arXiv subjects

Zhaohui Wu

Publications and source records attributed to Zhaohui Wu.

12 recordsLinked to original sources

arXiv2026

Single-shot in situ pulse-duration measurement using plasma grating

Accurate measurement of the pulse duration of ultrashort, ultra-intense laser pulses at focus is essential for strong-field science. Most existing diagnostics, however, cannot allow direct in situ measurement in the focal region because of damage-threshold limits and unavoidable spatial averaging. We present a direct single-shot far-field diagnostic based on a plasma grating. In this method, the pulse duration is encoded in the axial length of an interference-written plasma grating and retrieved from the corresponding Bragg-diffraction signal. Comparison with near-field (pre-focus) autocorrelator measurements and far-field (at-focus) scanning measurements confirms single-shot pulse-duration retrieval in the focal region over 35-130 fs, and the method remains effective at a peak intensity of $\sim 10^{16}{\rm W/cm^2}$. In principle, the measurable range can be extended to 15-300 fs and to higher peak intensities. The method is insensitive to the laser central wavelength and offers a practical approach to far-field diagnostics in high-power laser systems.

physics.optics↗

arXiv2025

Stimulated Brillouin Amplification with Flying Focus

Material damage thresholds pose a fundamental limit to chirped pulse amplification (CPA) in high-power laser systems. Plasma-based amplification via stimulated Brillouin scattering (SBS) offers a damage-free alternative, yet its effectiveness has been hindered by instabilities that constrain interaction length. In this study, we report the first experimental demonstration of SBS amplification driven by a flying focus in a 3-mm plasma channel. The flying focus is generated using chromatic aberration from spherical lenses, with its velocity precisely measured by an interferometric ionization method achieving 6.6 fs timing resolution. At a focus velocity near -c, SBS amplification is realized at pump and seed intensities more than two orders of magnitude lower than in conventional setups, yielding a conversion efficiency of 14.5%. These results validate flying focus as a powerful tool for extending interaction lengths and enabling efficient plasma-based laser amplification at reduced intensities.

physics.plasm-ph↗

arXiv2025

Spatiotemporal plasma hologram

We present the first experimental realization of a four-dimensional (4D) plasma hologram capable of recording and reconstructing the full spatiotemporal information of intense laser pulses. The holographic encoding is achieved through the interference of a long object pulse and a counter-propagating short reference pulse, generating an ionized plasma grating that captures both spatial and temporal characteristics of the laser field. A first-order diffractive probe enables the retrieval of encoded information, successfully reconstructing the spatiotemporal profiles of Gaussian and Laguerre-Gaussian beams. The experiment demonstrates the ability to encode artificial information into the laser pulse via spectral modulation and retrieve it through plasma grating diffraction, high-lighting potential applications in ultraintense optical data processing. Key innovations include a single-shot, background-free method for direct far-field spatiotemporal measurement and the obser-vation of laser focus propagation dynamics in plasma. The plasma grating exhibits a stable lifetime of 30-40 ps and supports high repetition rates, suggesting usage for high-speed optical switches and plasmatic analog memory. These advancements establish plasma holography as a robust platform for ultrafast laser manipulation, with implications for secure optical communication, analog computing,and precision spatiotemporal control of high-intensity lasers.

physics.plasm-ph↗

arXiv2023

Generation of subcycle isolated attosecond pulses by pumping ionizing gating

We present a novel approach named as pumping ionizing gating (PIG) for the generation of isolated attosecond pulses (IAPs). In this regime, a short laser is used to ionize a pre-existing gas grating, creating a fast-extending plasma grating(FEPG) having an ionization front propagating with the velocity of light. A low-intensity long counterpropagating pump pulse is then reflected by a very narrow region of the ionization front, only where the Bragg conditions for resonant reflection is satisfied. Consequently, the pump reflection is confined within a sub-cycle region called PIG, and forms a wide-band coherent IAP in combination with the frequency up-conversion effect due to the plasma gradient. This approach results in a new scheme to generate IAPs fromlong picosecond pump pulses. Three-dimensional (3D) simulations show that a 1.6-ps, 1-μm pump pulse can be used to generate a 330 as laser pulse with a peak intensity approximately 33 times that of the pump and a conversion efficiency of around 0.1%.These results highlight the potential of the PIG method for generating IAPs with high conversion efficiency and peak intensity.

physics.optics↗

arXiv2022

SPAIC: A Spike-based Artificial Intelligence Computing Framework

Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscience and deep learning. Currently, there have been various software frameworks developed for the related fields, but there is a lack of an efficient framework dedicated for spike-based computing models and algorithms. In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience. To integrate different methodologies from the two overwhelming disciplines, and balance between flexibility and efficiency, SPAIC is designed with neuroscience-style frontend and deep learning backend structure. We provide a wide range of examples including neural circuits Simulation, deep SNN learning and neuromorphic applications, demonstrating the concise coding style and wide usability of our framework. The SPAIC is a dedicated spike-based artificial intelligence computing platform, which will significantly facilitate the design, prototype and validation of new models, theories and applications. Being user-friendly, flexible and high-performance, it will help accelerate the rapid growth and wide applicability of neuromorphic computing research.

cs.NE↗

arXiv2021

Laser Compression via fast-extending plasma gratings

It is proposed a new method of compressing laser pulse by fast extending plasma gratings(FEPG), which is created by ionizing the hypersound wave generated by stimulated Brillouin scattering(SBS) in the background gas. Ionized by a short laser pulse, the phonon forms a light-velocity FEPG to fully reflect a resonant pump laser. As the reflecting surface moves with a light velocity, the reflected pulse is temporally overlapped and compressed. This regime is supported by the simulation results of a fully kinetic particle-in-cell(PIC) code Opic with a laser wavelength of 1um, displaying a pump pulse is compressed from 13ps to a few cycles(7.2fs), with an efficiency close to 80%. It is a promising method to produce critical laser powers due to several features: high efficiency without a linear stage, robustness to plasma instabilities, no seed and a wide range of pump intensity.

physics.plasm-ph↗

arXiv2021

Self-compression of stimulated Raman backscattering by flying focus

A novel regime of self-compression is proposed for plasma-based backward Raman amplification(BRA) upon flying focus. By using a pumping focus moving with a speed equal to the group velocity of stimulated Raman backscattering(SRBS), only a short part of SRBS which does always synchronize with the flying focus can be amplified. Due to the asymmetrical amplification, the pulse can be directly compressed in the linear stage of BRA. Therefore, instead of a short pulse, the Raman spontaneous or a long pulse can seed the BRA amplifiers. The regime is supported by the 2D particle-in-cell(PIC) simulation without a seed, presenting that the pump pulse is compressed from 26ps to 116fs, with an output amplitude comparable with the case of a well-synchronized short seed. This method provides a significant way to simplify the Raman amplifiers and overcome the issue of synchronization jitter between the pump and the seed.

physics.plasm-ph↗

arXiv2017

Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network

Spectral graph theory has been widely applied in unsupervised and semi-supervised learning. In this paper, we find for the first time, to our knowledge, that it also plays a concrete role in supervised classification. It turns out that two classifiers are inherently related to the theory: linear regression for classification (LRC) and normalized radial basis function network (nRBFN), corresponding to linear and nonlinear kernel respectively. The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk. With the theory, ideal working conditions for LRC and nRBFN are presented, which ensure not only zero fitting error but also low overfitting risk. For quantitative analysis, two concepts, the fitting error and the spectral risk (indicating overfitting), have been defined. Their bounds for nRBFN and LRC are derived. A special result shows that the spectral risk of nRBFN is lower bounded by the number of classes and upper bounded by the size of radial basis. When the conditions are not met exactly, the classifiers will pursue the minimum fitting error, running into the risk of overfitting. It turns out that $\ell_2$-norm regularization can be applied to control overfitting. Its effect is explored under the spectral context. It is found that the two terms in the $\ell_2$-regularized objective are one-one correspondent to the fitting error and the spectral risk, revealing a tradeoff between the two quantities. Concerning practical performance, we devise a basis selection strategy to address the main problem hindering the applications of (n)RBFN. With the strategy, nRBFN is easy to implement yet flexible. Experiments on 14 benchmark data sets show the performance of nRBFN is comparable to that of SVM, whereas the parameter tuning of nRBFN is much easier, leading to reduction of model selection time.

cs.LG↗

arXiv2017

Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory underlies a series of these elementary methods and can unify them into a complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations (e.g., method of optimal directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional scaling, Isomap, locally linear embedding), and subspace clustering (e.g., sparse subspace clustering, low-rank representation) are incorporated. We show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together. And when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. SSR combines merits of both sides: its sparse codes reduce dimensionality of data meanwhile revealing cluster structure. For its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. Scut, a clustering approach derived from SSR reaches the state-of-the-art performance in the spectral clustering family. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on various data sets demonstrate the properties and strengths of SSR, NSCrt, and Scut.

cs.CV↗

arXiv2017

Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices

With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power. Typical large Convolutional Neural Networks (CNNs) need large amounts of memory and computational power, and cannot be deployed on embedded devices efficiently. We present Two-Bit Networks (TBNs) for model compression of CNNs with edge weights constrained to (-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the memory usage and improve computational efficiency significantly while achieving good performance in terms of classification accuracy, thus representing a reasonable tradeoff between model size and performance.

cs.LG↗

arXiv2014

Science and Ethnicity: How Ethnicities Shape the Evolution of Computer Science Research Community

Globalization and the world wide web has resulted in academia and science being an international and multicultural community forged by researchers and scientists with different ethnicities. How ethnicity shapes the evolution of membership, status and interactions of the scientific community, however, is not well understood. This is due to the difficulty of ethnicity identification at the large scale. We use name ethnicity classification as an indicator of ethnicity. Based on automatic name ethnicity classification of 1.7+ million authors gathered from Web, the name ethnicity of computer science scholars is investigated by population size, publication contribution and collaboration strength. By showing the evolution of name ethnicity from 1936 to 2010, we discover that ethnicity diversity has increased significantly over time and that different research communities in certain publication venues have different ethnicity compositions. We notice a clear rise in the number of Asian name ethnicities in papers. Their fraction of publication contribution increases from approximately 10% to near 50% from 1970 to 2010. We also find that name ethnicity acts as a homophily factor on coauthor networks, shaping the formation of coauthorship as well as evolution of research communities.

cs.DL↗

arXiv2014

Sparse Principal Component Analysis via Rotation and Truncation

Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover the data subspace as much as possible. In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation. The algorithm of SPCArt consists of three alternating steps: rotate PCA basis, truncate small entries, and update the rotation matrix. Its performance bounds are also given. SPCArt is efficient, with each iteration scaling linearly with the data dimension. It is easy to choose parameters in SPCArt, due to its explicit physical explanations. Besides, we give a unified view to several existing sparse PCA methods and discuss the connection with SPCArt. Some ideas in SPCArt are extended to GPower, a popular sparse PCA algorithm, to overcome its drawback. Experimental results demonstrate that SPCArt achieves the state-of-the-art performance. It also achieves a good tradeoff among various criteria, including sparsity, explained variance, orthogonality, balance of sparsity among loadings, and computational speed.

cs.LG↗