271 research outputs found
Black Holes as Neutrino Factories
Ultralight bosons can grow substantially in the vicinity of a black hole,
through superradiant energy extraction. Consequently, such bosons can
potentially reach field values close to the Planck scale, making black holes
powerful transducers of such fields. If a scalar field couples to neutrino, it
can trigger parametric production of neutrinos, and potentially quench their
superradiant growth. During this saturation phase, scalar clouds can accelerate
neutrinos to the TeV energy scale, generating fluxes that surpass those
produced by atmospheric neutrinos.Comment: 13 pages, 3 figure
An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
High-dimensional linear regression model is the most popular statistical
model for high-dimensional data, but it is quite a challenging task to achieve
a sparse set of regression coefficients. In this paper, we propose a simple
heuristic algorithm to construct sparse high-dimensional linear regression
models, which is adapted from the shortest solution-guided decimation algorithm
and is referred to as ASSD. This algorithm constructs the support of regression
coefficients under the guidance of the least-squares solution of the
recursively decimated linear equations, and it applies an early-stopping
criterion and a second-stage thresholding procedure to refine this support. Our
extensive numerical results demonstrate that ASSD outperforms LASSO, vector
approximate message passing, and two other representative greedy algorithms in
solution accuracy and robustness. ASSD is especially suitable for linear
regression problems with highly correlated measurement matrices encountered in
real-world applications.Comment: 13 pages, 6 figure
Photon Ring Astrometry for Superradiant Clouds
Gravitational atoms produced from the superradiant extraction of rotational
energy of spinning black holes can reach energy densities significantly higher
than that of dark matter, turning black holes into powerful potential detectors
for ultralight bosons. These structures are formed by coherently oscillating
bosons, which induce oscillating metric perturbations, deflecting photon
geodesics passing through their interior. The deviation of nearby geodesics can
be further amplified near critical bound photon orbits. We discuss the prospect
of detecting this deflection using photon ring autocorrelations with the Event
Horizon Telescope and its next generation upgrade, which can probe a large
unexplored region of the cloud mass parameter space when compared with previous
constraints.Comment: 9 pages, 5 figure
Clustered Federated Learning based on Nonconvex Pairwise Fusion
This study investigates clustered federated learning (FL), one of the
formulations of FL with non-i.i.d. data, where the devices are partitioned into
clusters and each cluster optimally fits its data with a localized model. We
propose a novel clustered FL framework, which applies a nonconvex penalty to
pairwise differences of parameters. This framework can automatically identify
clusters without a priori knowledge of the number of clusters and the set of
devices in each cluster. To implement the proposed framework, we develop a
novel clustered FL method called FPFC. Advancing from the standard ADMM, our
method is implemented in parallel, updates only a subset of devices at each
communication round, and allows each participating device to perform a variable
amount of work. This greatly reduces the communication cost while
simultaneously preserving privacy, making it practical for FL. We also propose
a new warmup strategy for hyperparameter tuning under FL settings and consider
the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide
convergence guarantees of FPFC for general nonconvex losses and establish the
statistical convergence rate under a linear model with squared loss. Our
extensive experiments demonstrate the advantages of FPFC over existing methods.Comment: 46 pages, 9 figure
Composition of Rumen Bacterial Community in Dairy Cows With Different Levels of Somatic Cell Counts
Mastitis is an inflammatory disease, affects the dairy industry and has a severe economic impact. During subclinical mastitis, milk production and milk quality deteriorates. Recently, rumen microbial composition has been linked to rumen health, but few studies have investigated the effect of rumen microbiota on mammary health in cows. This study was undertaken to identify the rumen microbial composition and associated microbial fermentation in cows with different somatic cell counts (SCC), with the speculation that cows with different health statuses of the mammary gland have different rumen bacterial composition and diversity. A total of 319 Holstein dairy cows fed the same diet and under the same management were selected and divided into four groups as SCC1 (N = 175), SCC2 (N = 49), SCC3 (N = 49), and SCC4 (N = 46) with < 200,000, 200,001–500,000, 500,001–1,000,000, and >1,000,000 somatic cells/mL, respectively. Further, 20 cows with the lowest SCC and 20 cows with the highest SCC were identified. The rumen microbial composition was profiled using 16S rRNA sequencing, along with measurement of rumen fermentation parameters and milking performance. Compared to low SCC, cows with high SCC showed poorer milk yield, milk composition, and rumen volatile fatty acids concentration, but higher rumen bacterial diversity. Although the predominant rumen bacterial taxa did not vary among the SCC groups, the relative abundance of phyla SR1 and Actinobacteria, unclassified family Clostridiales and genus Butyrivibrio were significantly different. In addition, Proteobacteria and family Succinivibrionaceae were enriched in cows with low SCC. Our results suggest that specific rumen microbes are altered in cows with high SCC
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
Current video object detection (VOD) models often encounter issues with
over-aggregation due to redundant aggregation strategies, which perform feature
aggregation on every frame. This results in suboptimal performance and
increased computational complexity. In this work, we propose an image-level
Object Detection Difficulty (ODD) metric to quantify the difficulty of
detecting objects in a given image. The derived ODD scores can be used in the
VOD process to mitigate over-aggregation. Specifically, we train an ODD
predictor as an auxiliary head of a still-image object detector to compute the
ODD score for each image based on the discrepancies between detection results
and ground-truth bounding boxes. The ODD score enhances the VOD system in two
ways: 1) it enables the VOD system to select superior global reference frames,
thereby improving overall accuracy; and 2) it serves as an indicator in the
newly designed ODD Scheduler to eliminate the aggregation of frames that are
easy to detect, thus accelerating the VOD process. Comprehensive experiments
demonstrate that, when utilized for selecting global reference frames, ODD-VOD
consistently enhances the accuracy of Global-frame-based VOD models. When
employed for acceleration, ODD-VOD consistently improves the frames per second
(FPS) by an average of 73.3% across 8 different VOD models without sacrificing
accuracy. When combined, ODD-VOD attains state-of-the-art performance when
competing with many VOD methods in both accuracy and speed. Our work represents
a significant advancement towards making VOD more practical for real-world
applications.Comment: 11 pages, 6 figures, accepted by ACM MM202
Earth Shielding and Daily Modulation from Electrophilic Boosted Dark Particles
Boosted dark particles of astrophysical origin can lead to nonstandard
nuclear or electron recoil signals in direct detection experiments. %It has
been shown that this interpretation successfully explains the excess of keV
electron recoil events recently observed by the XENON1T experiment, and that a
daily modulation of the signal in the detector is expected. We conduct an
investigation of the daily modulation feature of a potential future signal of
this type. In particular, we perform simulations of the dark particle
interactions with electrons in atoms building up the Earth on its path to the
detector, and provide in-depth predictions for the expected daily changes in
the signal for various direct detection experiments, including XENONnT, PandaX,
and LUX-ZEPLIN.Comment: 15 pages, 12 figures, published version in PRD. For the code to
calculate the atomic ionization form factor, see
https://github.com/XueXiao-Physics/AtomIonCalc (AtomIonCalc) . For the Monte
Carlo simulation of the electrophilic dark particles traveling inside the
earth, see https://github.com/XueXiao-Physics/realEarthScatterDM
(realEarthScatterDM
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