594 research outputs found
Sure Screening for Gaussian Graphical Models
We propose {graphical sure screening}, or GRASS, a very simple and
computationally-efficient screening procedure for recovering the structure of a
Gaussian graphical model in the high-dimensional setting. The GRASS estimate of
the conditional dependence graph is obtained by thresholding the elements of
the sample covariance matrix. The proposed approach possesses the sure
screening property: with very high probability, the GRASS estimated edge set
contains the true edge set. Furthermore, with high probability, the size of the
estimated edge set is controlled. We provide a choice of threshold for GRASS
that can control the expected false positive rate. We illustrate the
performance of GRASS in a simulation study and on a gene expression data set,
and show that in practice it performs quite competitively with more complex and
computationally-demanding techniques for graph estimation
Layout and Location of Water IoT Device Based on Few-Shot Reinforcement Learning
After the traditional water equipment integrates the communication module, IoT (Internet of Things) device is formed. Whether these battery-powered IoT devices can be installed in a certain location depends on whether the power consumption of these IoT devices in these locations can meet the expected life cycle. In this paper, by adopting strategies to save the power consumption of IoT devices when sending data, more locations can be selected to install IoT devices. The process of IoT device sending data packet sequence needs to be aware of the environment, interact with the environment, then make a decision, and then adjust the policy according to the effect of the action. Therefore, in this paper, the process of IoT device sending data packet sequence is modelled as MDP (Markov Sequence Decision Process), and the real-time SINR of channel and the transmission delay of data packet sequence are defined as the state space, and the action space consists of immediate transmission and delayed transmission, with the minimum total power consumption as the objective function. Because IoT devices are very sensitive to power consumption and cannot collect a large amount of data for training, this paper uses the Proximal Policy Optimization algorithm based on prior distribution to conduct few-shot reinforcement learning to quickly obtain the optimal decision sequence of layout and location of IoT devices
Facial Motion Prior Networks for Facial Expression Recognition
Deep learning based facial expression recognition (FER) has received a lot of
attention in the past few years. Most of the existing deep learning based FER
methods do not consider domain knowledge well, which thereby fail to extract
representative features. In this work, we propose a novel FER framework, named
Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition
branch to generate a facial mask so as to focus on facial muscle moving
regions. To guide the facial mask learning, we propose to incorporate prior
domain knowledge by using the average differences between neutral faces and the
corresponding expressive faces as the training guidance. Extensive experiments
on three facial expression benchmark datasets demonstrate the effectiveness of
the proposed method, compared with the state-of-the-art approaches.Comment: VCIP 2019, Oral. Code is available at
https://github.com/donydchen/FMPN-FE
Recommended from our members
Credit-based Pricing for Multi-user Class Transportation Facilities
This paper proposes an innovative arc-based credit (ABC) congestion pricing scheme to improve the system performance in a transportation network. By associating each arc with apositive or negative credit rate, the strategy can accomplish multiple planning goals, such as efficiency, fairness, and public acceptance simultaneously. We first demonstrate that on a one-origin or one-destination network, a pareto-improving, system-optimal and revenue-neutral credit scheme always exists and can be obtained by solving a set of linear equations. Recognizing that such a credit scheme may not exist in a multi-origin network, we then define the maximum-revenue problem with pareto-improving constrains (MRPI): find the maximum possible revenue collected by the credit scheme with optimal arc flows and non-increasing origin-destination (OD) travel costs. We discover that the dual of MRPI is equivalent to a typical Transportation Problem which, therefore, provides a simple way to calculate the revenue by just examining the dual problem. At the end of the paper, a numerical example with a small synthetic network is provided for the comparison of the credit scheme with other existing toll schemes in terms of OD travel disutilities
Microsatellites within genes and ESTs of the Pacific oyster Crassostrea gigas and their transferability in five other Crassostrea species
We developed 15 novel polymorphic microsatellites for the Pacific oyster Crassostrea gigas by screening genes and expressed sequence tags (ESTs) found in GenBank. The number of alleles per locus ranged from two to 24 with an average of 8.7, and the values of observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.026 to 0.750 and from 0.120 to 0.947, respectively. No significant pairwise linkage disequilibrium was detected among loci and eight loci conformed to Hardy-Weinberg equilibrium. Transferability of the markers was examined on five other Crassostrea species and all the markers were amplified successfully in at least one species. These new microsatellites should be useful for population genetics, parentage analysis and genome mapping studies of C. gigas and closely related species. The nine markers identified from known genes are expected to be especially valuable for comparative mapping as type I markers
Conformal off-policy prediction
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. In this paper, we develop a novel procedure to produce reliable interval estimators for a target policy’s return starting from any initial state. Our proposal accounts for the variability of the return around its expectation, focuses on the individual effect and offers valid uncertainty quantification. Our main idea lies in designing a pseudo policy that generates subsamples as if they were sampled from the target policy so that existing conformal prediction algorithms are applicable to prediction interval construction. Our methods are justified by theories, synthetic data and real data from short-video platforms
3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping
We present 3DHumanGAN, a 3D-aware generative adversarial network that
synthesizes photorealistic images of full-body humans with consistent
appearances under different view-angles and body-poses. To tackle the
representational and computational challenges in synthesizing the articulated
structure of human bodies, we propose a novel generator architecture in which a
2D convolutional backbone is modulated by a 3D pose mapping network. The 3D
pose mapping network is formulated as a renderable implicit function
conditioned on a posed 3D human mesh. This design has several merits: i) it
leverages the strength of 2D GANs to produce high-quality images; ii) it
generates consistent images under varying view-angles and poses; iii) the model
can incorporate the 3D human prior and enable pose conditioning. Project page:
https://3dhumangan.github.io/.Comment: 9 pages, 8 figure
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
In real-world scenarios like traffic and energy, massive time-series data
with missing values and noises are widely observed, even sampled irregularly.
While many imputation methods have been proposed, most of them work with a
local horizon, which means models are trained by splitting the long sequence
into batches of fit-sized patches. This local horizon can make models ignore
global trends or periodic patterns. More importantly, almost all methods assume
the observations are sampled at regular time stamps, and fail to handle complex
irregular sampled time series arising from different applications. Thirdly,
most existing methods are learned in an offline manner. Thus, it is not
suitable for many applications with fast-arriving streaming data. To overcome
these limitations, we propose \ours: Bayesian Online Multivariate Time series
Imputation with functional decomposition. We treat the multivariate time series
as the weighted combination of groups of low-rank temporal factors with
different patterns. We apply a group of Gaussian Processes (GPs) with different
kernels as functional priors to fit the factors. For computational efficiency,
we further convert the GPs into a state-space prior by constructing an
equivalent stochastic differential equation (SDE), and developing a scalable
algorithm for online inference. The proposed method can not only handle
imputation over arbitrary time stamps, but also offer uncertainty
quantification and interpretability for the downstream application. We evaluate
our method on both synthetic and real-world datasets
- …