Quantum chaos for nonstandard symmetry classes in the Feingold-Peres model of coupled tops

We consider two coupled quantum tops with angular momentum vectors L and M. The coupling Hamiltonian defines the Feingold-Peres model, which is a known paradigm of quantum chaos. We show that this model has a nonstandard symmetry with respect to the Altland-Zirnbauer tenfold symmetry classification of quantum systems, which extends the well-known threefold way of Wigner and Dyson (referred to as “standard” symmetry classes here). We identify the nonstandard symmetry classes BDI0 (chiral orthogonal class with no zero modes), BDI1 (chiral orthogonal class with one zero mode), and CI (antichiral orthogonal class) as well as the standard symmetry class AI (orthogonal class). We numerically analyze the specific spectral quantum signatures of chaos related to the nonstandard symmetries. In the microscopic density of states and in the distribution of the lowest positive energy eigenvalue, we show that the Feingold-Peres model follows the predictions of the Gaussian ensembles of random-matrix theory in the appropriate symmetry class if the corresponding classical dynamics is chaotic. In a crossover to mix and near-integrable classical dynamics, we show that these signatures disappear or strongly change

2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection

This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we present a novel framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with multi-modal prompts for the regularization of cascaded modern foundation models. Inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly (SAA) to leverage diverse multi-modal prior knowledge for anomaly localization. Subsequently, we further introduce multimodal prompts (SAA$+$) derived from domain expert knowledge and target image context to enable the non-parameter adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will release the code of our winning solution for the CVPR2023 VAN.Comment: The first two author contribute equally. CVPR workshop challenge report. arXiv admin note: substantial text overlap with arXiv:2305.1072

Code Reviewer Recommendation Based on a Hypergraph with Multiplex Relationships

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing stateof-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.Comment: The 31st IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER

Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training

Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. However, the close relations between coarse- and fine-grained representations for each modality are important for image-text retrieval but almost neglected. As a result, such previous works inevitably suffer from low retrieval accuracy or heavy computational cost. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework. This framework is consistent with human cognition, as humans simultaneously pay attention to the entire sample and regional elements to understand the semantic content. To this end, a Token-Guided Dual Transformer (TGDT) architecture which consists of two homogeneous branches for image and text modalities, respectively, is proposed for image-text retrieval. The TGDT incorporates both coarse- and fine-grained retrievals into a unified framework and beneficially leverages the advantages of both retrieval approaches. A novel training objective called Consistent Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra- and inter-modal semantic consistencies between images and texts in the common embedding space. Equipped with a two-stage inference method based on the mixed global and local cross-modal similarity, the proposed method achieves state-of-the-art retrieval performances with extremely low inference time when compared with representative recent approaches.Comment: Code is publicly available: https://github.com/LCFractal/TGD

Heat shock transcription factor 1 preserves cardiac angiogenesis and adaptation during pressure overload

To examine how heat shock transcription factor 1 (HSF1) protects against maladaptive hypertrophy during pressure overload, we subjected HSF1 transgenic (TG), knockout (KO) and wild type (WT) mice to a constriction of transverse aorta (TAC), and found that cardiac hypertrophy, functions and angiogenesis were well preserved in TG mice but were decreased in KO mice compared to WT ones at 4 weeks, which was related to HIF-1 and p53 expression. Inhibition of angiogenesis suppressed cardiac adaptation in TG mice while overexpression of angiogenesis factors improved maladaptive hypertrophy in KO mice. In vitro formation of vasculatures by microvascular endothelial cells was higher in TG mice but lower in KO mice than in WT ones. A siRNA of p53 but not a HIF-1 gene significantly reversed maladaptive hypertrophy in KO mice whereas a siRNA of HIF-1 but not a p53 gene induced maladaptive hypertrophy in TG mice. Heart microRNA analysis showed that miR-378 and miR-379 were differently changed among the three mice after TAC, and miR-378 or siRNA of miR-379 could maintain cardiac adaptation in WT mice. These results indicate that HSF1 preserves cardiac adaptation during pressure overload through p53-HIF-1-associated angiogenesis, which is controlled by miR-378 and miR-379

Naringenin prevents TGF-β1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation

Presenting the incidence of pulmonary metastasis (mice with metastasis/total mice). Tumor-bearing mice treated with naringenin or 1D11 were imaged on day 24 using bags to avoid the bioluminescence from primary tumor. The mice with pulmonary metastases were numbered based on the bioluminescence signal. (TIF 26 kb

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

Transient Hemodynamic Changes upon Changing a BCPA into a TCPC in Staged Fontan Operation: A Computational Model Study

The clinical benefits of the Fontan operation in treating single-ventricle defects have been well documented. However, perioperative mortality or morbidity remains a critical problem. The purpose of the present study was to identify the cardiovascular factors that dominate the transient hemodynamic changes upon the change of a bidirectional cavopulmonary (Glenn) anastomosis (BCPA) into a total cavopulmonary connection (TCPC). For this purpose, two computational models were constructed to represent, respectively, a single-ventricle circulation with a BCPA and that with a TCPC. A series of model-based simulations were carried out to quantify the perioperative hemodynamic changes under various cardiovascular conditions. Obtained results indicated that the presence of a low pulmonary vascular resistance and/or a low lower-body vascular resistance is beneficial to the increase in transpulmonary flow upon the BCPA to TCPC change. Moreover, it was found that ventricular diastolic dysfunction and mitral valve regurgitation, despite being well-known risk factors for poor postoperative outcomes, do not cause a considerable perioperative reduction in transpulmonary flow. The findings may help physicians to assess the perioperative risk of the TCPC surgery based on preoperative measurement of cardiovascular function