Neocrangon orientalis, a new caridean shrimp species (Crustacea, Decapoda, Crangonidae) from the East China Sea

Han, Qingxi, Li, Xinzheng (2009): Neocrangon orientalis, a new caridean shrimp species (Crustacea, Decapoda, Crangonidae) from the East China Sea. Zootaxa 2050: 65-68, DOI: 10.5281/zenodo.18654

Seismic loss assessment of typical RC frame-core tube tall buildings in China and US using the FEMA P-58 procedure

Reinforced concrete (RC) frame-core tube buildings are widely constructed both in China and the United States (US). Their seismic performances greatly influence the economic loss of earthquakes. This study aims to compare the seismic losses of two typical RC frame-core tube tall buildings designed following the Chinese and the US seismic design codes. The prototype building is originally designed using the US seismic design codes, provided by the Tall Building Initiative (TBI) Project. Then the prototype building is redesigned according to the Chinese seismic design codes with the same design conditions and seismic hazard level. Detailed nonlinear finite element (FE) models are established for both designs. These models are used to evaluate their seismic responses at different earthquake intensities, including the service level earthquake (SLE), the design based earthquake (DBE) and the maximum considered earthquake (MCE). In addition, the collapse fragility functions of these two buildings are established using the incremental dynamic analysis (IDA). Subsequently, the seismic loss consequences (repair costs, repair workload, and casualties) of these two designs are calculated using the procedure proposed by FEMA P-58. The comparison shows that the Chinese design exhibits better seismic performances in most cases with smaller total repair cost, shorter repair time and a smaller number of casualties, except slightly longer repair time at the MCE level. For both designs, the repair cost of nonstructural components accounts for the majority of the total cost. The ceilings and elevators are the major causes of casualties at the MCE level

Current trends and developments in progressive collapse research on reinforced concrete flat plate structures

Progressive collapse of structures caused by extreme or accidental loads may lead to significant loss of life and property. Considerable research efforts have been made to date to mitigate the probability of progressive collapse and its consequences. This study summarises the fundamentals of progressive collapse in relation to the existing theoretical concepts and understanding. Specifically the existing theories pertinent to progressive collapse of building structures, in particular reinforced concrete (RC) flat plates, are examined from the following four key aspects: (1) definition of progressive collapse from deformation and/or strength perspectives with respect to the failure criteria of structural members and the entire structural system; (2) failure mechanisms of load-bearing systems undergoing progressive collapse with respect to the structural ultimate capacity, which has not been considered in the design process; (3) research methodologies for investigating collapse mechanisms, with emphases on experimental and numerical approaches; and (4) collapse-resistant design principles as covered in several international design standards in which a number of robustness requirements have been recognised. Based on the schematic review of the current trends and developments, gaps and limitations in progressive collapse research are identified and a new research direction is established to advance the progressive collapse study of RC flat plate structures

Class-Imbalanced Complementary-Label Learning via Weighted Loss

Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions. Unfortunately, existing CLL approaches have not investigate this problem. To alleviate this challenge, we propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification. To tackle this problem, we propose a novel CLL approach called Weighted Complementary-Label Learning (WCLL). The proposed method models a weighted empirical risk minimization loss by utilizing the class-imbalanced complementary labels, which is also applicable to multi-class imbalanced training samples. Furthermore, we derive an estimation error bound to provide theoretical assurance. To evaluate our approach, we conduct extensive experiments on several widely-used benchmark datasets and a real-world dataset, and compare our method with existing state-of-the-art methods. The proposed approach shows significant improvement in these datasets, even in the case of multiple class-imbalanced scenarios. Notably, the proposed method not only utilizes complementary labels to train a classifier but also solves the problem of class imbalance.Comment: 9 pages, 9 figures, 3 table

Host-specific bacterial communities associated with six cold-seep sponge species in the South China Sea

The cold-seep sponge holobionts are attracting growing attention in recent years. In this study, we utilized 16S rRNA amplicons to characterize the bacterial communities of six deep-sea sponge species found in sponge grounds at the Formosa Ridge cold seep in the South China Sea. Bacterial communities in these geographically proximal sponge species are dominated by Proteobacteria (mainly Gammaproteobacteria and Alphaproteobacteria) but exhibit distinct diversity and compositions among communities. Further analysis revealed that the SUP05 clade (Thioglobaceae) dominated most of the sponge samples. Meanwhile, phylogenetic analysis showed that the six sponge species harbored diverse SUP05 OTU phylotypes, indicating significant divergence within this clade. Additionally, operational taxonomic units (OTUs) of the family Methylomonadaceae, another abundant group in these sponges, displayed a significant genetic distance both from each other and from known species. Our findings support the hypothesis of the host-species specificity of sponge-associated bacterial communities, a widely accepted concept in shallow-water and other deep-sea sponges. The presence of dominant functional microbes, such as sulfur- and methanol-oxidizing bacteria, suggests their crucial role as chemosynthetic symbionts in facilitating the niche adaption of sponge hosts to the cold seep ecosystem. In conclusion, our study reveals the diverse and novel bacterial communities in deep-sea sponges from cold seep environments, contributing new knowledge to the host-species specificity of bacterial communities within sponges and highlighting the potential significance of functional microbes in cold seep ecosystems with dynamic energy supplies

Interfacial Polycondensation Synthesis of Optically Sensitive Polyurea Microcapsule

TMPTA prepolymer resin and photoinitiators of ITX/TPO had been encapsulated in core-shell structured microcapsules as optical responding ingredients based on interfacial polycondensation method, and polyurea structured microcapsule shell had been formed on the sheared O/W interface. The synthesized microcapsule had regular core-shell structure with the diameter of about 0.455 μm and shell thickness of about 40 nm. UV-visible absorption spectra indicated that the encapsulated ITX and TPO photoinitiators could efficiently absorb UV irradiation. Under exposure, the C=C bonds absorbance of the microencapsulated TMPTA decreased rapidly and then nearly unchanged during further exposure after 30 s. This implied that the optical response was achieved by C=C bond cleavage of TMPTA monomer initiated by the photoinitiator radicals, to form network polymers in microcapsules. The relative crosslinking rate was about 50%. Due to core polymer formation, the thermal phase change temperature of exposed microcapsules was narrowed and ranged from 105 to 205°C, compared with that from 125 to 260°C of unexposed microcapsules. Furthermore, the image density decrease at longer irradiation time had also verified the optical responding function of the synthesized microcapsules in macroscopic viewpoint

Complementary Labels Learning with Augmented Classes

Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training process might emerge in the testing phase. In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase. Specifically, by using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent. Moreover, we provide generalization error bound for proposed method which shows that the optimal parametric convergence rate is achieved for estimation error. Finally, the experimental results on several benchmark datasets verify the effectiveness of the proposed method

Learning from True-False Labels via Multi-modal Prompt Retrieving

Weakly supervised learning has recently achieved considerable success in reducing annotation costs and label noise. Unfortunately, existing weakly supervised learning methods are short of ability in generating reliable labels via pre-trained vision-language models (VLMs). In this paper, we propose a novel weakly supervised labeling setting, namely True-False Labels (TFLs) which can achieve high accuracy when generated by VLMs. The TFL indicates whether an instance belongs to the label, which is randomly and uniformly sampled from the candidate label set. Specifically, we theoretically derive a risk-consistent estimator to explore and utilize the conditional probability distribution information of TFLs. Besides, we propose a convolutional-based Multi-modal Prompt Retrieving (MRP) method to bridge the gap between the knowledge of VLMs and target learning tasks. Experimental results demonstrate the effectiveness of the proposed TFL setting and MRP learning method. The code to reproduce the experiments is at https://github.com/Tranquilxu/TMP.Comment: 15 pages, 4 figure

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

A comprehensive review of graph convolutional networks: approaches and applications

Convolutional neural networks (CNNs) utilize local translation invariance in the Euclidean domain and have remarkable achievements in computer vision tasks. However, there are many data types with non-Euclidean structures, such as social networks, chemical molecules, knowledge graphs, etc., which are crucial to real-world applications. The graph convolutional neural network (GCN), as a derivative of CNNs for non-Euclidean data, was established for non-Euclidean graph data. In this paper, we mainly survey the progress of GCNs and introduce in detail several basic models based on GCNs. First, we review the challenges in building GCNs, including large-scale graph data, directed graphs and multi-scale graph tasks. Also, we briefly discuss some applications of GCNs, including computer vision, transportation networks and other fields. Furthermore, we point out some open issues and highlight some future research trends for GCNs