274 research outputs found
Robust Bayesian Variable Selection for Gene-Environment Interactions
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 penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian 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, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++
How Does Folding Modulate Thermal Conductivity of Graphene
We study thermal transport in folded graphene nanoribbons using molecular
dynamics simulations and the non-equilibrium Green's function method. It is
found that the thermal conductivity of flat graphene nanoribbons can be
modulated by folding and changing interlayer couplings. The analysis of
transmission reveals that the reduction of thermal conductivity is due to
scattering of low frequency phonons by the folds. Our results suggest that
folding can be utilized in the modulation of thermal transport properties in
graphene and other two dimensional materials.Comment: published in Applied Physics Letters 201
Interaction Analysis of Repeated Measure Data
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University
Essays on financial communication in earnings conference calls
Earnings conference calls are an important platform of financial communication. They provide researchers with unique opportunities to observe firm managers’ and financial analysts’ interactions and natural communication style in a daily-task environment. Relying on multidisciplinary theories and methods, this dissertation studies financial communication in conference calls from both the managers’ and the sell-side analysts’ perspectives. It consists of three self-contained studies. Chapter 2 focuses on managers’ communication strategies in conference calls. It explores, in the small non-negative earnings surprises setting, whether non-manipulators design communication strategies to separate themselves from earnings manipulators, and whether manipulators pool through obfuscation. Chapters 3 and 4 focus on sell-side analysts’ communication behaviour in conference calls. Chapter 3 examines how analysts’ people skills affect their communication behaviour and relationships with firm management. Chapter 4 applies both qualitative and quantitative discourse analyses and investigates how analysts use linguistic politeness strategies to establish socially desirable identities in publicly accessible analyst-manager interactions. The three studies combined contribute to the accounting literature by furthering our understanding of managers’ and analysts’ financial communication incentives and behaviour from multiple perspectives
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Federated Learning (FL) requires frequent exchange of model parameters, which
leads to long communication delay, especially when the network environments of
clients vary greatly. Moreover, the parameter server needs to wait for the
slowest client (i.e., straggler, which may have the largest model size, lowest
computing capability or worst network condition) to upload parameters, which
may significantly degrade the communication efficiency. Commonly-used client
selection methods such as partial client selection would lead to the waste of
computing resources and weaken the generalization of the global model. To
tackle this problem, along a different line, in this paper, we advocate the
approach of model parameter dropout instead of client selection, and
accordingly propose a novel framework of Federated learning scheme with
Differential parameter Dropout (FedDD). FedDD consists of two key modules:
dropout rate allocation and uploaded parameter selection, which will optimize
the model parameter uploading ratios tailored to different clients'
heterogeneous conditions and also select the proper set of important model
parameters for uploading subject to clients' dropout rate constraints.
Specifically, the dropout rate allocation is formulated as a convex
optimization problem, taking system heterogeneity, data heterogeneity, and
model heterogeneity among clients into consideration. The uploaded parameter
selection strategy prioritizes on eliciting important parameters for uploading
to speedup convergence. Furthermore, we theoretically analyze the convergence
of the proposed FedDD scheme. Extensive performance evaluations demonstrate
that the proposed FedDD scheme can achieve outstanding performances in both
communication efficiency and model convergence, and also possesses a strong
generalization capability to data of rare classes
Supporting Information for “Robust Bayesian variable selection for gene-environment interactions”
NCACO-score: An effective main-chain dependent scoring function for structure modeling
<p>Abstract</p> <p>Background</p> <p>Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.</p> <p>Results</p> <p>Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.</p> <p>Conclusions</p> <p>Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.</p
Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization
To enhance the cross-target and cross-scene generalization of target-driven
visual navigation based on deep reinforcement learning (RL), we introduce an
information-theoretic regularization term into the RL objective. The
regularization maximizes the mutual information between navigation actions and
visual observation transforms of an agent, thus promoting more informed
navigation decisions. This way, the agent models the action-observation
dynamics by learning a variational generative model. Based on the model, the
agent generates (imagines) the next observation from its current observation
and navigation target. This way, the agent learns to understand the causality
between navigation actions and the changes in its observations, which allows
the agent to predict the next action for navigation by comparing the current
and the imagined next observations. Cross-target and cross-scene evaluations on
the AI2-THOR framework show that our method attains at least a
improvement of average success rate over some state-of-the-art models. We
further evaluate our model in two real-world settings: navigation in unseen
indoor scenes from a discrete Active Vision Dataset (AVD) and continuous
real-world environments with a TurtleBot.We demonstrate that our navigation
model is able to successfully achieve navigation tasks in these scenarios.
Videos and models can be found in the supplementary material.Comment: 11 pages, corresponding author: Kai Xu ([email protected]) and
Jun Wang ([email protected]
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
We present a target-driven navigation system to improve mapless visual
navigation in indoor scenes. Our method takes a multi-view observation of a
robot and a target as inputs at each time step to provide a sequence of actions
that move the robot to the target without relying on odometry or GPS at
runtime. The system is learned by optimizing a combinational objective
encompassing three key designs. First, we propose that an agent conceives the
next observation before making an action decision. This is achieved by learning
a variational generative module from expert demonstrations. We then propose
predicting static collision in advance, as an auxiliary task to improve safety
during navigation. Moreover, to alleviate the training data imbalance problem
of termination action prediction, we also introduce a target checking module to
differentiate from augmenting navigation policy with a termination action. The
three proposed designs all contribute to the improved training data efficiency,
static collision avoidance, and navigation generalization performance,
resulting in a novel target-driven mapless navigation system. Through
experiments on a TurtleBot, we provide evidence that our model can be
integrated into a robotic system and navigate in the real world. Videos and
models can be found in the supplementary material.Comment: 11 pages, accepted by IEEE Robotics and Automation Letter
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