Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “variable interactions”

Haptic Interactions Subject to Variable Latency

Chantal Hutchison, Joseph Hewlett, Siamak Arbatani et al. · IEEE Transactions on Haptics · 2024

Model-Mediated Teleoperation (MMT) between a haptic device and a remote or virtual environment uses a local model of the environment to compensate for latency of communication. MMT is often case-specific, and requires underlying latency distributions to be known. We propose a novel approach - which we refer to as the DelayRIM - which uses the time-stepping aspect of a Reduced Interface Model for the environment to render an up-to-date force to the haptic device from the delayed information. RIM is applicable to any physical or virtual system, and the DelayRIM itself makes no underlying assumption about the latency distribution. We show that for realistic variable delays, the DelayRIM improves transparency compared to other methods for a virtual drone bilateral teleoperation scenario.

Cited by 0

Variable Interactions in Query-Driven Visualization

Luke Gosink, John C. Anderson, Wes Bethel et al. · IEEE Transactions on Visualization and Computer Graphics · 2007

Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex datasets. This paper presents a new method that increases the utility of query-driven techniques by visually conveying statistical information about the trends that exist between variables in a query. In this method, correlation fields, created between pairs of variables, are used with the cumulative distribution functions of variables expressed in a user's query. This integrated use of cumulative distribution functions and correlation fields visually reveals, with respect to the solution space of the query, statistically important interactions between any three variables, and allows for trends between these variables to be readily identified. We demonstrate our method by analyzing interactions between variables in two flame-front simulations.

Cited by 51

Variable Interactions in Query-Driven Visualization

E. Wes Bethel, Luke Gosink, John C. Anderson et al. · University of North Texas Digital Library (University of North Texas) · 2007

One fundamental element of scientific inquiry is discoveringrelationships, particularly the interactions between different variablesin observed or simulated phenomena. Building upon our prior work in thefield of Query-Driven Visualization, where visual data analysisprocessing is focused on subsets of large data deemed to be"scientifically interesting," this new work focuses on a novel knowledgediscovery capability suitable for use with petascale class datasets. Itenables visual presentation of the presence or absence of relationships(correlations) between variables in data subsets produced by Query-Drivenmethodologies. This technique holds great potential for enablingknowledge discovery from large and complex datasets currently emergingfrom SciDAC and INCITE projects. It is sufficiently generally to beapplicable to any time of complex, time-varying, multivariate data fromstructured, unstructured or adaptive grids.

Cited by 1Open access

A variable shape and variable stiffness controller for haptic virtual interactions

Benjamin C. Mac Murray, Bryan Peele, Patricia Xu et al. · 2018

This paper presents an entirely compliant controller handle for use in virtual and augmented reality environments. The controller handle transitions between two static states: a semi-rigid, large diameter state when pneumatically pressurized and a soft, compressible, smaller diameter state when depressurized. We integrated the controller with a modified version of NVIDIA's VR Funhouse employing the two controller states to simulate the physical feel of two virtual objects. We used finite element modeling to downselect an internal elastomer lattice within the controller that controls deformation upon inflation. Finally, we show an example of using the compliance of the handle as an interaction input by designing an algorithm to identify rapid compressions of the handle as a signal to swap objects in the virtual environment.

Cited by 24

Analyzing biomolecular interactions by variable angle ellipsometry

J.Y. Wu, C.K. Lee, J.H. Lee et al. · 2001

In this paper, an innovative ellipsometer is developed and applied to metrology of the biomolecular interaction on a protein biochip. Both the theory, optical and opto-mechanical configurations of this newly developed ellipsometer and methodologies adopted in system design to improve the system performance are presented. It will be shown that by measuring the ellipsometric parameters, the corresponding concentration variation in biochemical reaction can be calculated according to stoichiometry analysis. By applying the variable angle ellipsometry to analysis of a multi-layered sample, the thickness and concentration are resolved. It is believed that the newly developed ellips ometer biosensor is able to undertake an accurate measurement on biomedical interaction.

Cited by 0

Penalized variable selection for gene-environment interactions

Du, Yinhao · K-State Research Exchange (Kansas State University) · 2021

Gene-environment (GE) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects.In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting GE interactions.Despite the success, variable selection is limited in the following aspects.First, multidimensional measurements have not been taken into fully account in interaction studies.Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes.Second, in the big data context, no variable selection method has been developed so far to conduct tailored interaction analysis.Third, the solution to case control association GE studies with high dimensional genomics variants in the big data context has not been made available so far.In this dissertation, we tackle these challenges rising from GE interaction studies in the modern era through the following projects.In the first project, we have developed a novel variable selection method to integrate multi-omics measurements in GE interaction studies.Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically but also resulting in improved identification and prediction performance.Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction and link the disease outcomes to multiple effects in the integrative GE studies via accommodating a sparse bi-level structure.Simulation studies show the integrative model leads to better identification of GE interactions and regulators than that of the alternative methods.In two GE lung cancer studies with high dimensional multiomics data, the integrative model leads to improved prediction and findings with important biological implications.In the second project, we propose to conduct interaction studies in the big data context by adopting the divide-and-conquer strategy.In particular, the sparse group variable selection for important GE effects has been developed within the framework of alternating direction method of multiplier (ADMM).To accommodate the large-scale data in terms of either samples or features, we have developed two novel parallel ADMM based variable selection methods across samples and features, respectively.The corresponding parallel algorithms can be efficiently implemented in distributed computing platforms.Simulation studies demonstrate that the parallel ADMM based penalization methods significantly improve the computational speed for analyzing large scale data from GE interaction studies with satisfactory identification and prediction performance.In the third project, we extend the proposed parallel ADMM based variable selection for GE interactions in the case-control association study of type 2 diabetes.Within the parallel computation framework, we have developed a penalized logistic regression model accommodating the bi-level selection tailored for the case control GE interaction study.The advantage of the proposed parallel penalization method has been fully illustrated in the distributed learning scenario.Simulation studies show the proposed method dramatically reduces the computational time while maintaining a competitive performance compared to the non-parallel counterparts.In the case study of type 2 diabetes with environmental factors and high dimensional SNP measurements, the proposed parallel penalization method leads to the identification of biologically important interaction effects.Penalized variable selection for gene-environment interactions by

Cited by 0Open access

Variable interactions in risk factors for dementia

Jim O’Donoghue, Mark Roantree, Andrew McCarren · 2016

Current estimates predict 1 in 3 people born today will develop dementia, suggesting a major impact on future population health. As such, research needs to connect specialist clinicians, data scientists and the general public. The In-MINDD project seeks to address this through the provision of a Profiler, a socio-technical information system connecting all three groups. The public interact, providing raw data; data scientists develop and refine prediction algorithms; and clinicians use in-built services to inform decisions. Common across these groups are Risk Factors, used for dementia-free survival prediction. Risk interactions could greatly inform prediction but determining these interactions is a problem underpinned by massive numbers of possible combinations. Our research employs a machine learning approach to automatically select best performing hyperparameters for prediction and learns variable interactions in a non-linear survival-analysis paradigm. Demonstrating effectiveness, we evaluate this approach using longitudinal data with a relatively small sample size.

Cited by 5

Quantum-enhanced sensing with variable-range interactions

Monika Mothsara, Leela Ganesh Chandra Lakkaraju, Srijon Ghosh et al. · Physical review. A/Physical review, A · 2025

The typical bound on parameter estimation, known as the standard quantum limit (SQL), can be surpassed by exploiting quantum resources such as entanglement. To estimate the magnetic probe field, we propose a quantum sensor based on a variable-range many-body quantum spin chain with a moderate transverse magnetic field. We report the threefold benefits of employing a long-range system as a quantum sensor. First, sensors with quasi-long-range interactions can always beat the SQL for all values of the coordination number, while a sensor with long-range interactions does not have this ubiquitous quantum advantage. Second, a long-range Hamiltonian outperforms a nearest-neighbor (NN) Hamiltonian in terms of both estimating precision and system-size scaling. Finally, we observe that the system with long-range interactions can go below the SQL in the presence of a high temperature of the initial state, while sensors having NN interactions cannot. Furthermore, a sensor based on the long-range Ising Hamiltonian proves to be robust against impurities in the magnetic field and when the time-inhomogeneous dephasing noise acts during interaction of the probe with the system.

Cited by 6

Variable Generation, Reserves, Flexibility and Policy Interactions

James P. Ryan, Erik Ela, Damian Flynn et al. · 2014

Operational issues associated with the integration of variable generation (VG) have led system operators and researchers to re-evaluate how reliability services are procured and at what levels they are required. Ramping reserve constraints are now being proposed and applied by several system operators to address the increasing variability and uncertainty in system schedules. The increasing environmental, economic, geographic and temporal interconnectedness of power systems means that the impact of additional reserve products can no longer be assessed in isolation. Although additional reserve categories are a practical short-term solution, in the longer term, greater coherence is required between policy objectives and operational strategies to avoid sub optimal operation.

Cited by 13Open access

Robust Bayesian Variable Selection for Gene–Environment Interactions

Jie Ren, Fei Zhou, Xiaoxi Li et al. · Biometrics · 2022

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Cited by 24Open access

State Variable Interactions of Dual Input SEPIC Converters

Allam Venkatesh, B. Amarendra Reddy, T. R. Jyothsna et al. · WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS · 2025

This article addresses the level of interactions between state variables and their eigenmodes of two-input integrated (TII) converters. Here, two-input Buck-SEPIC (TI-BS) and two-input modified Buck-SEPIC (TI-MBS) converters are considered for analysis. Small-signal models are obtained in continuous time and as well as discrete time, and eigenvalues are computed using system matrices “Aavg” and “Phi” respectively. The characteristic equations of TII converters are of fourth order and have four eigenvalues. Here, Participation Factors (PF) analysis is performed to identify the level of interaction between Converter State-Variables In Converter Eigen-Modes (CSVICEM), Converter Eigen-Modes In Converter State-Variables (CEMICSV), and Converter Eigen-Modes In Converter Output Variables (CEMICOV). The participation factor analysis helps to identify the sensitive elements that are present in the converter configuration. The simulation studies are performed for these converters considering a 36 V / 60 V to 48 V, 500 W dc-dc converter for Electric Vehicles. Here, continuous and discrete-time models are obtained, and using these models, CSVICEM, CEMICSV, and CEMICOV are computed, and experimental validation is performed through Hardware in Loop (HIL) using OPAL-RT.

Cited by 0Open access

High dimensional variable selection for gene-environment interactions

Cen Wu, Ping-shou Zhong, Yuehua Cui · 2014

Gene-environment (G×E) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environment factors are measured in a continuous scale, one can assess the genetic sensitivity over different environmental conditions on a disease phenotype. Motivated by the increasing awareness of the power of gene set based association analysis over single variant based approach, we proposed an additive varying-coefficient model to jointly model variants in a genetic system. The model allows us to examine how variants in a set are mediated by one or multiple environment factors to affect a disease phenotype. We approached the problem from a high dimensional variable selection perspective. In particular, we can select variants with varying, constant and zero coefficients, which correspond to cases of G×E interaction, no G×E interaction and no genetic effect, respectively. The procedure was implemented through a two stage iterative estimation algorithm via the Smoothly Clipped Absolute Deviation (SCAD) penalty function. Under certain regularity conditions, we established the consistency property in variable selection as well as effect separation of the two stage iterative estimators, and showed the optimal convergence rates of the estimates for varying effects. In addition, we showed that the estimate of non-zero constant coefficients enjoy the oracle property. The utility of our procedure was demonstrated through simulation studies and real data analysis. Key words: Nonlinear gene-environment interaction; SCAD penalty; Local quadratic approximation; Varying-coefficient model 1 1

Cited by 0

Variable autonomy assignment algorithms for human-robot interactions.

Chris Robinson · 2021

As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction has grown into an increasingly important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. However, such systems are often proscribed only at the most abstract level, with the meticulous details of implementation left to the designer's prerogative. Without a rigorous structure for implementing controls, the work of design is frequently left to ad hoc mechanism with only bespoke guarantees of systematic efficacy, if any such proof is forthcoming at all. Herein, I present two quantitatively defined models for implementing sliding-scale variable autonomy, in which levels of autonomy are determined by the relative efficacy of autonomous subroutines. I experimentally test the resulting Variable Autonomy Planning (VAP) algorithm and against a traditional traded control scheme in a pick-and-place task, and apply the Variable Autonomy Tasking algorithm to the implementation of a robot performing a complex sanitation task in real-world environs. Results show that prioritizing autonomy levels with higher success rates, as encoded into VAP, allows users to effectively and intuitively select optimal autonomy levels for efficient task completion. Further, the Pareto optimal design structure of the VAP+ algorithm allows for significant performance improvements to be made through intervention planning based on systematic input determining failure probabilities through sensorized measurements. This thesis describes the design, analysis, and implementation of these two algorithms, with a particular focus on the VAP+ algorithm. The core conceit is that they are methods for rigorously defining locally optimal plans for traded control being shared between a human and one or more autonomous processes. It is derived from an earlier algorithmic model, the VAP algorithm, developed to address the issue of rigorous, repeatable assignment of autonomy levels based on system data which provides guarantees on basis of the failure-rate sorting of paired autonomous and manual subtask achievement systems. Using only probability ranking to define levels of autonomy, the VAP algorithm is able to sort modules into optimizable ordered sets, but is limited to only solving sequential task assignments. By constructing a joint cost metric for the entire plan, and by implementing a back-to-front calculation scheme for this metric, it is possible for the VAP+ algorithm to generate optimal planning solutions which minimize the expected cost, as amortized over time, funds, accuracy, or any metric combination thereof. The algorithm

Cited by 0Open access

Bifurcations and periodic orbits in variable population interactions

Jorge Rebaza · Communications on Pure &amp Applied Analysis · 2013

Variable population interactions with harvesting on one of thespecies are studied. Existence and stability of equilibria andexistence of periodic solutions are established, existence of some bifurcation phenomenaare analytically and numerically studied, explicit threshold values are computed to determinethe kind of interaction (mutualism, competition, host-parasite) between the species, and severalnumerical examples are provided to illustrate the main results in this work. A brief discussionon the influence of the harvesting function on the dynamics of the model is also included.Hopf bifurcations and periodic solutions are found for the first time in this kind of models.

Cited by 1