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Researcher profile

Jie Liang

Jie Liang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.IVComputer VisionMachine LearningNetworking and Internet ArchitectureArtificial Intelligencecond-mat.quant-gasCryptography and SecurityInformation Theorymath.ITmath.NTMultimediaphysics.app-phphysics.atom-phphysics.chem-phphysics.opticsquant-phSoftware Engineering

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Published work

16 published item(s)

preprint2026arXiv

Enhanced Open-Source NWDAF for Event-Driven Analytics in 5G Networks

The network data analytics function (NWDAF) has been introduced in the fifth-generation (5G) core standards to enable event-driven analytics and support intelligent network automation. However, existing implementations remain largely proprietary, and open-source alternatives lack comprehensive support for end-to-end event subscription and notification. In this paper, we present an open source NWDAF framework integrated into an existing Free5GC implementation, which serves as an open-source 5G core implementation. Our implementation extends the session management function to support standardized event exposure interfaces and introduces custom-built notification mechanisms into the SMF and the access and mobility management function for seamless data delivery. The NWDAF subscribes to events and generates analytics on user equipment (UE) behavior, session lifecycle, and handover dynamics. We validate our system through a two-week deployment involving four virtual next-generation NodeBs (gNBs) and multiple virtual UEs with dynamic mobility patterns. To demonstrate predictive capabilities, we incorporate a mobility-aware module that achieves 80.65\% accuracy in forecasting the next gNB handover cell. The framework supports reliable UE registration, state tracking, and cross-cell handovers.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality

We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information for downstream models. We formulate machine-oriented quality as a latent machine utility and approximate it through pairwise predictive-consistency comparisons. To this end, we construct PCMP, a dataset of PSNR-matched distortion pairs labeled by consistency votes from multiple pretrained models. We further propose ML-CLIPSim, a differentiable quality metric built on a frozen CLIP visual encoder, which aggregates intermediate patch-token similarities and global image embeddings. Experiments on machine-preference benchmarks, human-IQA datasets, and learned image compression show that ML-CLIPSim better aligns with machine-oriented preferences than conventional fidelity and perceptual metrics, while remaining competitive for human quality prediction. Used as a compression distortion term, it improves rate--task trade-offs across multiple downstream tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering

Recently, deep learning-based image compression has made signifcant progresses, and has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC, in both subjective metric and the more challenging objective metric. However, a major problem is that many leading learned schemes cannot maintain a good trade-off between performance and complexity. In this paper, we propose an effcient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art. First, we develop an improved multi-scale residual block (MSRB) that can expand the receptive feld and is easier to obtain global information. It can further capture and reduce the spatial correlation of the latent representations. Second, a more advanced importance map network is introduced to adaptively allocate bits to different regions of the image. Third, we apply a 2D post-quantization flter (PQF) to reduce the quantization error, motivated by the Sample Adaptive Offset (SAO) flter in video coding. Moreover, We fnd that the complexity of encoder and decoder have different effects on image compression performance. Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance. Experimental results show that compared to the state-of-the-art method, the encoding and decoding time of the proposed method are about 17 times faster, and the R-D performance is only reduced by less than 1% on both Kodak and Tecnick datasets, which is still better than H.266/VVC(4:4:4) and other recent learning-based methods. Our source code is publicly available at https://github.com/fengyurenpingsheng.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Auto-Weighted Layer Representation Based View Synthesis Distortion Estimation for 3-D Video Coding

Recently, various view synthesis distortion estimation models have been studied to better serve for 3-D video coding. However, they can hardly model the relationship quantitatively among different levels of depth changes, texture degeneration, and the view synthesis distortion (VSD), which is crucial for rate-distortion optimization and rate allocation. In this paper, an auto-weighted layer representation based view synthesis distortion estimation model is developed. Firstly, the sub-VSD (S-VSD) is defined according to the level of depth changes and their associated texture degeneration. After that, a set of theoretical derivations demonstrate that the VSD can be approximately decomposed into the S-VSDs multiplied by their associated weights. To obtain the S-VSDs, a layer-based representation of S-VSD is developed, where all the pixels with the same level of depth changes are represented with a layer to enable efficient S-VSD calculation at the layer level. Meanwhile, a nonlinear mapping function is learnt to accurately represent the relationship between the VSD and S-VSDs, automatically providing weights for S-VSDs during the VSD estimation. To learn such function, a dataset of VSD and its associated S-VSDs are built. Experimental results show that the VSD can be accurately estimated with the weights learnt by the nonlinear mapping function once its associated S-VSDs are available. The proposed method outperforms the relevant state-of-the-art methods in both accuracy and efficiency. The dataset and source code of the proposed method will be available at https://github.com/jianjin008/.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bilateral series and Ramanujan's radial limits

Ramanujan's last letter to Hardy explored the asymptotic properties of modular forms, as well as those of certain interesting $q$-series which he called \emph{mock theta functions}. For his mock theta function $f(q)$, he claimed that as $q$ approaches an even order $2k$ root of unity $ζ$, \[\lim_{q\to ζ} \big(f(q) - (-1)^k (1-q)(1-q^3)(1-q^5)\cdots (1-2q + 2q^4 - \cdots)\big) = O(1),\] and hinted at the existence of similar statements for his other mock theta functions. Recent work of Folsom-Ono-Rhoades provides a closed formula for the implied constant in this radial limit of $f(q)$. Here, by different methods, we prove similar results for all of Ramanujan's 5th order mock theta functions. Namely, we show that each 5th order mock theta function may be related to a modular bilateral series, and exploit this connection to obtain our results. We further explore other mock theta functions to which this method can be applied.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution

Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets. Codes and models are available at https://github.com/csjliang/LDL.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution

Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to the unknown complex degradation of real-world images and the limited computation resources in practical applications. Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however, these methods largely rely on heavy backbone networks and they are inflexible to handle images of different degradation levels. In this paper, we propose an efficient and effective degradation-adaptive super-resolution (DASR) network, whose parameters are adaptively specified by estimating the degradation of each input image. Specifically, a tiny regression network is employed to predict the degradation parameters of the input image, while several convolutional experts with the same topology are jointly optimized to specify the network parameters via a non-linear mixture of experts. The joint optimization of multiple experts and the degradation-adaptive pipeline significantly extend the model capacity to handle degradations of various levels, while the inference remains efficient since only one adaptively specified network is used for super-resolving the input image. Our extensive experiments demonstrate that the proposed DASR is not only much more effective than existing methods on handling real-world images with different degradation levels but also efficient for easy deployment. Codes, models and datasets are available at https://github.com/csjliang/DASR.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Industry Practice of Coverage-Guided Enterprise-Level DBMS Fuzzing

As an infrastructure for data persistence and analysis, Database Management Systems (DBMSs) are the cornerstones of modern enterprise software. To improve their correctness, the industry has been applying blackbox fuzzing for decades. Recently, the research community achieved impressive fuzzing gains using coverage guidance. However, due to the complexity and distributed nature of enterprise-level DBMSs, seldom are these researches applied to the industry. In this paper, we apply coverage-guided fuzzing to enterprise-level DBMSs from Huawei and Bloomberg LP. In our practice of testing GaussDB and Comdb2, we found major challenges in all three testing stages. The challenges are collecting precise coverage, optimizing fuzzing performance, and analyzing root causes. In search of a general method to overcome these challenges, we propose Ratel, a coverage-guided fuzzer for enterprise-level DBMSs. With its industry-oriented design, Ratel improves the feedback precision, enhances the robustness of input generation, and performs an on-line investigation on the root cause of bugs. As a result, Ratel outperformed other fuzzers in terms of coverage and bugs. Compared to industrial black box fuzzers SQLsmith and SQLancer, as well as coverage-guided academic fuzzer Squirrel, Ratel covered 38.38%, 106.14%, 583.05% more basic blocks than the best results of other three fuzzers in GaussDB, PostgreSQL, and Comdb2, respectively. More importantly, Ratel has discovered 32, 42, and 5 unknown bugs in GaussDB, Comdb2, and PostgreSQL.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Learning-based Image Compression with Trellis Coded Quantization

Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder. In this paper, we propose to incorporate trellis coded quantizer (TCQ) into a deep learning based image compression framework. A soft-to-hard strategy is applied to allow for back propagation during training. We develop a simple image compression model that consists of three subnetworks (encoder, decoder and entropy estimation), and optimize all of the components in an end-to-end manner. We experiment on two high resolution image datasets and both show that our model can achieve superior performance at low bit rates. We also show the comparisons between TCQ and SQ based on our proposed baseline model and demonstrate the advantage of TCQ.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electrochemical Glucose Sensor using Single-Wall Carbon Nanotube Field Effect Transistor

In this paper, we present a simple yet sensitive method for glucose sensing using carbon nanotube field-effect transistor (CNTFET) based biosensor. The CNTs were well-dispersed to form CNT networks and maintain functional connectivity among CNTs, which increases the electron transfer through the network and thus, the electronic readout. Moreover, glucose oxidase (GOx) molecules are immobilized by CNT functionalization to form effective and sensitive CNT networks as FET channel. The CNTs are functionalized with linkers (1-pyrenebutanoic acid succinimidyl ester) to immobilize GOx on CNTs, where GOx serves as a mediator between CNTs and glucose for electron transfer. The liquid analyte glucose is adsorbed on CNTs via GOx and linkers by releasing additional electrons in the CNTFET channel and thus, increasing the CNTFET readout current. The binding of the target glucose molecules and GOx emulates the gate potential of FET channel and the electronic response of the sensor is recorded in real-time. Moreover, the variations in electronic readout of CNTFET biosensor are observed and is stipulated due to variation in CNT dispersion on each device. Overall, this work presents a simple, fast, sensitive, low-cost, and low concentration (0.01 mM) detection of glucose using CNTFET sensors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generalized Octave Convolutions for Learned Multi-Frequency Image Compression

Learned image compression has recently shown the potential to outperform the standard codecs. State-of-the-art rate-distortion (R-D) performance has been achieved by context-adaptive entropy coding approaches in which hyperprior and autoregressive models are jointly utilized to effectively capture the spatial dependencies in the latent representations. However, the latents are feature maps of the same spatial resolution in previous works, which contain some redundancies that affect the R-D performance. In this paper, we propose the first learned multi-frequency image compression and entropy coding approach that is based on the recently developed octave convolutions to factorize the latents into high and low frequency (resolution) components, where the low frequency is represented by a lower resolution. Therefore, its spatial redundancy is reduced, which improves the R-D performance. Novel generalized octave convolution and octave transposed-convolution architectures with internal activation layers are also proposed to preserve more spatial structure of the information. Experimental results show that the proposed scheme not only outperforms all existing learned methods as well as standard codecs such as the next-generation video coding standard VVC (4:2:0) on the Kodak dataset in both PSNR and MS-SSIM. We also show that the proposed generalized octave convolution can improve the performance of other auto-encoder-based computer vision tasks such as semantic segmentation and image denoising.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we propose a new variable-rate image compression framework, which employs generalized octave convolutions (GoConv) and generalized octave transposed-convolutions (GoTConv) with built-in generalized divisive normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and GoTConv-based residual blocks are also developed in the encoder and decoder networks. Our scheme also uses a stochastic rounding-based scalar quantization. To further improve the performance, we encode the residual between the input and the reconstructed image from the decoder network as an enhancement layer. To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Microwave electrometry via electromagnetically induced absorption in cold Rydberg atoms

The atom-based traceable standard for microwave electrometry shows promising advantages by enabling stable and uniform measurement. Here we theoretically propose and then experimentally realize an alternative direct International System of Units (SI)-traceable and self-calibrated method for measuring a microwave electric field strength based on electromagnetically induced absorption (EIA) in cold Rydberg atoms. Comparing with the method of electromagnetically induced transparency, we show that the equivalence relation between microwave Rabi frequency and Autler-Townes splitting is more valid and is even more robust against the experimental parameters in the EIA's linear region. Furthermore, a narrower linewidth of cold Rydberg EIA enables us to realize a direct SI-traceable microwave-electric-field measurement as small as $\sim$100 $μ\mathrm{\!V} \mathrm{cm}^{\!-\!1}$.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.

0 citations0 reviewsNo code linkedOpen access