arXiv subjects

Fan Li

Publications and source records attributed to Fan Li.

At least 289 records · Page 16Linked to original sources

arXiv2020

A Regression Discontinuity Design for Ordinal Running Variables: Evaluating Central Bank Purchases of Corporate Bonds

Regression discontinuity (RD) is a widely used quasi-experimental design for causal inference. In the standard RD, the assignment to treatment is determined by a continuous pretreatment variable (i.e., running variable) falling above or below a pre-fixed threshold. In the case of the corporate sector purchase programme (CSPP) of the European Central Bank, which involves large-scale purchases of securities issued by corporations in the euro area, such a threshold can be defined in terms of an ordinal running variable. This feature poses challenges to RD estimation due to the lack of a meaningful measure of distance. To evaluate such program, this paper proposes an RD approach for ordinal running variables under the local randomization framework. The proposal first estimates an ordered probit model for the ordinal running variable. The estimated probability of being assigned to treatment is then adopted as a latent continuous running variable and used to identify a covariate-balanced subsample around the threshold. Assuming local unconfoundedness of the treatment in the subsample, an estimate of the effect of the program is obtained by employing a weighted estimator of the average treatment effect. Two weighting estimators---overlap weights and ATT weights---as well as their augmented versions are considered. We apply the method to evaluate the causal effect of the CSPP and find a statistically significant and negative effect on corporate bond spreads at issuance.

stat.ME↗

arXiv2020

Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition

Diagnosis and therapeutic effect assessment of Parkinson disease based on voice data are very important,but its few-shot learning problem is challenging.Although deep learning is good at automatic feature extraction, it suffers from few-shot learning problem. Therefore, the general effective method is first conduct feature extraction based on prior knowledge, and then carry out feature reduction for subsequent classification. However, there are two major problems: 1) Structural information among speech features has not been mined and new features of higher quality have not been reconstructed. 2) Structural information between data samples has not been mined and new samples with higher quality have not been reconstructed. To solve these two problems, based on the existing Parkinson speech feature data set, a deep double-side learning ensemble model is designed in this paper that can reconstruct speech features and samples deeply and simultaneously. As to feature reconstruction, an embedded deep stacked group sparse auto-encoder is designed in this paper to conduct nonlinear feature transformation, so as to acquire new high-level deep features, and then the deep features are fused with original speech features by L1 regularization feature selection method. As to speech sample reconstruction, a deep sample learning algorithm is designed in this paper based on iterative mean clustering to conduct samples transformation, so as to obtain new high-level deep samples. Finally, the bagging ensemble learning mode is adopted to fuse the deep feature learning algorithm and the deep samples learning algorithm together, thereby constructing a deep double-side learning ensemble model. At the end of this paper, two representative speech datasets of Parkinson's disease were used for verification. The experimental results show that the proposed algorithm are effective.

cs.CV↗

arXiv2020

Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy

This paper evaluates the effects of being an only child in a family on psychological health, leveraging data on the One-Child Policy in China. We use an instrumental variable approach to address the potential unmeasured confounding between the fertility decision and psychological health, where the instrumental variable is an index on the intensity of the implementation of the One-Child Policy. We establish an analytical link between the local instrumental variable approach and principal stratification to accommodate the continuous instrumental variable. Within the principal stratification framework, we postulate a Bayesian hierarchical model to infer various causal estimands of policy interest while adjusting for the clustering data structure. We apply the method to the data from the China Family Panel Studies and find small but statistically significant negative effects of being an only child on self-reported psychological health for some subpopulations. Our analysis reveals treatment effect heterogeneity with respect to both observed and unobserved characteristics. In particular, urban males suffer the most from being only children, and the negative effect has larger magnitude if the families were more resistant to the One-Child Policy. We also conduct sensitivity analysis to assess the key instrumental variable assumption.

stat.AP↗

arXiv2020

WasteNet: Waste Classification at the Edge for Smart Bins

Smart Bins have become popular in smart cities and campuses around the world. These bins have a compaction mechanism that increases the bins' capacity as well as automated real-time collection notifications. In this paper, we propose WasteNet, a waste classification model based on convolutional neural networks that can be deployed on a low power device at the edge of the network, such as a Jetson Nano. The problem of segregating waste is a big challenge for many countries around the world. Automated waste classification at the edge allows for fast intelligent decisions in smart bins without needing access to the cloud. Waste is classified into six categories: paper, cardboard, glass, metal, plastic and other. Our model achieves a 97\% prediction accuracy on the test dataset. This level of classification accuracy will help to alleviate some common smart bin problems, such as recycling contamination, where different types of waste become mixed with recycling waste causing the bin to be contaminated. It also makes the bins more user friendly as citizens do not have to worry about disposing their rubbish in the correct bin as the smart bin will be able to make the decision for them.

cs.CV↗

arXiv2020

Distributed Equivalent Substitution Training for Large-Scale Recommender Systems

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduces fully synchronous training to large-scale recommendation system for the first time by reducing communication, thus making the training of commercial recommender systems converge faster and reach better CTR. DES requires much less communication by substituting the weights-rich operators with the computationally equivalent sub-operators and aggregating partial results instead of transmitting the huge sparse weights directly through the network. Due to the use of synchronous training on large-scale Deep Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under ROC). We successfully apply DES training on multiple popular DLRMs of industrial scenarios. Experiments show that our implementation outperforms the state-of-the-art PS-based training framework, achieving up to 68.7% communication savings and higher throughput compared to other PS-based recommender systems.

cs.LG↗

arXiv2020

Accurate nuclear symmetry energy at finite temperature within a BHF approach

We compute the free energy of asymmetric nuclear matter in a Brueckner-Hartree-Fock approach at finite temperature, paying particular attention to the dependence on isospin asymmetry. The first- and second-order symmetry energies are determined as functions of density and temperature and useful parametrizations are provided. We find small deviations from the quadratic isospin dependence and very small corresponding effects on (proto)neutron star structure.

nucl-th↗

arXiv2020

Towards 5G: Joint Optimization of Video Segment Cache, Transcoding and Resource Allocation for Adaptive Video Streaming in a Muti-access Edge Computing Network

The cache and transcoding of the multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact and determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients in MEC networks. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients' QoE. Therefore, we propose a joint optimization framework of video segment cache and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop a MEC cache management mechanism that consists of the MEC cache partition, video segment deletion, and MEC cache space transfer. Then, a joint optimization algorithm that combines video segment cache and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients' channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment presentation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients' QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment cache and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment presentation switch and system backhaul traffic.

cs.MM↗

arXiv2020

In situ modification of delafossite type PdCoO2 bulk single crystal for reversible hydrogen sorption and fast hydrogen evolution

The observation of extraordinarily high conductivity in delafossite-type PdCoO2 is of great current interest, and there is some evidence that electrons behave like a fluid when flowing in bulk crystals of PdCoO2. Thus, this material is an ideal platform for the study of the electron transfer processes in heterogeneous reactions. Here, we report the use of bulk single crystal PdCoO2 as a promising electrocatalyst for hydrogen evolution reactions (HERs). An overpotential of only 31 mV results in a current density of 10 mA cm^(-2), accompanied by high long-term stability. We have precisely determined that the crystal surface structure is modified after electrochemical activation with the formation of strained Pd nanoclusters in the surface layer. These nanoclusters exhibit reversible hydrogen sorption and desorption, creating more active sites for hydrogen access. The bulk PdCoO2 single crystal with ultra-high conductivity, which acts as a natural substrate for the Pd nanoclusters, provides a high-speed channel for electron transfer

cond-mat.mtrl-sci↗

arXiv2020

Training-Set Distillation for Real-Time UAV Object Tracking

Correlation filter (CF) has recently exhibited promising performance in visual object tracking for unmanned aerial vehicle (UAV). Such online learning method heavily depends on the quality of the training-set, yet complicated aerial scenarios like occlusion or out of view can reduce its reliability. In this work, a novel time slot-based distillation approach is proposed to efficiently and effectively optimize the training-set's quality on the fly. A cooperative energy minimization function is established to score the historical samples adaptively. To accelerate the scoring process, frames with high confident tracking results are employed as the keyframes to divide the tracking process into multiple time slots. After the establishment of a new slot, the weighted fusion of the previous samples generates one key-sample, in order to reduce the number of samples to be scored. Besides, when the current time slot exceeds the maximum frame number, which can be scored, the sample with the lowest score will be discarded. Consequently, the training-set can be efficiently and reliably distilled. Comprehensive tests on two well-known UAV benchmarks prove the effectiveness of our method with real-time speed on a single CPU.

cs.CV↗

arXiv2019

A More Refined Mobile Edge Cache Replacement Scheme for Adaptive Video Streaming with Mutual Cooperation in Multi-MEC Servers

In this paper, we propose a more refined video segment based Mobile Edge Computing (MEC) enhanced cache update strategy, which takes into account the client's playback status and transmission state, MEC cache capacity and the popularity of each segment, to improve the quality of experience (QoE) of clients and the use ratio of MEC cache. In each cache update period, the segments which cannot bring significant QoE improvement will be deleted and meanwhile more suitable representations of the segments will be cahed instead. First, we divide the MEC cache space into three parts based on segment popularity and segment importance level. In addition, the size of different cache parts can be transformed to each other to further exploit the MEC cache space. For different parts, the corresponding deleted strategy and caching strategy are formulated, where the deleted strategy combines the requested times of segment and transmission capability of clients, and the caching strategy utilizes the playback state of clients to calculate the caching priority. Furthermore, we formulate the cache update problem to maximize the utility function of clients subject to the constraint of MEC cache size and transmission capacity. The brand and branch method is emplyed to obatain the optimal solution. Simulation results show that our proposed algorithm can improve system throughput and hit ratio of video segment, and at the same time decrease playback frozen time and system backhaul traffic compared with other existing algorithms.

eess.IV↗

arXiv2019

Distributed Computing with Heterogeneous Communication Constraints: The Worst-Case Computation Load and Proof by Contradiction

We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). For this setting, we characterize the minimum total computation load and the minimum worst-case computation load in some cases, under the heterogeneous communication load constraints. While the total computation load depends on the sum of the computation loads of all the nodes, the worst-case computation load depends on the computation load of a node with the heaviest job. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and the data shuffling. Beyond the cut-set bound, a novel converse is proposed using the proof by contradiction. For the general case, we identify two extreme regimes in which both the scheme with coding and the scheme without coding are optimal, respectively.

cs.IT↗

arXiv2019

BGD-based Adam algorithm for time-domain equalizer in PAM-based optical interconnects

To the best of our knowledge, for the first time, we propose adaptive moment estimation (Adam) algorithm based on batch gradient descent (BGD) to design a time-domain equalizer (TDE) for PAM-based optical interconnects. Adam algorithm has been widely applied in the fields of artificial intelligence. For TDE, BGD-based Adam algorithm can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Therefore, fast and stable convergence can be achieved by BGD-based Adam algorithm with low mean square error. Meanwhile, BGD-based Adam algorithm is implemented by parallel processing, which is more efficient than conventional serial algorithms, such as least mean square and recursive least square algorithms. The experimental results demonstrate that BGD-based Adam feed-forward equalizer works well in 120-Gbit/s PAM8 optical interconnects. In conclusion, BGD-based Adam algorithm shows great potential for converging the tap coefficients of TDE in future optical interconnects.

cs.IT↗

arXiv2019

Adding Common Randomness Can Remove the Secrecy Constraints in Communication Networks

In communication networks secrecy constraints usually incur an extra limit in capacity or generalized degrees-of-freedom (GDoF), in the sense that a penalty in capacity or GDoF is incurred due to the secrecy constraints. Over the past decades a significant amount of effort has been made by the researchers to understand the limits of secrecy constraints in communication networks. In this work, we focus on how to remove the secrecy constraints in communication networks, i.e., how to remove the GDoF penalty due to secrecy constraints. We begin with three basic settings: a two-user symmetric Gaussian interference channel with confidential messages, a symmetric Gaussian wiretap channel with a helper, and a two-user symmetric Gaussian multiple access wiretap channel. Interestingly, in this work we show that adding common randomness at the transmitters can totally remove the penalty in GDoF or GDoF region of the three settings considered here. The results reveal that adding common randomness at the transmitters is a powerful way to remove the secrecy constraints in communication networks in terms of GDoF performance. Common randomness can be generated offline. The role of the common randomness is to jam the information signal at the eavesdroppers, without causing too much interference at the legitimate receivers. To accomplish this role, a new method of Markov chain-based interference neutralization is proposed in the achievability schemes utilizing common randomness. From the practical point of view, we hope to use less common randomness to remove secrecy constraints in terms of GDoF performance. With this motivation, for most of the cases we characterize the minimal GDoF of common randomness to remove secrecy constraints, based on our derived converses and achievability.

cs.IT↗

arXiv2019

Propensity Score Weighting for Causal Inference with Multiple Treatments

Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common pre-specified target population. The class of balancing weights include several existing approaches such as the inverse probability weights and trimming weights as special cases. Within this framework, we propose a set of target estimands based on linear contrasts. We further develop the generalized overlap weights, constructed as the product of the inverse probability weights and the harmonic mean of the generalized propensity scores. The generalized overlap weighting scheme corresponds to the target population with the most overlap in covariates across the multiple treatments. These weights are bounded and thus bypass the problem of extreme propensities. We show that the generalized overlap weights minimize the total asymptotic variance of the moment weighting estimators for the pairwise contrasts within the class of balancing weights. We consider two balance check criteria and propose a new sandwich variance estimator for estimating the causal effects with generalized overlap weights. We apply these methods to study the racial disparities in medical expenditure between several racial groups using the 2009 Medical Expenditure Panel Survey (MEPS) data. Simulations were carried out to compare with existing methods.

stat.ME↗

arXiv2019

A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment

Difference-in-differences is a widely-used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trends, which is scale dependent and may be questionable in some applications. A common alternative is a regression model that adjusts for the lagged dependent variable, which rests on the assumption of ignorability conditional on past outcomes. In the context of linear models, \citet{APbook} show that the difference-in-differences and lagged-dependent-variable regression estimates have a bracketing relationship. Namely, for a true positive effect, if ignorability is correct, then mistakenly assuming parallel trends will overestimate the effect; in contrast, if the parallel trends assumption is correct, then mistakenly assuming ignorability will underestimate the effect. We show that the same bracketing relationship holds in general nonparametric (model-free) settings. We also extend the result to semiparametric estimation based on inverse probability weighting. We provide three examples to illustrate the theoretical results with replication files in \citet{ding2019bracketingData}.

stat.AP↗

arXiv2019

Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation

Difference-in-differences (DID) is a widely used approach for drawing causal inference from observational panel data. Two common estimation strategies for DID are outcome regression and propensity score weighting. In this paper, motivated by a real application in traffic safety research, we propose a new double-robust DID estimator that hybridizes regression and propensity score weighting. We particularly focus on the case of discrete outcomes. We show that the proposed double-robust estimator possesses the desirable large-sample robustness property. We conduct a simulation study to examine its finite-sample performance and compare with alternative methods. Our empirical results from a Pennsylvania Department of Transportation data suggest that rumble strips are marginally effective in reducing vehicle crashes.

stat.AP↗

arXiv2019

IoT Virtualization: A Survey of Software Definition & Function Virtualization Techniques for Internet of Things

Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for next generation Internet. The deployment and applications of IoT ranges from smart cities to urban computing, and from ubiquitous healthcare to tactile Internet. For this reason the physical infrastructure of heterogeneous network systems has become more complicated, and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks (SDN) and Network Function Virtualization (NFV) has been extensively researched for IoT in recent past. In this article we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software defined networks designed for IoT, function virtualization for IoT networks, and software defined IoT networks. These categories are further divided into works which present architectural, security, and management solutions. In addition, the paper highlights a number of short term and long term research challenges and open issues related to adoption of software defined Internet of things.

cs.NI↗

arXiv2019

A Comprehensive Survey of Interface Protocols for Software Defined Networks

Software Defined Networks has seen tremendous growth and deployment in different types of networks. Compared to traditional networks it decouples the control logic from network layer devices, and centralizes it for efficient traffic forwarding and flow management across the domain. This multi-layered architecture has data forwarding devices at the bottom in data plane, which are programmed by controllers in the control plane. The high level application or management plane interacts with control layer to program the whole network and enforce different policies. The interaction among these layers is done through interfaces which work as communication/programming protocols. In this survey, we present a comprehensive study of such interfaces available for southbound, northbound, and east/westbound communication. We have classified each type into different categories based on their properties and capabilities. Virtualization of networks devices is a common practice in Software Defined Networks. This paper also analyzes interface solution which work with different virtualization schemes. In addition, the paper highlights a number of short term and long term research challenges and open issues related to SDN interfaces.

cs.NI↗