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Shallow neural networks process the features directly, while deep networks extract features automatically along with the training. Both models suffer from overfitting or poor generalization in many cases. Deep networks include more hyper-parameters than shallow ones that increase the overfitting probability. This paper states a systematic review of the overfit controlling methods and categorizes them into passive, active, and semi-active subsets. A passive method designs a neural network before training, while an active method adapts a neural network along with the training process. A semi-active method redesigns a neural network when the training performance is poor. This review includes the theoretical and experimental backgrounds of these methods, their strengths and weaknesses, and the emerging techniques for overfitting detection. The adaptation of model complexity to the data complexity is another point in this review. The relation between overfitting control, regularization, network compression, and network simplification is also stated. The paper ends with some concluding lessons from the literature.

Text is by far the most ubiquitous source of knowledge and information and should be made easily accessible to as many people as possible; however, texts often contain complex words that hinder reading comprehension and accessibility. Therefore, suggesting simpler alternatives for complex words without compromising meaning would help convey the information to a broader audience. This paper proposes mTLS, a multilingual controllable Transformer-based Lexical Simplification (LS) system fined-tuned with the T5 model. The novelty of this work lies in the use of language-specific prefixes, control tokens, and candidates extracted from pretrained masked language models to learn simpler alternatives for complex words. The evaluation results on three well-known LS datasets – LexMTurk, BenchLS, and NNSEval – show that our model outperforms the previous state-of-the-art models like LSBert and ConLS. Moreover, further evaluation of our approach on the part of the recent TSAR-2022 multilingual LS shared-task dataset shows that our model performs competitively when compared with the participating systems for English LS and even outperforms the GPT-3 model on several metrics. Moreover, our model obtains performance gains also for Spanish and Portuguese.

Mesh simplification is an effective way to solve the contradiction between 3D models and limited transmission bandwidth and smooth model rendering. The existing mesh simplification algorithms usually have problems of texture distortion, deformation of different degrees, and no texture simplification. In this paper, a model simplification algorithm suitable for 3D reconstruction is proposed by taking full advantage of the recovered 3D scene structure and calibrated images. First, the reference 3D model scene is constructed on the basis of the original mesh; second, the images are collected on the basis of the reference 3D model scene; then, the mesh and texture are simplified by using the reference image set combined with the QEM algorithm. Lastly, the 3D model data of a town in Tengzhou are used for experimental verification. The results show that the algorithm proposed in this paper basically has no texture distortion and deformation problems in texture simplification and can effectively reduce the amount of texture data, with good feasibility.

Agricultural intensification resulting in the simplification of agricultural landscapes is known to negatively impact the delivery of key ecosystem services such as the biological control of crop pests. Both conservation and classical biological control may be influenced by the landscape context in which they are deployed; yet studies examining the role of landscape structure in the establishment and success of introduced natural enemies and their interactions with native communities are lacking. In this study, we investigated the relationship between landscape simplification, classical and conservation biological control services and importantly, the outcome of these interactions for crop yield. We showed that agricultural simplification at the landscape scale is associated with an overall reduction in parasitism rates of crop pests. Additionally, only introduced parasitoids were identified, and no native parasitoids were found in crop habitat, irrespective of agricultural landscape simplification. Pest densities in the crop were lower in landscapes with greater proportions of semi-natural habitats. Furthermore, farms with less semi-natural cover in the landscape and consequently, higher pest numbers, had lower yields than farms in less agriculturally dominated landscapes. Our study demonstrates the importance of landscape scale agricultural simplification in mediating the success of biological control programs and highlights the potential risks to native natural enemies in classical biological control programs against native insects. Our results represent an important contribution to an understanding of the landscape-mediated impacts on crop yield tha t will be essential to implementing effective policies that simultaneously conserve biodiversity and ecosystem services.

Agricultural landscape intensification has enabled food production to meet growing demand. However, there are concerns that more simplified cropland with lower crop diversity, less noncrop habitat, and larger fields results in increased use of pesticides due to a lack of natural pest control and more homogeneous crop resources. Here, we use data on crop production and insecticide use from over 100,000 field-level observations from Kern County, California, encompassing the years 2005–2013 to test if crop diversity, field size, and cropland extent affect insecticide use in practice. Overall, we find that higher crop diversity does reduce insecticide use, but the relationship is strongly influenced by the differences in crop types between diverse and less diverse landscapes. Further, we find insecticide use increases with increasing field size. The effect of cropland extent is distance-dependent, with nearby cropland decreasing insecticide use, whereas cropland further away increases insecticide use. This refined spatial perspective provides unique understanding of how different components of landscape simplification influence insecticide use over space and for different crops. Our results indicate that neither the traditionally conceived “simplified” nor “complex” agricultural landscape is most beneficial to reducing insecticide inputs; reality is far more complex.

Efficient simplification of 3D models is essential for mobile and other resource-constrained application scenarios. Industrial 3D assemblies, typically composed of numerous components and dense triangular meshes, often pose significant challenges in rendering and transmission due to their large scale and high complexity. The Quadric Error Metrics (QEM) algorithm offers a practical balance between simplification accuracy and computational efficiency. However, its application to large-scale industrial models remain limited by performance bottlenecks, especially when combined with curvature-based optimization techniques that improve fidelity at the cost of increased computation. Therefore, this paper presents a parallel implementation of the QEM algorithm and its curvature-optimized variant using the OpenMP framework. By identifying key bottlenecks in the serial workflow, this research parallelizes critical processes such as curvature estimation, error metric computation, and data structure manipulation. Experiments on large industrial assembly models at a simplification ratio of 0.3, 0.5, and 0.7 demonstrate that the proposed parallel algorithms achieve significant speedups, with a maximum observed speedup of 5.5×, while maintaining geometric quality and topological consistency. The proposed approach significantly improves model processing efficiency, particularly for medium- to large-scale industrial models, and provides a scalable and practical solution for real-time loading and interaction in engineering applications.

The increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their effects on network output or pruning model components after the often-extensive time-consuming training. It is shown in this study that model simplification can benefit from investigating the network node, the most fundamental unit of neural networks, during training. Whereas studies on simplification of model structure have mostly required repeated model training, assessing evolving trends in node weights toward model stabilization may circumvent that requirement. Node magnitude stability, defined as the number of epochs where node weights retained their magnitude within a tolerance value, was the central construct in this study. To test evolving trends, a manipulated, a contrived, and two life science data sets were used. Data sets were run on convolutional and deep neural network models. Findings indicated that neural network progress toward stability differed by model, where CNNs tended to add influential nodes early during training. The magnitude stability approach of this study showed superior time efficiencies, which may assist in XAI research toward producing more transparent models and clear outcomes to technical and non-technical audiences.

The negative effects of landscape simplification on bee communities are well documented. To reverse these effects, flowering habitat enhancements are designed to provide supplemental nutritional resources for wild bees and are particularly important when few resources are available in the surrounding landscape. Yet, it is not known whether or how habitat enhancements support bee populations under varying landscape contexts. Body size is a morphological trait that is strongly linked to foraging ability, immune function, and fitness in bees. Landscape simplification has been associated with size declines across bee taxa and smaller body size can be an early indicator of environmental stress. To determine whether the negative effects of landscape simplification on body size can be improved by adding floral resources to farm landscapes, we measured the body size of 10 wild bee species collected at 70 sites with or without habitat enhancements in Michigan and New York. Bees were collected at sites with varying amounts of agriculture in the surrounding landscape, allowing us to test whether morphological responses to enhancements are affected by landscape simplification. Half of the bee species measured exhibited declining body size across the landscape gradient. Among these species, declines were buffered by the presence of habitat enhancements suggesting this response is the result of improved nutrition, reduced need for long‐distance foraging, enhanced recruitment of larger individuals or a combination of these mechanisms. Declines in body size were strongest in both the smallest and the largest species. Large and medium sized species exhibited the greatest response to flowering habitat enhancements. Synthesis and applications. At sites with high agricultural cover, we observed intraspecific body size declines among many species; however, we did not observe decreased body size in any species at sites with a flowering habitat enhancement. Therefore, our findings suggest that the presence of flowering habitat enhancements can support wild bees experiencing stress from intensively managed agricultural landscapes across multiple cropping systems and regions. At sites with high agricultural cover, we observed intraspecific body size declines among many species; however, we did not observe decreased body size in any species at sites with a flowering habitat enhancement. Therefore, our findings suggest that the presence of flowering habitat enhancements can support wild bees experiencing stress from intensively managed agricultural landscapes across multiple cropping systems and regions.

With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization.

We propose a modification of the exhaustivity operator from Fox (in: Sauerland and Stateva (eds) Presupposition and implicature in compositional semantics, Palgrave Macmillan, London, pp 71–120, 2007. https://doi.org/10.1057/9780230210752_4 ) that on top of negating all the Innocently Excludable alternatives affirms all the ‘Innocently Includable’ ones. The main result of supplementing the notion of Innocent Exclusion with that of Innocent Inclusion is that it allows the exhaustivity operator to identify cells in the partition induced by the set of alternatives (assign a truth value to every alternative) whenever possible. We argue for this property of ‘cell identification’ based on the simplification of disjunctive antecedents and the effects on free choice that arise as the result of the introduction of universal quantifiers. We further argue for our proposal based on the interaction of only with free choice disjunction.