Q-Switched 2 Micron Solid-State Lasers and Their Applications

In this chapter, we overview the Q-switched 2 μm solid-state laser development achieved in recent years, including flash- and diode-pumped solid-state lasers based on active and passive modulators. In summary, active Q-switching is still the first choice for obtaining large pulse energy at 2 μm currently, while passive Q-switching based on saturable absorbers (SAs), especially the newly emerging broadband low-dimension nanomaterial, is becoming promising approach in generating Q-switched 2 μm lasers specially with high repetition rate, although the output power, pulse duration, and pulse energy needs further enhancement. Besides, some important applications of 2 μm lasers, such as medicine, laser radar, and infrared directional interference, have also been introduced in brief

Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback

In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-feedback control, output-feedback control is more prevalent since the underlying state of the system may not be fully observed in many practical settings. This paper analyzes the optimization landscape inherent to policy gradient methods when applied to static output feedback (SOF) control in discrete-time LTI systems subject to quadratic cost. We begin by establishing crucial properties of the SOF cost, encompassing coercivity, L-smoothness, and M-Lipschitz continuous Hessian. Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method. Moreover, we provide proof that the vanilla policy gradient method exhibits linear convergence towards local minima when initialized near such minima. The paper concludes by presenting numerical examples that validate our theoretical findings. These results not only characterize the performance of gradient descent for optimizing the SOF problem but also provide insights into the effectiveness of general policy gradient methods within the realm of reinforcement learning

Saliency Driven Vasculature Segmentation with Infinite Perimeter Active Contour Model

Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors

A novel investment strategy for renewable-dominated power distribution networks

Aiming at the problem of insufficient adaptability to the new elements of the new power system in the current distribution network investment method, this paper innovatively proposes a distribution network investment method based on the new power system. By constructing a source-grid-load-storage-side investment calculation model, the investment in the new power system can be accurately calculated. First, the distributed power investment is calculated from the two aspects of new construction and renovation. Secondly, construct the grid investment demand and grid investment capacity measurement model, and obtain the grid side investment model by weighted summation. Then, a model for calculating the scale of investment that can be saved due to demand-side response is constructed, and the cost of demand response is subtracted to obtain a model for calculating the scale of investment that can be saved on the load side. Finally, the energy storage side investment calculation model is constructed from the power supply side, grid side, user-side energy storage investment, and energy storage investment benefit. The research results are applied to the empirical area, and scientific guidance is provided to realize the precise investment in the area

Tripartite motif containing 28 (TRIM28) promotes breast cancer metastasis by stabilizing TWIST1 protein.

TRIM28 regulates its target genes at both transcriptional and posttranscriptional levels. Here we report that a TRIM28-TWIST1-EMT axis exists in breast cancer cells and TRIM28 promotes breast cancer metastasis by stabilizing TWIST1 and subsequently enhancing EMT. We find that TRIM28 is highly expressed in both cancer cell lines and advanced breast cancer tissues, and the levels of TRIM28 and TWIST1 are positively correlated with the aggressiveness of breast carcinomas. Overexpression and depletion of TRIM28 up- and down-regulates the protein, but not the mRNA levels of TWIST1, respectively, suggesting that TRIM28 upregulates TWIST1 post-transcriptionally. Overexpression of TRIM28 in breast cancer cell line promotes cell migration and invasion. Knockdown of TRIM28 reduces the protein level of TWIST1 with concurrent upregulation of E-cadherin and downregulation of N-cadherin and consequently inhibits cell migration and invasion. Furthermore, Immunoprecipitation and GST pull-down assays demonstrated that TRIM28 interacts with TWIST1 directly and this interaction is presumed to protect TWIST1 from degradation. Our study revealed a novel mechanism in breast cancer cells that TRIM28 enhances metastasis by stabilizing TWIST1, suggesting that targeting TRIM28 could be an efficacious strategy in breast cancer treatment

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Oxidative stress impairs cognitive function by affecting hippocampal fimbria volume in drug-naïve, first-episode schizophrenia

ObjectiveThe aim of the present study was to explore influencing factors of cognitive impairments and their interrelationships in drug-naïve, first-episode schizophrenia (SCZ).MethodsPatients with drug naïve, first episode SCZ and healthy controls (HCs) were enrolled. Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Serum levels of oxidative stress indices, including folate, superoxide dismutase (SOD), uric acid (UA) and homocysteine (Hcy), were determined after an overnight fast. Hippocampal subfield volumes were measured using FreeSurfer. Mediation models were conducted using the SPSS PROCESS v3.4 macro. A false discovery rate (FDR) correction was applied for multiple comparisons.ResultsSixty-seven patients with SCZ and 65 HCs were enrolled in our study. The patient group had significantly lower serum levels of folate and SOD and higher serum levels of HCY compared with the HCs (all p &lt; 0.05). The patient group had a significantly smaller volume of the whole hippocampus than the HC group (p &lt; 0.05). We also found significant volume differences between the two groups in the following subfields: CA1, molecular layer, GC-ML-DG and fimbria (all p &lt; 0.05, uncorrected). The partial correlation analysis controlling for age and sex showed that the fimbria volume in the patient group was significantly positively associated with NAB scores (r = 0.382, pFDR = 0.024); serum levels of SOD in the patient group showed a significantly positive correlation with fimbria volume (r = 0.360, pFDR = 0.036). Mediation analyses controlling for age and sex showed that the serum levels of SOD in patients with SCZ had significant indirect effects on the NAB scores which were mediated by the fimbria volume [indirect effect = 0.0565, 95% CI from the bootstrap test excluding zero (0.0066 to 0.0891)].ConclusionOxidative stress, a reduction in hippocampal subfield volumes and cognitive impairments occur in early SCZ. Oxidative stress impairs cognitive function by affecting hippocampal subfield volumes

Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator

The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sample two-timescale AC for solving the canonical linear quadratic regulator (LQR) problem, where the actor and the critic update only once with a single sample in each iteration on an unbounded continuous state and action space. Existing analysis cannot conclude the convergence for such a challenging case. We develop a new analysis framework that allows establishing the global convergence to an epsilon-optimal solution with at most an order of epsilon to -2.5 sample complexity. To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality. The sample complexity improves those of other variants by orders, which sheds light on the practical wisdom of single sample algorithms. We also further validate our theoretical findings via comprehensive simulation comparisons

Optimization Landscape of Gradient Descent for Discrete-time Static Output Feedback

In this paper, we analyze the optimization landscape of gradient descent methods for static output feedback (SOF) control of discrete-time linear time-invariant systems with quadratic cost. The SOF setting can be quite common, for example, when there are unmodeled hidden states in the underlying process. We first establish several important properties of the SOF cost function, including coercivity, L-smoothness, and M-Lipschitz continuous Hessian. We then utilize these properties to show that the gradient descent is able to converge to a stationary point at a dimension-free rate. Furthermore, we prove that under some mild conditions, gradient descent converges linearly to a local minimum if the starting point is close to one. These results not only characterize the performance of gradient descent in optimizing the SOF problem, but also shed light on the efficiency of general policy gradient methods in reinforcement learning

An Orbital-Angular-Momentum- and Wavelength-Tunable 2 &mu;m Vortex Laser

In this paper, dual tuning of orbital angular momentum (OAM) and the wavelength of a Tm:YLF vortex laser was realized by off-axis pumping and F-P etalon. The tuning of Hermite&ndash;Gaussian (HG) modes by off-axis pumping was theoretically analyzed. In the experiment, the highest 17th order HG17,0 mode was realized by off-axis pumping. The threshold power increased from 2 to 17.51 W with the increase in off-axis distance, and the curve of threshold power vs. off-axis distance was partially consistent with the theoretical simulation analysis. The Laguerre&ndash;Gaussian (LG) modes carrying OAM were produced by mode converter, and the beam quality of LG modes was good. The phase distribution of the LG modes was verified by interference. Subsequently, an F-P etalon was inserted into the resonant cavity to tune the wavelength. Finally, the OAM tuning of the vortex beam from LG1,0(OAM = &minus;1&#8463;) to LG16,0(OAM = &minus;16&#8463;) was realized, and the corresponding wavelength tuning range was from 1898&ndash;1943 nm to 1898&ndash;1937 nm