longitudinal ventilation can be improved by placing axial circulation fans with vertical upward outlets in the center of the workshop

CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic Detection

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant's traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods.Comment: Submitted to IEEE TIF

A Dynamic Feature Interaction Framework for Multi-task Visual Perception

Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve multiple common perception tasks, including instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Simply sharing the same visual feature representations for these tasks impairs the performance of tasks, while independent task-specific feature extractors lead to parameter redundancy and latency. Thus, we design two feature-merge branches to learn feature basis, which can be useful to, and thus shared by, multiple perception tasks. Then, each task takes the corresponding feature basis as the input of the prediction task head to fulfill a specific task. In particular, one feature merge branch is designed for instance-level recognition the other for dense predictions. To enhance inter-branch communication, the instance branch passes pixel-wise spatial information of each instance to the dense branch using efficient dynamic convolution weighting. Moreover, a simple but effective dynamic routing mechanism is proposed to isolate task-specific features and leverage common properties among tasks. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception. In addition, as tasks benefit from co-training with each other, our solution achieves on par results on partially labeled settings on nuScenes and outperforms previous works for 3D detection and depth estimation on the Cityscapes dataset with full supervision.Comment: Accepted by International Journal of Computer Vision. arXiv admin note: text overlap with arXiv:2011.0979

The social and environmental costs associated with water management practices in state environmental protection projects in Xinjiang, China

Since the late 1970s the central government of China has initiated several ecological environmental protection projects. The most significant of these has been the tui geng huan lin (returning cultivated land to forest and pasture) project in operation since the late 1990s. China's northwest region is characterized by lack of water resources, yet such resources are of vital importance. There is scant discussion in the literature (including in China) on the linkages between the environmental protection projects and water management practices. This paper analyses how central government environmental protection projects are interpreted in the local setting, and how local water management policies and practices correspond to the projects. The conclusion is that local water management policies and practices are interlinked with both central government and local government policies on the environmental protection projects, and a new process for the redistribution of water has been established. When equity and social costs are not factored into the planning of new environmental protection projects, the social costs may be as high as the environmental costs. (C) 2009 Elsevier Ltd. All rights reserved

Follow-up on the Supermassive Black Hole Binary Candidate J1048+7143: Successful Prediction of the Next Gamma-ray Flare and Refined Binary Parameters in the Framework of Jet Precession Model

Analyzing single-dish and VLBI radio, as well as \textit{Fermi}-LAT $\gamma$-ray observations, we explained the three major flares in the $\gamma$-ray light curve of FSRQ J1048+7143 with the spin--orbit precession of the dominant mass black hole in a supermassive black hole binary system. Here, we report on the detection of a fourth $\gamma$-ray flare from J1048+7143, appearing in the time interval which was predicted in our previous work. Including this new flare, we constrained the mass ratio into a narrow range of $0.062<q<0.088$, and consequently we were able to further constrain the parameters of the hypothetical supermassive binary black hole at the heart of J1048+7143. We predict the occurrence of the fifth major $\gamma$-ray flare that would appear only if the jet will still lay close to our line sight. The fourth major $\gamma$-ray flare also shows the two-subflare structure, further strengthening our scenario in which the occurrence of the subflares is the signature of the precession of a spine--sheath jet structure that quasi-periodically interacts with a proton target, e.g. clouds in the broad-line region.Comment: 9 pages, 4 figures, 3 tables. Accepted to ApJ

Comparison of the therapeutic effects of mesenchymal stem cells derived from human dental pulp (DP), adipose tissue (AD), placental amniotic membrane (PM), and umbilical cord (UC) on postmenopausal osteoporosis

Background: Osteoporosis is a systemic bone disease characterized by bone loss and microstructural degeneration. Recent preclinical and clinical trials have further demonstrated that the transplantation of mesenchymal stem cells (MSCs) derived from human adipose tissue (AD), dental pulp (DP), placental amniotic membrane (AM), and umbilical cord (UC) tissues can serve as an effective form of cell therapy for osteoporosis. However, MSC-mediated osteoimmunology and the ability of these cells to regulate osteoclast-osteoblast differentiation varies markedly among different types of MSCs.Methods: In this study, we investigated whether transplanted allogeneic MSCs derived from AD, DP, AM, and UC tissues were able to prevent osteoporosis in an ovariectomy (OVX)-induced mouse model of osteoporosis. The homing and immunomodulatory ability of these cells as well as their effects on osteoblastogenesis and the maintenance of bone formation were compared for four types of MSCs to determine the ideal source of MSCs for the cell therapy-based treatment of OVX-induced osteoporosis. The bone formation and bone resorption ability of these four types of MSCs were analyzed using micro-computed tomography analyses and histological staining. In addition, cytokine array-based analyses of serological markers and bioluminescence imaging assays were employed to evaluate cell survival and homing efficiency. Immune regulation was determined by flow cytometer assay to reflect the mechanisms of osteoporosis treatment.Conclusion: These analyses demonstrated that MSCs isolated from different tissues have the capacity to treat osteoporosis when transplanted in vivo. Importantly, DP-MSCs infusion was able to maintain trabecular bone mass more efficiently with corresponding improvements in trabecular bone volume, mineral density, number, and separation. Among the tested MSC types, DP-MSCs were also found to exhibit greater immunoregulatory capabilities, regulating the Th17/Treg and M1/M2 ratios. These data thus suggest that DP-MSCs may represent an effective tool for the treatment of osteoporosis

Amorphous-Like Ultralow Thermal Transport in Crystalline Argyrodite Cu7PS6

Due to their amorphous-like ultralow lattice thermal conductivity both below and above the superionic phase transition, crystalline Cu- and Ag-based superionic argyrodites have garnered widespread attention as promising thermoelectric materials. However, despite their intriguing properties, quantifying their lattice thermal conductivities and a comprehensive understanding of the microscopic dynamics that drive these extraordinary properties are still lacking. Here, an integrated experimental and theoretical approach is adopted to reveal the presence of Cu-dominated low-energy optical phonons in the Cu-based argyrodite Cu7PS6. These phonons yield strong acoustic-optical phonon scattering through avoided crossing, enabling ultralow lattice thermal conductivity. The Unified Theory of thermal transport is employed to analyze heat conduction and successfully reproduce the experimental amorphous-like ultralow lattice thermal conductivities, ranging from 0.43 to 0.58 W m−1 K−1, in the temperature range of 100–400 K. The study reveals that the amorphous-like ultralow thermal conductivity of Cu7PS6 stems from a significantly dominant wave-like conduction mechanism. Moreover, the simulations elucidate the wave-like thermal transport mainly results from the contribution of Cu-associated low-energy overlapping optical phonons. This study highlights the crucial role of low-energy and overlapping optical modes in facilitating amorphous-like ultralow thermal transport, providing a thorough understanding of the underlying complex dynamics of argyrodites