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Wen Zhou

Publications and source records attributed to Wen Zhou.

At least 55 records · Page 3Linked to original sources
arXiv2024

Uncertainty relations based on the $ρ$-absolute variance for quantum channels

Uncertainty principle reveals the intrinsic differences between the classical and quantum worlds, which plays a significant role in quantum information theory. By using $ρ$-absolute variance, we introduce the uncertainty of quantum channels and explore its properties. By using Cauchy-Schwarz inequality and the parallelogram law, we establish the product and summation forms of the uncertainty relations for arbitrary two quantum channels, respectively. The summation form of the uncertainty inequalities based on the $ρ$-absolute variance for arbitrary $N$ quantum channels are also investigated and the optimal lower bounds are presented. We illustrate our results by several typical examples.

quant-ph↗
arXiv2024

Tighter Constraints of Multi-Qubit Entanglement in Terms of Nonconvex Entanglement Measures LCREN and LCRENoA

The monogamy property of entanglement is an intriguing feature of multipartite quantum entanglement. Most entanglement measures satisfying the monogamy inequality are turned out to be convex. Whether nonconvex entanglement measures obeys the monogamy inequalities remains less known at present. As a well known measure of entanglement, the logarithmic negativity is not convex. We elucidate the constraints of multi-qubit entanglement based on the logarithmic convex-roof extended negativity (LCREN) and the logarithmic convex-roof extended negativity of assistance (LCRENoA). Using the Hamming weight derived from the binary vector associated with the distribution of subsystems, we establish monogamy inequalities for multi-qubit entanglement in terms of the $α$th-power ($α\geq 4\ln2$) of LCREN, and polygamy inequalities utilizing the $α$th-power ($0 \leq α\leq 2$) of LCRENoA. We demonstrate that these inequalities give rise to tighter constraints than the existing ones. Furthermore, our monogamy inequalities are shown to remain valid for the high dimensional states that violate the CKW monogamy inequality. Detailed examples are presented to illustrate the effectiveness of our results in characterizing the multipartite entanglement distributions.

quant-ph↗
arXiv2024

Optimal Nonparametric Inference on Network Effects with Dependent Edges

Testing network effects in weighted directed networks is a foundational problem in econometrics, sociology, and psychology. Yet, the prevalent edge dependency poses a significant methodological challenge. Most existing methods are model-based and come with stringent assumptions, limiting their applicability. In response, we introduce a novel, fully nonparametric framework that requires only minimal regularity assumptions. While inspired by recent developments in $U$-statistic literature (arXiv:1712.00771, arXiv:2004.06615), our approach notably broadens their scopes. Specifically, we identified and carefully addressed the challenge of indeterminate degeneracy in the test statistics $-$ a problem that aforementioned tools do not handle. We established Berry-Esseen type bound for the accuracy of type-I error rate control. Using original analysis, we also proved the minimax optimality of our test's power. Simulations underscore the superiority of our method in computation speed, accuracy, and numerical robustness compared to competing methods. We also applied our method to the U.S. faculty hiring network data and discovered intriguing findings.

stat.ME↗
arXiv2023

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering. Nonetheless, cutting-edge dynamic neural rendering methods rely heavily on these implicit representations, which frequently struggle to capture the intricate details of objects in the scene. Furthermore, implicit methods have difficulty achieving real-time rendering in general dynamic scenes, limiting their use in a variety of tasks. To address the issues, we propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead, which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world datasets. Through a differential Gaussian rasterizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed, making it well-suited for tasks such as novel-view synthesis, time interpolation, and real-time rendering.

cs.CV↗
arXiv2023

Multiscale simulations of growth-dominated Sb$_2$Te phase-change material for non-volatile photonic applications

Chalcogenide phase-change materials (PCMs) are widely applied in electronic and photonic applications, such as non-volatile memory and neuro-inspired computing. Doped Sb$_2$Te alloys are now gaining increasing attention for on-chip photonic applications, due to their growth-driven crystallization features. However, it remains unknown whether Sb$_2$Te also forms a metastable crystalline phase upon nanoseconds crystallization in devices, similar to the case of nucleation-driven Ge-Sb-Te alloys. Here, we carry out ab initio simulations to understand the changes in optical properties of amorphous Sb$_2$Te upon crystallization and post annealing. During the continuous transformation process, changes in the dielectric function are highly wavelength-dependent from the visible-light range towards the telecommunication band. Our finite-difference time-domain simulations based on the ab initio input reveal key differences in device output for color display and photonic memory applications upon tellurium ordering. Our work serves as an example of how multiscale simulations of materials can guide practical photonic phase-change applications.

cond-mat.mtrl-sci↗
arXiv2023

In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ per dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (greater than 87.36) that leads to an enhanced computing accuracy (standard deviation less than 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

physics.app-ph↗
arXiv2023

A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research.

cs.CV↗
arXiv2023

VMCDL: Vulnerability Mining Based on Cascaded Deep Learning Under Source Control Flow

With the rapid development of the computer industry and computer software, the risk of software vulnerabilities being exploited has greatly increased. However, there are still many shortcomings in the existing mining techniques for leakage source research, such as high false alarm rate, coarse-grained detection, and dependence on expert experience. In this paper, we mainly use the c/c++ source code data of the SARD dataset, process the source code of CWE476, CWE469, CWE516 and CWE570 vulnerability types, test the Joern vulnerability scanning function of the cutting-edge tool, and propose a new cascading deep learning model VMCDL based on source code control flow to effectively detect vulnerabilities. First, this paper uses joern to locate and extract sensitive functions and statements to form a sensitive statement library of vulnerable code. Then, the CFG flow vulnerability code snippets are generated by bidirectional breadth-first traversal, and then vectorized by Doc2vec. Finally, the cascade deep learning model based on source code control flow is used for classification to obtain the classification results. In the experimental evaluation, we give the test results of Joern on specific vulnerabilities, and give the confusion matrix and label data of the binary classification results of the model algorithm on single vulnerability type source code, and compare and verify the five indicators of FPR, FNR, ACC, P and F1, respectively reaching 10.30%, 5.20%, 92.50%,85.10% and 85.40%,which shows that it can effectively reduce the false alarm rate of static analysis.

cs.AI↗
arXiv2022

DCDetector: An IoT terminal vulnerability mining system based on distributed deep ensemble learning under source code representation

Context: The IoT system infrastructure platform facility vulnerability attack has become the main battlefield of network security attacks. Most of the traditional vulnerability mining methods rely on vulnerability detection tools to realize vulnerability discovery. However, due to the inflexibility of tools and the limitation of file size, its scalability It is relatively low and cannot be applied to large-scale power big data fields. Objective: The goal of the research is to intelligently detect vulnerabilities in source codes of high-level languages such as C/C++. This enables us to propose a code representation of sensitive sentence-related slices of source code, and to detect vulnerabilities by designing a distributed deep ensemble learning model. Method: In this paper, a new directional vulnerability mining method of parallel ensemble learning is proposed to solve the problem of large-scale data vulnerability mining. By extracting sensitive functions and statements, a sensitive statement library of vulnerable codes is formed. The AST stream-based vulnerability code slice with higher granularity performs doc2vec sentence vectorization on the source code through the random sampling module, obtains different classification results through distributed training through the Bi-LSTM trainer, and obtains the final classification result by voting. Results: This method designs and implements a distributed deep ensemble learning system software vulnerability mining system called DCDetector. It can make accurate predictions by using the syntactic information of the code, and is an effective method for analyzing large-scale vulnerability data. Conclusion: Experiments show that this method can reduce the false positive rate of traditional static analysis and improve the performance and accuracy of machine learning.

cs.CR↗
arXiv2022

Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression

In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. Here, we consider a threshold model that all continuous features are discretized into ordinal levels, which further determine the binary responses. Both the threshold points and regression coefficients are unknown and to be estimated. For high dimensional data, we propose a fusion penalized logistic threshold regression (FILTER) model, where a fused lasso penalty is employed to control the total variation and shrink the coefficients to zero as a method of variable selection. Under mild conditions on the estimate of unknown threshold points, we establish the non-asymptotic error bound for coefficient estimation and the model selection consistency. With a careful characterization of the error propagation, we have also shown that the tree-based method, such as CART, fulfill the threshold estimation conditions. We find the FILTER model is well suited in the problem of early detection and prediction for chronic disease like diabetes, using physical examination data. The finite sample behavior of our proposed method are also explored and compared with extensive Monte Carlo studies, which supports our theoretical discoveries.

stat.ME↗
arXiv2021

Improving Code Autocompletion with Transfer Learning

Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integrations. Recently, accuracy in autocompletion prediction improved 12.8% from training on a real-world dataset collected from programmers' IDE activity. But what if limited examples of IDE autocompletion in the target programming language are available for model training? In this paper, we investigate the efficacy of pretraining autocompletion models on non-IDE, non-autocompletion, and different-language example code sequences. We find that these unsupervised pretrainings improve model accuracy by over 50% on very small fine-tuning datasets and over 10% on 50k labeled examples. We confirm the real-world impact of these pretrainings in an online setting through A/B testing on thousands of IDE autocompletion users, finding that pretraining is responsible for increases of up to 6.63% autocompletion usage.

cs.SE↗
arXiv2021

Tracking the Transmission Dynamics of COVID-19 with a Time-Varying Coefficient State-Space Model

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and non-homogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, detected non-infectious, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several US states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.

stat.AP↗
arXiv2019

Hyperuniform disordered photonic crystal polarizers

An ultra-broadband transverse magnetic (TM) pass hyperuniform disordered photonic crystal (HUDPC) polarizer is proposed and demonstrated on a silicon-on-insulator platform. Propagation of the transverse electric mode is blocked by three combined effects, including the photonic bandgap (PBG) effect, diffusive (non-resonant) scattering, and bandedge resonances. Specially, the designed 30-dB bandwidth in polarization extinction ratio (PER) of 265 nm is much larger than the spectral width of the PBG (149 nm) due to using the bandedge resonances. The TM mode is in the subwavelength regime of the HUDPC and thus has a low insertion loss (IL). An ultrawide 30-dB bandwidth in PER of 210 nm (1.44-1.65 um) is experimentally demonstrated in a 12.9-um-long HUDPC polarizer with spectrally averaged PER of 39.6 dB and IL for the TM mode of 1.1 dB (IL = 0.6 dB at 1.55 um). The HUDPC polarizers can be an excellent candidate for ultra-broadband polarization filtering in the silicon photonic platform.

physics.optics↗
arXiv2019

G-flocking: Flocking Model Optimization based on Genetic Framework

Flocking model has been widely used to control robotic swarm. However, with the increasing scalability, there exist complex conflicts for robotic swarm in autonomous navigation, brought by internal pattern maintenance, external environment changes, and target area orientation, which results in poor stability and adaptability. Hence, optimizing the flocking model for robotic swarm in autonomous navigation is an important and meaningful research domain.

cs.MA↗
arXiv2019

SAWL:A Self-adaptive Wear-leveling NVM Scheme for High Performance Storage Systems

In order to meet the needs of high performance computing (HPC) in terms of large memory, high throughput and energy savings, the non-volatile memory (NVM) has been widely studied due to its salient features of high density, near-zero standby power, byte-addressable and non-volatile properties. In HPC systems, the multi-level cell (MLC) technique is used to significantly increase device density and decrease the cost, which however leads to much weaker endurance than the single-level cell (SLC) counterpart. Although wear-leveling techniques can mitigate this weakness in MLC, the improvements upon MLC-based NVM become very limited due to not achieving uniform write distribution before some cells are really worn out. To address this problem, our paper proposes a self-adaptive wear-leveling (SAWL) scheme for MLC-based NVM. The idea behind SAWL is to dynamically tune the wear-leveling granularities and balance the writes across the cells of entire memory, thus achieving suitable tradeoff between the lifetime and cache hit rate. Moreover, to reduce the size of the address-mapping table, SAWL maintains a few recently-accessed mappings in a small on-chip cache. Experimental results demonstrate that SAWL significantly improves the NVM lifetime and the performance for HPC systems, compared with state-of-the-art schemes.

cs.AR↗