Quantum state engineering with flux-biased Josephson phase qubits by Stark-chirped rapid adiabatic passages

In this paper, the scheme of quantum computing based on Stark chirped rapid adiabatic passage (SCRAP) technique [L. F. Wei et al., Phys. Rev. Lett. 100, 113601 (2008)] is extensively applied to implement the quantum-state manipulations in the flux-biased Josephson phase qubits. The broken-parity symmetries of bound states in flux-biased Josephson junctions are utilized to conveniently generate the desirable Stark-shifts. Then, assisted by various transition pulses universal quantum logic gates as well as arbitrary quantum-state preparations could be implemented. Compared with the usual PI-pulses operations widely used in the experiments, the adiabatic population passage proposed here is insensitive the details of the applied pulses and thus the desirable population transfers could be satisfyingly implemented. The experimental feasibility of the proposal is also discussed.Comment: 9 pages, 4 figure

Defective Tmprss3-Associated Hair Cell Degeneration in Inner Ear Organoids

Mutations in the gene encoding the type II transmembrane protease 3 (TMPRSS3) cause human hearing loss, although the underlying mechanisms that result in TMPRSS3-related hearing loss are still unclear. We combined the use of stem cell-derived inner ear organoids with single-cell RNA sequencing to investigate the role of TMPRSS3. Defective Tmprss3 leads to hair cell apoptosis without altering the development of hair cells and the formation of the mechanotransduction apparatus. Prior to degeneration, Tmprss3-KO hair cells demonstrate reduced numbers of BK channels and lower expressions of genes encoding calcium ion-binding proteins, suggesting a disruption in intracellular homeostasis. A proteolytically active TMPRSS3 was detected on cell membranes in addition to ER of cells in inner ear organoids. Our in vitro model recapitulated salient features of genetically associated inner ear abnormalities and will serve as a powerful tool for studying inner ear disorders

A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.

Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets

Flexible unsupervised feature extraction for image classification

Dimensionality reduction is one of the fundamental and important topics in the fields of pattern recognition and machine learning. However, most existing dimensionality reduction methods aim to seek a projection matrix W such that the projection W T x is exactly equal to the true low-dimensional representation. In practice, this constraint is too rigid to well capture the geometric structure of data. To tackle this problem, we relax this constraint but use an elastic one on the projection with the aim to reveal the geometric structure of data. Based on this context, we propose an unsupervised dimensionality reduction model named flexible unsupervised feature extraction (FUFE) for image classification. Moreover, we theoretically prove that PCA and LPP, which are two of the most representative unsupervised dimensionality reduction models, are special cases of FUFE, and propose a non-iterative algorithm to solve it. Experiments on five real-world image databases show the effectiveness of the proposed model

Macroscopic Quantum Phase Interference in Antiferromagnetic Particles

The tunnel splitting in biaxial antiferromagnetic particles is studied with a magnetic field applied along the hard anisotropy axis. We observe the oscillation of tunnel splitting as a function of the magnetic field due to the quantum phase interference of two tunneling paths of opposite windings. The oscillation is similar to the recent experimental result with Fe}$_8$\textrm{\ molecular clusters.}Comment: 8 pages, 2 postscript figures, to appear in J. Phys.: Condes. Matte

PLM-IPE: A Pixel-Landmark Mutual Enhanced Framework for Implicit Preference Estimation

In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, without any kind of active engagement, is in fact an essential block to achieve interactive applications. We propose a pixel-landmark mutual enhanced framework for implicit preference estimation, named PLM-IPE, which is capable of inferring the user's implicit preferences exploiting visual cues, without any active or conscious engagement. PLM-IPE consists of three key modules: pixel-based estimator, landmark-based estimator and mutual learning based optimization. The former two modules work on capturing the implicit reaction of the user from the pixel level and landmark level, respectively. The last module serves to transfer knowledge between the two parallel estimators. Towards evaluation, we collected a real-world dataset, named SentiGarment, which contains 3,345 facial reaction videos paired with suggested outfits and human labeled reaction scores. Extensive experiments show the superiority of our model over state-of-the-art approaches

Interplay of Spin-Orbit Interactions, Dimensionality, and Octahedral Rotations in Semimetallic SrIrO3_3

We employ reactive molecular-beam epitaxy to synthesize the metastable perovskite SrIrO$_{3}$ and utilize {\it in situ} angle-resolved photoemission to reveal its electronic structure as an exotic narrow-band semimetal. We discover remarkably narrow bands which originate from a confluence of strong spin-orbit interactions, dimensionality, and both in- and out-of-plane IrO$_6$ octahedral rotations. The partial occupation of numerous bands with strongly mixed orbital characters signals the breakdown of the single-band Mott picture that characterizes its insulating two-dimensional counterpart, Sr$_{2}$IrO$_{4}$, illustrating the power of structure-property relations for manipulating the subtle balance between spin-orbit interactions and electron-electron interactions