In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several crit...
Semantic segmentation for autonomous driving is challenged by limited field-of-view (FoV), where occlusions and restricted viewpoints reduce available visual information. Existing approaches often incur considerable computational overhead, which is u...
As vital equipment in modern industry, the health state of rotating machinery influences the production process and equipment safety. However, rotating machinery generally operates in a normal state most of the time, which results in limited fault da...
This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network. Previous research has shown that using the scattering transfor...
Railway turnout systems are important components of railway signaling infrastructure, and timely fault diagnosis is essential for ensuring operational safety and maintenance efficiency. In practical applications, turnout fault diagnosis based on powe...
The Transformer architecture drives modern Artificial Intelligence (AI), yet the physical principles that may constrain self-attention training remain poorly characterized. We develop a thermodynamic framework for attention training, drawing on the e...
Accurate prediction of reference evapotranspiration (ET0) using integrated deep learning approaches with limited meteorological data is highly significant for efficient water resource utilization and management in arid regions. Nevertheless, paramete...
Navigating crowded environments poses significant challenges for mobile robots, particularly as traditional Simultaneous Localization and Mapping (SLAM)-based methods often struggle with dynamic and unpredictable settings. This paper proposes a visua...
Building energy consumption prediction (BECP) is the essential foundation for attaining energy efficiency in buildings, contributing significantly to tackling global energy challenges and facilitating energy sustainability. However, while data-driven...
To address the challenges of poor model generalization and suboptimal recognition accuracy stemming from limited and imbalanced sample sizes in tomato leaf disease identification, this study proposes a novel recognition strategy. This approach synerg...
In the context of choice with limited consideration, where the decision-maker may not pay attention to all available options, the consideration function of a decision maker is path-independent if her choice cannot be manipulated by the presentation o...
With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. T...
Accurate surface defect classification is a critical requirement in structural health monitoring and infrastructure inspection, where defects, including cracks, spalling, delamination and noncrack regions, often appear with low-contrast and complex b...
Although automatic target recognition (ATR) with synthetic aperture radar (SAR) images has been one of the most important research topics, there is an inherent problem of performance degradation when the number of labeled SAR target images for traini...
Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challe...
When participants are asked to identify expressed emotions from pictures, fear is often confused with surprise. The present study explored this confusion by utilizing one prototype of surprise and three prototypes of fear varying as a function of dis...
Fertilizer reduction and improved fertilization management are important approaches to achieving green and organic grape production and vineyard management. This study aims to examine whether digital agricultural extension tools can facilitate fertil...
We present a tractable generalization of quantal response equilibrium via non-expected utility preferences. In particular, we introduce concave perturbed utility games in which an individual has strategy-specific utility indices that depend on the ou...
Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral...
Animal disease is a major threat to the sustainability of the global livestock market. We explore the price risk spillover of avian influenza to the broiler market, from the perspective of public opinion. Unlike in previous work, where avian influenz...
Two arguments in the literature bear on how accounting information should be priced in emerging markets. The first holds that where enforcement of financial reporting is weak, investors favour book value over earnings because book value depends less...
Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock...
The existing literature shows that, due to locality and familiarity, spatial investor–firm adjacency plays a key role in determining stock investor attention, as proxied by the location where investors initiate an Internet search of the ticker...
Is momentum a psychology-based phenomenon or is momentum driven by earning fundamentals? Applying predictive processing from brain sciences, I show that a new explanation emerges in which momentum is jointly driven by earning fundamentals and psychol...
Accurate ship target recognition remains challenging in complex maritime environments due to background clutter, multiscale target appearance, and limited discriminative features extracted by single-type networks. To address these issues, this paper...
Concrete crack identification methods based on machine learning can greatly improve extraction efficiency and precision. However, in many cases, model training requires a large amount of sample data, and insufficient data makes it difficult to effect...
As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering th...
By way of a field experiment conducted at a university cafeteria this paper finds that placing a vegetarian option instead of a meat option at the top of a menu decreases the share of meat dishes sold by 11%. This translates to a 6% decrease of daily...
This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (Φ), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D c...
Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction...
Background/Objectives: This study examines the effectiveness of calorie labeling on restaurant menus by investigating how visual attention is allocated between calorie information and food cues. Although calorie labeling policies are widely implement...
Classroom attention is a fundamental cognitive function that is crucial to effective learning and significantly influences academic performance. Recent advances in investigating neural correlates of attention in classroom environments provide insight...
Knowledge tracing (KT) is a fundamental task in student modeling within online education systems, enabling the tracking of a student’s learning progress and the prediction of future performance. Recent attention-based KT methods have leveraged...
Selective attention is the strategy used by biological systems to cope with the inherent limits in their available computational resources, in order to efficiently process sensory information. The same strategy can be used in artificial systems that...
Twitter may be considered to be a decentralized social information processing platform whose users constantly receive their followees’ information feeds, which they may in turn dispatch to their followers. This decentralization is not devoid of...
Although improving energy efficiency has many benefits, including not only reducing pollution and climate change but also enhancing productivity and competitiveness, many firms still do not adopt energy efficiency innovation. In this study, we sugges...
Despite the extensive discussion on the educational potential of GIS and the changes made in the curricula in many countries, the implementation of GIS in classrooms has still been relatively slow. This is because of variables limiting the process of...
The combination of onboard estimation and data-driven methods is widely applied for sea state parameter prediction. However, conventional data-driven approaches often exhibit limited adaptability to this task, resulting in suboptimal prediction perfo...
Attention deficit hyperactivity disorder (ADHD) results in significant functional impairment. Current treatments, particularly for adults, are limited. Previous research indicates that exercise may offer an alternative approach to managing ADHD, but...
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition characterized by impairments in attention, inhibitory control, working memory, and executive functions, largely associated with dysfunction of pre...
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification d...
Linear attention has gained popularity in recent years due to its lower computational complexity compared to Softmax attention. However, its relatively lower performance has limited its widespread application. To address this issue, we propose a plug...
In driver attention zone prediction tasks, accurately identifying and locating the driver’s attention zone is crucial. Traditional models have significant limitations in complex driving scenarios due to their failure to fully utilize multidimen...
To overcome the limitations of inadequate local feature representation and the underutilization of global information in dynamic graph convolutions, we propose a network that combines attention mechanisms with dual graph convolutions. Firstly, we con...
As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlayin...
Aiming at the large-angle maneuver control problem of tracking spacecraft attitude in non-cooperative target rendezvous and proximity tasks, under the condition that the target spacecraft attitude information is unknown and the actuator output has ph...
Graph neural networks have attracted much attention and performed well in many downstream tasks. However, due to issues such as oversmoothing, existing graph neural networks are limited in their ability to quantitatively exploit higher-order neighbor...
Enterprise green innovation (GI) is the micro foundation for coping with the challenges of the ecological environment and achieving sustainable development. The top management team’s environmental attention (TMTEA) is crucial for guiding enviro...
This article presents some pressing issues on roboethics, which lie at the frontier between roboethics and information ethics. It relates them to the well-established field of marketing ethics, stressing two main points. First, that human attention a...
As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognit...
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