Internal magnetic field distribution of a type II high Tc superconductor with non-conducting inclusions

The internal magnetic field distributions for a type II superconductor (a single crystal YBa2Cu3O7-delta ) with large normal inclusions (YBa2Cu3O 7-delta) are studied. A model based on the London Equations has been successfully developed and applied to the interpretation of the pSR data on this system. In our model, these inclusions are assumed to be cylindrical in shape and infinite in length. Therefore, this model should be especially appropriate for the prediction of field distributions in single crystal superconductors in which columnar defects have been purposely introduced to enhance pinning.;muSR experiments on a large single-crystal sample of YBa2C u3O7-delta with non-conducting YBa2Cu3 O7-delta inclusions show some interesting characteristics, especially the magnetic field distribution in the inclusion regions. In our model, the difference between the field value in the inclusions and the value at the saddle point is sensitive to the penetration depth. Comparing the calculated to observed field differences provides a new method for determining the penetration depth

Research on the performance and causes of the company’s abusive leadership - Taking "A" company in China as example

As a negative leadership behavior, abusive leadership has gradually attracted the attention of scholars and business managers. It not only harms the benefits of employees and enterprises but also has negative effects on the development of the social economy. Among the existing studies, the research on the phenomenon of abusive leadership is mostly focused on enterprises in Western countries, while the research on Chinese enterprises is rare. To make up for this deficiency in existing research, the researcher decides to explore the specific performance and causes of abusive leadership in a Chinese company. So as to lay a foundation for Chinese enterprises to reduce and eliminate this kind of leadership behavior in the future. The researcher chooses employees in a Chinese company called “A” as a research sample. According to her research results, she finds the abusive leadership exists in this company. And the results of the study on the causes of abusive leadership coincide with the preliminary expectation of the researcher. These four causes are: organizational factors, work factors, supervisor factors, and subordinate factors. The researcher also finds the most frequently mentioned factors by interviewees is the supervisor factors. The findings of this study can be used as a reference for future scholars who want to study this topic. Moreover, “A” Company and other similar enterprises in China can directly formulate corresponding solutions to reduce the conflict between managers and employees according to the causes of abusive leadership summarized in this study

Research on the performance and causes of the company’s abusive leadership - Taking "A" company in China as example

As a negative leadership behavior, abusive leadership has gradually attracted the attention of scholars and business managers. It not only harms the benefits of employees and enterprises but also has negative effects on the development of the social economy. Among the existing studies, the research on the phenomenon of abusive leadership is mostly focused on enterprises in Western countries, while the research on Chinese enterprises is rare. To make up for this deficiency in existing research, the researcher decides to explore the specific performance and causes of abusive leadership in a Chinese company. So as to lay a foundation for Chinese enterprises to reduce and eliminate this kind of leadership behavior in the future. The researcher chooses employees in a Chinese company called “A” as a research sample. According to her research results, she finds the abusive leadership exists in this company. And the results of the study on the causes of abusive leadership coincide with the preliminary expectation of the researcher. These four causes are: organizational factors, work factors, supervisor factors, and subordinate factors. The researcher also finds the most frequently mentioned factors by interviewees is the supervisor factors. The findings of this study can be used as a reference for future scholars who want to study this topic. Moreover, “A” Company and other similar enterprises in China can directly formulate corresponding solutions to reduce the conflict between managers and employees according to the causes of abusive leadership summarized in this study

RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation

Recently, the RGB images and point clouds fusion methods have been proposed to jointly estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras and LiDAR sensors adopt a frame-based data acquisition mechanism, their performance is limited by the fixed low sampling rates, especially in highly-dynamic scenes. By contrast, the event camera can asynchronously capture the intensity changes with a very high temporal resolution, providing complementary dynamic information of the observed scenes. In this paper, we incorporate RGB images, Point clouds and Events for joint optical flow and scene flow estimation with our proposed multi-stage multimodal fusion model, RPEFlow. First, we present an attention fusion module with a cross-attention mechanism to implicitly explore the internal cross-modal correlation for 2D and 3D branches, respectively. Second, we introduce a mutual information regularization term to explicitly model the complementary information of three modalities for effective multimodal feature learning. We also contribute a new synthetic dataset to advocate further research. Experiments on both synthetic and real datasets show that our model outperforms the existing state-of-the-art by a wide margin. Code and dataset is available at https://npucvr.github.io/RPEFlow.Comment: ICCV 2023. Project page: https://npucvr.github.io/RPEFlow Code: https://github.com/danqu130/RPEFlo

Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection. Based on our multimodal representation learning framework, we introduce an asymmetric feature extractor for our multimodal data, which is proven more effective than the conventional symmetric backbone setting. We also introduce multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction. Experimental results on benchmark RGB-D salient object detection datasets verify both effectiveness of our explicit multimodal disentangled representation learning method and the stochastic prediction refinement strategy, achieving comparable performance with the state-of-the-art fully supervised models. Our code and data are available at: https://github.com/baneitixiaomai/MIRV.Comment: IEEE Transactions on Circuits and Systems for Video Technology 202

Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion

RGB-guided depth completion aims at predicting dense depth maps from sparse depth measurements and corresponding RGB images, where how to effectively and efficiently exploit the multi-modal information is a key issue. Guided dynamic filters, which generate spatially-variant depth-wise separable convolutional filters from RGB features to guide depth features, have been proven to be effective in this task. However, the dynamically generated filters require massive model parameters, computational costs and memory footprints when the number of feature channels is large. In this paper, we propose to decompose the guided dynamic filters into a spatially-shared component multiplied by content-adaptive adaptors at each spatial location. Based on the proposed idea, we introduce two decomposition schemes A and B, which decompose the filters by splitting the filter structure and using spatial-wise attention, respectively. The decomposed filters not only maintain the favorable properties of guided dynamic filters as being content-dependent and spatially-variant, but also reduce model parameters and hardware costs, as the learned adaptors are decoupled with the number of feature channels. Extensive experimental results demonstrate that the methods using our schemes outperform state-of-the-art methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at the time of submission. Meanwhile, they also achieve comparable performance on the NYUv2 dataset. In addition, our proposed methods are general and could be employed as plug-and-play feature fusion blocks in other multi-modal fusion tasks such as RGB-D salient object detection

Improving Audio-Visual Segmentation with Bidirectional Generation

The aim of audio-visual segmentation (AVS) is to precisely differentiate audible objects within videos down to the pixel level. Traditional approaches often tackle this challenge by combining information from various modalities, where the contribution of each modality is implicitly or explicitly modeled. Nevertheless, the interconnections between different modalities tend to be overlooked in audio-visual modeling. In this paper, inspired by the human ability to mentally simulate the sound of an object and its visual appearance, we introduce a bidirectional generation framework. This framework establishes robust correlations between an object's visual characteristics and its associated sound, thereby enhancing the performance of AVS. To achieve this, we employ a visual-to-audio projection component that reconstructs audio features from object segmentation masks and minimizes reconstruction errors. Moreover, recognizing that many sounds are linked to object movements, we introduce an implicit volumetric motion estimation module to handle temporal dynamics that may be challenging to capture using conventional optical flow methods. To showcase the effectiveness of our approach, we conduct comprehensive experiments and analyses on the widely recognized AVSBench benchmark. As a result, we establish a new state-of-the-art performance level in the AVS benchmark, particularly excelling in the challenging MS3 subset which involves segmenting multiple sound sources. To facilitate reproducibility, we plan to release both the source code and the pre-trained model.Comment: Dawei Hao and Yuxin Mao contribute equality to this paper. Yiran Zhong is the corresponding author. The code will be released at https://github.com/OpenNLPLab/AVS-bidirectiona

Pre-configured Error Pattern Ordered Statistics Decoding for CRC-Polar Codes

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. Offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity

Preliminary study on mesenchymal stem cells in repairing nerve injury in pelvic floor denervation

Introduction: Nerve injury is considered one of the causes of pelvic floor dysfunction. Mesenchymal stem cells (MSCs) transplantation provides new possibilities for refractory degenerative diseases. This study aimed to explore the possibility and strategy of mesenchymal stem cells in treating pelvic floor dysfunction nerve injury.Methods: MSCs were isolated from human adipose tissue and cultured. A MSCs suspension (40 µL at 5 × 107/mL) was loaded on a gelatin scaffold. A rat model of anterior vaginal wall nerve injury was established by bilateral pudendal nerve denervation. The nerve tissue repair effect of mesenchymal stem cells transplanted into the anterior vaginal wall of a rat model was explored and compared in the following three groups: blank gelatin scaffold group (GS group), mesenchymal stem cell injection group (MSC group), and mesenchymal stem cells loaded on the gelatin scaffold group (MSC-GS group). Nerve fiber counting under a microscope and mRNA expression of neural markers were tested. Moreover, mesenchymal stem cells were induced into neural stem cells in vitro, and their therapeutic effect was explored.Results: Rat models of anterior vaginal wall nerve injury induced by bilateral pudendal nerve denervation showed a decreased number of nerve fibers in the anterior vaginal wall. qRT-PCR revealed that the content of neurons and nerve fibers in the rat model began to decrease 1 week after the operation and this could continue for 3 months. In vivo experiments showed that MSC transplantation improved the nerve content, and MSCs loaded on the gelatin scaffold had an even better effect. mRNA expression analysis demonstrated that MSCs loaded on gelatin scaffolds induced a higher and earlier gene expression of neuron-related markers. Induced neural stem cell transplantation was superior in improving the nerve content and upregulating the mRNA expression of neuron-related markers in the early stage.Conclusion: MSCs transplantation showed a promising repair capacity for nerve damage in the pelvic floor. The supporting role of gelatin scaffolds might promote and strengthen the nerve repair ability at an early stage. Preinduction schemes could provide an improved regenerative medicine strategy for innervation recovery and functional restoration in pelvic floor disorders in the future

Transformer Transforms Salient Object Detection and Camouflaged Object Detection

The transformer networks are particularly good at modeling long-range dependencies within a long sequence. In this paper, we conduct research on applying the transformer networks for salient object detection (SOD). We adopt the dense transformer backbone for fully supervised RGB image based SOD, RGB-D image pair based SOD, and weakly supervised SOD within a unified framework based on the observation that the transformer backbone can provide accurate structure modeling, which makes it powerful in learning from weak labels with less structure information. Further, we find that the vision transformer architectures do not offer direct spatial supervision, instead encoding position as a feature. Therefore, we investigate the contributions of two strategies to provide stronger spatial supervision through the transformer layers within our unified framework, namely deep supervision and difficulty-aware learning. We find that deep supervision can get gradients back into the higher level features, thus leads to uniform activation within the same semantic object. Difficulty-aware learning on the other hand is capable of identifying the hard pixels for effective hard negative mining. We also visualize features of conventional backbone and transformer backbone before and after fine-tuning them for SOD, and find that transformer backbone encodes more accurate object structure information and more distinct semantic information within the lower and higher level features respectively. We also apply our model to camouflaged object detection (COD) and achieve similar observations as the above three SOD tasks. Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task. The source code and experimental results are available via our project page: https://github.com/fupiao1998/TrasformerSOD.Comment: Technical report, 18 pages, 22 figure