15,325 research outputs found
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.Comment: CVPR 201
Hybridizing two-step growth mixture model and exploratory factor analysis to examine heterogeneity in nonlinear trajectories
Empirical researchers are usually interested in investigating the impacts of
baseline covariates have when uncovering sample heterogeneity and separating
samples into more homogeneous groups. However, a considerable number of studies
in the structural equation modeling (SEM) framework usually start with vague
hypotheses in terms of heterogeneity and possible reasons. It suggests that (1)
the determination and specification of a proper model with covariates is not
straightforward, and (2) the exploration process may be computational intensive
given that a model in the SEM framework is usually complicated and the pool of
candidate covariates is usually huge in the psychological and educational
domain where the SEM framework is widely employed. Following
\citet{Bakk2017two}, this article presents a two-step growth mixture model
(GMM) that examines the relationship between latent classes of nonlinear
trajectories and baseline characteristics. Our simulation studies demonstrate
that the proposed model is capable of clustering the nonlinear change patterns,
and estimating the parameters of interest unbiasedly, precisely, as well as
exhibiting appropriate confidence interval coverage. Considering the pool of
candidate covariates is usually huge and highly correlated, this study also
proposes implementing exploratory factor analysis (EFA) to reduce the dimension
of covariate space. We illustrate how to use the hybrid method, the two-step
GMM and EFA, to efficiently explore the heterogeneity of nonlinear trajectories
of longitudinal mathematics achievement data.Comment: Draft version 1.6, 08/08/2020. This paper has not been peer reviewed.
Please do not copy or cite without author's permissio
Universal contributions to charge fluctuations in spin chains at finite temperature
At finite temperature, conserved charges undergo thermal fluctuations in a
quantum many-body system in the grand canonical ensemble. The full structure of
the fluctuations of the total U(1) charge can be succinctly captured by the
generating function . For a
1D translation-invariant spin chain, in the thermodynamic limit the magnitude
scales with the system size as , where is the
scale-invariant contribution and may encode universal information about the
underlying system. In this work we investigate the behavior and physical
meaning of when the system is periodic. We find that
only takes non-zero values at isolated points of ,
which is for all our examples. In two exemplary lattice systems we
show that takes quantized values when the U(1) symmetry exhibits
a specific type of 't Hooft anomaly with other symmetries. In other cases, we
investigate how depends on microscopic conditions (such as the
filling factor) in field theory and exactly solvable lattice models.Comment: 19 pages, 2 figure
Microbial community pattern detection in human body habitats via ensemble clustering framework
The human habitat is a host where microbial species evolve, function, and
continue to evolve. Elucidating how microbial communities respond to human
habitats is a fundamental and critical task, as establishing baselines of human
microbiome is essential in understanding its role in human disease and health.
However, current studies usually overlook a complex and interconnected
landscape of human microbiome and limit the ability in particular body habitats
with learning models of specific criterion. Therefore, these methods could not
capture the real-world underlying microbial patterns effectively. To obtain a
comprehensive view, we propose a novel ensemble clustering framework to mine
the structure of microbial community pattern on large-scale metagenomic data.
Particularly, we first build a microbial similarity network via integrating
1920 metagenomic samples from three body habitats of healthy adults. Then a
novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is
proposed and applied onto the network to detect clustering pattern. Extensive
experiments are conducted to evaluate the effectiveness of our model on
deriving microbial community with respect to body habitat and host gender. From
clustering results, we observed that body habitat exhibits a strong bound but
non-unique microbial structural patterns. Meanwhile, human microbiome reveals
different degree of structural variations over body habitat and host gender. In
summary, our ensemble clustering framework could efficiently explore integrated
clustering results to accurately identify microbial communities, and provide a
comprehensive view for a set of microbial communities. Such trends depict an
integrated biography of microbial communities, which offer a new insight
towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
A New Powerful Nonparametric Rank Test for Ordered Alternative Problem
We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain
A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe
in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late
March. As the U.S. begins to gradually resume economic activity, it is
imperative for policymakers and power system operators to take a scientific
approach to understanding and predicting the impact on the electricity sector.
Here, we release a first-of-its-kind cross-domain open-access data hub,
integrating data from across all existing U.S. wholesale electricity markets
with COVID-19 case, weather, cellular location, and satellite imaging data.
Leveraging cross-domain insights from public health and mobility data, we
uncover a significant reduction in electricity consumption across that is
strongly correlated with the rise in the number of COVID-19 cases, degree of
social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript
can also be accessed from EnerarXiv:
http://www.enerarxiv.org/page/thesis.html?id=198
LocustDB: a relational database for the transcriptome and biology of the migratory locust (Locusta migratoria)
BACKGROUND: The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects for biological studies. Its transcriptomic data provide invaluable information for molecular entomology and pave a way for the comparative research of other medically, agronomically, and ecologically relevant insects. We developed the first transcriptomic database of the locust (LocustDB), building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. DESCRIPTION: LocustDB currently hosts 45,474 high-quality EST sequences from the locust, which were assembled into 12,161 unigenes. It, through user-friendly web interfaces, allows investigators to freely access sequence data, including homologous/orthologous sequences, functional annotations, and pathway analysis, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, including the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. The website address of LocustDB is . CONCLUSION: LocustDB starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporating in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies
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