Showing papers on "Sampling (signal processing) published in 2022"

When Should You Adjust Standard Errors for Clustering?

Abstract: Abstract Clustered standard errors, with clusters defined by factors such as geography, are widespread in empirical research in economics and many other disciplines. Formally, clustered standard errors adjust for the correlations induced by sampling the outcome variable from a data-generating process with unobserved cluster-level components. However, the standard econometric framework for clustering leaves important questions unanswered: (i) Why do we adjust standard errors for clustering in some ways but not others, for example, by state but not by gender, and in observational studies but not in completely randomized experiments? (ii) Is the clustered variance estimator valid if we observe a large fraction of the clusters in the population? (iii) In what settings does the choice of whether and how to cluster make a difference? We address these and other questions using a novel framework for clustered inference on average treatment effects. In addition to the common sampling component, the new framework incorporates a design component that accounts for the variability induced on the estimator by the treatment assignment mechanism. We show that, when the number of clusters in the sample is a nonnegligible fraction of the number of clusters in the population, conventional clustered standard errors can be severely inflated, and propose new variance estimators that correct for this bias.

Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System

Abstract: The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this article proposes an adaptive reference vector reinforcement learning (RVRL) approach to decomposition-based algorithms for industrial copper burdening optimization. The proposed approach involves two main operations, that is: 1) a reinforcement learning (RL) operation and 2) a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the RL operation treats the reference vector adaption process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm.

Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]

Abstract: Enhanced sampling methods for molecular dynamics simulations [Article v1.0] Jérôme Hénin1,2*, Tony Lelièvre3*, Michael R. Shirts4*, Omar Valsson5,6*, Lucie Delemotte7* 1Laboratoire de Biochimie Théorique UPR 9080, CNRS, Paris, France; 2Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, Paris, France; 3CERMICS, Ecole des Ponts, INRIA, Marne-la-Vallée, France; 4Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA, 80309; 5University of North Texas, Department of Chemistry, Denton, TX, USA; 6Max Planck Institute for Polymer Research, Mainz, Germany; 7KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden

Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples

Abstract: Adaptive sampling is a method of software-controlled enrichment unique to nanopore sequencing platforms. To test its potential for enrichment of rarer species within metagenomic samples, we create a synthetic mock community and construct sequencing libraries with a range of mean read lengths. Enrichment is up to 13.87-fold for the least abundant species in the longest read length library; factoring in reduced yields from rejecting molecules the calculated efficiency raises this to 4.93-fold. Finally, we introduce a mathematical model of enrichment based on molecule length and relative abundance, whose predictions correlate strongly with mock and complex real-world microbial communities.

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Abstract: Graph convolutional networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

Distributed Resilient Secondary Control for DC Microgrids Against Heterogeneous Communication Delays and DoS Attacks

Abstract: In this article, a cooperative resilient control method for dc microgrid (MG) is proposed to dispel the adverse influences of both communication delays and denial-of-service (DoS) attacks. To avoid that the sampling period is captured by intelligent attackers, a new time-varying sampling period, and an improved communication mechanism are first introduced under the sampling control framework. Based on the designed sampling period and communication mechanism, a resilient secondary controller is designed. It is theoretically shown that the developed method can achieve the goals of bus voltage restoration and current sharing even in the presence of both DoS attacks and heterogeneous communication delays. Finally, a dc MG test system is built in a controller-hardware-in-the-loop testing platform to illustrate and verify the effectiveness of our developed method against both communication delays and DoS attacks.

Quantum computational advantage via high-dimensional Gaussian boson sampling

Abstract: A programmable quantum computer based on fiber optics outperforms classical computers with a high level of confidence.

Characterization of Joint Roughness Heterogeneity and Its Application in Representative Sample Investigations

Abstract: Abstract Rock joint surface roughness is usually characterized by heterogeneity, but the determination of a required number of samples for achieving a reasonable heterogeneity assessment remains a challenge. In this paper, a novel method, the global search method, was proposed to investigate the heterogeneity of rock joint roughness. In this method, the roughness heterogeneity was characterized based on a statistical analysis of the roughness of all samples extracted from different locations of a given rock joint. Analyses of the effective sample number were conducted, which showed that sampling bias was caused by an inadequate number of samples. To overcome this drawback, a large natural slate joint sample (1000 mm × 1000 mm in size) was digitized in a laboratory using a high-accuracy laser scanner. The roughness heterogeneities of both two-dimensional (2D) profiles and three-dimensional (3D) surface topographies were systematically investigated. The results show that the expected value obtained from conventional methods failed to accurately represent the overall roughness. The relative errors between the population parameter and the expected value varied not only from sample to sample but also with the scale. The roughness heterogeneity characteristics of joint samples of various sizes can be obtained using the global search method. This new method could facilitate the determination of the most representative samples and their positions.

GEDI launches a new era of biomass inference from space

Abstract: Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

Sampling in software engineering research: a critical review and guidelines

Abstract: Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a critical review of the state of sampling in recent, high-quality software engineering research. The key findings are: (1) random sampling is rare; (2) sophisticated sampling strategies are very rare; (3) sampling, representativeness and randomness often appear misunderstood. These findings suggest that software engineering research has a generalizability crisis. To address these problems, this paper synthesizes existing knowledge of sampling into a succinct primer and proposes extensive guidelines for improving the conduct, presentation and evaluation of sampling in software engineering research. It is further recommended that while researchers should strive for more representative samples, disparaging non-probability sampling is generally capricious and particularly misguided for predominately qualitative research.

Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

Abstract: For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation

Abstract: 3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still cannot generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance mani folds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency. ^{1 1} Project page: https://yudeng.github.io/GRAM/

Fuzzy Intermittent Extended Dissipative Control for Delayed Distributed Parameter Systems With Stochastic Disturbance: A Spatial Point Sampling Approach

Abstract: In this article, the extended dissipative performance of distributed parameter systems (DPSs) with stochastic disturbances and multiple time-varying delays is studied by using a new fuzzy aperiodic intermittent sampled-data control strategy. Different from the previous fuzzy sampled-data control results, the state sampling of the proposed sampled-data controller occurs only in space and is intermittent rather than continuous in the time domain. By introducing a novel multitime-delay-dependent switched Lyapunov functional to explore the dynamic characteristics of the controlled system, and by means of the famous Jensen’s inequality with reciprocally convex approach, Wirtinger’s inequality, the criterion of the system’s mean square stabilization is established based on the LMI technique, which quantitatively reveals the relationship between the control period, the control length, and the upper bound of the control sampling interval. Especially, the optimal control gain is given by designing an optimized algorithm in the article, which greatly reduces the cost. Finally, two numerical examples are presented to demonstrate the effectiveness and superiority of the proposed approach.

GNNLab: a factored system for sample-based GNN training over GPUs

Abstract: We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to the task of graph sampling or model training. It accelerates both tasks by eliminating GPU memory contention. To balance GPU workloads, GNNLab applies a global queue to bridge GPUs asynchronously and adopts a simple yet effective method to adaptively allocate GPUs for different tasks. GNNLab further leverages temporarily switching to avoid idle waiting on GPUs. Furthermore, GNNLab proposes a new pre-sampling based caching policy that takes both sampling algorithms and GNN datasets into account, and shows an efficient and robust caching performance. Evaluations on three representative GNN models and four real-life graphs show that GNNLab outperforms the state-of-the-art GNN systems DGL and PyG by up to 9.1× (from 2.4×) and 74.3× (from 10.2×), respectively. In addition, our pre-sampling based caching policy achieves 90% -- 99% of the optimal cache hit rate in all experiments.

Design and Control of a Highly Redundant Rigid-flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling

Abstract: The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.