437 research outputs found
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
Many problems encountered in science and engineering can be formulated as
estimating a low-rank object (e.g., matrices and tensors) from incomplete, and
possibly corrupted, linear measurements. Through the lens of matrix and tensor
factorization, one of the most popular approaches is to employ simple iterative
algorithms such as gradient descent (GD) to recover the low-rank factors
directly, which allow for small memory and computation footprints. However, the
convergence rate of GD depends linearly, and sometimes even quadratically, on
the condition number of the low-rank object, and therefore, GD slows down
painstakingly when the problem is ill-conditioned. This chapter introduces a
new algorithmic approach, dubbed scaled gradient descent (ScaledGD), that
provably converges linearly at a constant rate independent of the condition
number of the low-rank object, while maintaining the low per-iteration cost of
gradient descent for a variety of tasks including sensing, robust principal
component analysis and completion. In addition, ScaledGD continues to admit
fast global convergence to the minimax-optimal solution, again almost
independent of the condition number, from a small random initialization when
the rank is over-specified in the presence of Gaussian noise. In total,
ScaledGD highlights the power of appropriate preconditioning in accelerating
nonconvex statistical estimation, where the iteration-varying preconditioners
promote desirable invariance properties of the trajectory with respect to the
symmetry in low-rank factorization without hurting generalization.Comment: Book chapter for "Explorations in the Mathematics of Data Science -
The Inaugural Volume of the Center for Approximation and Mathematical Data
Analytics". arXiv admin note: text overlap with arXiv:2104.1452
RTVis: Research Trend Visualization Toolkit
When researchers and practitioners are about to start a new project or have
just entered a new research field, choosing a proper research topic is always
challenging. To help them have an overall understanding of the research trend
in real-time and find out the research topic they are interested in, we develop
the Research Trend Visualization toolkit (RTVis) to analyze and visualize the
research paper information. RTVis consists of a field theme river, a
co-occurrence network, a specialized citation bar chart, and a word frequency
race diagram, showing the field change through time respectively, cooperating
relationship among authors, paper citation numbers in different venues, and the
most common words in the abstract part. Moreover, RTVis is open source and easy
to deploy. The demo of our toolkit and code with detailed documentation are
both available online.Comment: Work submitted to IEEE VIS 2023 (Poster). 2 pages, 1 figure. For our
demo page, visit http://www.rtvis.design
Hierarchical-level rain image generative model based on GAN
Autonomous vehicles are exposed to various weather during operation, which is
likely to trigger the performance limitations of the perception system, leading
to the safety of the intended functionality (SOTIF) problems. To efficiently
generate data for testing the performance of visual perception algorithms under
various weather conditions, a hierarchical-level rain image generative model,
rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on
the generative adversarial network (GAN) and can generate images of light,
medium, and heavy rain. Different rain intensities are introduced as labels in
conditional GAN (CGAN). Meanwhile, the model structure is optimized and the
training strategy is adjusted to alleviate the problem of mode collapse. In
addition, natural rain images of different intensities are collected and
processed for model training and validation. Compared with the two baseline
models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of
RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the
structural similarity (SSIM) is improved by 18% and 8%, respectively. The
ablation experiments are also carried out to validate the effectiveness of the
model tuning
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Microbial functional traits are sensitive indicators of mild disturbance by lamb grazing.
Mild disturbances are prevalent in the environment, which may not be easily notable but could have considerable ecological consequences over prolonged periods. To evaluate this, a field study was designed to examine the effects of very light-intensity lamb grazing on grassland soil microbiomes with different soil backgrounds. No significant change (P > 0.05) was observed in any vegetation and soil variables. Nonetheless, hundreds of microbial functional gene families, but not bacterial taxonomy, were significantly (P < 0.05) shifted. The relative abundances of both taxonomic markers and functional genes related to nitrifying bacteria were also changed. The observation highlighted herein, showing a high level of sensitivity with respect to functional traits (functionally categorized taxa or genes) in differentiating mild environmental disturbance, suggests that the key level at which to address microbial responses may not be "species" (by means of rRNA taxonomy), but rather at the functional gene level
Divergent taxonomic and functional responses of microbial communities to field simulation of aeolian soil erosion and deposition.
Aeolian soil erosion and deposition have worldwide impacts on agriculture, air quality and public health. However, ecosystem responses to soil erosion and deposition remain largely unclear in regard to microorganisms, which are the crucial drivers of biogeochemical cycles. Using integrated metagenomics technologies, we analysed microbial communities subjected to simulated soil erosion and deposition in a semiarid grassland of Inner Mongolia, China. As expected, soil total organic carbon and plant coverage were decreased by soil erosion, and soil dissolved organic carbon (DOC) was increased by soil deposition, demonstrating that field simulation was reliable. Soil microbial communities were altered (p < .039) by both soil erosion and deposition, with dramatic increase in Cyanobacteria related to increased stability in soil aggregates. amyA genes encoding α-amylases were specifically increased (p = .01) by soil deposition and positively correlated (p = .02) to DOC, which likely explained changes in DOC. Surprisingly, most of microbial functional genes associated with carbon, nitrogen, phosphorus and potassium cycling were decreased or unaltered by both erosion and deposition, probably arising from acceleration of organic matter mineralization. These divergent responses support the necessity to include microbial components in evaluating ecological consequences. Furthermore, Mantel tests showed strong, significant correlations between soil nutrients and functional structure but not taxonomic structure, demonstrating close relevance of microbial function traits to nutrient cycling
Multidifferential study of identified charged hadron distributions in -tagged jets in proton-proton collisions at 13 TeV
Jet fragmentation functions are measured for the first time in proton-proton
collisions for charged pions, kaons, and protons within jets recoiling against
a boson. The charged-hadron distributions are studied longitudinally and
transversely to the jet direction for jets with transverse momentum 20 GeV and in the pseudorapidity range . The
data sample was collected with the LHCb experiment at a center-of-mass energy
of 13 TeV, corresponding to an integrated luminosity of 1.64 fb. Triple
differential distributions as a function of the hadron longitudinal momentum
fraction, hadron transverse momentum, and jet transverse momentum are also
measured for the first time. This helps constrain transverse-momentum-dependent
fragmentation functions. Differences in the shapes and magnitudes of the
measured distributions for the different hadron species provide insights into
the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb
public pages
Study of the decay
The decay is studied
in proton-proton collisions at a center-of-mass energy of TeV
using data corresponding to an integrated luminosity of 5
collected by the LHCb experiment. In the system, the
state observed at the BaBar and Belle experiments is
resolved into two narrower states, and ,
whose masses and widths are measured to be where the first uncertainties are statistical and the second
systematic. The results are consistent with a previous LHCb measurement using a
prompt sample. Evidence of a new
state is found with a local significance of , whose mass and width
are measured to be and , respectively. In addition, evidence of a new decay mode
is found with a significance of
. The relative branching fraction of with respect to the
decay is measured to be , where the first
uncertainty is statistical, the second systematic and the third originates from
the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb
public pages
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