Unextendible Maximally Entangled Bases in Cpd⊗Cqd\mathbb{C}^{pd}\otimes \mathbb{C}^{qd}

The construction of unextendible maximally entangled bases is tightly related to quantum information processing like local state discrimination. We put forward two constructions of UMEBs in $\mathbb {C}^{pd}\otimes \mathbb {C}^{qd}$($p\leq q$) based on the constructions of UMEBs in $\mathbb {C}^{d}\otimes \mathbb {C}^{d}$ and in $\mathbb {C}^{p}\otimes \mathbb {C}^{q}$, which generalizes the results in [Phys. Rev. A. 94, 052302 (2016)] by two approaches. Two different 48-member UMEBs in $\mathbb {C}^{6}\otimes \mathbb {C}^{9}$ have been constructed in detail

Classification of Diabetic Foot Ulcers Using Class Knowledge Banks

Diabetic foot ulcers (DFUs) are one of the most common complications of diabetes. Identifying the presence of infection and ischemia in DFU is important for ulcer examination and treatment planning. Recently, the computerized classification of infection and ischaemia of DFU based on deep learning methods has shown promising performance. Most state-of-the-art DFU image classification methods employ deep neural networks, especially convolutional neural networks, to extract discriminative features, and predict class probabilities from the extracted features by fully connected neural networks. In the testing, the prediction depends on an individual input image and trained parameters, where knowledge in the training data is not explicitly utilized. To better utilize the knowledge in the training data, we propose class knowledge banks (CKBs) consisting of trainable units that can effectively extract and represent class knowledge. Each unit in a CKB is used to compute similarity with a representation extracted from an input image. The averaged similarity between units in the CKB and the representation can be regarded as the logit of the considered input. In this way, the prediction depends not only on input images and trained parameters in networks but the class knowledge extracted from the training data and stored in the CKBs. Experimental results show that the proposed method can effectively improve the performance of DFU infection and ischaemia classifications

Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation

Learning per-point semantic features from the hierarchical feature pyramid is essential for point cloud semantic segmentation. However, most previous methods suffered from ambiguous region features or failed to refine per-point features effectively, which leads to information loss and ambiguous semantic identification. To resolve this, we propose Retro-FPN to model the per-point feature prediction as an explicit and retrospective refining process, which goes through all the pyramid layers to extract semantic features explicitly for each point. Its key novelty is a retro-transformer for summarizing semantic contexts from the previous layer and accordingly refining the features in the current stage. In this way, the categorization of each point is conditioned on its local semantic pattern. Specifically, the retro-transformer consists of a local cross-attention block and a semantic gate unit. The cross-attention serves to summarize the semantic pattern retrospectively from the previous layer. And the gate unit carefully incorporates the summarized contexts and refines the current semantic features. Retro-FPN is a pluggable neural network that applies to hierarchical decoders. By integrating Retro-FPN with three representative backbones, including both point-based and voxel-based methods, we show that Retro-FPN can significantly improve performance over state-of-the-art backbones. Comprehensive experiments on widely used benchmarks can justify the effectiveness of our design. The source is available at https://github.com/AllenXiangX/Retro-FPNComment: Accepted by ICCV 202

Limits on scalar-induced gravitational waves from the stochastic background by pulsar timing array observations

Recently, the NANOGrav, PPTA, EPTA, and CPTA collaborations independently reported their evidence of the Stochastic Gravitational Waves Background (SGWB). While the inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from the population of supermassive black-hole binaries (SMBHBs), the search for new physics remains plausible in this observational window. In this work, we explore the possibility of explaining such a signal by the scalar-induced gravitational waves (IGWs) in the very early universe. We use a parameterized broken power-law function as a general description of the energy spectrum of the SGWB and fit it to the new results of NANOGrav, PPTA and EPTA. We find that this approach can put constraints on the parameters of IGW energy spectrum and further yield restrictions on various inflation models that may produce primordial black holes (PBHs) in the early universe, which is also expected to be examined by the forthcoming space-based GW experiments.Comment: 7 pages, 2 figures, update some reference

Anatomical study of simple landmarks for guiding the quick access to humeral circumflex arteries

BACKGROUND: The posterior and anterior circumflex humeral artery (PCHA and ACHA) are crucial for the blood supply of humeral head. We aimed to identify simple landmarks for guiding the quick access to PCHA and ACHA, which might help to protect the arteries during the surgical management of proximal humeral fractures. METHODS: Twenty fresh cadavers were dissected to measure the distances from the origins of PCHA and ACHA to the landmarks (the acromion, the coracoid, the infraglenoid tubercle, the midclavicular line) using Vernier calipers. RESULTS: The mean distances from the origin of PCHA to the infraglenoid tubercle, the coracoid, the acromion and the midclavicular line were 27.7 mm, 50.2 mm, 68.4 mm and 75.8 mm. The mean distances from the origin of ACHA to the above landmarks were 26.9 mm, 49.2 mm, 67.0 mm and 74.9 mm. CONCLUSION: Our study provided a practical method for the intraoperative identification as well as quick access of PCHA and ACHA based on a series of anatomical measurements

2.75D: Boosting Learning Efficiency and Capability by Representing 3D Features in 2D

In medical imaging, 3D convolutional neural networks (CNNs) have shown superior performance to 2D CNN in numerous deep learning tasks with high dimensional input, proving the added value of 3D spatial information in feature representation. However, 3D CNN requires more training samples to converge, and more computational resources and execution time for both training and inference. Meanwhile, applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D networks. To tackle with these issues, we propose a novel 2D strategical representation of volumetric data, namely 2.75D approach. In our method, the spatial information of 3D images was captured in a single 2D view by a spiral-spinning technique. Therefore, our CNN is intrinsically a 2D network, which can fully leverage pre-trained 2D CNNs for downstream vision problems. We evaluated the proposed method on LUNA16 nodule detection challenge, by comparing the proposed 2.75D method with 2D, 2.5D, 3D counterparts in the nodule false positive reduction. Results show that the proposed method outperforms other counterparts when all methods were trained from scratch. Such performance gain is more pronounced when introducing transfer learning or when training data is limited. In addition, our method achieves a substantial reduce in time consumption of training and inference comparing with the 3D method. Our code will be publicly available

B0−B0ˉB^{0}-\bar{B^{0}} mixing in the U(1)XU(1)_XSSM

$U(1)_X$SSM is a non-universal Abelian extension of the Minimal Supersymmetric Standard Model (MSSM) and its local gauge group is extended to $SU(3)_C\times SU(2)_L \times U(1)_Y\times U(1)_X$. Based on the latest data of neutral meson mixing and experimental limitations, we investigate the process of $B^{0}-\bar{B^{0}}$ mixing in $U(1)_X$SSM. Using the effective Hamiltonian method, the Wilson coefficients and mass difference $\triangle m_{B}$ are derived. The abundant numerical results verify that $~v_S,~M^2_D,~\lambda_C,~{\mu},~M_2,~\tan{\beta},~g_{YX},~M_1$ and $~\lambda_H$ are sensitive parameters to the process of $B^{0}-\bar{B^{0}}$ mixing. With further measurement in the experiment, the parameter space of the $U(1)_X$SSM will be further constrained during the mixing process of $B^{0}-\bar{B^{0}}$