arXiv subjects

Feifei Wang

Publications and source records attributed to Feifei Wang.

53 records · Page 3Linked to original sources

arXiv2023

Shedding Light on Rechargeable Na/Cl$_2$ Battery

Advancing new ideas of rechargeable batteries represents an important path to meeting the ever increasing energy storage needs. Recently we showed rechargeable sodium/chlorine (Na/Cl$_2$) (or lithium/chlorine Li/Cl$_2$) batteries that used a Na (or Li) metal negative electrode, a microporous amorphous carbon nanosphere (aCNS) positive electrode and an electrolyte containing dissolved AlCl$_3$ and fluoride additives in thionyl chloride (SOCl$_2$). The main battery redox reaction involved conversion between NaCl and Cl$_2$ trapped in the carbon positive electrode, delivering a cyclable capacity of up to 1200 mAh g$^{-1}$ (based on positive electrode mass) at a ~ 3.5 V discharge voltage. Here, we discovered by X-ray photoelectron spectroscopy (XPS) that upon charging a Na/Cl$_2$ battery, chlorination of carbon in the positive electrode occurred to form C-Cl accompanied by molecular Cl$_2$ infiltrating the porous aCNS, consistent with Cl$_2$ probed by mass spectrometry. Synchrotron X-ray diffraction observed the development of graphitic ordering in the initially amorphous aCNS under battery charging when the carbon matrix was oxidized/chlorinated and infiltrated with Cl$_2$. The C-Cl, Cl$_2$ species and graphitic ordering were reversible upon discharge, accompanied by NaCl formation. The results revealed redox conversion between NaCl and Cl$_2$, reversible graphitic ordering/amorphourization of carbon through battery charge/discharge, and for the first time probed trapped Cl$_2$ in porous carbon by XPS.

physics.app-ph↗

arXiv2023

Diversity-Aware Meta Visual Prompting

We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone. A challenging issue in visual prompting is that image datasets sometimes have a large data diversity whereas a per-dataset generic prompt can hardly handle the complex distribution shift toward the original pretraining data distribution properly. To address this issue, we propose a dataset Diversity-Aware prompting strategy whose initialization is realized by a Meta-prompt. Specifically, we cluster the downstream dataset into small homogeneity subsets in a diversity-adaptive way, with each subset has its own prompt optimized separately. Such a divide-and-conquer design reduces the optimization difficulty greatly and significantly boosts the prompting performance. Furthermore, all the prompts are initialized with a meta-prompt, which is learned across several datasets. It is a bootstrapped paradigm, with the key observation that the prompting knowledge learned from previous datasets could help the prompt to converge faster and perform better on a new dataset. During inference, we dynamically select a proper prompt for each input, based on the feature distance between the input and each subset. Through extensive experiments, our DAM-VP demonstrates superior efficiency and effectiveness, clearly surpassing previous prompting methods in a series of downstream datasets for different pretraining models. Our code is available at: \url{https://github.com/shikiw/DAM-VP}.

cs.CV↗

arXiv2023

Network Autoregression for Incomplete Matrix-Valued Time Series

We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects. We incorporate covariate information and a low rank intercept matrix. We allow incomplete observations in the matrices and the missing mechanism can be covariate dependent. To estimate the model, a two-step estimation procedure is proposed. The first step aims to estimate the network autoregression coefficients, and the second step aims to estimate the regression parameters, which are matrices themselves. Theoretically, we first separately establish the asymptotic properties of the autoregression coefficients and the error bounds of the regression parameters. Subsequently, a bias reduction procedure is proposed to reduce the asymptotic bias and the theoretical property of the debiased estimator is studied. Lastly, we illustrate the usefulness of the proposed method through a number of numerical studies and an analysis of a Yelp data set.

stat.ME↗

arXiv2022

Interplay between spin wave and magnetic vortex

In this paper, the interplay between spin wave and magnetic vortex is studied. We find three types of magnon scatterings: skew scattering, symmetric side deflection and back reflection, which associate with respectively magnetic topology, energy density distribution and linear momentum transfer torque within vortex. The vortex core exhibits two translational modes: the intrinsic circular mode and a coercive elliptical mode, which can be excited based on permanent and periodic magnon spin-transfer torque effects of spin wave. Lastly, we propose a vortex-based spin wave valve in which via inhomogeneity modulation we access arbitrary control of the phase shift.

cond-mat.mes-hall↗

arXiv2022

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.

eess.IV↗

arXiv2022

High-Capacity Rechargeable $Li/Cl_2$ Batteries with Graphite Positive Electrodes

Developing new types of high-capacity and high-energy density rechargeable battery is important to future generations of consumer electronics, electric vehicles, and mass energy storage applications. Recently we reported ~ 3.5 V sodium/chlorine $(Na/Cl_2)$ and lithium/chlorine $(Li/Cl_2)$ batteries with up to 1200 mAh $g^{-1}$ reversible capacity, using either a Na or Li metal as the negative electrode, an amorphous carbon nanosphere (aCNS) as the positive electrode, and aluminum chloride $(AlCl_3)$ dissolved in thionyl chloride $(SOCl_2)$ with fluoride-based additives as the electrolyte. The high surface area and large pore volume of aCNS in the positive electrode facilitated NaCl or LiCl deposition and trapping of $Cl_2$ for reversible $NaCl/Cl_2$ or $LiCl/Cl_2$ redox reactions and battery discharge/charge cycling. Here we report an initially low surface area/porosity graphite (DGr) material as the positive electrode in a $Li/Cl_2$ battery, attaining high battery performance after activation in carbon dioxide $(CO_2)$ at 1000 °C (DGr_ac) with the first discharge capacity ~ 1910 mAh $g^{-1}$ and a cycling capacity up to 1200 mAh $g^{-1}$. Ex situ Raman spectroscopy and X-ray diffraction (XRD) revealed the evolution of graphite over battery cycling, including intercalation/de-intercalation and exfoliation that generated sufficient pores for hosting $LiCl/Cl_2$ redox. This work opens up widely available, low-cost graphitic materials for high-capacity alkali metal/$Cl_2$ batteries. Lastly, we employed mass spectrometry to probe the $Cl_2$ trapped in the graphitic positive electrode, shedding light into the $Li/Cl_2$ battery operation.

physics.app-ph↗

arXiv2022

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic forgetting across real-world heterogeneous devices. In this paper, we demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. Concretely, we conduct the first rigorous empirical investigation of different neural architectures across a range of federated algorithms, real-world benchmarks, and heterogeneous data splits. Our experiments show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. We release our code and pretrained models at https://github.com/Liangqiong/ViT-FL-main to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front.

cs.LG↗

arXiv2021

Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora

Topic evolution modeling has received significant attentions in recent decades. Although various topic evolution models have been proposed, most studies focus on the single document corpus. However in practice, we can easily access data from multiple sources and also observe relationships between them. Then it is of great interest to recognize the relationship between multiple text corpora and further utilize this relationship to improve topic modeling. In this work, we focus on a special type of relationship between two text corpora, which we define as the "lead-lag relationship". This relationship characterizes the phenomenon that one text corpus would influence the topics to be discussed in the other text corpus in the future. To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus. With the recognized lead-lag relationship, the similarities of the two text corpora can be figured out and the quality of topic learning in both corpora can be improved. We numerically investigate the performance of the jointly dynamic topic modeling approach using synthetic data. Finally, we apply the proposed model on two text corpora consisting of statistical papers and the graduation theses. Results show the proposed model can well recognize the lead-lag relationship between the two corpora, and the specific and shared topic patterns in the two corpora are also discovered.

cs.CL↗

arXiv2021

Implicit Profiling Estimation for Semiparametric Models with Bundled Parameters

Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method becomes quite slow and unstable with intensive calculation of the large Hessian matrix and its inverse. Iterative methods separately update parameters for finite dimensional component and infinite dimensional component have been developed to speed up single iteration, but they often take more steps until convergence or even sometimes sacrifice estimation precision due to sub-optimal update direction. We propose a computationally efficient implicit profiling algorithm that achieves simultaneously the fast iteration step in iterative methods and the optimal update direction in the Newton's method by profiling out the infinite dimensional component as the function of the finite dimensional component. We devise a first order approximation when the profiling function has no explicit analytical form. We show that our implicit profiling method always solve any local quadratic programming problem in two steps. In two numerical experiments under semiparametric transformation models and GARCH-M models, we demonstrated the computational efficiency and statistical precision of our implicit profiling method.

stat.CO↗

arXiv2021

Some $α$-spectral extremal results for some digraphs

In this paper, we characterize the extremal digraphs with the maximal or minimal $α$-spectral radius among some digraph classes such as rose digraphs, generalized theta digraphs and tri-ring digraphs with given size $m$. These digraph classes are denoted by $\mathcal{R}_{m}^k$, $\widetilde{\boldsymbolΘ}_k(m)$ and $\INF(m)$ respectively. The main results about spectral extremal digraph by Guo and Liu in \cite{MR2954483} and Li and Wang in \cite{MR3777498} are generalized to $α$-spectral graph theory. As a by-product of our main results, an open problem in \cite{MR3777498} is answered. Furthermore, we determine the digraphs with the first three minimal $α$-spectral radius among all strongly connected digraphs. Meanwhile, we determine the unique digraph with the fourth minimal $α$-spectral radius among all strongly connected digraphs for $0\le α\le \frac{1}{2}$.

math.CO↗

arXiv2020

The smallest spectral radius of bicyclic uniform hypergraphs with a given size

Identifying graphs with extremal properties is an extensively studied topic in spectral graph theory. In this paper, we study the log-concavity of a type of iteration sequence related to the $α$-normal weighted incidence matrices which is presented by Lu and Man for computing the spectral radius of hypergraphs. By using results obtained about the sequence and the method of some edge operations, we will characterize completely extremal k-graphs with the smallest spectral radius among bicyclic hypergraphs with given size.

math.CO↗

arXiv2020

Efficient Estimation for Generalized Linear Models on a Distributed System with Nonrandomly Distributed Data

Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear models on a distributed system with nonrandomly distributed data. We develop a Pseudo-Newton-Raphson algorithm for efficient estimation. In this algorithm, we first obtain a pilot estimator based on a small random sample collected from different Workers. Then conduct one-step updating based on the computed derivatives of log-likelihood functions in each Worker at the pilot estimator. The final one-step estimator is proved to be statistically efficient as the global estimator, even with nonrandomly distributed data. In addition, it is computationally efficient, in terms of both communication cost and storage usage. Based on the one-step estimator, we also develop a likelihood ratio test for hypothesis testing. The theoretical properties of the one-step estimator and the corresponding likelihood ratio test are investigated. The finite sample performances are assessed through simulations. Finally, an American Airline dataset is analyzed on a Spark cluster for illustration purpose.

stat.ME↗

arXiv2019

User Association and Path Planning for UAV-Aided Mobile Edge Computing with Energy Restriction

Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.

cs.IT↗

arXiv2019

On some properties of the $α$-spectral radius of the $k$-uniform hypergraph

In this paper we show how the $α$-spectral radius changes under the edge grafting operations on connected $k$-uniform hypergraphs. We characterize the extremal hypertree for $α$-spectral radius among $k$-uniform non-caterpillar hypergraphs with given order, size and diameter. we also characerize the second largest $α$-spectral radius among all $k$-uniform supertrees on $n$ vertices by two methods.

math.CO↗

arXiv2019

A comprehensive statistical study on gamma-ray bursts

In order to obtain an overview of the gamma-ray bursts (GRBs), we need a full sample. In this paper, we collected 6289 GRBs (from GRB 910421 to GRB 160509A) from the literature, including prompt emission, afterglow and host galaxy properties. We hope to use this large sample to reveal the intrinsic properties of GRB. We have listed all the data in machine readable tables, including the properties of the GRBs, correlation coefficients and linear regression results of two arbitrary parameters, and linear regression results of any three parameters. These machine readable tables could be used as a data reservoir for further studies on the classifications or correlations. One may find some intrinsic properties from these statistical results. With this comprehensive table, it is possible to find relations between different parameters, and to classify the GRBs into different kinds of sub-groups. With the completion, it may reveal the nature of GRBs and may be used as tools like pseudo-redshift indicators, standard candles, etc. All the machine readable data and statistical results are available on the website of the journal.

astro-ph.HE↗

arXiv2017

Research on Human Dynamics of Information Release of WeChat Users

The information release behavior of WeChat users is influenced by many factors, and studying the rules of the behavior of users in WeChat can provide theoretical help for the dynamic research of mobile social network users. By crawling WeChat moments information of nine users within 5 years, we used the human behavioral dynamics system to analyze users' behavior. The results show that the information distribution behavior of WeChat users is consistent with the power-law distribution for a certain period of time. Meanwhile, there is an anti-memory characteristic in information release behavior of WeChat users, which is significantly different from other user behavior patterns in online social networks. The results of the study provide theoretical support for the further study of information release behavior of Wechat users.

cs.SI↗

arXiv2016

Disease Mapping with Generative Models

Disease mapping focuses on learning about areal units presenting high relative risk. Disease mapping models for disease counts specify Poisson regressions in relative risks compared with the expected counts. These models typically incorporate spatial random effects to accomplish spatial smoothing. Fitting of these models customarily computes expected disease counts via internal standardization. This places the data on both sides of the model, i.e., the counts are on the left side but they are also used to obtain the expected counts on the right side. As a result, these internally standardized models are incoherent and not generative; probabilistically, they could not produce the observed data. Here, we argue for adopting the direct generative model for disease counts. We model disease incidence instead of relative risks, using a generalized logistic regression. We extract relative risks post model fitting. We also extend the generative model to dynamic settings. We compare the generative models with internally standardized models through simulated datasets and a well-examined lung cancer morbidity data in Ohio. Each model is a spatial smoother and they smooth the data similarly with regard to relative risks. However, the generative models tend to provide tighter credible intervals. Since the generative specification is no more difficult to fit, is coherent, and is at least as good inferentially, we suggest it should be the model of choice for spatial disease mapping.

stat.ME↗