Miracle-dz/UniFAR-FTUs huggingface dataset:
OAMSFNet: Orientation-Aware and Multi-Scale Feature Fusion Network for shadow detection in remote sensing images via pseudo shadow

ABSTRACT Remote sensing images (RSI) of urban regions often exhibit shadows cast by buildings and other objects, which may result in imprecise analysis and interpretation. Therefore, shadow detection plays a significant role in RSI scene understanding. Current approaches have paid little attention to the presence of pseudo shadows and their confounding effects on detection results. We tackle these questions in the spatial context of orientation-awareness, effectively capturing the intricate relationships between shadows and ground objects. Here, we introduce a novel deep learning network named the Orientation-Aware and Multi-Scale Feature Fusion Network (OAMSFNet) due to noise reduction considerations. The proposed OAMSFNet comprises Shadow Aware Feature Encoder (SFE), Orientation-Aware Context Module (OCM), and Multi-Scale Feature Pyramid (MFP). Furthermore, a multi-scale feature fusion algorithm has been devised to enhance the detection and segmentation capabilities of the model in shadow regions. Finally, a comparative study was conducted on the Aerial Imagery dataset for Shadow Detection (AISD) in both quantitative and qualitative aspects. The experimental results show that our method outperforms state-of-the-art methods while maintaining a lightweight model design, indicating that our approach exhibits remarkable accuracy and stability in excellent agreement with predictions. OAMSFNet achieved an average F-score of 89.85%, surpassing the straightforward semantic segmentation model SegNet by 9.94%, and demonstrated remarkable efficiency enhancements, with FLOPs reduced to as low as 11.48 G, resulting in efficiency improvements of 66.7%-96.5% compared to other shadow detection models.
Information Criteria for Comparing Partition Schemes

Abstract When inferring phylogenies, one important decision is whether and how nucleotide substitution parameters should be shared across different subsets or partitions of the data. One sort of partitioning error occurs when heterogeneous subsets are mistakenly lumped together and treated as if they share parameter values. The opposite kind of error is mistakenly treating homogeneous subsets as if they result from distinct sets of parameters. Lumping and splitting errors are not equally bad. Lumping errors can yield parameter estimates that do not accurately reflect any of the subsets that were combined whereas splitting errors yield estimates that did not benefit from sharing information across partitions. Phylogenetic partitioning decisions are often made by applying information criteria such as the Akaike information criterion (AIC). As with other information criteria, the AIC evaluates a model or partition scheme by combining the maximum log‐likelihood value with a penalty that depends on the number of parameters being estimated. For the purpose of selecting an optimal partitioning scheme, we derive an adjustment to the AIC that we refer to as the AICSymbol and that is motivated by the idea that splitting errors are less serious than lumping errors. We also introduce a similar adjustment to the Bayesian information criterion (BIC) that we refer to as the BICSymbol. Via simulation and empirical data analysis, we contrast AIC and BIC behavior to our suggested adjustments. We discuss these results and also emphasize why we expect the probability of lumping errors with the AICSymbol and the BICSymbol to be relatively robust to model parameterization. Symbol. No caption available.
Gender heterophily and equality: a contribution to gender equality in the Chilean scientific sector

Women’s insertion or consolidation in science has been thoroughly researched. Such discussion could be particularly relevant concerning sustainable development goal five (SDG 5) on Gender Equality advancement. However, the debate is focused on women percentages inserted into scientific labor, leaving the issue of symbolic experience for women in research unaddressed and with little empirical support. The data come from a survey developed under a FONDECYT project, which studied knowledge production in Chile. Researchers obtained contacts through invitations on social networks such as Twitter, Facebook, and LinkedIn and databases containing scientists’ emails working in Chile. The non-probabilistic sample collected 583 cases, with participants residing in 15 of the 16 country regions. As a result, this document presents the findings of a study on symbolic experience using an instrument to determine whether there are any homophily patterns. It aims to determine if scientists tend to cite others as referents only when they encounter a situation like their own. The findings reveal a clear way scientists estimate the effect of others in terms of their gender homophily. This intervening factor could be crucial in reproducing the disparities and asymmetries that characterize Chile’s scientific field.
Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

INTRODUCTION: Machine translation is a modern natural language processing research field with important scientific and practical significance. In practice, the variation of languages, the limitation of semantic knowledge, and the lack of parallel language resources limit the development of machine translation. OBJECTIVES: This paper aims to avoid duplicating neural networks during the learning process and improve the ability to generalize complex neural network machine translation models with limited resources. METHODS: Textual material in the source language was studied, and a suitable textual material representation model was used to express complex, high-level, and abstract semantic information. Then, a more efficient neural network machine translation integration model was developed based on the control of written data and algorithms. RESULTS: Data mining must be applied to complex neural network machine translation systems based on transfer learning to standardize finite neural network models. CONCLUSION: Neural network-based embedded machine translation systems based on migration training require a small number of labelled samples to improve the system's permeability. However, this adaptive migration learning region approach can easily lead to over-learning problems in neural network machine translation models, thus avoiding excessive correspondences during the learning process and improving the generalization ability of the translation model with limited neural network resources.
A 3D WebGIS-Enhanced Representation Method Fusing Surveillance Video Information

In Internet applications, there are more and more demands for geographic information expression technology that integrates video surveillance information, but there are generally problems such as camera position, direction, field of view and other parameters that are difficult to calculate accurately, texture occlusion, etc., which seriously affect the fusion expression of 3D scenes and video information Effect. To address the above problems, in this study, movable objects and trees are removed from the model surface, and an enhanced representation framework is designed for the fusion of video and 3D WebGIS under weak constraints. At the same time, the proposed method is verified and analyzed from three aspects: feature point matching, line coincidence degree and occlusion effect. The experimental results show the following. 1) In terms of the feature point matching ability, the feature point matching ability of the proposed method is 22.22% higher than that under strong constraints. 2) In terms of the occlusion effect, the proposed method achieves a 74.12% reduction in the area of the occlusion area, which is 1.63% higher than the occlusion area reduction rate after video projection under strong constraints. 3) In terms of the straight line coincidence, the experimental results of the proposed method are significantly better than those obtained using the strong constraints method. Both the length difference of the straight line with the same name and the relative offset angle are smaller than the result of the method using the strong constraint conditions. Also, the FPS is stabilized at about 30f/s during system operation with high rendering efficiency, which further proves the feasibility and stability of this system. Experiments show that using the proposed method can reflect real geospatial information, better enhance geographic information expression and be easy to implement, providing a valuable reference for the construction of applications such as real-world 3D models and smart cities.
Chemical changes during endoskarn and porphyry‐style alteration and Cu—Fe exoskarn mineralization in the Tonglushan system, eastern China

Element mobility and chemical mass transfer are evaluated in the formation of Cu—Fe exoskarn deposits and endoskarn and minor porphyry‐style alteration in the Tonglushan quartz monzodiorite (QMD) system, eastern China. Endoskarn formation involved the migration of Ca into the QMD from the exoskarnification of carbonates (now marble) xenoliths and wall rocks, addition of Fe and Mn by magmatic‐hydrothermal fluids emanating from the interior of the QMD, and removal of alkali elements due to the replacement of feldspars and mica by prograde skarn minerals. Zirconium, Hf, U, and rare earth elements (REE) were added by hydrothermal fluids which were able to carry these often poorly mobile high field strength elements (HFSE) due to elevated F activity. Additions of Al were likely from Na‐rich fluids that also caused sodic alteration. Several factors favored mineralization within the exoskarns rather than endoskarns and QMD. The endoskarns were relatively oxidizing, as evidenced by a significant addition of Fe3+, which caused Cu to remain in magmatic‐hydrothermal fluids until they entered and precipitated sulphides in the more reducing environment of the exoskarns. Fluid migration from the QMD through the endoskarns and into the exoskarns was favored due to decarbonation of wall rock carbonates and related upwards migration of CO2 to produce a self‐sustaining chimney effect, which drew further fluids towards the carbonates to form, alter and mineralize the exoskarns. The higher porosity and permeability of the endoskarns compared with the QMD further promoted the lateral flow of Cu‐bearing fluids towards the exoskarns and limited porphyry‐style alteration and mineralization within the QMD. This proposed mechanism is only likely to be relevant for porphyry‐type systems developed predominantly within carbonate host rocks. Its significance for exploration models is that relatively poorly mineralized porphyry stocks in this setting may be associated with more substantive exoskarn deposits on their margins.
Deep Learning Approach for Optimal Localization Using an mm-Wave Sensor

Short-range indoor localization is one of the key necessities in automation industries and healthcare setups. With its increasing demand, the need for more precise positioning systems is rapidly increasing. Millimeter-wave (mm-wave) technology is emerging to enable highly precise localization performance. However, due to the limited availability of low-cost mm-wave sensors, it is challenging to accelerate research on real data. Furthermore, noise due to the hardware components of a sensor incurs perturbation in the received signal, which corrupts the estimation of range and the angle of arrival (AoA). Due to the huge success of data-driven algorithms in solving regression problems, we propose a data-driven approach, which employs two deep learning (DL)-based regression models, i.e., dense neural network and convolutional neural network, and compare their performance with two machine learning-based regression models, linear regression and support vector regression, to reduce errors in the estimate of AoA and range obtained via an mm-wave sensor. Our main goal is to optimize the localization measurements acquired from a low-cost mm-wave sensor for short-range applications. This will accelerate the development of proof of concept and foster research on cost-effective mm-wave-based indoor positioning systems. All experiments were conducted using over-the-air data collected with an mm-wave sensor, and the validity of the experiments was verified in unseen environments. The results obtained from our experimental evaluations, both for in-sample and out-of-sample testing, indicate improvements in the estimation of AoA and range with our proposed DL models. The improvements achieved were greater than 15% for AoA estimation and over 85% for range estimation compared to the baseline methods.
The 2023 Impact of Inflammatory Bowel Disease in Canada: Treatment Landscape

Abstract The therapeutic landscape for inflammatory bowel disease (IBD) has changed considerably over the past two decades, owing to the development and widespread penetration of targeted therapies, including biologics and small molecules. While some conventional treatments continue to have an important role in the management of IBD, treatment of IBD is increasingly moving towards targeted therapies given their greater efficacy and safety in comparison to conventional agents. Early introduction of these therapies—particularly in persons with Crohn’s disease—combining targeted therapies with traditional anti-metabolite immunomodulators and targeting objective markers of disease activity (in addition to symptoms), have been shown to improve health outcomes and will be increasingly adopted over time. The substantially increased costs associated with targeted therapies has led to a ballooning of healthcare expenditure to treat IBD over the past 15 years. The introduction of less expensive biosimilar anti-tumour necrosis factor therapies may bend this cost curve downwards, potentially allowing for more widespread access to these medications. Newer therapies targeting different inflammatory pathways and complementary and alternative therapies (including novel diets) will continue to shape the IBD treatment landscape. More precise use of a growing number of targeted therapies in the right individuals at the right time will help minimize the development of expensive and disabling complications, which has the potential to further reduce costs and improve outcomes.
Design Methodology of Dual-Polarized Angle-Selective Surface Based on Three-Layer Frequency-Selective Surfaces

This article presents the design methodology of dual-polarized angle-selective surface (ASS) based on three-layer frequency-selective surface (FSS). The entire structure is analyzed with an equivalent circuit model, and the corresponding bandpass condition in terms of the FSS’s normalized susceptance is first derived. The wideband and stopband conditions are also derived analytically, and a general design methodology is proposed to realize an ASS of wideband, high angular selectivity, and dual-polarization operation. To verify the proposed design methodology, a low-pass ASS example based on the complementary Swastika-shaped resonance is proposed. Simulation results show that the proposed ASS achieves 5% bandwidth (from 2.94 to 3.1 GHz) with a passband (<inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{21} \vert \,\, > \,\,-1$ </tex-math></inline-formula> dB) from 0° to 15° and a stopband (<inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{21} \vert < -20$ </tex-math></inline-formula> dB) from 30°. The proposed ASS is then above a high-gain horn antenna, and it can effectively suppress the horn antenna’s sidelobes. A prototype of the proposed ASS is fabricated, and measured results agree well with the simulated ones.
A weapon to fight against pervasive Omicron: systematic actions transiting to pre-COVID normal

The Coronavirus Disease-2019 (COVID-19) pandemic is not just a health crisis but also a social crisis. Confronted with the resurgence of variants with massive infections, the triggered activities from personal needs may promote the spread, which should be considered in risk management. Meanwhile, it is important to ensure that the policy responses on citizen life to a lower level. In the face of Omicron mutations, we need to sum up the control experience accumulated, adapting strategies in the dynamic coevolution process while balancing life resumption and pandemic control, to meet challenges of future crises. We collected 46 cases occurring between 2021 and 2022, mainly from China, but also including five relevant cases from other countries around the world. Based on case studies, we combine micro-view individual needs/behaviors with macro-view management measures linking Maslow’s hierarchy of needs with the transmission chain of Omicron clusters. The proposed loophole chain could help identify both individual and management loopholes in the spread of the virus. The systematic actions that were taken have effectively combated these ubiquitous vulnerabilities at lower costs and lesser time. In the dynamic coevolution process, the Chinese government has made effective and more socially acceptable prevention policies while meeting the divergent needs of the entire society at the minimum costs. Systematic actions do help maintain the balance between individuals’ satisfaction and pandemic containment. This implies that risk management policies should reasonably consider individual needs and improve the cooperation of various stakeholders with targeted flexible measures, securing both public health and life resumption.
A bytecode‐based integrated detection and repair method for reentrancy vulnerabilities in smart contracts

The reentrancy vulnerability in smart contracts has caused significant losses in the digital currency economy. Existing solutions for detecting and repairing this vulnerability are limited in scope and lack a comprehensive framework. Additionally, there is currently a lack of guidance methods for effectively pinpointing the location of vulnerabilities. The proposed bytecode‐level method addresses these challenges by incorporating a detection module, an auxiliary localization module, and a repair module. An opcode classification method is introduced using vulnerability features and a BiLSTM‐Attention‐based sequence model to enhance detection accuracy. To overcome difficulties in vulnerability localization, an auxiliary localization method based on data flow and control flow analysis is proposed, enabling developers to better locate vulnerabilities. Current reentrancy vulnerability repair methods are analyzed and strategies for three reachable patterns are proposed. The bytecode rewriting strategy utilizes Trampoline technology for repair, while a fuel optimization method reduces bytecode generation length to optimize gas costs. Through extensive experimental validation, the effectiveness and superiority of the proposed methods are confirmed, further validating the feasibility of the entire framework. Experimental results demonstrate that the framework offers enhanced protection against reentrancy vulnerability attacks in smart contracts.
Eucalyptus cryptica (Myrtaceae): a critically endangered new species

ABSTRACT Recognition that the critically endangered mallee Eucalyptus sp. Cattai (Gregson s.n. 28 Aug 1954) is a distinct species has been complicated by close morphological similarity between it and other members of E. subgenus Symphyomyrtus section Latoangulatae series Annulares. Recent genomic evidence has demonstrated that it is distinct from other species. In this study, we provide E. sp. Cattai with the new species name, E. cryptica T.C. Wilson, S.Rutherf. & S.M. Douglas, and use genomic scans of adults and seedlings to assist in its description and support its conservation by identifying hybrids. Accompanying the description of E. cryptica are images, diagnostic illustrations and an updated part of the Eucalyptus key for the Flora of New South Wales.
Policy strategies in planning adaptation of the sustainable palm oil industry in Merauke Regency Papua Province of Indonesia

In the Papua region, the expansion of the palm oil industry increased by up to 71% from 2011 to 2019, with the largest affected area located in Merauke regency. The objective of this research is to develop policy strategies with impact simulations related to economic, socio-cultural, and environmental issues for the sustainability of the palm oil industry with or without a moratorium on further expansion of land area. This study employs a system dynamics approach. The dynamic system is carried out through conceptual development, model specification, model verification, scenario development, and validity testing by measuring the absolute percentage error (MAPE). Model development and validation were carried out using PowerSim v.7 software. The policy scenario is simulated from the palm oil expansion policy beguan in 2018, until the research year ends in 2030. The validated results, forming the basis for simulations, exhibit high accuracy with a MAPE of less than 5%. The percentage deviations of 4.21% for production and 0.28% for land area is observed based on actual data from 2018 to 2022. The simulation of a 20% expansion scenario shows a significant increase in production to 1 million metric tons per year but also a 1.3% rise in waste generation, with an average waste volume of 350,000 tons per year. The expanded area will experience a 50% increase in the labor force to meet the higher production demand. The proposed comprehensive strategy includes regional contract regulations, diligent monitoring of land clearing, community empowerment, and indigenous peoples' involvement. It also promotes smallholder plantations, the utilization of waste for energy, and alternative markets for crude palm oil (CPO). Conversely, the dynamic model scenario with a moratorium on land expansion resulted in a production output of 600,000 metric tons, slower job market growth, and a 1.3% increase in waste generation, with an average waste volume of 195,000 tons per year. The strategy proposed uses proper waste processing, production adjustments, regulation of community rights and boundaries, resource development, and involvement of local palm farmers. These simulations offer valuable insights for sustainable decision-making, emphasizing the need to balance economic growth, environmental protection, and community well-being in the development of the palm oil industry. Key words: dynamical system, moratorium, palm oil, scenario strategy, sustainability