Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning

Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor

First-principles and model simulation of all-optical spin reversal

All-optical spin switching is a potential trailblazer for information storage and communication at an unprecedented fast rate and free of magnetic fields. However, the current wisdom is largely based on semiempirical models of effective magnetic fields and heat pulses, so it is difficult to provide high-speed design protocols for actual devices. Here, we carry out a massively parallel first-principles and model calculation for thirteen spin systems and magnetic layers, free of any effective field, to establish a simpler and alternative paradigm of laser-induced ultrafast spin reversal and to point out a path to a full-integrated photospintronic device. It is the interplay of the optical selection rule and sublattice spin orderings that underlines seemingly irreconcilable helicity-dependent/independent switchings. Using realistic experimental parameters, we predict that strong ferrimagnets, in particular, Laves phase C15 rare-earth alloys, meet the telecommunication energy requirement of 10 fJ, thus allowing a cost-effective subpicosecond laser to switch spin in the GHz region.Comment: 23 pages, 6 figures and one tabl

Triaxially deformed relativistic point-coupling model for Λ\Lambda hypernuclei: a quantitative analysis of hyperon impurity effect on nuclear collective properties

The impurity effect of hyperon on atomic nuclei has received a renewed interest in nuclear physics since the first experimental observation of appreciable reduction of $E2$ transition strength in low-lying states of hypernucleus $^{7}_\Lambda$Li. Many more data on low-lying states of $\Lambda$ hypernuclei will be measured soon for $sd$-shell nuclei, providing good opportunities to study the $\Lambda$ impurity effect on nuclear low-energy excitations. We carry out a quantitative analysis of $\Lambda$ hyperon impurity effect on the low-lying states of $sd$-shell nuclei at the beyond-mean-field level based on a relativistic point-coupling energy density functional (EDF), considering that the $\Lambda$ hyperon is injected into the lowest positive-parity ($\Lambda_s$) and negative-parity ($\Lambda_p$) states. We adopt a triaxially deformed relativistic mean-field (RMF) approach for hypernuclei and calculate the $\Lambda$ binding energies of hypernuclei as well as the potential energy surfaces (PESs) in $(\beta, \gamma)$ deformation plane. We also calculate the PESs for the $\Lambda$ hypernuclei with good quantum numbers using a microscopic particle rotor model (PRM) with the same relativistic EDF. The triaxially deformed RMF approach is further applied in order to determine the parameters of a five-dimensional collective Hamiltonian (5DCH) for the collective excitations of triaxially deformed core nuclei. Taking $^{25,27}_{\Lambda}$Mg and $^{31}_{\Lambda}$Si as examples, we analyse the impurity effects of $\Lambda_s$ and $\Lambda_p$ on the low-lying states of the core nuclei...Comment: 15 pages with 18 figures and 1 table (version to be published in Physical Review C

Liquid Crystal-Solid Interface Structure at the Antiferroelectric-Ferroelectric Phase Transition

Total Internal Reflection (TIR) is used to probe the molecular organization at the surface of a tilted chiral smectic liquid crystal at temperatures in the vicinity of the bulk antiferroelectric-ferroelectric phase transition. Data are interpreted using an exact analytical solution of a real model for ferroelectric order at the surface. In the mixture T3, ferroelectric surface order is expelled with the bulk ferroelectric-antiferroelectric transition. The conditions for ferroelectric order at the surface of an antiferroelectric bulk are presented

FETNet: Feature exchange transformer network for RGB-D object detection

In RGB-D object detection, due to the inherent difference between the RGB and Depth modalities, it remains challenging to simultaneously leverage sensed photometric and depth information. In this paper, to address this issue, we propose a Feature Exchange Transformer Network (FETNet), which consists of two well-designed components: the Feature Exchange Module (FEM), and the Multi-modal Vision Transformer (MViT). Specially, we propose the FEM to exchange part of the channels between RGB and depth features at each backbone stage, which facilitates the information flow, and bridges the gap, between the two modalities. Inspired by the success of Vision Transformer (ViT), we develop the variant MViT to effectively fuse multi-modal features and exploit the attention between the RGB and depth features. Different from previous methods developing from specified RGB detection algorithm, our proposal is generic. Extensive experiments prove that, when the proposed modules are integrated into mainstream RGB object detection methods, their RGB-D counterparts can obtain significant performance gains. Moreover, our FETNet surpasses state-of-the-art RGB-D detectors by 7.0% mAP on SUN RGB-D and 1.7% mAP on NYU Depth v2, which also well demonstrates the effectiveness of the proposed method

First-principles study of native point defects in Bi2Se3

Using first-principles method within the framework of the density functional theory, we study the influence of native point defect on the structural and electronic properties of Bi$_2$Se$_3$. Se vacancy in Bi$_2$Se$_3$ is a double donor, and Bi vacancy is a triple acceptor. Se antisite (Se$_{Bi}$) is always an active donor in the system because its donor level ($\varepsilon$(+1/0)) enters into the conduction band. Interestingly, Bi antisite(Bi$_{Se1}$) in Bi$_2$Se$_3$ is an amphoteric dopant, acting as a donor when $\mu$$_e$$<$0.119eV (the material is typical p-type) and as an acceptor when $\mu$$_e$$>$0.251eV (the material is typical n-type). The formation energies under different growth environments (such as Bi-rich or Se-rich) indicate that under Se-rich condition, Se$_{Bi}$ is the most stable native defect independent of electron chemical potential $\mu$$_e$. Under Bi-rich condition, Se vacancy is the most stable native defect except for under the growth window as $\mu$$_e$$>$0.262eV (the material is typical n-type) and $\Delta$$\mu$$_{Se}$$<$-0.459eV(Bi-rich), under such growth windows one negative charged Bi$_{Se1}$ is the most stable one.Comment: 7 pages, 4 figure

Spring Cold Bias of SST and minimal wind mixing in the Equatorial Pacific Cold Tongue

AbstractThe authors investigate the relationship between bias in simulated sea surface temperature (SST) in the equatorial eastern Pacific cold tongue during the boreal spring as simulated by an oceanic general circulation model (OGCM) and minimal wind mixing (MWM) at the surface. The cold bias of simulated SST is the greatest during the boreal spring, at approximately 3°C. A sensitivity experiment reducing MWM by one order of magnitude greatly alleviates cold biases, especially in March-April. The decrease in bias is primarily due to weakened vertical mixing, which preserves heat in the uppermost layer and results in warmer simulated SST. The reduction in vertical mixing also leads to a weak westward current in the upper layer, which further contributes to SST warming. These findings imply that there are large uncertainties about simple model parameters such as MWM at the oceanic surface

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

© 2013 IEEE. Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability

On-Chip Matching Networks for Radio-Frequency Single-Electron-Transistors

In this letter, we describe operation of a radio-frequency superconducting single electron transistor (RF-SSET) with an on-chip superconducting LC matching network consisting of a spiral inductor L and its capacitance to ground. The superconducting network has a lower parasitic capacitance and gives a better matching for the RF-SSET than does a commercial chip inductor. Moreover, the superconducting network has negligibly low dissipation, leading to sensitive response to changes in the RF-SSET impedance. The charge sensitivity 2.4*10^-6 e/(Hz)^1/2 in the sub-gap region and energy sensitivity of 1.9 hbar indicate that the RF-SSET is operating in the vicinity of the shot noise limit.Comment: 3 pages, 3 figures, REVTeX 4. To appear in Appl. Phys. Let