Bismuth-doped fiber amplifier based on a linear cavity double pass structure operating in the O + E band.

Finding suitable fiber amplifiers is one of the key strategies to increase the transmission capacity of fiber links. Recently, bismuth-doped fiber amplifiers (BDFAs) have attracted much attention due to their distinctive ultra-wideband luminescence properties. In this paper, we propose a linear cavity double pass structure for BDFA operating in the O and E bands. The design creates a linear cavity within the amplifier by combining a fiber Bragg grating (FBG) and a fiber mirror to achieve dual-wavelength pump at 1240 nm and 1310 nm. Meanwhile, the configuration of a circulator and mirror facilitates bidirectional signal propagation through the BDFA, resulting in a double-pass amplification structure. We have tested and analyzed the performance of the linear cavity double pass structure BDFA under different pump schemes and compared it with the conventional structure BDFA. The results show that the gain spectrum of the new structure is shifted toward longer wavelengths, and the gain band is extended from the O band to the O and E bands compared with the conventional structure. In particular, the linear cavity double pass structure BDFA has more relaxed requirements on the stability of the pump and signal power. This work provides a positive reference for the design, application, and development of BDFAs.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Inducing aortic aneurysm/dissection in zebrafish: evaluating the efficacy of ß-Aminopropionic Nitrile as a model.

Aortic aneurysm/dissection (AAD) poses a life-threatening cardiovascular emergency with complex mechanisms and a notably high mortality rate. Zebrafish (Danio rerio) serve as valuable models for AAD due to the conservation of their three-layered arterial structure and genome with that of humans. However, the existing studies have predominantly focused on larval zebrafish, leaving a gap in our understanding of adult zebrafish. In this study, we utilized ß-Aminopropionic Nitrile (BAPN) impregnation to induce AAD in both larval and adult zebrafish. Following induction, larval zebrafish exhibited a 28% widening of the dorsal aortic diameter (p < 0.0004, n = 10) and aortic arch malformations, with a high malformation rate of 75% (6/8). Conversely, adult zebrafish showed a 41.67% (5/12) mortality rate 22 days post-induction. At this time point, the dorsal aortic area had expanded by 2.46 times (p < 0.009), and the vessel wall demonstrated significant thickening (8.22 ± 2.23 µM vs. 26.38 ± 10.74 µM, p < 0.05). Pathological analysis revealed disruptions in the smooth muscle layer, contributing to a 58.33% aneurysm rate. Moreover, the expression levels of acta2, tagln, cnn1a, and cnn1b were decreased, indicating a weakened contractile phenotype. Transcriptome sequencing showed a significant overlap between the molecular features of zebrafish tissues post-BAPN treatment and those of AAD patients. Our findings present a straightforward and practical method for generating AAD models in both larval and adult zebrafish using BAPN.

Experimental investigation on the internal stress of bismuth-doped fiber and its effect on the noise figure of Bismuth-doped fiber amplifier.

The noise figure (NF) of a fiber amplifier is one of the key measures of amplification performance, which characterizes the quality of the amplified signal. Residual stresses are inevitably generated during the manufacturing process of optical fibers, and this can lead to changes in the refractive index (RI) distribution of the fiber. Further, the change in RI distribution causes the mode-field characteristics of the fiber to change as well, and this ultimately has an impact on the NF performance of the amplifier. However, until now, there have been fewer studies on the effect of residual stress on the NF of the fiber amplifiers. In this work, we took a commercial single-mode bismuth-doped fiber (BDF) as an example and used a self-developed stress test device to measure its residual stress and refractive index distribution and compare it with that of a passive fiber. We also comprehensively compared the distribution of residual stress and refractive index of the fiber at different pump powers and pump wavelengths. Finally, we performed numerical simulations of the bismuth-doped fiber amplifier (BDFA) based on the BDF under the theoretical mode field area and BDF after the expansion of the mode field area due to stresses to compare the NF performance. The results demonstrate that: the entire cross-section (core and cladding) of the BDF exhibits tensile stress (>0 MPa), where the residual stress at the core of the BDF is nearly 9.8 MPa higher than that of the passive fiber; The residual stress makes the mode-field area of the BDF expand by 26.7% compared with the theoretical values, which ultimately makes the NF of the BDFA rise from 4.6 dB to 4.7 dB; The stress at the BDF core is exacerbated by pump excitation, where it is elevated by about 26% and 5% compared to vacancy at 1240 nm and 1310 nm pumps, which is most likely attributed to thermal effects. Therefore, it is necessary to consider the effect of residual stresses in the fabrication of optical fibers to better achieve the radius of the expected indicators. This work contributes to the better development of O-band BDFAs, especially for pre-simulation of the actual performance of BDFAs with a practical reference.

Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention.

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In-memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first-order dynamics. Here, a second-order memristor is experimentally demonstrated using yttria-stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second-order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL-based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second-order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high-efficiency, compact footprint, and hardware-encoded plasticity.

Dynamic Refractive Index-Matching for Adaptive Thermoresponsive Smart Windows.

Thermoresponsive smart windows (TRSWs) take great advantages in energy-efficient buildings and on-demand devices owing to their self-adaptiveness and external energy consumption-free nature. Currently used TRSWs largely rely on thermal-induced phase transitions in single-material systems, however, the intrinsic characteristics of which may not be suited for practical window utilization, such as poor luminous transparency and fixed critical temperature (Tc ). Herein, an adaptive TRSW based on dynamic refractive index (RI) matching between two phases is demonstrated, which is facilely fabricated by embedding ethylene glycol solution microdroplets into polydimethylsiloxane (PDMS) via a one-step emulsification approach, realizing a smart temperature response in PDMS. The TRSW presents high transparency (≈92%) and bidirectional transparency-temperature response (≈20% at 73 °C, ≈40% at 8 °C). Moreover, the RI dispersion generates a unique effect of wavelength selectivity with temperature. Notably, the effective optical-temperature response with variable Tc could be tuned over a wide range of 13-68 °C by adjusting the EGS concentration. The proposed strategy with dynamic RI matching allows TRSW construction to extend beyond phase transitional materials and greatly broadens the applicable scope of TRSWs, which is promising in the fields of smart optical devices such as smart windows, anti-counterfeiting, optical switches, and optical selection.

Modeling of a ring-core trench-assisted few-mode BDFA for seven-mode signal gain equalization.

In this paper, a ring-core trench-assisted few-mode bismuth-doped fiber amplifier (BDFA) is simulated on the basis of the three-energy level. The fiber is designed to support four modes of signal group transmission for practical considerations, including LP01, LP11, LP21, and LP31. The results suggest that (1) it is possible to obtain gain equalization of the three signal groups by using the LP21 mode pump independently, where the maximum difference in modal gain (MAX DMG) is about 0.9 dB, except for the LP31 mode signal; (2) by combining the LP01 and LP31 mode pumps, the average gain of the groups increases by 14%, and the MAX DMG decreases by nearly 60% (3.8 to 1.5 dB) compared to the LP01 pump alone; and (3) with the same combination of mode pumps, the ring-core BDFA (1.5 dB) achieves better gain equalization than the single-core BDFA (2.8 dB). The analysis is informative for the future development of a multimode BDFA.

Class Incremental Learning With Few-Shots Based on Linear Programming for Hyperspectral Image Classification.

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification.

Giant exchange bias induced via tuning interfacial spins in polycrystalline Fe3O4/CoO bilayers.

A giant exchange bias (EB) of 9600 Oe was observed in polycrystalline Fe3O4/CoO layers at 10 K after 20 kOe field cooling, and was attributed to the strong exchange coupling formed by the interfacial spins between the polycrystalline Fe3O4 and the CoO layer. It was found that at 10 K, the magnetic-moment difference (ΔM) between the zero field cooling curves and field cooling curves first increases and then decreases with the change of the field, and it reaches the maximum value at a field of 20 kOe, which suggests that the interfacial spins can be tuned by the cooling field. Furthermore, other magnetic properties, including field dependence, temperature dependence, and training effects, were investigated, which further confirmed that the interfacial spins play an important role in the EB effect. This work provides a method to tune the magnitude of the EB effect and reveals the mechanism of the dependency of EB on interfacial spins, which could guide the design of giant-EB-effect materials.

Machine Learning Magnetic Parameters from Spin Configurations.

Hamiltonian parameters estimation is crucial in condensed matter physics, but is time- and cost-consuming. High-resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.