1,103 research outputs found
Dust Scattering In Turbulent Media: Correlation Between The Scattered Light and Dust Column Density
Radiative transfer models in a spherical, turbulent interstellar medium (ISM)
in which the photon source is situated at the center are calculated to
investigate the correlation between the scattered light and the dust column
density. The medium is modeled using fractional Brownian motion structures that
are appropriate for turbulent ISM. The correlation plot between the scattered
light and optical depth shows substantial scatter and deviation from simple
proportionality. It was also found that the overall density contrast is
smoothed out in scattered light. In other words, there is an enhancement of the
dust-scattered flux in low-density regions, while the scattered flux is
suppressed in high-density regions. The correlation becomes less significant as
the scattering becomes closer to be isotropic and the medium becomes more
turbulent. Therefore, the scattered light observed in near-infrared wavelengths
would show much weaker correlation than the observations in optical and
ultraviolet wavelengths. We also find that the correlation plot between
scattered lights at two different wavelengths shows a tighter correlation than
that of the scattered light versus the optical depth.Comment: 6 pages, 5 figure, accepted for publication in the ApJ Letter
Friction force microscopy : a simple technique for identifying graphene on rough substrates and mapping the orientation of graphene grains on copper
At a single atom thick, it is challenging to distinguish graphene from its substrate using conventional techniques. In this paper we show that friction force microscopy (FFM) is a simple and quick technique for identifying graphene on a range of samples, from growth substrates to rough insulators. We show that FFM is particularly effective for characterizing graphene grown on copper where it can correlate the graphene growth to the three-dimensional surface topography. Atomic lattice stickâslip friction is readily resolved and enables the crystallographic orientation of the graphene to be mapped nondestructively, reproducibly and at high resolution. We expect FFM to be similarly effective for studying graphene growth on other metal/locally crystalline substrates, including SiC, and for studying growth of other two-dimensional materials such as molybdenum disulfide and hexagonal boron nitride
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Federated learning (FL) is a promising approach that enables distributed
clients to collaboratively train a global model while preserving their data
privacy. However, FL often suffers from data heterogeneity problems, which can
significantly affect its performance. To address this, clustered federated
learning (CFL) has been proposed to construct personalized models for different
client clusters. One effective client clustering strategy is to allow clients
to choose their own local models from a model pool based on their performance.
However, without pre-trained model parameters, such a strategy is prone to
clustering failure, in which all clients choose the same model. Unfortunately,
collecting a large amount of labeled data for pre-training can be costly and
impractical in distributed environments. To overcome this challenge, we
leverage self-supervised contrastive learning to exploit unlabeled data for the
pre-training of FL systems. Together, self-supervised pre-training and client
clustering can be crucial components for tackling the data heterogeneity issues
of FL. Leveraging these two crucial strategies, we propose contrastive
pre-training-based clustered federated learning (CP-CFL) to improve the model
convergence and overall performance of FL systems. In this work, we demonstrate
the effectiveness of CP-CFL through extensive experiments in heterogeneous FL
settings, and present various interesting observations.Comment: Published in Neural Network
Weighing the Universe with Photometric Redshift Surveys and the Impact on Dark Energy Forecasts
With a wariness of Occam's razor awakened by the discovery of cosmic
acceleration, we abandon the usual assumption of zero mean curvature and ask
how well it can be determined by planned surveys. We also explore the impact of
uncertain mean curvature on forecasts for the performance of planned dark
energy probes. We find that weak lensing and photometric baryon acoustic
oscillation data, in combination with CMB data, can determine the mean
curvature well enough that the residual uncertainty does not degrade
constraints on dark energy. We also find that determinations of curvature are
highly tolerant of photometric redshift errors.Comment: 6 pages, submitted to Ap
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Federated learning (FL) enables a decentralized machine learning paradigm for
multiple clients to collaboratively train a generalized global model without
sharing their private data. Most existing works simply propose typical FL
systems for single-modal data, thus limiting its potential on exploiting
valuable multimodal data for future personalized applications. Furthermore, the
majority of FL approaches still rely on the labeled data at the client side,
which is limited in real-world applications due to the inability of
self-annotation from users. In light of these limitations, we propose a novel
multimodal FL framework that employs a semi-supervised learning approach to
leverage the representations from different modalities. Bringing this concept
into a system, we develop a distillation-based multimodal embedding knowledge
transfer mechanism, namely FedMEKT, which allows the server and clients to
exchange the joint knowledge of their learning models extracted from a small
multimodal proxy dataset. Our FedMEKT iteratively updates the generalized
global encoders with the joint embedding knowledge from the participating
clients. Thereby, to address the modality discrepancy and labeled data
constraint in existing FL systems, our proposed FedMEKT comprises local
multimodal autoencoder learning, generalized multimodal autoencoder
construction, and generalized classifier learning. Through extensive
experiments on three multimodal human activity recognition datasets, we
demonstrate that FedMEKT achieves superior global encoder performance on linear
evaluation and guarantees user privacy for personal data and model parameters
while demanding less communication cost than other baselines
Consistency test of general relativity from large scale structure of the Universe
We construct a consistency test of General Relativity (GR) on cosmological
scales. This test enables us to distinguish between the two alternatives to
explain the late-time accelerated expansion of the universe, that is, dark
energy models based on GR and modified gravity models without dark energy. We
derive the consistency relation in GR which is written only in terms of
observables - the Hubble parameter, the density perturbations, the peculiar
velocities and the lensing potential. The breakdown of this consistency
relation implies that the Newton constant which governs large-scale structure
is different from that in the background cosmology, which is a typical feature
in modified gravity models. We propose a method to perform this test by
reconstructing the weak lensing spectrum from measured density perturbations
and peculiar velocities. This reconstruction relies on Poisson's equation in GR
to convert the density perturbations to the lensing potential. Hence any
inconsistency between the reconstructed lensing spectrum and the measured
lensing spectrum indicates the failure of GR on cosmological scales. The
difficulties in performing this test using actual observations are discussed.Comment: 7 pages, 1 figur
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