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It is well-known that maximally entangled GHZ states can achieve perfect teleportation and superdense coding, whereas maximally entangled W states cannot. However, it has been demonstrated that there exists a special class of non-maximally entangled W states, called as \textit{W-like} states, which can overcome this limitation. Therefore, it is of great significance to prepare such W-like states for efficient quantum communication. Here, we propose two kinds of novel and efficient fusion schemes for atomic W-like states based on the large-detuning interactions between several atoms and a single-mode cavity field, with which large-scale atomic $|\mathcal{W}_{N+M-1}\rangle$ and $|\mathcal{W}_{N+M+T-2}\rangle$ states can be prepared, respectively, from two small-scale atomic $|\mathcal{W}_{N}\rangle$ and $|\mathcal{W}_{M}\rangle$ states and three small-scale atomic $|\mathcal{W}_{N}\rangle$, $|\mathcal{W}_{M}\rangle$ and $|\mathcal{W}_{T}\rangle$ states, by detecting the states of one or two of the fused atoms. Particularly, although the fusion process of our scheme involves particle loss, the corresponding success probability is high and fixed, which may induce high fusion efficiency. Furthermore, through the investigation of the resource cost and feasibility analysis, our protocol is simple and feasible under the current experimental conditions. All these suggest that it provides an alternative strategy for preparing large-scale atomic W-like states for perfect teleportation and superdense coding., Comment: 8 pages, 4 figures, 1 table

Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In this work, we investigate the shape of the brain's 3D white matter connections and its potential predictive relationship to human cognitive function. We reconstruct brain connections as sequences of 3D points using diffusion magnetic resonance imaging (dMRI) tractography. To describe each connection, we extract 12 shape descriptors in addition to traditional dMRI connectivity and tissue microstructure features. We introduce a novel framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a multi-head cross-attention feature fusion module to predict subject-specific language performance based on dMRI tractography. We assess the performance of the method on a large dataset including 1065 healthy young adults. The results demonstrate that both the transformer-based SFFormer model and its inter/intra feature fusion with shape, microstructure, and connectivity are informative, and together, they improve the prediction of subject-specific language performance scores. Overall, our results indicate that the shape of the brain's connections is predictive of human language function., Comment: 2 figures, 11 pages

White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings.

Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from diffusion MRI tractography, without considering the shape of the connections reconstructed by tractography. In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features. We focus on three basic shape features: length, diameter, and elongation. Two different prediction methods are used, including a traditional regression method and a deep-learning-based prediction method. Experiments use an efficient two-stage fusion strategy for prediction using microstructure, connectivity, and shape measures. To reduce predictive bias due to brain size, normalized shape features are also investigated. Experimental results on the Human Connectome Project (HCP) young adult dataset (n=1065) demonstrate that individual shape features are predictive of non-imaging phenotypes. When combined with microstructure and connectivity features, shape features significantly improve performance for predicting the cognitive score TPVT (NIH Toolbox picture vocabulary test). Overall, this study demonstrates that the shape of fiber tracts contains useful information for the description and study of the living human brain using machine learning.

Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not included in the existing annotated WM tracts, are to be segmented, additional annotations of these novel WM tracts need to be collected. Since tract annotation is time-consuming and costly, it is desirable to make only a few annotations of novel WM tracts for training the segmentation model, and previous work has addressed this problem by transferring the knowledge learned for segmenting existing WM tracts to the segmentation of novel WM tracts. However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts. In this work, we explore the problem of one-shot segmentation of novel WM tracts. Since in the one-shot setting the annotated training data is extremely scarce, based on the existing knowledge transfer framework, we propose to further perform extensive data augmentation for the single annotated scan, where synthetic annotated training data is produced. We have designed several different strategies that mask out regions in the single annotated scan for data augmentation. Our method was evaluated on public and in-house datasets. The experimental results show that our method improves the accuracy of one-shot segmentation of novel WM tracts.

In the paper, we study the production of pentaquark $\bar{c}q qqq$ from singly bottom baryon. The tensor representations of pentaquark $\bar{c}q qqq$ are completed at first. Under the light quark symmetry analysis, we decompose the singly charmed pentaquarks into $6$, $15$ and $15'$ multiple states, and deduce the representations of these states. Then we construct the production Hamiltonian of the multiple states in proper order. For completeness, we systematically study the production channels of pentaquark states, including the possible production amplitudes and cross section relations between different channels. Ultimately, we screen out some advantageous channels, which expected to be fairly valuable supports for future search experiments., Comment: 18 pages,2 figures