A precise computer-aided diagnosis (CAD) system is introduced to predict two different tumor responses to neoadjuvant chemotherapy (NAC) by studying the correlation between radiological and clinical markers, and treat...
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Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone cente...
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Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.
electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes an...
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Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional var...
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer’s Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.
The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production ...
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Inspired by the visual system of Papilio xuthus butterfly, w e f abricated a ultraviolet-visible multispectral imager which combines perovskite nanocrystals and vertically stacked silicon photodetectors. High-resoluti...
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We investigated the effect of copper ion concentration in zinc-copper dual-ion electrolytes to suppress dendrites and extend the cycle life of zinc ion capacitors. The devices were characterized in terms of changes in...
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ISBN:
(数字)9798331529468
ISBN:
(纸本)9798331529475
We investigated the effect of copper ion concentration in zinc-copper dual-ion electrolytes to suppress dendrites and extend the cycle life of zinc ion capacitors. The devices were characterized in terms of changes in microstructure and cycling stability. The nuclei size was decreased in the optimal copper ion concentration to promote lateral deposition and avoid vertical dendrites. The device exhibited stable cycling performance with a capacitance retention of 95% after 10,000 redox cycles, compared to the device with single zinc ion electrolyte which short circuited at around 1,250 redox cycles.
In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize...
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A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...
GeSn alloys hold great promise for the development of Si photonics due to its direct bandgap with sufficient high Sn composition, tunable bandgap energies covering broad infrared wavelength range, and compatibility wi...
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GeSn alloys hold great promise for the development of Si photonics due to its direct bandgap with sufficient high Sn composition, tunable bandgap energies covering broad infrared wavelength range, and compatibility with the complementary metal-oxide-semiconductor (CMOS) process. For the past decades, tremendous efforts have been made to develop material growth recipes to obtain device-level material quality using chemical vapor deposition (CVD) reactors. A common issue in material growth is the formation of Sn droplets (i.e., Sn segregation) that seriously degrades the material quality. However, although industrial CVD has delivered high-quality materials for device demonstration, the dynamic process of material growth remains unavailable, and only ex-situ material characterizations could provide feedback. In this work, a specially designed ultra-high-vacuum CVD (UHV-CVD) was used to study the growth dynamic by employing an in-situ optical system, which enables us to monitor the formation of Sn droplets in real-time. The Sn segregation occurring at the cooling stage after growth completion rather than during material growth was discovered, indicating that a post-growth treatment is needed to minimize the Sn droplet formation. By extending the GeSn growth time and controlling the cooling cycle, the number of Sn droplets dramatically reduced, leading to a high-quality strain-relaxed GeSn layer with 10.2% Sn composition and over 730 nm thickness. Material quality is comparable with those grown using commercial RPCVD in the early stage, which was further confirmed by the demonstration of an optically pumped laser. A threshold of ∼600 kW/cm 2 at 77 K and a maximum operating temperature of 100 K were obtained, with the lasing peak at ∼2250 nm.
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