arXiv subjects

Xingjun Wang

Publications and source records attributed to Xingjun Wang.

33 records · Page 2Linked to original sources

arXiv2023

High-coherence parallelization in integrated photonics

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications.

physics.optics↗

arXiv2023

SF-IDS: An Imbalanced Semi-Supervised Learning Framework for Fine-grained Intrusion Detection

Deep learning-based fine-grained network intrusion detection systems (NIDS) enable different attacks to be responded to in a fast and targeted manner with the help of large-scale labels. However, the cost of labeling causes insufficient labeled samples. Also, the real fine-grained traffic shows a long-tailed distribution with great class imbalance. These two problems often appear simultaneously, posing serious challenges to fine-grained NIDS. In this work, we propose a novel semi-supervised fine-grained intrusion detection framework, SF-IDS, to achieve attack classification in the label-limited and highly class imbalanced case. We design a self-training backbone model called RI-1DCNN to boost the feature extraction by reconstructing the input samples into a multichannel image format. The uncertainty of the generated pseudo-labels is evaluated and used as a reference for pseudo-label filtering in combination with the prediction probability. To mitigate the effects of fine-grained class imbalance, we propose a hybrid loss function combining supervised contrastive loss and multi-weighted classification loss to obtain more compact intra-class features and clearer inter-class intervals. Experiments show that the proposed SF-IDS achieves 3.01% and 2.71% Marco-F1 improvement on two classical datasets with 1% labeled, respectively.

cs.CR↗

arXiv2023

A Lightweight Approach for Network Intrusion Detection based on Self-Knowledge Distillation

Network Intrusion Detection (NID) works as a kernel technology for the security network environment, obtaining extensive research and application. Despite enormous efforts by researchers, NID still faces challenges in deploying on resource-constrained devices. To improve detection accuracy while reducing computational costs and model storage simultaneously, we propose a lightweight intrusion detection approach based on self-knowledge distillation, namely LNet-SKD, which achieves the trade-off between accuracy and efficiency. Specifically, we carefully design the DeepMax block to extract compact representation efficiently and construct the LNet by stacking DeepMax blocks. Furthermore, considering compensating for performance degradation caused by the lightweight network, we adopt batch-wise self-knowledge distillation to provide the regularization of training consistency. Experiments on benchmark datasets demonstrate the effectiveness of our proposed LNet-SKD, which outperforms existing state-of-the-art techniques with fewer parameters and lower computation loads.

cs.CR↗

arXiv2023

Rethink Long-tailed Recognition with Vision Transformers

In the real world, data tends to follow long-tailed distributions w.r.t. class or attribution, motivating the challenging Long-Tailed Recognition (LTR) problem. In this paper, we revisit recent LTR methods with promising Vision Transformers (ViT). We figure out that 1) ViT is hard to train with long-tailed data. 2) ViT learns generalized features in an unsupervised manner, like mask generative training, either on long-tailed or balanced datasets. Hence, we propose to adopt unsupervised learning to utilize long-tailed data. Furthermore, we propose the Predictive Distribution Calibration (PDC) as a novel metric for LTR, where the model tends to simply classify inputs into common classes. Our PDC can measure the model calibration of predictive preferences quantitatively. On this basis, we find many LTR approaches alleviate it slightly, despite the accuracy improvement. Extensive experiments on benchmark datasets validate that PDC reflects the model's predictive preference precisely, which is consistent with the visualization.

cs.CV↗

arXiv2023

Slow light silicon modulator beyond 110 GHz bandwidth

Silicon modulators are key components in silicon photonics to support the dense integration of electro-optic (EO) functional elements on a compact chip for various applications including high-speed data transmission, signal processing, and photonic computing. Despite numerous advances in promoting the operation speed of silicon modulators, a bandwidth ceiling of 67 GHz emerges in practices and becomes an obstacle to paving silicon photonics toward Tbps level data throughput on a single chip. Here, we theoretically propose and experimentally demonstrate a design strategy for silicon modulators by employing the slow light effect, which shatters the present bandwidth ceiling of silicon modulators and pushes its limit beyond 110 GHz in a small footprint. The proposed silicon modulator is built on a coupled-resonator optical waveguide (CROW) architecture, in which a set of Bragg gratings are appropriately cascaded to give rise to a slow light effect. By comprehensively balancing a series of merits including the group index, photon lifetime, electrical bandwidth, and losses, we found the modulators can benefit from the slow light for better modulation efficiency and compact size while remaining their bandwidth sufficiently high to support ultra-high-speed data transmission. Consequently, we realize a modulator with an EO bandwidth of 110 GHz in a length of 124 μm, and demonstrate a data rate beyond 110 Gbps by applying simple on-off keying modulation for a DSP-free operation. Our work proves that silicon modulators beyond 110 GHz are feasible, thus shedding light on the potentials of silicon photonics in ultra-high-bandwidth applications such as data communication, optical interconnection, and photonic machine learning.

physics.optics↗

arXiv2022

M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes

In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine removal module. Firstly, the highlight feature extractor module can directly separate the highlight feature and non-highlight feature from the original highlight image. Then highlight removal image is obtained using a coarse highlight removal network. To further improve the highlight removal effect, the refined highlight removal image is finally obtained using refine highlight removal module based on contextual highlight attention mechanisms. Extensive experimental results in multiple scenes indicate that the proposed framework can obtain excellent visual effects of highlight removal and achieve state-of-the-art results in several quantitative evaluation metrics. Our algorithm is applied for the first time in video highlight removal with promising results.

cs.CV↗

arXiv2022

HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing

Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logic-form SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pre-trained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-the-art results on the two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.

cs.DB↗

arXiv2022

Fully on-chip microwave photonics system

Microwave photonics (MWP), harnessing the tremendous bandwidth of light to generate, process and measure wideband microwave signals, are poised to spark a new revolution for the information and communication fields. Within the past decade, new opportunity for MWP has emerged driven by the advances of integrated photonics. However, despite significant progress made in terms of integration level, a fully on-chip MWP functional system comprising all the necessary photonic and electronic components, is yet to be demonstrated. Here, we break the status quo and provide a complete on-chip solution for MWP system, by exploiting hybrid integration of indium phosphide, silicon photonics and complementary metal-oxide-semiconductor (CMOS) electronics platforms. Applying this hybrid integration methodology, a fully chip-based MWP microwave instantaneous frequency measurement (IFM) system is experimentally demonstrated. The unprecedented integration level brings great promotion to the compactness, reliability, and performances of the overall MWP IFM system, including a wide frequency measurement range (2-34 GHz), ultralow estimation errors (10.85 MHz) and a fast response speed (0.3 ns). Furthermore, we deploy the chip-scale MWP IFM system into realistic application tasks, where diverse microwave signals with rapid-varying frequencies at X-band (8-12 GHz) are accurately identified in real-time. This demonstration marks a milestone for the development of integrated MWP, by providing the technology basis for the miniaturization and massive implementations of various MWP functional systems.

physics.app-ph↗

arXiv2021

Sub-milliwatt, widely-tunable coherent microcomb generation with feedback-free operation

Microcombs are revolutionizing optoelectronics by providing parallelized, mutually coherent wavelength channels for time-frequency metrology and information processing. To implement this essential function in integrated photonic systems, it is desirable to drive microcombs directly with an on-chip laser in a simple and flexible way. However, two major difficulties are preventing this goal: 1) generating mode-locked comb states usually requires a significant amount of pump power and 2) the requirement to align laser and resonator frequency significantly complicates operation and limits the tunability of the comb lines. Here, we address these problems by using microresonators on an AlGaAs on-insulator platform to generate dark-pulse microcombs. This highly nonlinear platform dramatically relaxes fabrication requirements and leads to a record-low pump power of less than 1 mW for coherent comb generation. Dark-pulse microcombs facilitated by thermally-controlled avoided mode-crossings are accessed by direct DFB laser pumping. Without any feedback or control circuitries, the comb shows good coherence and stability. This approach also leads to an unprecedented wide chirping range of all the comb lines. Our work provides a route to realize power-efficient, simple and reconfigurable microcombs that can be seamlessly integrated with a wide range of photonic systems.

physics.optics↗

arXiv2021

Optical coherent dot-product chip for sophisticated deep learning regression

Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in complete real-value domain instead of in only positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chip. It is anticipated that the OCDC can promote novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

cs.ET↗

arXiv2021

Bridging microcombs and silicon photonic engines for optoelectronics systems

Microcombs have sparked a surge of applications over the last decade, ranging from optical communications to metrology. Despite their diverse deployment, most microcomb-based systems rely on a tremendous amount of bulk equipment to fulfill their desired functions, which is rather complicated, expensive and power-consuming. On the other hand, foundry-based silicon photonics (SiPh) has had remarkable success in providing versatile functionality in a scalable and low-cost manner, but its available chip-based light sources lack the capacity for parallelization, which limits the scope of SiPh applications. Here, we bridge these two technologies by using a power-efficient and operationally-simple AlGaAs on insulator microcomb source to drive CMOS SiPh engines. We present two important chip-scale photonic systems for optical data transmissions and microwave photonics respectively: The first microcomb-based integrated photonic data link is demonstrated, based on a pulse-amplitude 4-level modulation scheme with 2 Tbps aggregate rate, and a highly reconfigurable microwave photonic filter with unprecedented integration level is constructed, using a time stretch scheme. Such synergy of microcomb and SiPh integrated components is an essential step towards the next generation of fully integrated photonic systems.

physics.app-ph↗

arXiv2021

Exciton emissions in bilayer WSe2 tuned by the ferroelectric polymer

In this work, a hybrid integration of few-layer transition metal dichalcogenides (TMDCs) and ferroelectric polymer is designed to achieve passive control of optical properties in-situ. The electrical polarization in ferroelectric P(VDF-TrFE) polymer can regulate the photoluminescence (PL) in few-layer TMDCs. The total PL intensity is substantially suppressed or enhanced under opposite polarization in bilayer WSe2. This is because electrons transfer between valley K and Λ in the conduction band induced by the built-in electric field in P(VDF-TrFE) polymer. This charge transfer further changes the competing dynamics between direct and indirect exciton recombination path and overall optical radiation efficiency. We also illustrate that the engineered PL originates from external electric field dependent transferred electron effect. The theoretical result matches the experimental data well. This work demonstrates a device platform in which passive regulation is achieved using 2D TMDCs modulated by polarized ferroelectric materials.

physics.optics↗

arXiv2020

Ultrahigh-Q AlGaAs-on-insulator microresonators for integrated nonlinear photonics

Aluminum gallium arsenide (AlGaAs) and related III-V semiconductors have excellent optoelectronic properties. They also possess strong material nonlinearity as well as high refractive indices. In view of these properties, AlGaAs is a promising candidate for integrated photonics, including both linear and nonlinear devices, passive and active devices, and associated applications. For integrated photonics low propagation loss is essential, particularly in nonlinear applications. However, achieving low-loss and high-confinement AlGaAs photonic integrated circuits poses a challenge. Here we show an effective reduction of surface-roughness-induced scattering loss in fully etched high-confinement AlGaAs-on-insulator nanowaveguides, by using a heterogeneous wafer-bonding approach and optimizing fabrication techniques. We demonstrate ultrahigh-quality AlGaAs microring resonators and realize quality factors up to 3.52E6 and finesses as high as 1.4E4. We also show ultra-efficient frequency comb generations in those resonators and achieve record-low threshold powers on the order of ~20 uW and ~120 uW for the resonators with 1 THz and 90 GHz free-spectral ranges, respectively. Our result paves the way for the implementation of AlGaAs as a novel integrated material platform specifically for nonlinear photonics, and opens a new window for chip-based efficiency-demanding practical applications.

physics.optics↗

arXiv2019

Ultra-efficient frequency comb generation in AlGaAs-on-insulator microresonators

Recent advances in nonlinear optics have revolutionized the area of integrated photonics, providing on-chip solutions to a wide range of new applications. Currently, the state of the art integrated nonlinear photonic devices are mainly based on dielectric material platforms, such as Si3N4 and SiO2. While semiconductor materials hold much higher nonlinear coefficients and convenience in active integration, they suffered in the past from high waveguide losses that prevented the realization of highly efficient nonlinear processes on-chip. Here we challenge this status quo and demonstrate an ultra-low loss AlGaAs-on-insulator (AlGaAsOI) platform with anomalous dispersion and quality (Q) factors beyond 1.5*10^6. Such a high quality factor, combined with the high nonlinear coefficient and the small mode volume, enabled us to demonstrate a record low Kerr frequency comb generation threshold of ~36 uW for a resonator with a 1 THz free spectral range (FSR), ~100 times lower compared to that in previous semiconductor platform. Combs with >250 nm broad span have been generated under a pump power lower than the threshold power of state of the art dielectric micro combs. A soliton-step transition has also been observed for the first time from an AlGaAs resonator. This work is an important step towards ultra-efficient semiconductor-based nonlinear photonics and will lead to fully integrated nonlinear photonic integrated circuits (PICs) in near future.

physics.optics↗

arXiv2013

Nonlocal constitutive laws generated by matrix functions: Lattice Dynamics Models and their Continuum Limits

We analyze one-dimensional discrete and quasi-continuous linear chains of $N>>1$ equidistant and identical mass points with periodic boundary conditions and generalized nonlocal interparticle interactions in the harmonic approximation. We introduce elastic potentials which define by Hamilton's principle discrete "Laplacian operators" ("Laplacian matrices") which are operator functions ($N\times N$-matrix functions) of the Laplacian of the Born-von-Karman linear chain with next neighbor interactions. The non-locality of the constitutive law of the present model is a natural consequence of the {\it non-diagonality} of these Laplacian matrix functions in the $N$ dimensional vector space of particle displacement fields where the periodic boundary conditions (cyclic boundary conditions) and as a consequence the (Bloch-) eigenvectors of the linear chain are maintained. In the quasi-continuum limit (long-wave limit) the Laplacian matrices yield "Laplacian convolution kernels" (and the related elastic modulus kernels) of the non-local constitutive law. The elastic stability is guaranteed by the positiveness of the elastic potentials. We establish criteria for "weak" and "strong" nonlocality of the constitutive behavior which can be controlled by scaling behavior of material constants in the continuum limit when the interparticle spacing $h\rightarrow 0$. The approach provides a general method to generate physically admissible (elastically stable) {\it non-local constitutive laws} by means of "simple" Laplacian matrix functions. The model can be generalized to model non-locality in $n=2,3,..$ dimensions of the physical space.

math-ph↗