A-priori Validation of Subgrid-scale Models for Astrophysical Turbulence

We perform a-priori validation tests of subgrid-scale (SGS) models for the turbulent transport of momentum, energy and passive scalars. To this end, we conduct two sets of high-resolution hydrodynamical simulations with a Lagrangian code: an isothermal turbulent box with rms Mach number of 0.3, 2 and 8, and the classical wind tunnel where a cold cloud traveling through a hot medium gradually dissolves due to fluid instabilities. Two SGS models are examined: the eddy diffusivity (ED) model wildly adopted in astrophysical simulations and the "gradient model" due to Clark et al. (1979). We find that both models predict the magnitude of the SGS terms equally well (correlation coefficient > 0.8). However, the gradient model provides excellent predictions on the orientation and shape of the SGS terms while the ED model predicts poorly on both, indicating that isotropic diffusion is a poor approximation of the instantaneous turbulent transport. The best-fit coefficient of the gradient model is in the range of [0.16, 0.21] for the momentum transport, and the turbulent Schmidt number and Prandtl number are both close to unity, in the range of [0.92, 1.15].Comment: ApJ accepted; analysis code available at https://github.com/huchiayu/Lapriori.j

FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy. Due to the heterogeneous nature of local datasets, updated client models may overfit and diverge from one another, commonly known as the problem of client drift. In this paper, we propose FedBug (Federated Learning with Bottom-Up Gradual Unfreezing), a novel FL framework designed to effectively mitigate client drift. FedBug adaptively leverages the client model parameters, distributed by the server at each global round, as the reference points for cross-client alignment. Specifically, on the client side, FedBug begins by freezing the entire model, then gradually unfreezes the layers, from the input layer to the output layer. This bottom-up approach allows models to train the newly thawed layers to project data into a latent space, wherein the separating hyperplanes remain consistent across all clients. We theoretically analyze FedBug in a novel over-parameterization FL setup, revealing its superior convergence rate compared to FedAvg. Through comprehensive experiments, spanning various datasets, training conditions, and network architectures, we validate the efficacy of FedBug. Our contributions encompass a novel FL framework, theoretical analysis, and empirical validation, demonstrating the wide potential and applicability of FedBug.Comment: Submitted to NeurIPS'2

Loss of vesicular dopamine release precedes tauopathy in degenerative dopaminergic neurons in a Drosophila model expressing human tau.

While a number of genome-wide association studies have identified microtubule-associated protein tau as a strong risk factor for Parkinson's disease (PD), little is known about the mechanism through which human tau can predispose an individual to this disease. Here, we demonstrate that expression of human wild-type tau is sufficient to disrupt the survival of dopaminergic neurons in a Drosophila model. Tau triggers a synaptic pathology visualized by vesicular monoamine transporter-pHGFP that precedes both the age-dependent formation of tau-containing neurofibrillary tangle-like pathology and the progressive loss of DA neurons, thereby recapitulating the pathological hallmarks of PD. Flies overexpressing tau also exhibit progressive impairments of both motor and learning behaviors. Surprisingly, contrary to common belief that hyperphosphorylated tau could aggravate toxicity, DA neuron degeneration is alleviated by expressing the modified, hyperphosphorylated tau(E14). Together, these results show that impairment of VMAT-containing synaptic vesicle, released to synapses before overt tauopathy may be the underlying mechanism of tau-associated PD and suggest that correction or prevention of this deficit may be appropriate targets for early therapeutic intervention

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1

Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization

Astrocyte-specific regulation of hMeCP2 expression in \u3ci\u3eDrosophila\u3c/i\u3e

Alterations in the expression of Methyl-CpG-binding protein 2 (MeCP2) either by mutations or gene duplication leads to a wide spectrum of neurodevelopmental disorders including Rett Syndrome and MeCP2 duplication disorder. Common features of Rett Syndrome (RTT), MeCP2 duplication disorder, and neuropsychiatric disorders indicate that even moderate changes in MeCP2 protein levels result in functional and structural cell abnormalities. In this study, we investigated two areas of MeCP2 pathophysiology using Drosophila as a model system: the effects of MeCP2 glial gain-of-function activity on circuits controlling sleep behavior, and the cell-type specific regulation of MeCP2 expression. In this study, we first examined the effects of elevated MeCP2 levels on microcircuits by expressing human MeCP2 (hMeCP2) in astrocytes and distinct subsets of amine neurons including dopamine and octopamine (OA) neurons. Depending on the celltype, hMeCP2 expression reduced sleep levels, altered daytime/ nighttime sleep patterns, and generated sleep maintenance deficits. Second, we identified a 498 base pair region of the MeCP2e2 isoform that is targeted for regulation in distinct subsets of astrocytes. Levels of the full-length hMeCP2e2 and mutant RTT R106W protein decreased in astrocytes in a temporally and spatially regulated manner. In contrast, expression of the deletion D166 hMeCP2 protein was not altered in the entire astrocyte population. qPCR experiments revealed a reduction in full-length hMeCP2e2 transcript levels suggesting transgenic hMeCP2 expression is regulated at the transcriptional level. Given the phenotypic complexities that are caused by alterations in MeCP2 levels, our results provide insight into distinct cellular mechanisms that control MeCP2 expression and link microcircuit abnormalities with defined behavioral deficits

Untargeted, spectral libraryâ free analysis of dataâ independent acquisition proteomics data generated using Orbitrap mass spectrometers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/1/pmic12370_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/2/pmic12370.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/3/pmic12370-sup-0001-SupplementaryInfo.pd

UNDERSTANDING COMPETITIVE PERFORMANCE OF SOFTWARE-AS-A-SERVICE (SAAS)—THE COMPETITIVE DYNAMICS PERSPECTIVE

Understanding the antecedents and consequences of a firm’s agility in cloud software applications is important. This papers draws on the competitive dynamics perspective to develop a model that explains the relationships between collaboration with vendors, agility, and competitive performance in software-as-a-service (SaaS) context. Collaboration reflects a firm’s ability to leverage interfirm resources, characterized as knowledge sharing and process alignment. Agility is measured by a firm’s strategy-oriented agility and service-oriented agility. This study also investigates the moderating effect of environmental turbulence. The proposed hypotheses are supported by the empirical data. The results show that competitive performance is affected by ability, which, in turn, is impacted by collaboration. Environmental turbulence positively moderates the relationship between agility and performance. Finally, we discuss the implications of our results