A Hybrid Rydberg Quantum Gate for Quantum Network

The high fidelity storage, distribution and processing of quantum information prefers qubits with different physical properties. Thus, hybrid quantum gates interfacing different types of qubits are essential for the realization of complex quantum network structures. A Rydberg-atom based physical quantum CZ gate is proposed to hybridly process the polarisation-encoded single-photon optical qubit and the "Schroedinger cat" microwave qubit. The degradation of the fidelity under the influence of various noise channels, such as microwave cavity loss, sponetanous emission of atom states, and non-adiabaticity effect, etc, has been analyised through detailed theoretical analysis by deriving input-output relation of qubit fields. The feasibility and the challenges of the protocol within current technology are also discussed by analysing the possible experimental parameter settings

Seasonal Variations in the Organization and Structure of Apis cerana cerana Swarm Queen Cells

This paper describes the organization and structure of the swarm queen cells of Apis cerana cerana in spring, summer, and autumn in Kunming, Yunnan Province, China. We measured the following indices to reveal the organization rule of swarm cells: number of swarm cells built by each colony during different seasons; the shortest distance between two adjacent swarm cells on the comb; distance between swarm cell base and bottom bar of movable frame. We revealed the swarm cells structural characteristics using the following indicators: maximum diameter of swarm cell, the length between mouth and bottom of swarm cell, depth between maximum diameter and bottom of swarm cell, and the ratio of maximum diameter to depth between maximum diameter and bottom of swarm cell. Regarding seasonal differences, results indicated a significant variation in the distance between the swarm cell base and the bottom bar of the movable frame. Still, no such effect was observed in the shortest distance between two adjacent swarm cells. The maximum swarm cell diameter was not considerably influenced either, while the distance between the maximum diameter and the bottom of the swarm cell had substantial variation. The detected ratio of the maximum diameter to the depth between the maximum diameter and the bottom of theswarm cell indicated seasonal changes in the bottom shape of the swarm cell. This study clarifies the temporal and spatial distribution and structure of swarm cells of A. c. cerana. It establishes the basis for predicting the time and position of appearing swarm cells, thus allowing for a more precise determination of the shape and size of queen-cell punch and the ideal position of a cell cup on the bar of queen cup frames in artificial queen rearing

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAMComment: The last two authors contributed equally. Accepted to Medical Imaging with Deep Learning (MIDL) 202

FVIFormer: flow-guided global-local aggregation transformer network for video inpainting

Video inpainting has been extensively used in recent years. Established works usually utilise the similarity between the missing region and its surrounding features to inpaint in the visually damaged content in a multi-stage manner. However, due to the complexity of the video content, it may result in the destruction of structural information of objects within the video. In addition to this, the presence of moving objects in the damaged regions of the video can further increase the difficulty of this work. To address these issues, we propose a flow-guided global-Local aggregation Transformer network for video inpainting. First, we use a pre-trained optical flow complementation network to repair the defective optical flow of video frames. Then, we propose a content inpainting module, which use the complete optical flow as a guide, and propagate the global content across the video frames using efficient temporal and spacial Transformer to inpaint in the corrupted regions of the video. Finally, we propose a structural rectification module to enhance the coherence of content around the missing regions via combining the extracted local and global features. In addition, considering the efficiency of the overall framework, we also optimized the self-attention mechanism to improve the speed of training and testing via depth-wise separable encoding. We validate the effectiveness of our method on the YouTube-VOS and DAVIS video datasets. Extensive experiment results demonstrate the effectiveness of our approach in edge-complementing video content that has undergone stabilisation algorithms

Elasticity-Controlled Jamming Criticality in Soft Composite Solids

Soft composite solids are made of dispersed inclusions within soft matrices. They are ubiquitous in nature and form the basis of many biological tissues. In the field of materials science, synthetic soft composites are promising candidates for constructing various engineering devices due to their highly programmable features. However, when the volume fraction of inclusions increases, predicting the mechanical properties of these materials poses a significant challenge for the classical theories in composite mechanics. The difficulty arises from the inherently disordered, multi-scale interactions between the inclusions and matrix. To address this challenge, we conducted systematic investigations on the mechanics of densely-filled soft elastomers containing stiff microspheres. We experimentally demonstrated how the strain-stiffening response of the soft composites is governed by the critical scalings in the vicinity of a continuous phase transition, which depend on both the elasticity of the elastomer matrix and the particles. The critical points signify a shear-jamming transition of the included particles in the absence of matrix elasticity. The proposed criticality framework quantitatively predicts diverse mechanical responses observed in experiments across a wide range of material parameters. The findings uncover a novel design paradigm of composite mechanics that relies on engineering the jamming-criticality of the embedded inclusions

Correlation model between mesostructure and gradation of asphalt mixture based on statistical method

Asphalt mixture has complex gradation and mesostructure. Accurate prediction of the relationship between gradation and mesostructure is of great significance for the establishment of mesostructure numerical simulation model and image-based gradation detection. In this paper, featurization, stepwise regression, econometric hypothesis test are utilized for establishing the predicting models. Firstly, asphalt mixtures with 64 kinds of gradation are scanned by Computed Tomography (CT) to obtain the mesostructure images; Then a series of mesostructure parameters of voids and aggregates are put forward. On this basis, the relationship model between gradation and mesostructure is established and verified by featurization and statistical modeling method. The results show that for predicting the passing percentage of the 4.75 mm sieve and the mean value of average distance between aggregate centroids for 9.5–4.75 mm aggregates, the prediction error of passing percentage is acceptable. It illustrates that the relationship model between gradation and mesostructure established by statistical method is effective, and it is significance for material design and testing under the condition of big data in the future

FGF, Mechanism of Action, Role in Parkinson’s Disease, and Therapeutics

Parkinson’s disease (PD) is a neurodegenerative disease associated with severe disability and adverse effects on life quality. In PD, motor dysfunction can occur, such as quiescence, muscle stiffness, and postural instability. PD is also associated with autonomic nervous dysfunction, sleep disorders, psychiatric symptoms, and other non-motor symptoms. Degeneration of dopaminergic neurons in the substantia nigra compact (SNPC), Lewy body, and neuroinflammation are the main pathological features of PD. The death or dysfunction of dopaminergic neurons in the dense part of the substantia nigra leads to dopamine deficiency in the basal ganglia and motor dysfunction. The formation of the Lewy body is associated with the misfolding of α-synuclein, which becomes insoluble and abnormally aggregated. Astrocytes and microglia mainly cause neuroinflammation, and the activation of a variety of pro-inflammatory transcription factors and regulatory proteins leads to the degeneration of dopaminergic neurons. At present, PD is mainly treated with drugs that increase dopamine concentration or directly stimulate dopamine receptors. Fibroblast growth factor (FGF) is a family of cellular signaling proteins strongly associated with neurodegenerative diseases such as PD. FGF and its receptor (FGFR) play an essential role in the development and maintenance of the nervous system as well as in neuroinflammation and have been shown to improve the survival rate of dopaminergic neurons. This paper summarized the mechanism of FGF and its receptors in the pathological process of PD and related signaling pathways, involving the development and protection of dopaminergic neurons in SNPC, α-synuclein aggregation, mitochondrial dysfunction, and neuroinflammation. It provides a reference for developing drugs to slow down or prevent the potential of PD

Molecular Doping Inhibits Charge Trapping in Low-Temperature-Processed ZnO toward Flexible Organic Solar Cells

There has been a growing interest in the development of efficient flexible organic solar cells (OSCs) due to their unique capacity to provide energy sources for flexible electronics. To this end, it is required to design a compatible interlayer with low processing temperature and high electronic quality. In this work, we present that the electronic quality of the ZnO interlayer fabricated from a low-temperature (130 °C) sol–gel method can be significantly improved by doping an organic small molecule, TPT-S. The doped TPT-S, on the one hand, passivates uncoordinated Zn-related defects by forming N–Zn bonds. On the other hand, photoinduced charge transfer from TPT-S to ZnO is confirmed, which further fills up electron-deficient trap states. This renders ZnO improved electron transport capability and reduced charge recombination. By illuminating devices with square light pulses of varying intensities, we also reveal that an unfavorable charge trapping/detrapping process observed in low-temperature-processed devices is significantly inhibited after TPT-S doping. OSCs based on PBDB-T-2F:IT-4F with ZnO:TPT-S being the cathode interlayer yield efficiencies of 12.62 and 11.33% on rigid and flexible substrates, respectively. These observations convey the practicality of such hybrid ZnO in high-performance flexible devices