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 stick–slip 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