119 research outputs found
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes
Bayesian optimization (BO), while proved highly effective for many black-box
function optimization tasks, requires practitioners to carefully select priors
that well model their functions of interest. Rather than specifying by hand,
researchers have investigated transfer learning based methods to automatically
learn the priors, e.g. multi-task BO (Swersky et al., 2013), few-shot BO
(Wistuba and Grabocka, 2021) and HyperBO (Wang et al., 2022). However, those
prior learning methods typically assume that the input domains are the same for
all tasks, weakening their ability to use observations on functions with
different domains or generalize the learned priors to BO on different search
spaces. In this work, we present HyperBO+: a pre-training approach for
hierarchical Gaussian processes that enables the same prior to work universally
for Bayesian optimization on functions with different domains. We propose a
two-step pre-training method and analyze its appealing asymptotic properties
and benefits to BO both theoretically and empirically. On real-world
hyperparameter tuning tasks that involve multiple search spaces, we demonstrate
that HyperBO+ is able to generalize to unseen search spaces and achieves lower
regrets than competitive baselines.Comment: Full version of the workshop paper at 2022 NeurIPS Workshop on
Gaussian Processes, Spatiotemporal Modeling, and Decision-making System
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
Bayesian optimization (BO) is a popular black-box function optimization
method, which makes sequential decisions based on a Bayesian model, typically a
Gaussian process (GP), of the function. To ensure the quality of the model,
transfer learning approaches have been developed to automatically design GP
priors by learning from observations on "training" functions. These training
functions are typically required to have the same domain as the "test" function
(black-box function to be optimized). In this paper, we introduce MPHD, a model
pre-training method on heterogeneous domains, which uses a neural net mapping
from domain-specific contexts to specifications of hierarchical GPs. MPHD can
be seamlessly integrated with BO to transfer knowledge across heterogeneous
search spaces. Our theoretical and empirical results demonstrate the validity
of MPHD and its superior performance on challenging black-box function
optimization tasks
Angle-selective perfect absorption with two-dimensional materials
Two-dimensional (2D) materials have great potential in photonic and optoelectronic devices. However, the relatively weak light absorption in 2D materials hinders their application in practical devices. Here, we propose a general approach to achieve angle-selective perfect light absorption in 2D materials. As a demonstration of the concept, we experimentally show giant light absorption by placing large-area single-layer graphene on a structure consisting of a chalcogenide layer atop a mirror and achieving a total absorption of 77.6% in the mid-infrared wavelength range (~13 μm), where the graphene contributes a record-high 47.2% absorptivity of mid-infrared light. Construction of such an angle-selective thin optical element is important for solar and thermal energy harvesting, photo-detection and sensing applications. Our study points to a new opportunity to combine 2D materials with photonic structures to enable novel device applications
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
Planar object tracking is a critical computer vision problem and has drawn
increasing interest owing to its key roles in robotics, augmented reality, etc.
Despite rapid progress, its further development, especially in the deep
learning era, is largely hindered due to the lack of large-scale challenging
benchmarks. Addressing this, we introduce PlanarTrack, a large-scale
challenging planar tracking benchmark. Specifically, PlanarTrack consists of
1,000 videos with more than 490K images. All these videos are collected in
complex unconstrained scenarios from the wild, which makes PlanarTrack,
compared with existing benchmarks, more challenging but realistic for
real-world applications. To ensure the high-quality annotation, each frame in
PlanarTrack is manually labeled using four corners with multiple-round careful
inspection and refinement. To our best knowledge, PlanarTrack, to date, is the
largest and most challenging dataset dedicated to planar object tracking. In
order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and
conduct comprehensive comparisons and in-depth analysis. Our results, not
surprisingly, demonstrate that current top-performing planar trackers
degenerate significantly on the challenging PlanarTrack and more efforts are
needed to improve planar tracking in the future. In addition, we further derive
a variant named PlanarTrack for generic object tracking from
PlanarTrack. Our evaluation of 10 excellent generic trackers on
PlanarTrack manifests that, surprisingly,
PlanarTrack is even more challenging than several popular
generic tracking benchmarks and more attention should be paid to handle such
planar objects, though they are rigid. All benchmarks and evaluations will be
released at the project webpage.Comment: Tech. Repor
A multi-variable predictive warning model for cervical cancer using clinical and SNPs data
IntroductionCervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).MethodsClinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models’ efficiencies. The performance of models was validated using decision curve analysis (DCA).ResultsThe LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.DiscussionThe predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management
A Novel N-Arylpyridone Compound Alleviates the Inflammatory and Fibrotic Reaction of Silicosis by Inhibiting the ASK1-p38 Pathway and Regulating Macrophage Polarization
Silicosis is one of the potentially fatal occupational diseases characterized by respiratory dysfunction, chronic interstitial inflammation, and fibrosis, for which treatment options are limited. Previous studies showed that a novel N-arylpyridone compound named AKEX0011 exhibited anti-inflammatory and anti-fibrotic effects in bleomycin-induced pulmonary fibrosis; however, it is unknown whether it could also be effective against silicosis. Therefore, we sought to investigate the preventive and therapeutic roles of AKEX0011 in a silicosis rodent model and in a silica-stimulated macrophage cell line. In vivo, our results showed that AKEX0011 ameliorated silica-induced imaging lung damages, respiratory dysfunction, reduced the secretion of inflammatory and fibrotic factors (TNF-α, IL-1β, IL-6, TGF-β, IL-4, and IL-10), and the deposition of fibrosis-related proteins (collagen I, fibronectin, and α-SMA), regardless of early or advanced therapy. Specifically, we found that AKEX0011 attenuated silicosis by inhibiting apoptosis, blocking the ASK1-p38 MAPK signaling pathway, and regulating polarization of macrophages. In vitro, AKEX0011 inhibited macrophages from secreting inflammatory cytokines and inhibited apoptosis of macrophages in pre-treated and post-treated models, concurrent with blocking the ASK1-p38 pathway and inhibiting M1 polarization. Collectively, AKEX0011, as a novel N-arylpyridone compound, exerted protective effects for silica-induced pulmonary inflammation and fibrosis both in vivo and in vitro, and hence, it could be a strong drug candidate for the treatment of silicosis
Addendum: A novel N-Arylpyridone compound alleviates the inflammatory and fibrotic reaction of silicosis by inhibiting the ASK1-p38 pathway and regulating macrophage polarization
Corrigendum: A novel N-arylpyridone compound alleviates the inflammatory and fibrotic reaction of silicosis by inhibiting the ASK1-p38 pathway and regulating macrophage polarization
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