Image Super-resolution with An Enhanced Group Convolutional Neural Network

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

With considering sewage pipe network upgrading projects in the “villages” in cities, the optimization of construction resources and the assessment of delay risks could be achieved. Based on the schedule-cost hypothetical theory, the mathematical model with constraint indicators was established to obtain the expression of optimal resource input, and conclude the method to analyze the schedule uncertainties. The analysis showed that cyclical footage of pipe could be regarded as a relatively fixed value, and the cost can be regarded as a function that depending on the number of working teams. The optimal number of teams and the optimal schedule occurred when the minimum total cost achieved. In the case of insufficient meteorological data, the Monte Carlo simulation method and uncertainty analysis method can be applied to assess the impact of rainfall on the total construction period, correspondingly the probability of such risk could be derived. The calculation showed that the risk of overdue completion varied significantly according to the construction starting time. It was necessary to take rainfall risk into consideration and make corresponding strategies and measures

From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs

The distinction between humans and animals lies in the unique ability of humans to use and create tools. Tools empower humans to overcome physiological limitations, fostering the creation of magnificent civilizations. Similarly, enabling foundational models like Large Language Models (LLMs) with the capacity to learn external tool usage may serve as a pivotal step toward realizing artificial general intelligence. Previous studies in this field have predominantly pursued two distinct approaches to augment the tool invocation capabilities of LLMs. The first approach emphasizes the construction of relevant datasets for model fine-tuning. The second approach, in contrast, aims to fully exploit the inherent reasoning abilities of LLMs through in-context learning strategies. In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs. This pipeline mirrors the human task-solving process, addressing complicated real-life user queries. At each step, we guide LLMs to summarize the achieved results and determine the next course of action. We term this pipeline `from Summary to action', Sum2Act for short. Empirical evaluations of our Sum2Act pipeline on the ToolBench benchmark show significant performance improvements, outperforming established methods like ReAct and DFSDT. This highlights Sum2Act's effectiveness in enhancing LLMs for complex real-world tasks

Optimizing Deep Learning Inference via Global Analysis and Tensor Expressions

We thank our shepherd, Vinod Grover, and the anonymous reviewers for their constructive feedback. For the purpose of open access, the authors have applied a Creative Commons Attribution (CCBY) license to any Author Accepted Manuscript versionarising from this submission

Pollution Characteristics and Risk Assessment of Typical Antibiotics and Persistent Organic Pollutants in Reservoir Water Sources

The major task of Chinese water governance has gradually shifted away from water environment protection to water ecology remediation, and the governance of trace organic pollutants, including persistent organic pollutants and antibiotics, has attracted growing concern. The present study examined the seasonal distribution and sources of typical persistent organic pollutants and antibiotics in six representative water sources in the lower reaches of the Yangtze River, as well as their ecological risk to the environment. Six representative surface water and surface sediment samples were collected at different time intervals, i.e., December 2018, March 2019, and June 2019, and the concentrations of nineteen organochlorine pesticides (OCPs), seventeen polychlorinated biphenyls (PCBs), and eight polybrominated diphenyl ethers (PBDEs) were analyzed by GC-MS. The major findings are listed below: ① Endosulfan sulfate, Beta-endosulfan, and methoxychlor were the major persistent organic pollutants (POPs) detected in the sediments from Gaoyou Lake, Gonghu Lake, and Gehu Lake, with concentrations ranging from 9.0 to 10.6 ng/g. ② The target antibiotics in water sources were at relatively low levels. Occurrences of sulfonamide antibiotics in water and surface sediments were NF~37.4 ng·L−1 and NF~47.3 ng·g−1. Concentrations of quinolone antibiotics in the two media were NF~5.3 ng·L−1, 0.4~32.5 ng·g−1. ③ The combined toxicity of antibiotics (risk quotient, RQ) in Lake Gehu was 0.18, which was at a moderate risk level. There was no obvious ecological risk in most water sources affected by POPs. However, there were certain ecological risks in the water sources of Gaoyou Lake, Gonghu Lake, and Sanjiangying, induced by OCPs and PCBs. This study provides a scientific basis for the treatment of antibiotics and organic pollutants in reservoir water sources

Determination of water content for early-age concrete based on dielectric constant

A new model for determining the water content of early-age concrete is proposed in this paper. The water-cement (w/c) ratios of early-age concrete can be determined based on the measured dielectric constant. The experimental results show that the relation between the relative dielectric constant (ϵr') and w/c can be modelled by a linear function at microwave frequency of 2 GHz. The model has verified and shown to have good repeatability and, hence, can be used to monitor the concrete curing and structural health conditions

Dielectric characterization of Guo Biao concrete

This paper presents an application of noninvasive near-field technique for wireless structural health monitoring. The dielectric constants of Guo Biao concrete specimens were studied by means of regression modelling. The aim is to obtain the nominal values of dielectric constant of Guo Biao concrete

Improving the wear resistance of HVOF sprayed WC-Co coatings by adding submicron-sized WC particles at the splats' interfaces

In this paper, the submicron-sized WC particles (similar to 300 nm) with the content of 3 wt% and 5 wt.% are incorporated into high velocity oxy-fuel (HVOF) sprayed WC-Co coatings with the aim of improving properties of the coatings. XRD analyses suggest a small amount of decarburization of the incorporated WC phase after the composite coating deposition. The SEM microstructure showed even distribution of WC particles at the interfaces of WC-Co splats, indicating significantly enhanced wear resistance of the coatings with the wear rate as much as similar to 10(-7) mm(3)/N. m. The content of submicron-sized WC particles plays an important role in determining the wear performances of the coatings. The increment of submicron-sized WC particles causes a decrease in wear rate from 6.09 x 10(-7) mm(3)/N.m to 5.15 x 10(-7) mm(3)/N.m. Also, the Vickers microhardness of the coatings enhances as the increasing of WC particle ratio (reaches 1365 HV with the content of the WC particles of 5 wt.%). The wear failure analysis gives further insight into the mechanism of the property enhancement. The change of stress state and crack initiation at splats' interfaces act as the predominant mechanism, which is caused by the presence of submicron-sized WC particles at splats' interfaces. (C) 2015 Elsevier B.V. All rights reserved