BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Lixin Zhang

Lixin Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.mtrl-sciPerformanceDistributed, Parallel, and Cluster ComputingInformation Retrievalmath.STcond-mat.mes-hallCryptography and SecurityHardware ArchitectureMachine Learningmath.DSmath.PRphysics.chem-phphysics.opticsStatistics Theory

Catalog footprint

What is connected

14works
14topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

14 published item(s)

preprint2026arXiv

Byzantine-Robust Distributed Sparse Learning Revisited

We revisit Byzantine robust distributed estimation for high-dimensional sparse linear models. By combining local $\ell_1$-regularized robust estimation with robust aggregation at the server, the framework applies to pseudo-Huber regression, quantile regression, and sparse SVM. We show that the resulting estimators yield non-asymptotic guarantees and attain near-optimal statistical rates under mild conditions, while remaining communication-efficient. Simulations confirm strong robustness in estimation, support recovery and classification accuracy under various Byzantine attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Small perturbations may change the sign of Lyapunov exponents for linear SDEs

In this paper, we study the existence of $n$-dimensional linear stochastic differential equations (SDEs) such that the sign of Lyapunov exponents is changed under an exponentially decaying perturbation. First, we show that the equation with all positive Lyapunov exponents will have $n-1$ linearly independent solutions with negative Lyapunov exponents under the perturbation. Meanwhile, we prove that the equation with all negative Lyapunov exponents will also have solutions with positive Lyapunov exponents under another similar perturbation. Finally, we also show that other three kinds of perturbations which appear at different positions of the equation will change the sign of Lyapunov exponents.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust parameter estimation of regression model under weakened moment assumptions

This paper provides some extended results on estimating parameter matrix of several regression models when the covariate or response possesses weaker moment condition. We study the $M$-estimator of Fan et al. (Ann Stat 49(3):1239--1266, 2021) for matrix completion model with $(1+ε)$-th moment noise. The corresponding phase transition phenomenon is observed. When $1> ε>0$, the robust estimator possesses a slower convergence rate compared with previous literature. For high dimensional multiple index coefficient model, we propose an improved estimator via applying the element-wise truncation method to handle heavy-tailed data with finite fourth moment. The extensive simulation study validates our theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Single-shot Embedding Dimension Search in Recommender System

As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR prediction tasks, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90\% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

UFNRec: Utilizing False Negative Samples for Sequential Recommendation

Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential component in learning the evolving user preferences through historical records. Except for randomly sampling negative samples from a uniformly distributed subset, many delicate methods have been proposed to mine negative samples with high quality. However, due to the inherent randomness of negative sampling, false negative samples are inevitably collected in model training. Current strategies mainly focus on removing such false negative samples, which leads to overlooking potential user interests, lack of recommendation diversity, less model robustness, and suffering from exposure bias. To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance. We first devise a simple strategy to extract false negative samples and then transfer these samples to positive samples in the following training process. Furthermore, we construct a teacher model to provide soft labels for false negative samples and design a consistency loss to regularize the predictions of these samples from the student model and the teacher model. To the best of our knowledge, this is the first work to utilize false negative samples instead of simply removing them for the sequential recommendation. Experiments on three benchmark public datasets are conducted using three widely applied SOTA models. The experiment results demonstrate that our proposed UFNRec can effectively draw information from false negative samples and further improve the performance of SOTA models. The code is available at https://github.com/UFNRec-code/UFNRec.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Lightweight Isolation Mechanism for Secure Branch Predictors

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different programs, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Instead of flush-based or physical isolation of hardware resources, we want to achieve isolation of the content in these hardware tables with some lightweight processing using randomization as follows. (1) Content encoding. We propose to use hardware-based thread-private random numbers to encode the contents of the branch predictor tables (both direction and destination histories) which we call XOR-BP. Specifically, the data is encoded by XOR operation with the key before written in the table and decoded after read from the table. Such a mechanism obfuscates the information adding difficulties to cross-process or cross-privilege level analysis and perception. It achieves a similar effect of logical isolation but adds little in terms of space or time overheads. (2) Index encoding. We propose a randomized index mechanism of the branch predictor (Noisy-XOR-BP). Similar to the XOR-BP, another thread-private random number is used together with the branch instruction address as the input to compute the index of the branch predictor. This randomized indexing mechanism disrupts the correspondence between the branch instruction address and the branch predictor entry, thus increases the noise for malicious perception attacks. Our analyses using an FPGA-based RISC-V processor prototype and additional auxiliary simulations suggest that the proposed mechanisms incur a very small performance cost while providing strong protection.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Interaction between Mo and intrinsic or extrinsic defects of Mo doped LiNbO$_3$ from first-principles calculations

Lithium niobate (LiNbO$_3$, LN) plays an important role in holographic storage, and molybdenum doped LiNbO$_3$ (LN:Mo) is an excellent candidate for holographic data storage. In this paper, the basic features of Mo doped LiNbO$_3$, such as the site preference, electronic structure, and the lattice distortions, have been explored from first-principles calculations. Mo substituting Nb with its highest charge state of +6 is found to be the most stable point defect form. The energy levels formed by Mo with different charge states are distributed in the band gap, which are responsible for the absorption in the visible region. The transition of Mo in different charge states implies molybdenum can serve as a photorefractive center in LN:Mo. In addition, the interactions between Mo and intrinsic or extrinsic point defects are also investigated in this work. Intrinsic defects $\tt V_{Li}^-$ could cause the movement of the $\tt Mo_{Nb}^+$ energy levels. The exploration of Mo, Mg co-doped LiNbO$_3$ reveals that although Mg ion could not shift the energy level of Mo, it can change the distribution of electrons in Mo and Mg co-doped LN (LN:Mo,Mg) which help with the photorefractive phenomenon.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Energy calculations of charged point defects on surfaces

We present a virtual ionic crystal (VIC) method to calculate energies of charged point defects on surfaces. No artificial charge but an actual zero-dimensional (0D) species is introduced to charge a defect. Effect of dielectric substrate on lattice energy is depressed through suitable configuration of the unit cell. The lattice energy approximates to Madelung energy with defect and 0D species considered as point charges in vacuum. Energy required to charge the defect is derived from charge quantity on the defect in VIC, energy of unit cell, and energy required to charge the 0D species.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Understanding Big Data Analytic Workloads on Modern Processors

Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve the performance of data center computer systems, in which characterizing representative workloads is a key practical problem. In this paper, after investigating three most impor- tant application domains in terms of page views and daily visitors, we chose 11 repre- sentative data analytics workloads and characterized their micro-architectural behaviors by using hardware performance counters, so as to understand the impacts and implications of data analytics workloads on the systems equipped with modern superscalar out-of-order processors. Our study reveals that big data analytics applications themselves share many inherent characteristics, which place them in a different class from traditional workloads and scale-out services. To further understand the characteristics of big data analytics work- loads we performed a correlation analysis of CPI (cycles per instruction) with other micro- architecture level characteristics and an investigation of the big data software stack impacts on application behaviors. Our correlation analysis showed that even though big data ana- lytics workloads own notable pipeline front end stalls, the main factors affecting the CPI performance are long latency data accesses rather than the front end stalls. Our software stack investigation found that the typical big data software stack significantly contributes to the front end stalls and incurs bigger working set. Finally we gave several recommen- dations for architects, programmers and big data system designers with the knowledge acquired from this paper.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Characterizing Data Analysis Workloads in Data Centers

As the amount of data explodes rapidly, more and more corporations are using data centers to make effective decisions and gain a competitive edge. Data analysis applications play a significant role in data centers, and hence it has became increasingly important to understand their behaviors in order to further improve the performance of data center computer systems. In this paper, after investigating three most important application domains in terms of page views and daily visitors, we choose eleven representative data analysis workloads and characterize their micro-architectural characteristics by using hardware performance counters, in order to understand the impacts and implications of data analysis workloads on the systems equipped with modern superscalar out-of-order processors. Our study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop (SPEC CPU2006), HPC (HPCC), and service workloads, including traditional server workloads (SPECweb2005) and scale-out service workloads (four among six benchmarks in CloudSuite), and accordingly we give several recommendations for architecture and system optimizations. On the basis of our workload characterization work, we released a benchmark suite named DCBench for typical datacenter workloads, including data analysis and service workloads, with an open-source license on our project home page on http://prof.ict.ac.cn/DCBench. We hope that DCBench is helpful for performing architecture and small-to-medium scale system researches for datacenter computing.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

High Volume Computing: Identifying and Characterizing Throughput Oriented Workloads in Data Centers

For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coin a new term high volume computing (in short HVC) to describe those workloads and data center computer systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center computer systems for HVC workloads.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems

Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Ab initio study of the formation of transparent carbon under pressure

A body-centered tetragonal carbon (bct-Carbon) allotrope has been predicted to be a transparent carbon polymorph obtained under pressure. The structural transition pathways from graphite to diamond, M-Carbon, and bct-Carbon are simulated and the lowest activation barrier is found for the graphite-bct transition. Furthermore, bct-Carbon has higher shear strength than diamond due to its perpendicular graphene-like structure. Our results provide a possible explanation for the formation of a transparent carbon allotrope via the cold compression of graphite. We also verify that this allotrope is hard enough to crack diamond.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Unusual Compression Behavior of Columbite TiO2 via First-Principles Calculations

The physical mechanisms behind the reduction of the bulk modulus of a high-pressure cubic TiO2 phase are confirmed by first-principles calculations. An unusual and abrupt change occurs in the dependence of energy on pressure at 43 GPa, indicating a pressure-induced phase transition from columbite TiO2 to a newly-identified modified fluorite TiO2 with a Pca21 symmetry. Oxygen atom displacement in Pca21 TiO2 unexpectedly reduces the bulk modulus by 34% relative to fluorite TiO2. This discovering provides a direct evidence for understanding the compressive properties of such groups of homologous materials

0 citations0 reviewsNo code linkedOpen access