Ground-state energy of a quantum chain with competing interactions

We show rigorously that the ground state of a quantum chain with competing ferromagnetic nearest and antiferromagnetic next nearest interactions undergoes a transition from ferromagnetic to helical type, in the isotropic case, for a certain value of the relevant ratio of coupling constants. Boundaries of the phase diagram are also determined in the anisotropic case. The stability of a special quantum state (corresponding to a classical modulated phase of =/3) is analyzed by an extension of Holstein-Primakoff arguments, along a line of constant ratio of couplings, showing in particular a sequence of (instability) gaps. Finally, a natural adaptation of a variational wave function due to Huse and Elser is used to study several portions of the phase diagram, with very good agreement with previous theoretical results.     

Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.     

固体激光器晶体吸收光场的设计仿真

设计了3类8种二极管侧面泵浦结构,构建了相应的泵浦光学理论模型,采用有限元分析方法,计算了不同结构下的吸收效率和矩阵元素标准差。首先,在给定泵浦总功率、芯片间距、掺杂浓度等前提下,随着泵浦距离在0～5 mm内递增,8种泵浦结构中激光晶体的吸收效率呈降低趋势,矩阵元素标准差也逐渐减小,其中部分结构在近距离(0～0.3 mm)泵浦时矩阵元素标准差变化程度较大,在实际应用中应避免泵浦距离处于近距离区间。其次,在相同结构参量下,随着晶体掺杂浓度在0.6%～1.0%内增加,激光晶体的吸收效率提高,同时,掺杂浓度提升也是造成截面中心峰值降低或半径方向上光场凹陷的原因。因此,掺杂浓度和泵浦距离的匹配将是提升半径方向上均匀性分布的有效手段。     

基于背景感知的相关滤波抗遮挡优化算法

针对相关滤波目标跟踪中由于目标遮挡、出视野引起的跟踪失败等问题,以背景感知相关滤波(background aware correlation filters, BACF)算法为基础,提出一种抗遮挡优化算法。首先特征提取部分融合方向梯度直方图(histogram of oriented gradient, HOG)特征与颜色名称(color name, CN)特征;其次提出层级遮挡优化算法,在目标跟踪过程中根据平均峰值相关能量(average peak to correlation energy,APCE)和最大响应峰值判断目标外观是否发生较大变化,并通过巴氏距离进一步计算目标相邻滤波器模板特征相似程度,以此判断造成目标外观变化的真正原因,进而决定是否更新模板;针对目标出视野问题,提出模板筛查策略,跟踪过程中每隔K帧对滤波器模板进行置信度考查,若目标丢失,则生成新的模板并对目标进行全局搜索重新抓取目标。通过在目标跟踪数据集OTB2015上测试,优化算法精确度与成功率分别为83.0%与78.8%,有效提高了算法性能。     

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self-Powered Human–Machine Interaction

MXene materials emerge as promising candidates for energy harvesting and storage application. In this study, the effect of the surface chemistry on the work function of MXenes, which determines the performance of MXene-based triboelectric nanogenerator (TENG), is elucidated. First-principles calculations reveal that the surface functional group greatly influences MXene work function:  OH termination reduces the work function with respect to that of bare surface, while  F and  Cl increase it. Then, work functions are experimentally determined by Kelvin probe force microscopy. The MXene prepared by gentle etching at 40 °C for 48 h (GE_40/48) has the largest work function. Furthermore, an electron-cloud potential-well model is established to explain the mechanism of electron emission-dominated charge transfer and assemble a triboelectric device to verify experimentally its conclusions. It is found that GE_40/48 has the best performance with a 281 V open-circuit voltage, 9.7 µA short-current current, and storing 1.019 µC of charge, which is consistent with the model. Last, a patterned TENG is demonstrated for self-powered human–machine interaction application. This finding enhances the understanding of the inherent mechanism between the surface structure and the output performance of MXene-based TENG, which can be applied to other TENG based on 2D materials.     

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.     

Solid Ink Laser Patterning for High-Resolution Information Labels with Supervised Learning Readout

Tagging, tracking, or validation of products are often facilitated by inkjet-printed optical information labels. However, this requires thorough substrate pretreatment, ink optimization, and often lacks in printing precision/resolution. Herein, a printing method based on laser-driven deposition of solid polymer ink that allows for printing on various substrates without pretreatment is demonstrated. Since the deposition process has a precision of <1 µm, it can introduce the concept of sub-positions with overlapping spots. This enables high-resolution fluorescent labels with comparable spot-to-spot distance of down to 15 µm (444,444 spots cm⁻²) and rapid machine learning-supported readout based on low-resolution fluorescence imaging. Furthermore, the defined thickness of the printed polymer ink spots can be used to fabricate multi-channel information labels. Additional information can be stored in different fluorescence channels or in a hidden topography channel of the label that is independent of the fluorescence.     

Direct Freeform Laser Fabrication of 3D Conformable Electronics

3D conformable electronic devices on freeform surfaces show superior performance to the conventional, planar ones. They represent a trend of future electronics and have witnessed exponential growth in various applications. However, their potential is largely limited by a lack of sophisticated fabrication techniques. To tackle this challenge, a new direct freeform laser (DFL) fabrication method enabled by a 5-axis laser processing platform for directly fabricating 3D conformable electronics on targeted arbitrary surfaces is reported. Accordingly, representative laser-induced graphene (LIG), metals, and metal oxides are successfully fabricated as high-performance sensing and electrode materials from different material precursors on various types of substrates for applications in temperature/light/gas sensing, energy storage, and printed circuit board for circuit. Last but not the least, to demonstrate an application in smart homes, LIG-based conformable strain sensors are fabricated and distributed in designated locations of an artificial tree. The distributed sensors have the capability of monitoring the wind speed and direction with the assistance of well-trained machine-learning models. This novel process will pave a new and general route to fabricating 3D conformable electronic devices, thus creating new opportunities in robotics, biomedical sensing, structural health, environmental monitoring, and Internet of Things applications.     

A matching game framework for users clustering and resource allocation with wireless power transfer in a CR-NOMA network

This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.     

Kernel-based adversarial attacks and defenses on support vector classification

While malicious samples are widely found in many application fields of machine learning, suitable countermeasures have been investigated in the field of adversarial machine learning. Due to the importance and popularity of Support Vector Machines (SVMs), we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper. The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier. Specially, we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers. Utilizing this vulnerability function, we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack. Our defense method is verified to be very effective on the benchmark datasets, and the SVM classifier becomes more robust after using our kernel optimization scheme.