arXiv subjects

Wei Cao

Publications and source records attributed to Wei Cao.

At least 73 records · Page 4Linked to original sources

arXiv2022

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting. In this paper, we introduce a deep expansion learning framework, DEPTS, for PTS forecasting. DEPTS starts with a decoupled formulation by introducing the periodic state as a hidden variable, which stimulates us to make two dedicated modules to tackle the aforementioned two challenges. First, we develop an expansion module on top of residual learning to perform a layer-by-layer expansion of those complicated dependencies. Second, we introduce a periodicity module with a parameterized periodic function that holds sufficient capacity to capture diversified periods. Moreover, our two customized modules also have certain interpretable capabilities, such as attributing the forecasts to either local momenta or global periodicity and characterizing certain core periodic properties, e.g., amplitudes and frequencies. Extensive experiments on both synthetic data and real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most cases, DEPTS achieves significant improvements over the best baseline. Specifically, the error reduction can even reach up to 20% for a few cases. Finally, all codes are publicly available.

cs.LG↗

arXiv2022

Compositional Model Checking of Consensus Protocols Specified in TLA+ via Interaction-Preserving Abstraction

Consensus protocols are widely used in building reliable distributed software systems and its correctness is of vital importance. TLA+ is a lightweight formal specification language which enables precise specification of system design and exhaustive checking of the design without any human effort. The features of TLA+ make it widely used in the specification and model checking of consensus protocols, both in academia and industry. However, the application of TLA+ is limited by the state explosion problem in model checking. Though compositional model checking is essential to tame the state explosion problem, existing compositional checking techniques do not sufficiently consider the characteristics of TLA+. In this work, we propose the Interaction-Preserving Abstraction (IPA) framework, which leverages the features of TLA+ and enables practical and efficient compositional model checking of consensus protocols specified in TLA+. In the IPA framework, system specification is partitioned into multiple modules, and each module is divided to the internal part and the interaction part. The basic idea of the interaction-preserving abstraction is to omit the internal part of each module, such that another module cannot distinguish whether it is interacting with the original module or the coarsened abstract one. We use the IPA framework to the compositional checking of the TLA+ specification of two consensus protocols Raft and ParallelRaft. Raft is a consensus protocol which is originally developed in the academia and then widely used in industry. ParallelRaft is the replication protocol in PolarFS, the distributed file system for the commercial database Alibaba PoloarDB. We demonstrate that the IPA framework is easy to use in realistic scenarios and at the same time significantly reduces the model checking cost.

cs.DC↗

arXiv2022

Fingerprint of the Interbond Electron Hopping in Second-Order Harmonic Generation

We experimentally explore the fingerprint of the microscopic electron dynamics in second-order harmonic generation (SHG). It is shown that the interbond electron hopping induces a novel source of nonlinear polarization and plays an important role even when the driving laser intensity is 2 orders of magnitude lower than the characteristic atomic field. Our model predicts anomalous anisotropic structures of the SHG yield contributed by the interbond electron hopping, which is identified in our experiments with ZnO crystals. Moreover, a generalized second-order susceptibility with an explicit form is proposed, which provides a unified description in both the weak and strong field regimes. Our work reveals the nonlinear responses of materials at the electron scale and extends the nonlinear optics to a previously unexplored regime, where the nonlinearity related to the interbond electron hopping becomes dominant. It paves the way for realizing controllable nonlinearity on an ultrafast time scale.

physics.optics↗

arXiv2021

Forecast with Forecasts: Diversity Matters

Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combination has flourished. Although this idea has been proved to be beneficial in several forecasting competitions, it may not be practical in many situations. For example, the task of selecting appropriate features to build forecasting models is often challenging. Even if there was an acceptable way to define the features, existing features are estimated based on the historical patterns, which are likely to change in the future. Other times, the estimation of the features is infeasible due to limited historical data. In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features. We use out-of-sample forecasts to obtain weights for forecast combinations by amplifying the diversity of the pool of methods being combined. A rich set of time series is used to evaluate the performance of the proposed method. Experimental results show that our diversity-based forecast combination framework not only simplifies the modelling process but also achieves superior forecasting performance in terms of both point forecasts and prediction intervals. The value of our proposition lies on its simplicity, transparency, and computational efficiency, elements that are important from both an optimisation and a decision analysis perspective.

stat.ME↗

arXiv2021

Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study

In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which resulted in a large nationwide and even worldwide wave. While there have been indications that precaution fatigue could be a key factor, no scientific evidence has been provided so far. We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020. We incorporated non-pharmaceutical interventions (NPIs) into this model by assuming that the precaution strength grows with positive cases and studied two types of pandemic fatigue. We found that people in most states and in the whole US respond to the outbreak in a sublinear manner (with exponent k=0.5), while only three states (Massachusetts, New York and New Jersey) have linear reaction (k=1). Case fatigue (decline in people's vigilance to positive cases) is responsible for 58% of cases, while precaution fatigue (decay of maximal fraction of vigilant group) accounts for 26% cases. If there were no pandemic fatigue (no case fatigue and no precaution fatigue), total positive cases would have reduced by 68% on average. Our study shows that pandemic fatigue is the major cause of the worsening situation of COVID-19 in United States. Reduced vigilance is responsible for most positive cases, and higher mortality rate tends to push local people to react to the outbreak faster and maintain vigilant for longer time.

q-bio.PE↗

arXiv2021

First-Principles Prediction of Two-Dimensional B3C2P3 and B2C4P2: Structural Stability, Fundamental Properties, and Renewable Energy Applications

The existence of two novel hybrid two-dimensional (2D) monolayers, 2D B3C2P3 and 2D B2C4P2, has been predicted based on the density functional theory calculations. It has been shown that these materials possess structural and thermodynamic stability. 2D B3C2P3 is a moderate band gap semiconductor, while 2D B2C4P2 is a zero band gap semiconductor. It has also been shown that 2D B3C2P3 has a highly tunable band gap under the effect of strain and substrate engineering. Moreover, 2D B3C2P3 produces low barriers for dissociation of water and hydrogen molecules on its surface, and shows fast recovery after desorption of the molecules. The novel materials can be fabricated by carbon doping of boron phosphide, and directly by arc discharge and laser ablation and vaporization. Applications of 2D B3C2P3 in renewable energy and straintronic nanodevices have been proposed.

cond-mat.mtrl-sci↗

arXiv2021

Pinpointing the Memory Behaviors of DNN Training

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators. Characterizing the memory behaviors of DNN training is critical to optimize the device memory pressures. In this work, we pinpoint the memory behaviors of each device memory block of GPU during training by instrumenting the memory allocators of the runtime system. Our results show that the memory access patterns of device memory blocks are stable and follow an iterative fashion. These observations are useful for the future optimization of memory-efficient training from the perspective of raw memory access patterns.

cs.PF↗

arXiv2021

Incorporation of Si atoms into CrCoNiFe high-entropy alloy: a DFT study

Density functional theory based computational study has been conducted in order to investigate the effect of substitution of Cr and Co components by Si on the structure, mechanical, electronic, and magnetic properties of the high entropy alloy CrCoNiFe. It is found that the presence of a moderate concentration of Si substitutes (up to 12.5 %) does not significantly reduce the structural and mechanical stability of CrCoNiFe while it may modify its electronic and magnetic properties. Based on that, Si is proposed as a cheap and functional material for partial substitution of Cr or Co in CrCoNiFe.

cond-mat.mtrl-sci↗

arXiv2020

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https://github.com/ZhiningLiu1998/mesa.

cs.LG↗

arXiv2020

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scale but extremely imbalance and low-quality datasets. Most of existing learning methods suffer from poor performance or low computation efficiency under such a scenario. To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers. Taking those factors into consideration, we propose a novel framework for imbalance classification that aims to generate a strong ensemble by self-paced harmonizing data hardness via under-sampling. Extensive experiments have shown that this new framework, while being very computationally efficient, can lead to robust performance even under highly overlapping classes and extremely skewed distribution. Note that, our methods can be easily adapted to most of existing learning methods (e.g., C4.5, SVM, GBDT and Neural Network) to boost their performance on imbalanced data.

cs.LG↗

arXiv2020

Interfacial Ferroelectricity by van-der-Waals Sliding

Despite their ionic nature, many layered diatomic crystals avoid internal electric polarization by forming a centrosymmetric lattice at their optimal anti-parallel van-der-Waals stacking. Here, we report a stable ferroelectric order emerging at the interface between two naturally-grown flakes of hexagonal-boron-nitride, which are stacked together in a metastable non-centrosymmetric parallel orientation. We observe alternating domains of inverted normal polarization, caused by a lateral shift of one lattice site between the domains. Reversible polarization switching coupled to lateral sliding is achieved by scanning a biased tip above the surface. Our calculations trace the origin of the phenomenon to a subtle interplay between charge redistribution and ionic displacement, and our minimal cohesion model predicts further venues to explore the unique "slidetronics" switching.

cond-mat.mes-hall↗

arXiv2020

Strong field induced electron density tide in high harmonic generation from solids

In strong laser fields, the electron density in solids can show up tidal motions caused by the laser ``pulling'' on the electrons, which forms an electron density tide (EDT). However, the strong field processes in solids are always explained by the single-active-electron (SAE) model and the fluctuation of background electrons is neglected previously. Here, we demonstrate the strong field induced EDT effect and propose a model for revealing its role in high harmoinc generation (HHG) from solids. We show that the EDT effect induces an additional polarization current beyond the SAE approximation. It gives a new mechanism for HHG and leads to new anisotropic structures, which are experimentally observed with MgO. Our experiment indicates that the EDT effect becomes more obvious with increasing the laser intensity. Our work establishes the bridge between the HHG and the microscopic EDT in solids induced by the strong laser field, which paves the way to probe the ultrafast electron dynamics.

physics.optics↗

arXiv2020

Nonparametric Estimation of the Fisher Information and Its Applications

This paper considers the problem of estimation of the Fisher information for location from a random sample of size $n$. First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived. Second, a new estimator, termed a clipped estimator, is proposed. Superior upper bounds on the rates of convergence can be shown for the new estimator compared to the Bhattacharya estimator, albeit with different regularity conditions. Third, both of the estimators are evaluated for the practically relevant case of a random variable contaminated by Gaussian noise. Moreover, using Brown's identity, which relates the Fisher information and the minimum mean squared error (MMSE) in Gaussian noise, two corresponding consistent estimators for the MMSE are proposed. Simulation examples for the Bhattacharya estimator and the clipped estimator as well as the MMSE estimators are presented. The examples demonstrate that the clipped estimator can significantly reduce the required sample size to guarantee a specific confidence interval compared to the Bhattacharya estimator.

cs.IT↗

arXiv2020

A 6G White Paper on Connectivity for Remote Areas

In many places all over the world rural and remote areas lack proper connectivity that has led to increasing digital divide. These areas might have low population density, low incomes, etc., making them less attractive places to invest and operate connectivity networks. 6G could be the first mobile radio generation truly aiming to close the digital divide. However, in order to do so, special requirements and challenges have to be considered since the beginning of the design process. The aim of this white paper is to discuss requirements and challenges and point out related, identified research topics that have to be solved in 6G. This white paper first provides a generic discussion, shows some facts and discusses targets set in international bodies related to rural and remote connectivity and digital divide. Then the paper digs into technical details, i.e., into a solutions space. Each technical section ends with a discussion and then highlights identified 6G challenges and research ideas as a list.

eess.SP↗

arXiv2020

Coherent spin pumping in a strongly coupled magnon-magnon hybrid system

We experimentally identify coherent spin pumping in the magnon-magnon hybrid modes of permalloy/yttrium iron garnet (Py/YIG) bilayers. Using broadband ferromagnetic resonance, an "avoided crossing" is observed between the uniform mode of Py and the spin wave mode of YIG due to the fieldlike interfacial exchange coupling. We also identify additional linewidth suppression and enhancement for the in-phase and out-of-phase hybrid modes, respectively, \textcolor{black}{which can be interpreted as concerted dampinglike torque from spin pumping}. Our analysis predicts inverse proportionality of both fieldlike and dampinglike torques to the square root of the Py thickness, which quantitatively agrees with experiments.

cond-mat.mes-hall↗

arXiv2020

Progressive Neural Index Search for Database System

As a key ingredient of the DBMS, index plays an important role in the query optimization and processing. However, it is a non-trivial task to apply existing indexes or design new indexes for new applications, where both data distribution and query distribution are unknown. To address the issue, we propose a new indexing approach, NIS (Neural Index Search), which searches for the optimal index parameters and structures using a neural network. In particular, NIS is capable for building a tree-like index automatically for an arbitrary column that can be sorted/partitioned using a customized function. The contributions of NIS are twofold. First, NIS constructs a tree-like index in a layer-by-layer way via formalizing the index structure as abstract ordered and unordered blocks. Ordered blocks are implemented using B+-tree nodes or skip lists, while unordered blocks adopt hash functions with different configurations. Second, all parameters of the building blocks (e.g., fanout of B+-tree node, bucket number of hash function and etc.) are tuned by NIS automatically. We achieve the two goals for a given workload and dataset with one RNN-powered reinforcement learning model. Experiments show that the auto-tuned index built by NIS can achieve a better performance than the state-of-the-art index.

cs.DB↗

arXiv2020

Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction

Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the training data is fixed and applied directly in the test data. Although such assumption has made traditional machine learning techniques succeed in many real-world tasks, the highly dynamic nature of the stock market invalidates the strict assumption in stock prediction. To address this challenge, we propose the second-order identical distribution assumption, where the data distribution is assumed to be fluctuating over time with certain patterns. Based on such assumption, we develop a second-order learning paradigm with multi-scale patterns. Extensive experiments on real-world Chinese stock data demonstrate the effectiveness of our second-order learning paradigm in stock prediction.

q-fin.ST↗

arXiv2020

Relational State-Space Model for Stochastic Multi-Object Systems

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time-series datasets.

cs.LG↗