Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration

Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized model when collaborating with others. A key question in PFL is to decide which parameters of a client should be localized or shared with others. In current mainstream approaches, all layers that are sensitive to non-IID data (such as classifier layers) are generally personalized. The reasoning behind this approach is understandable, as localizing parameters that are easily influenced by non-IID data can prevent the potential negative effect of collaboration. However, we believe that this approach is too conservative for collaboration. For example, for a certain client, even if its parameters are easily influenced by non-IID data, it can still benefit by sharing these parameters with clients having similar data distribution. This observation emphasizes the importance of considering not only the sensitivity to non-IID data but also the similarity of data distribution when determining which parameters should be localized in PFL. This paper introduces a novel guideline for client collaboration in PFL. Unlike existing approaches that prohibit all collaboration of sensitive parameters, our guideline allows clients to share more parameters with others, leading to improved model performance. Additionally, we propose a new PFL method named FedCAC, which employs a quantitative metric to evaluate each parameter's sensitivity to non-IID data and carefully selects collaborators based on this evaluation. Experimental results demonstrate that FedCAC enables clients to share more parameters with others, resulting in superior performance compared to state-of-the-art methods, particularly in scenarios where clients have diverse distributions.Comment: Accepted by ICCV202

Searching for radio pulsation from SGR 1935+2154 with the Parkes Ultra-Wideband Low receiver

Magnetars have been proposed to be the origin of FRBs soon after its initial discovery. The detection of the first Galactic FRB 20200428 from SGR 1935+2154 has made this hypothesis more convincing. In October 2020, this source was supposed to be in an extremely active state again. We then carried out a 1.6-hours follow-up observation of SGR 1935+2154 using the new ultra-wideband low (UWL) receiver of the Parkes 64\,m radio telescope covering a frequency range of 704$-$4032 MHz. However, no convincing signal was detected in either of our single pulse or periodicity searches. We obtained a limit on the flux density of periodic signal of $\rm 3.6\,\mu Jy$ using the full 3.3GHz bandwidth data sets, which is the strictest limit for that of SGR 1935+2154. Our full bandwidth limit on the single pulses fluence is 35mJy ms, which is well below the brightest single pulses detected by the FAST radio telescope just two before our observation. Assuming that SGR 1935+2154 is active during our observation, our results suggest that its radio bursts are either intrinsically narrowband or show a steep spectrum

Generative-based Fusion Mechanism for Multi-Modal Tracking

Generative models (GMs) have received increasing research interest for their remarkable capacity to achieve comprehensive understanding. However, their potential application in the domain of multi-modal tracking has remained relatively unexplored. In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking. In this paper, we delve into two prominent GM techniques, namely, Conditional Generative Adversarial Networks (CGANs) and Diffusion Models (DMs). Different from the standard fusion process where the features from each modality are directly fed into the fusion block, we condition these multi-modal features with random noise in the GM framework, effectively transforming the original training samples into harder instances. This design excels at extracting discriminative clues from the features, enhancing the ultimate tracking performance. To quantitatively gauge the effectiveness of our approach, we conduct extensive experiments across two multi-modal tracking tasks, three baseline methods, and three challenging benchmarks. The experimental results demonstrate that the proposed generative-based fusion mechanism achieves state-of-the-art performance, setting new records on LasHeR and RGBD1K

The human parvovirus B19 non-structural protein 1 N-terminal domain specifically binds to the origin of replication in the viral DNA

The non-structural protein 1 (NS1) of human parvovirus B19 plays a critical role in viral DNA replication. Previous studies identified the origin of replication in the viral DNA, which contains four DNA elements, namely NSBE1 to NSBE4, that are required for optimal viral replication (Guan et al, 2009, J. Virology, 83, 9541-9553). Here we have demonstrated in vitro that the NS1 N-terminal domain (NS1N) binds to the origin of replication in a sequence-specific, length-dependent manner that requires NSBE1 and NSBE2, while NSBE3 and NSBE4 are dispensable. Mutagenesis analysis has identified nucleotides in NSBE1 and NSBE2 that are critical for NS1N binding. These results suggest that NS1 binds to the NSBE1-NSBE2 region in the origin of replication, while NSBE3 and NSBE4 may provide binding sites for potential cellular factors. Such a specialized nucleoprotein complex may enable NS1 to nick the terminal resolution site and separate DNA strands during replication

The human parvovirus B19 non-structural protein 1 N-terminal domain specifically binds to the origin of replication in the viral DNA

The non-structural protein 1 (NS1) of human parvovirus B19 plays a critical role in viral DNA replication. Previous studies identified the origin of replication in the viral DNA, which contains four DNA elements, namely NSBE1 to NSBE4, that are required for optimal viral replication (Guan et al, 2009, J. Virology, 83, 9541-9553). Here we have demonstrated in vitro that the NS1 N-terminal domain (NS1N) binds to the origin of replication in a sequence-specific, length-dependent manner that requires NSBE1 and NSBE2, while NSBE3 and NSBE4 are dispensable. Mutagenesis analysis has identified nucleotides in NSBE1 and NSBE2 that are critical for NS1N binding. These results suggest that NS1 binds to the NSBE1-NSBE2 region in the origin of replication, while NSBE3 and NSBE4 may provide binding sites for potential cellular factors. Such a specialized nucleoprotein complex may enable NS1 to nick the terminal resolution site and separate DNA strands during replication

LIDER: cell embedding based deep neural network classifier for supervised cell type identification

Background Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. Results LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER

Plasma noise in TianQin time delay interferometry

TianQin is a proposed geocentric space-based gravitational wave observatory mission, which requires time-delay interferometry (TDI) to cancel laser frequency noise. With high demands for precision, solar-wind plasma environment at $\sim 10^5$ km above the Earth may constitute a non-negligible noise source to laser interferometric measurements between satellites, as charged particles perturb the refractivity along light paths. In this paper, we first assess the plasma noises along single links from space-weather models and numerical orbits, and analyze the time and frequency domain characteristics. Particularly, to capture the plasma noise in the entire measurement band of $10^{-4} - 1$ Hz, we have performed additional space-weather magnetohydrodynamic simulations in finer spatial and temporal resolutions and utilized Kolmogorov spectra in high-frequency data generation. Then we evaluate the residual plasma noises of the first- and second-generation TDI combinations. Both analytical and numerical estimations have shown that under normal solar conditions the plasma noise after TDI is less than the secondary noise requirement. Moreover, TDI is shown to exhibit moderate suppression on the plasma noise below $\sim 10^{-2}$ Hz due to noise correlation between different arms, when compared with the secondary noise before and after TDI.Comment: 12 pages, 15 figures, accepted by Phys. Rev.