Multi-auxiliary domain transfer learning for diagnosis of MCI conversion.

In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.

Position linearity analysis of circular arc terminated resistive anode using finite element method for photon-counting imaging detectors.

This study proposes a comprehensive model of the circular arc terminated (CAT) resistive anode based on the finite element method to explore the dynamic process of charge diffusion on this anode and its position linearity performance. The waveforms of charges of the electrodes on the anode are calculated for different electrical parameters and their influence on positional linearity is investigated. The influence of the signal development time and the non-uniformity of the resistance per square of the anode on positional linearity is also analyzed. The results of simulations show that the non-linearity of the image varies monotonically with the termination resistance and the non-uniformity of the resistance per square of the anode, but has a non-linear relationship with the signal development time and the ratio of the resistance per square. A CAT resistive anode with capacitance c and a resistance per square of the sensitive area of R▱ can be used to recover an image with a root mean-squared non-linearity of 2%, when the charge signals of the electrode are collected for at least 0.6R▱c s. The reliability of the results of the simulations was verified with experimental measurements.

Three-dimensional reconstruction of laser-imploded targets from simulated pinhole images.

This paper proposes an integral method to achieve a more accurate weighting matrix that makes very positive contributions to the image reconstruction in inertial confinement fusion research. Standard algebraic reconstruction techniques with a positivity constraint included are utilized. The final normalized mean-square error between the simulated and reconstructed projection images is 0.000365%, which is a nearly perfect result, indicating that the weighting matrix is very important. Compared with the error between the simulated and reconstructed phantoms, which is 2.35%, it seems that the improvement of the accuracy of the projection image does not mean the improvement of the phantom. The proposed method can reconstruct a simulated laser-imploded target consisting of 100×100×100 voxels.

[Research on the method of transient spectrum detection based on array CCD].

Based on the characteristic of high speed line scanning for CCD in transient spectrum detection, a method of transient spectrum detection with array CCD is presented. The high speed line scanning with array CCD was realized by changing the mode of charge transfer. In order to explore the feasibility of this method, a fast detection system of single point based on linear CCD was designed and fabricated. Seven different LED pulses were measured when the system worked at fast detection mode of single point and normal mode respectively. The results demonstrate that the method of fast detection of single point based on linear CCD is feasible, and the rate of single point detection reaches up to 20 MHz. Thus, in theory, it was proved that transient spectrum detection with array CCD by changing the mode of charge transfer is also feasible.

Chimera states and cluster solutions in Hindmarsh-Rose neural networks with state resetting process.

The neuronal state resetting model is a hybrid system, which combines neuronal system with state resetting process. As the membrane potential reaches a certain threshold, the membrane potential and recovery current are reset. Through the resetting process, the neuronal system can produce abundant new firing patterns. By integrating with the state resetting process, the neuronal system can generate irregular limit cycles (limit cycles with impulsive breakpoints), resulting in repetitive spiking or bursting with firing peaks which can not exceed a presetting threshold. Although some studies have discussed the state resetting process in neurons, it has not been addressed in neural networks so far. In this paper, we consider chimera states and cluster solutions in Hindmarsh-Rose neural networks with state resetting process. The network structures are based on regular ring structures and the connections among neurons are assumed to be bidirectional. Chimera and cluster states are two types of phenomena related to synchronization. For neural networks, the chimera state is a self-organization phenomenon in which some neuronal nodes are synchronous while the others are asynchronous. Cluster synchronization divides the system into several subgroups based on their synchronization characteristics, with neuronal nodes in each subgroup being synchronous. By improving previous chimera measures, we detect the spike inspire time instead of the state variable and calculate the time between two adjacent spikes. We then discuss the incoherence, chimera state, and coherence of the constructed neural networks using phase diagrams, time series diagrams, and probability density histograms. Besides, we further contrast the cluster solutions of the system under local and global coupling, respectively. The subordinate state resetting process enriches the firing mode of the proposed Hindmarsh-Rose neural networks.

Collective energy-spectrum broadening of a proton beam in a gas-discharge plasma.

An accurate understanding of ion-beam transport in plasmas is crucial for applications in inertial fusion energy and high-energy-density physics. We present an experimental measurement on the energy spectrum of a proton beam at 270 keV propagating through a gas-discharge hydrogen plasma. We observe the energies of the beam protons changing as a function of the plasma density and spectrum broadening due to a collective beam-plasma interaction. Supported by linear theory and three-dimensional particle-in-cell simulations, we attribute this energy modulation to a two-stream instability excitation and further saturation by beam ion trapping in the wave. The widths of the energy spectrum from both experiment and simulation agree with the theory.

High-Sensitivity and Long-Life Microchannel Plate Processed by Atomic Layer Deposition.

As a key component of electron multiplier device, a microchannel plate (MCP) can be applied in many scientific fields. Pure aluminum oxide (Al2O3) as secondary electron emission (SEE) layer were deposited in the pores of MCP via atomic layer deposition (ALD) to overcome problems such as high dark current and low lifetime which often occur on traditional MCP. In this paper, we systematically investigate the morphology, element distribution, and structure of samples by scanning electron microscopy (SEM) and energy disperse spectroscopy (EDS), respectively. Output current of different thickness of Al2O3 was studied and an optimal thickness was found. Experimental tests show that the average gain of ALD-MCP was nearly five times better than that of traditional MCP, and the ALD-MCP showed better sensitivity and longer lifetime.

Disentangling the Luminescent Mechanism of Cs4PbBr6 Single Crystals from an Ultrafast Dynamics Perspective.

New all-inorganic perovskites like Cs4PbBr6 provide rich luminescent tools and particularly novel physical insights, including their zero-dimensional structure and controversial emitting mechanism. The ensuing debate over the origin of the luminescence of Cs4PbBr6 inspired us to tackle the issue through fabricating high-quality Cs4PbBr6 single crystals and employing ultrafast dynamics study. Upon photoexcitation, Cs4PbBr6 underwent dynamics steps distinct from that of CsPbBr3, including exciton migration to the defect level on a time scale of several hundred femtoseconds, exciton relaxation within the defect states on the picosecond time scale, and exciton recombination from the subnanosecond to nanosecond time scale. The observation disclosed that crystal defects of Cs4PbBr6 induced green emission while CsPbBr3 mainly relied on quantum confinement to emit at room temperature. The study provides an in-depth understanding of the photoinduced multistep dynamics steps of Cs4PbBr6 associated with display and photovoltaic applications, establishing Cs4PbBr6 as a new candidate for uses associated with the perovskite family of materials.