arXiv subjects

Shu Yang

Publications and source records attributed to Shu Yang.

At least 235 records · Page 13Linked to original sources

arXiv2020

A Distributed Training Algorithm of Generative Adversarial Networks with Quantized Gradients

Training generative adversarial networks (GAN) in a distributed fashion is a promising technology since it is contributed to training GAN on a massive of data efficiently in real-world applications. However, GAN is known to be difficult to train by SGD-type methods (may fail to converge) and the distributed SGD-type methods may also suffer from massive amount of communication cost. In this paper, we propose a {distributed GANs training algorithm with quantized gradient, dubbed DQGAN,} which is the first distributed training method with quantized gradient for GANs. The new method trains GANs based on a specific single machine algorithm called Optimistic Mirror Descent (OMD) algorithm, and is applicable to any gradient compression method that satisfies a general $δ$-approximate compressor. The error-feedback operation we designed is used to compensate for the bias caused by the compression, and moreover, ensure the convergence of the new method. Theoretically, we establish the non-asymptotic convergence of {DQGAN} algorithm to first-order stationary point, which shows that the proposed algorithm can achieve a linear speedup in the parameter server model. Empirically, our experiments show that our {DQGAN} algorithm can reduce the communication cost and save the training time with slight performance degradation on both synthetic and real datasets.

cs.LG↗

arXiv2020

A Fully Integrated Sensor-Brain-Machine Interface System for Restoring Somatosensation

Sensory feedback is critical to the performance of neural prostheses that restore movement control after neurological injury. Recent advances in direct neural control of paralyzed arms present new requirements for miniaturized, low-power sensor systems. To address this challenge, we developed a fully-integrated wireless sensor-brain-machine interface (SBMI) system for communicating key somatosensory signals, fingertip forces and limb joint angles, to the brain. The system consists of a tactile force sensor, an electrogoniometer, and a neural interface. The tactile force sensor features a novel optical waveguide on CMOS design for sensing. The electrogoniometer integrates an ultra low-power digital signal processor (DSP) for real-time joint angle measurement. The neural interface enables bidirectional neural stimulation and recording. Innovative designs of sensors and sensing interfaces, analog-to-digital converters (ADC) and ultra wide-band (UWB) wireless transceivers have been developed. The prototypes have been fabricated in 180nm standard CMOS technology and tested on the bench and in vivo. The developed system provides a novel solution for providing somatosensory feedback to next-generation neural prostheses.

q-bio.NC↗

arXiv2020

SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization

With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy. However, existing GNNs suffer from high memory footprints (e.g., node embedding features). This high memory footprint hurdles the potential applications towards memory-constrained devices, such as the widely-deployed IoT devices. To this end, we propose a specialized GNN quantization scheme, SGQuant, to systematically reduce the GNN memory consumption. Specifically, we first propose a GNN-tailored quantization algorithm design and a GNN quantization fine-tuning scheme to reduce memory consumption while maintaining accuracy. Then, we investigate the multi-granularity quantization strategy that operates at different levels (components, graph topology, and layers) of GNN computation. Moreover, we offer an automatic bit-selecting (ABS) to pinpoint the most appropriate quantization bits for the above multi-granularity quantizations. Intensive experiments show that SGQuant can effectively reduce the memory footprint from 4.25x to 31.9x compared with the original full-precision GNNs while limiting the accuracy drop to 0.4% on average.

cs.LG↗

arXiv2020

Robust inference of conditional average treatment effects using dimension reduction

It is important to make robust inference of the conditional average treatment effect from observational data, but this becomes challenging when the confounder is multivariate or high-dimensional. In this article, we propose a double dimension reduction method, which reduces the curse of dimensionality as much as possible while keeping the nonparametric merit. We identify the central mean subspace of the conditional average treatment effect using dimension reduction. A nonparametric regression with prior dimension reduction is also used to impute counterfactual outcomes. This step helps improve the stability of the imputation and leads to a better estimator than existing methods. We then propose an effective bootstrapping procedure without bootstrapping the estimated central mean subspace to make valid inference.

stat.ME↗

arXiv2020

Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness

The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation's accuracy and precision with that of multiple imputation.

stat.ME↗

arXiv2020

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.

stat.ME↗

arXiv2020

Multiply robust matching estimators of average and quantile treatment effects

Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however requiring stringent model assumptions. In this article, we propose double score matching(DSM) for general causal estimands utilizing two balancing scores including the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the de-biasing DSM estimator achieves the multiple robustness property in that it is consistent for the true causal estimand if any model of the propensity score or prognostic score is correct.

stat.ME↗

arXiv2020

Generalized propensity score approach to causal inference with spatial interference

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005--2018.

stat.ME↗

arXiv2020

A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output

Wildland fire smoke contains hazardous levels of fine particulate matter PM2.5, a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM2.5 and PM2.5 from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM2.5 under counterfactual scenarios. The chemical model representation of PM2.5 for this analysis is simulated using Community Multi-Scale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of absolute, relative and cumulative contributions of wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke.

stat.ME↗

arXiv2020

Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations

Structural Nested Mean Models (SNMMs) are useful for causal inference of treatment effects in longitudinal observational studies. Most existing works assume that the data are collected at pre-fixed time points for all subjects, which, however, is restrictive in practice. To deal with irregularly spaced observations, we assume a class of continuous-time SNMMs and a martingale condition of no unmeasured confounding (NUC) to identify the causal parameters. We develop the first semiparametric efficiency theory and locally efficient estimators for continuous-time SNMMs. This task is non-trivial due to the restrictions from the NUC assumption imposed on the SNMM parameter. In the presence of dependent censoring, we propose an inverse probability of censoring weighting estimator, which achieves a multiple robustness feature in that it is unbiased if either the model for the treatment process or the potential outcome mean function is correctly specified, regardless whether the censoring model is correctly specified. The new framework allows us to conduct causal analysis respecting the underlying continuous-time nature of the data processes. We estimate the effect of time to initiate highly active antiretroviral therapy on the CD4 count at year 2 from the observational Acute Infection and Early Disease Research Program database.

stat.ME↗

arXiv2020

Statistical Data Integration in Survey Sampling: A Review

Finite population inference is a central goal in survey sampling. Probability sampling is the main statistical approach to finite population inference. Challenges arise due to high cost and increasing non-response rates. Data integration provides a timely solution by leveraging multiple data sources to provide more robust and efficient inference than using any single data source alone. The technique for data integration varies depending on types of samples and available information to be combined. This article provides a systematic review of data integration techniques for combining probability samples, probability and non-probability samples, and probability and big data samples. We discuss a wide range of integration methods such as generalized least squares, calibration weighting, inverse probability weighting, mass imputation and doubly robust methods. Finally, we highlight important questions for future research.

stat.ME↗

arXiv2020

Effects of Inflow Turbulence on Structural Deformation of Wind Turbine Blades

The present investigation provides the first field characterization of the influence of turbulent inflow on the blade structural response of a utility-scale wind turbine (2.5MW), using the unique facility available at the Eolos Wind Energy Research Station of the University of Minnesota. A representative one-hour dataset under a stable atmosphere is selected for the characterization, including the inflow turbulent data measured from the meteorological tower, high-resolution blade strain measurement at different circumferential and radiation positions along the blade, and the wind turbine operational conditions. The results indicate that the turbulent inflow modulates the turbine blade structural response in three representative frequency ranges: a lower frequency range (corresponding to modulations due to large eddies in the atmosphere), a higher frequency range (corresponding to flow structures in scales smaller than the rotor diameter), and an intermediate-range in between. The blade structure responds strongly to the turbulent inflow in the lower and intermediate ranges, while it is primarily dominated by the rotation effect and other high-frequency characteristics of wind turbines in the higher frequency range. Moreover, the blade structural behaviors at different azimuth angles, circumferential and radial locations along the blade are also compared, suggesting the comparatively high possibility of structural failure at certain positions. Further, the present study also uncovers the linkage between the turbulent inflow and blade structural response using temporal correlation. The derived findings provide insights into the development of advanced control strategies or blade design to mitigate the structural impact and increase blade longevity for the safer and more efficient operation of large-scale wind turbines.

physics.flu-dyn↗

arXiv2019

A Tunably Compliant Origami Mechanism for Dynamically Dexterous Robots

We present an approach to overcoming challenges in dynamical dexterity for robots through tunable origami structures. Our work leverages a one-parameter family of flat sheet crease patterns that folds into origami bellows, whose axial compliance can be tuned to select desired stiffness. Concentrically arranged cylinder pairs reliably manifest additive stiffness, extending the tunable range by nearly an order of magnitude and achieving bulk axial stiffness spanning 200-1500 N/m using 8 mil thick polyester-coated paper. Accordingly, we design origami energy-storing springs with a stiffness of 1035 N/m each and incorporate them into a three degree-of-freedom (DOF) tendon-driven spatial pointing mechanism that exhibits trajectory tracking accuracy less than 15% rms error within a ~2 cm^3 volume. The origami springs can sustain high power throughput, enabling the robot to achieve asymptotically stable juggling for both highly elastic (1~kg resilient shot put ball) and highly damped ("medicine ball") collisions in the vertical direction with apex heights approaching 10 cm. The results demonstrate that "soft" robotic mechanisms are able to perform a controlled, dynamically actuated task.

cs.RO↗

arXiv2019

Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data

Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for general probability samples. The major technical hurdle is due to the possible dependence of the sample under the finite population framework. To overcome this challenge, we construct martingales which enable us to apply Bernstein concentration inequality for martingales. In the second step, we focus on a doubly robust estimator of the finite population mean and re-estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first-step selection error and renders the doubly robust estimator root-n consistent if either the sampling probability or the outcome model is correctly specified.

stat.ME↗

arXiv2019

Two Dimensional Router: Design and Implementation

Higher dimensional classification has attracted more attentions with increasing demands for more flexible services in the Internet. In this paper, we present the design and implementation of a two dimensional router (TwoD router), that makes forwarding decisions based on both destination and source addresses. This TwoD router is also a key element in our current effort towards two dimensional IP routing. With one more dimension, the forwarding table will grow explosively given a straightforward implementation. As a result, it is impossible to fit the forwarding table to the current TCAM, which is the de facto standard despite its limited capacity. To solve the explosion problem, we propose a forwarding table structure with a novel separation of TCAM and SRAM. As such, we move the redundancies in expensive TCAM to cheaper SRAM, while the lookup speed is comparable with conventional routers. We also design the incremental update algorithms that minimize the number of accesses to memory. We evaluate our design with a real implementation on a commercial router, Bit-Engine 12004, with real data sets. Our design does not need new devices, which is favorable for adoption. The results also show that the performance of our TwoD router is promising.

cs.NI↗

arXiv2019

Matrix Completion for Survey Data Prediction with Multivariate Missingness

The National Health and Nutrition Examination Survey (NHANES) studies the nutritional and health status over the whole U.S. population with comprehensive physical examinations and questionnaires. However, survey data analyses become challenging due to inevitable missingness in almost all variables. In this paper, we develop a new imputation method to deal with multivariate missingness at random using matrix completion. In contrast to existing imputation schemes either conducting row-wise or column-wise imputation, we treat the data matrix as a whole which allows exploiting both row and column patterns to impute the missing values in the whole data matrix at one time. We adopt a column-space-decomposition model for the population data matrix with easy-to-obtain demographic data as covariates and a low-rank structured residual matrix. A unique challenge arises due to lack of identification of parameters in the sample data matrix. We propose a projection strategy to uniquely identify the parameters and corresponding penalized estimators, which are computationally efficient and possess desired statistical properties. The simulation study shows that the doubly robust estimator using the proposed matrix completion for imputation has smaller mean squared error than other competitors. To demonstrate practical relevance, we apply the proposed method to the 2015-2016 NHANES Questionnaire Data.

stat.ME↗

arXiv2019

Two-body bound state of ultracold Fermi atoms with two-dimensional spin-orbit coupling

In a recent experiment, a two-dimensional spin-orbit coupling (SOC) was realized for fermions in the continuum [Nat. Phys. 12, 540 (2016)], which represents an important step forward in the study of synthetic gauge field using cold atoms. In the experiment, it was shown that a Raman-induced two-dimensional SOC exists in the dressed-state basis close to a Dirac point of the single-particle spectrum. By contrast, the short-range inter-atomic interactions of the system are typically expressed in the hyperfine-spin basis. The interplay between synthetic SOC and interactions can potentially lead to interesting few- and many-body phenomena but has so far eluded theoretical attention. Here we study in detail properties of two-body bound states of such a system. We find that, due to the competition between SOC and interaction, the stability region of the two-body bound state is in general reduced. Particularly, the threshold of the lowest two-body bound state is shifted to a positive, SOC-dependent scattering length. Furthermore, the center-of-mass momentum of the lowest two-body bound state becomes nonzero, suggesting the emergence of Fulde-Ferrell pairing states in a many-body setting. Our results reveal the critical difference between the experimentally realized two-dimensional SOC and the more symmetric Rashba or Dresselhaus SOCs in an interacting system, and paves the way for future characterizations of topological superfluid states in the experimentally relevant systems.

cond-mat.quant-gas↗

arXiv2019

Monolithic shape-programmable dielectric liquid crystal elastomer actuators

Macroscale robotic systems have demonstrated great capabilities of high speed, precise, and agile functions. However, the ability of soft robots to perform complex tasks, especially in centimeter and millimeter scale, remains limited due to the unavailability of fast, energy-efficient soft actuators that can programmably change shape. Here, we combine desirable characteristics from two distinct active materials: fast and efficient actuation from dielectric elastomers and facile shape programmability from liquid crystal elastomers into a single shape changing electrical actuator. Uniaxially aligned monoliths achieve strain rates over 120%/s with energy conversion efficiency of 20% while moving loads over 700 times the actuator weight. The combined actuator technology offers unprecedented opportunities towards miniaturization with precision, efficiency, and more degrees of freedom for applications in soft robotics and beyond.

cond-mat.soft↗