Forward modelling 3-D geophysical electromagnetic field data with meshfree methods

Simulating geophysical electromagnetic (EM) data over real-life conductivity models requires numerical algorithms that can incorporate realistically complex geometry and topography. The most successful way to incorporate them is to use unstructured meshes in the discretization of an Earth model. Current mesh-based numerical methods that are capable of using such meshes have inherent drawbacks caused by generating 3-D unstructured meshes conforming to irregular geometries. Such a mesh generation process may become computationally expensive and unstable, and particularly so for EM inversion computations in which the forward modelling may be required many times. In this thesis I investigate the feasibility and applicability of radial basis function-based finite difference (RBF-FD), a meshfree method, in forward modelling 3-D EM data. In the meshfree method, the physical model is represented using only a set of unconnected points, effectively overcoming the issues related to the mesh generation. To improve numerical efficiency, unstructured point sets are used in the computation for the first time for EM problems. The computation is further accelerated by introducing a new type of radial basis function in the RBFFD method. The convergence and accuracy of the proposed RBF-FD method are demonstrated first via forward modelling gravity and gravity gradient data. The computational efficiency of the meshfree method is compared with that of using a more traditional finite element method. The meshfree method is then applied to forward model magnetotelluric data of which the effectiveness is demonstrated using three benchmark conductivity models from the literature. Faithful reproduction of the physics in the EM fields, e.g. discontinuous electric fields across the conductivity contrasts, is achieved by proposing a hybrid meshfree scheme which is a modification to standard meshfree algorithms. The hybrid method is also applied to simulate controlled-source EM data in the frame of both total-field and primary-secondary field approaches, in which the problems in dealing with singular source functions that cause singularities in the EM fields are addressed. For these two approaches, the accuracies of the proposed hybrid meshfree method in forward modelling the controlled-source EM data are demonstrated by using idealized 1-D layered models and a 3-D marine canonical disk model. The successful applications of the proposed meshfree method in modelling the above EM data suggest that the meshfree technique has the potential of becoming an important numerical method for simulating EM responses over complicated conductivity models

Mutual Exclusive Modulator for Long-Tailed Recognition

The long-tailed recognition (LTR) is the task of learning high-performance classifiers given extremely imbalanced training samples between categories. Most of the existing works address the problem by either enhancing the features of tail classes or re-balancing the classifiers to reduce the inductive bias. In this paper, we try to look into the root cause of the LTR task, i.e., training samples for each class are greatly imbalanced, and propose a straightforward solution. We split the categories into three groups, i.e., many, medium and few, according to the number of training images. The three groups of categories are separately predicted to reduce the difficulty for classification. This idea naturally arises a new problem of how to assign a given sample to the right class groups? We introduce a mutual exclusive modulator which can estimate the probability of an image belonging to each group. Particularly, the modulator consists of a light-weight module and learned with a mutual exclusive objective. Hence, the output probabilities of the modulator encode the data volume clues of the training dataset. They are further utilized as prior information to guide the prediction of the classifier. We conduct extensive experiments on multiple datasets, e.g., ImageNet-LT, Place-LT and iNaturalist 2018 to evaluate the proposed approach. Our method achieves competitive performance compared to the state-of-the-art benchmarks

More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching

Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Re-sourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supervise the training of cross-modal attention models in a contrastive learning manner without requiring explicit attention annotations. They are plug-in training strategies and can be easily integrated into existing cross-modal attention models. Additionally, we introduce three metrics including Attention Precision, Recall, and F1-Score to quantitatively measure the quality of learned attention models. We evaluate the proposed constraints by incorporating them into four state-of-the-art cross-modal attention-based image-text matching models. Experimental results on both Flickr30k and MS-COCO datasets demonstrate that integrating these constraints improves the model performance in terms of both retrieval performance and attention metrics.Comment: Accepted to WACV 202

Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning

Despite the success of fully-supervised human skeleton sequence modeling, utilizing self-supervised pre-training for skeleton sequence representation learning has been an active field because acquiring task-specific skeleton annotations at large scales is difficult. Recent studies focus on learning video-level temporal and discriminative information using contrastive learning, but overlook the hierarchical spatial-temporal nature of human skeletons. Different from such superficial supervision at the video level, we propose a self-supervised hierarchical pre-training scheme incorporated into a hierarchical Transformer-based skeleton sequence encoder (Hi-TRS), to explicitly capture spatial, short-term, and long-term temporal dependencies at frame, clip, and video levels, respectively. To evaluate the proposed self-supervised pre-training scheme with Hi-TRS, we conduct extensive experiments covering three skeleton-based downstream tasks including action recognition, action detection, and motion prediction. Under both supervised and semi-supervised evaluation protocols, our method achieves the state-of-the-art performance. Additionally, we demonstrate that the prior knowledge learned by our model in the pre-training stage has strong transfer capability for different downstream tasks.Comment: Accepted to ECCV 202

Preparation of fluorescence-encoded microspheres in a core-shell structure for suspension arrays

Fluorescence-encoded microspheres are widely used in the detection and analysis of biological molecules, especially in suspension arrays. Here, we report an efficient strategy for the preparation of fluorescence-encoded polystyrene microspheres with desirable optical and surface properties. The micron-sized, monodisperse polystyrene seed beads were first synthesized by dispersion polymerization. Then, dye molecules and carboxyl functional groups were copolymerized on the surface of the seed beads by forming a core-shell structure. Rhodamine 6G (R6G) was used as a model dye molecule to prepare the fluorescent beads, and the fluorescence intensity of the beads can be precisely controlled by adjusting the quantity of R6G. These fluorescent beads were characterized by environmental scanning electron microscopy, laser scanning confocal microscopy, and spectrofluorometry. The differences of the fluorescence spectra between fluorescent beads and R6G in solution were investigated. Twelve kinds of fluorescent beads encoded with different R6G fluorescence intensities were prepared, and they can be clearly distinguished on a conventional flow cytometer. Furthermore, the encoded beads are stable in water and resistant to photobleaching, which is crucial for their potential applications in diagnostic assays and imaging. Detection of human alpha fetoprotein antigen via a sandwich microsphere-based immunoassay yielded a detection limit of 80 pg mL(-1), demonstrating that the fluorescence-encoded microspheres synthesized herein are efficient in serving as the microcarriers in suspension arrays. As both the encoding and functionalizing procedures are made simultaneously, the newly designed technique is extremely simple and time-saving. Moreover, it could be readily applicable to the preparation of a wide size range of fluorescent particles made by polymerization.National Natural Science Foundation of China [20675070]; Program for New Century Excellent Talents in University [NCET-07-0729]; NFFTBS [J0630429]; Scientific Research Foundation ; State Education Ministr

Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering similar to 1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture

Genetic and Biochemical Investigation of Seed Fatty Acid Accumulation in Arabidopsis

As a vegetable oil, consisting principally of triacylglycerols, is the major storage form of photosynthetically-fixed carbon in oilseeds which are of significant agricultural and industrial value. Photosynthesis in chlorophyll-containing green seeds, along with photosynthesis in leaves and other green organs, generates ATP and reductant (NADPH and NADH) needed for seed fatty acid production. However, contribution of seed photosynthesis to fatty acid accumulation in seeds have not been well-defined. Here, we report the contribution of seed-photosynthesis to fatty acid production by probing segregating green (photosynthetically-competent) and non-green or yellow (photosynthetically-non-competent) seeds in siliques of an Arabidopsis chlorophyll synthase mutant. Using this mutant, we found that yellow seeds lacking photosynthetic capacity reached 80% of amounts of oil in green seeds at maturity. Combining this with studies using shaded siliques, we determined that seed-photosynthesis accounts for 20% and silique and leaf/stem photosynthesis each account for ~40% of the ATP and reductant for seed oil production. Transmission electron microscopy (TEM) and pyridine nucleotides and ATP analyses revealed that seed photosynthesis provides ATP and reductant for oil production mostly during early development, as evidenced by delayed oil accumulation in non-green seeds. Transcriptomic analyses suggests that the oxidative pentose phosphate pathway could be the source of carbon, energy and reductants required for fatty acid synthesis beyond the early stages of seed development

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error

In large ports, shipyards, and other places of handling operations, gantry cranes are widely used. As a typical slewing crane, its slewing-bearing large gear ring is prone to tooth breakage problems due to the existence of long-term complex alternating load. This paper presents a new fault analysis method based on the gear position accuracy error. Firstly, the relative displacement relationship between the pinion and large gear ring and the large gear ring gear teeth stress nature were analyzed through the establishment of the rotary table structure, slewing bearing, and cylinder structure assembly finite element model. In addition, a dynamic data acquisition instrument and resistance strain gauge data were separately applied to analyze the pinion and large gear ring displacement along with cylinder structure stress for the test position accuracy error. The final results show that under the frontal load condition, the horizontal displacement deviation of the pinion gear and the large gear ring is approximately 2.9 mm, which leads to an increase of 23% in the stress value of the upper tooth root at the engagement of the large gear ring, causing extrusion and accelerating the fatigue and even fracture of the gear teeth