New measurements and reanalysis of 14N elastic scattering on 10B target

The angular distributions of elastic scattering of ¹⁴N ions on ¹⁰B targets have been measured at incident beam energies of 21.0 and 24.5 MeV. Angular distributions at higher energies 38–94.0 MeV (previously measured) were also included in the analysis. All data were analyzed within the framework of the optical model and the distorted waves Born approximation method. The observed rise in cross sections at large angles was interpreted as a possible contribution of the α-cluster exchange mechanism. Spectroscopic amplitudes SA₂ and SA₄ for the configuration ¹⁴N→ ¹⁰B +α were extracted. Their average values are 0.58±0.10 and 0.81±0.12 for SA₂ and SA₄, respectively, suggesting that the exchange mechanism is a major component of the elastic scattering for this system. The energy dependence of the depths for the real and imaginary potentials was found.     

增材制造金属反射镜的发展综述

随着光学测量与遥感领域的不断发展,折反式光学系统对重量、体积和环境适应性等需求不断提高。基于增材制造技术的金属反射镜以其便于实现优化设计、快速制造和加工工艺性好等优点,逐渐获得国内外学者的关注与研究。与传统金属反射镜相比,增材制造金属反射镜可以提高反射镜的结构刚度,同时可实现更高程度的轻量化。增材制造反射镜可以满足光学系统对环境适应性和快速性的需求。本文首先讨论了金属反射镜的评价指标;其次,综述了国内外在基于增材制造技术制备金属反射镜领域的发展现状和技术参数,从增材制造金属反射镜的基体设计与制备和基体的后处理2个方面展开论述;然后,通过分析,总结了增材制造金属反射镜的技术路线和关键技术;最后,对增材制造反射镜的应用前景提出了展望。     

基于自由曲面反射镜的低投射比超短焦投影物镜的光学设计

为了设计低投射比的超短焦投影物镜,本文采用自由曲面和折反式的光路结构设计了一种具有低投射比的超短焦投影物镜系统。该物镜由一个旋转对称的折射透镜组和一个自由曲面反射镜组成。采用11.938 mm的数字微镜器件(DMD)作为空间光调制器产生图像源。采用法线加权迭代优化的方法计算自由曲面。最后,分析了系统的性能。仿真结果表明:超短焦投影物镜可在580 mm的投影距离处实现3048 mm尺寸的大屏幕投影,系统的投射比低至0.19,系统的最大畸变小于0.72%。能够满足低投射比超短焦投影物镜的设计要求。该投影系统具有低投射比、低畸变、投影效果好等优点,可为超短焦投影系统的进一步发展提供有益参考。     

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.     

FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects

Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional error control in the design of CM systems with high-performance requirements remains urgent. As an additional control of CM systems, we propose to use indivisible error detection codes based on a positional number system. In this work, we evaluated the indivisible code using the average probability method (APM) for the binary symmetric channel (BSC), which has the simplicity, versatility and reliability of the estimate, which is close to reality. The APM allows for evaluation and compares indivisible codes according to parameters of correct transmission, and detectable and undetectable errors. Indivisible codes allow for the end-to-end (E2E) control of the transmission and processing of information in digital systems and design devices with a regular structure and high speed. This study researched a fractal decoder device for additional error control, implemented in field-programmable gate array (FPGA) software with FEC for short-reach optical interconnects with multilevel pulse amplitude (PAM-M) modulated with Gray code mapping. Indivisible codes with natural redundancy require far fewer hardware costs to develop and implement encoding and decoding devices with a sufficiently high error detection efficiency. We achieved a reduction in hardware costs for a fractal decoder by using the fractal property of the indivisible code from 10% to 30% for different n while receiving the reciprocal of the golden ratio.     

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.     

A Classical Formulation of Quantum Theory?

We explore a particular way of reformulating quantum theory in classical terms, starting with phase space rather than Hilbert space, and with actual probability distributions rather than quasiprobabilities. The classical picture we start with is epistemically restricted, in the spirit of a model introduced by Spekkens. We obtain quantum theory only by combining a collection of restricted classical pictures. Our main challenge in this paper is to find a simple way of characterizing the allowed sets of classical pictures. We present one promising approach to this problem and show how it works out for the case of a single qubit.     

DCE-MRI定量参数对子宫内膜癌的诊断价值及与临床病理因素的相关性

本文选取了子宫内膜癌患者108例作为观察组,另选取子宫内膜良性病变患者105例作为对照组,均行DCE-MRI检查后发现,观察组容量转移常数(Ktrans)、速率常数(Kep)、血管外细胞外间隙容积比值(Ve)高于对照组(P＜0.05);Ktrans、Kep、Ve值联合诊断子宫内膜癌的曲线下面积(AUC)为0.841;K...