GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization

Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradients calculated from their local data to a central server. Recent studies find that the exchanged gradients also take the risk of privacy leakage, e.g., an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. However, performing gradient inversion attacks in the latent space of the GAN model limits their expression ability and generalizability. To tackle these challenges, we propose \textbf{G}radient \textbf{I}nversion over \textbf{F}eature \textbf{D}omains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers. Instead of optimizing only over the initial latent code, we progressively change the optimized layer, from the initial latent space to intermediate layers closer to the output images. In addition, we design a regularizer to avoid unreal image generation by adding a small ${l_1}$ ball constraint to the searching range. We also extend GIFD to the out-of-distribution (OOD) setting, which weakens the assumption that the training sets of GANs and FL tasks obey the same data distribution. Extensive experiments demonstrate that our method can achieve pixel-level reconstruction and is superior to the existing methods. Notably, GIFD also shows great generalizability under different defense strategy settings and batch sizes.Comment: ICCV 202

Recent Advances in Visible-Light Driven Photocatalysis

Semiconductor photocatalysis has been considered a potentially promising approach for renewable energy and environmental remediation with abundant solar light. However, the currently available semiconductor materials are generally limited by either the harvesting of solar energy or insufficient charge separation ability. To overcome the serious drawbacks of narrow light-response range and low efficiency in most photocatalysts, many strategies have been developed in the past decades. This article reviews the recent advancements of visible-light-driven photocatalysts and attempts to provide a comprehensive update of some strategies to improve the efficiency, such as doping, coupling with graphene, precipitating with metal particles, crystal growth design, and heterostructuring. A brief introduction to photocatalysts is given first, followed by an explanation of the basic rules and mechanisms of photocatalysts. This chapter focuses on recent progress in exploring new strategies to design TiO2-based photocatalysts that aim to extend the light absorption of TiO2 from UV wavelengths into the visible region. Subsequently, some strategies are also used to endow visible-light-driven Ag3PO4 with high activity in photocatalytic reactions. Next, a novel approach, using long afterglow phosphor, has been used to associate a fluorescence-emitting support to continue the photocatalytic reaction after turning off the light. The last section proposes some challenges to design high efficiency of photocatalytic systems

The two-loop contributions to muon MDM in U(1)XU(1)_X SSM

The MSSM is extended to the $U(1)_X$SSM, whose local gauge group is $SU(3)_C \times SU(2)_L \times U(1)_Y \times U(1)_X$. To obtain the $U(1)_X$SSM, we add the new superfields to the MSSM, namely: three Higgs singlets $\hat{\eta},~\hat{\bar{\eta}},~\hat{S}$ and right-handed neutrinos $\hat{\nu}_i$. It can give light neutrino tiny mass at the tree level through the seesaw mechanism. The study of the contribution of the two-loop diagrams to the MDM of muon under $U(1)_X$SSM provides the possibility for us to search for new physics. In the analytical calculation of the loop diagrams (one-loop and two-loop diagrams), the effective Lagrangian method is used to derive muon MDM. Here, the considered two-loop diagrams include Barr-Zee type diagrams and rainbow type two-loop diagrams, especially Z-Z rainbow two-loop diagram is taken into account. The obtained numerical results can reach $7.4\times10^{-10}$, which can remedy the deviation between SM prediction and experimental data to some extent.Comment: 19 pages, 8 figure

Dihydromyricetin attenuates depressive-like behaviors in mice by inhibiting the AGE-RAGE signaling pathway

Depression is a complex mental disorder, affecting approximately 280 million individuals globally. The pathobiology of depression is not fully understood, and the development of new treatments is urgently needed. Dihydromyricetin (DHM) is a natural flavanone, mainly distributed in Ampelopsis grossedentata. DHM has demonstrated a protective role against cardiovascular disease, diabetes, liver disease, cancer, kidney injury and neurodegenerative disorders. In the present study, we examined the protective effect of DHM against depression in a chronic depression mouse model induced by corticosterone (CORT). Animals exposed to CORT displayed depressive-like behaviors; DHM treatment reversed these behaviors. Network pharmacology analyses showed that DHM’s function against depression involved a wide range of targets and signaling pathways, among which the inflammation-linked targets and signaling pathways were critical. Western blotting showed that CORT-treated animals had significantly increased levels of the advanced glycation end product (AGE) and receptor of AGE (RAGE) in the hippocampus, implicating activation of the AGE-RAGE signaling pathway. Furthermore, enzyme-linked immunosorbent assay (ELISA) detected a marked increase in the production of proinflammatory cytokines, interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNFα) in the hippocampus of CORT-treated mice. DHM administration significantly counteracted these CORT-induced changes. These findings suggest that protection against depression by DHM is mediated by suppression of neuroinflammation, predominantly via the AGE-RAGE signaling pathway

Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time

Complete genome sequencing and analysis of six enterovirus 71 strains with different clinical phenotypes

BACKGROUND: Hand, foot and mouth diseases (HFMD) caused by enterovirus 71(EV71) presents a broad spectrum of clinical manifestations ranging from mild febrile disease to fatal neurolocal disease. However, the mechanism of virulence is unknown. METHODS: We isolated 6 strains of EV71 from HFMD patients with or without neurological symptoms, and sequenced the whole genomes of the viruses to reveal the virulence factors of EV71. RESULTS: Phylogenetic tree based on VP1 region showed that all six strains clustered into C4a of C4 sub-genotype. In the complete polypeptide, 298 positions were found to be variable in all strains, and three of these positions (Val(P814)/Ile(P814) in VP1, Val(P1148)/Ile(P1148) in 3A and Ala (P1728)/Cys (P1728)/Val (P1728) in 3C) were conserved among the strains with neurovirulence, but variable in strains without neurovirulence. In the 5(′)-UTR region, it showed that the first 10 nucleotides were mostly conserved, however from the 11th nucleotide, nucleotide insertions and deletions were quite common. The secondary structure prediction of 5(′)-UTR sequences showed that two of three strains without neurovirulence (SDLY11 and SDLY48) were almost the same, and all strains with neurovirulence (SDLY96, SDLY107 and SDLY153) were different from each other. SDLY107 (a fatal strain) was found different from other strains on four positions (C(P241)/T(P241), A(P571)/T(P571), C(P579)/T(P579) in 5(′)-UTR and T(P7335)/C(P7335) in 3(′)-UTR). CONCLUSIONS: The three positions (Val(P814)/Ile(P814) in VP1, Val(P1148)/Ile(P1148) in 3A and Ala (P1728)/Cys (P1728)/Val (P1728) in 3C), were different between two phenotypes. These suggested that the three positions might be potential virulent positions. And the three varied positions were also found to be conserved in strains with neurovirulence, and variable in strains without neurovirulence. These might reveal that the conservation of two of the three positions or the three together were specific for the strains with neurovirulence. Varation of secondary structure of 5(′)-UTR, might be correlated to the changes of viral virulence. SDLY107 (a fatal strain) was found different from other strains on four positions, these positions might be related with death