1,512 research outputs found
A Causal, Data-Driven Approach to Modeling the Kepler Data
Astronomical observations are affected by several kinds of noise, each with
its own causal source; there is photon noise, stochastic source variability,
and residuals coming from imperfect calibration of the detector or telescope.
The precision of NASA Kepler photometry for exoplanet science---the most
precise photometric measurements of stars ever made---appears to be limited by
unknown or untracked variations in spacecraft pointing and temperature, and
unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for
Kepler data, a data-driven model intended to capture variability but preserve
transit signals. The CPM works at the pixel level so that it can capture very
fine-grained information about the variation of the spacecraft. The CPM
predicts each target pixel value from a large number of pixels of other stars
sharing the instrument variabilities while not containing any information on
possible transits in the target star. In addition, we use the target star's
future and past (auto-regression). By appropriately separating, for each data
point, the data into training and test sets, we ensure that information about
any transit will be perfectly isolated from the model. The method has four
hyper-parameters (the number of predictor stars, the auto-regressive window
size, and two L2-regularization amplitudes for model components), which we set
by cross-validation. We determine a generic set of hyper-parameters that works
well for most of the stars and apply the method to a corresponding set of
target stars. We find that we can consistently outperform (for the purposes of
exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for
exoplanet discovery.Comment: Accepted for publication in the PAS
Pseudo-Derivative-Feedback Current Control for Three-Phase Grid-Connected Inverters With LCL Filters
Ceria–terbia solid solution nanobelts with high catalytic activities for CO oxidation
Ceria–terbia solid solution nanobelts were prepared by an electrochemical route and tested as catalysts of high activity for CO oxidation
Federated Generalization via Information-Theoretic Distribution Diversification
Federated Learning (FL) has surged in prominence due to its capability of
collaborative model training without direct data sharing. However, the vast
disparity in local data distributions among clients, often termed the
non-Independent Identically Distributed (non-IID) challenge, poses a
significant hurdle to FL's generalization efficacy. The scenario becomes even
more complex when not all clients participate in the training process, a common
occurrence due to unstable network connections or limited computational
capacities. This can greatly complicate the assessment of the trained models'
generalization abilities. While a plethora of recent studies has centered on
the generalization gap pertaining to unseen data from participating clients
with diverse distributions, the divergence between the training distributions
of participating clients and the testing distributions of non-participating
ones has been largely overlooked. In response, our paper unveils an
information-theoretic generalization framework for FL. Specifically, it
quantifies generalization errors by evaluating the information entropy of local
distributions and discerning discrepancies across these distributions. Inspired
by our deduced generalization bounds, we introduce a weighted aggregation
approach and a duo of client selection strategies. These innovations aim to
bolster FL's generalization prowess by encompassing a more varied set of client
data distributions. Our extensive empirical evaluations reaffirm the potency of
our proposed methods, aligning seamlessly with our theoretical construct
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