Towards Personalized Federated Learning via Heterogeneous Model Reassembly

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner

Turning dead leaves into an active multifunctional material as evaporator, photocatalyst, and bioplastic

Large numbers of leaves fall on the earth each autumn. The current treatments of dead leaves mainly involve completely destroying the biocomponents, which causes considerable energy consumption and environmental issues. It remains a challenge to convert waste leaves into useful materials without breaking down their biocomponents. Here, we turn red maple dead leaves into an active three-component multifunctional material by exploiting the role of whewellite biomineral for binding lignin and cellulose. Owing to its intense optical absorption spanning the full solar spectrum and the heterogeneous architecture for effective charge separation, films of this material show high performance in solar water evaporation, photocatalytic hydrogen production, and photocatalytic degradation of antibiotics. Furthermore, it also acts as a bioplastic with high mechanical strength, high-temperature tolerance, and biodegradable features. These findings pave the way for the efficient utilization of waste biomass and innovations of advanced materials

Consistent Targets Provide Better Supervision in Semi-supervised Object Detection

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo targets undermine the training of an accurate semi-supervised detector. It not only inject noise into student training but also lead to severe overfitting on the classification task. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment~(ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo bounding boxes; Then we calibrate the subtask predictions by designing a 3D feature alignment module~(FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of the pseudo-bboxes, which stabilizes the number of ground-truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10\% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.2 mAP. Our code will be open-sourced soon

Immobilization of Enzymes by Polymeric Materials

Enzymes are the highly efficient biocatalyst in modern biotechnological industries. Due to the fragile property exposed to the external stimulus, the application of enzymes is highly limited. The immobilized enzyme by polymer has become a research hotspot to empower enzymes with more extraordinary properties and broader usage. Compared with free enzyme, polymer immobilized enzymes improve thermal and operational stability in harsh environments, such as extreme pH, temperature and concentration. Furthermore, good reusability is also highly expected. The first part of this study reviews the three primary immobilization methods: physical adsorption, covalent binding and entrapment, with their advantages and drawbacks. The second part of this paper includes some polymer applications and their derivatives in the immobilization of enzymes

Synthesis and Characterization of Mesoporous Silica Nanoparticles Loaded with Pt Catalysts

Coating the catalyst with a nanoporous layer has been demonstrated to be an effective approach to improve catalyst stability. Herein, we systematically investigate two types of core-shell mesoporous silica nanoparticles with a platinum nanocatalyst using a variety of characterization methods. One of the mesoporous particles has a unique amine ring structure in the middle of a shell (Ring-mSiO2/Pt-5.0/SiO2), and the other one has no ring structure (mSiO2/Pt-5.0/SiO2). Brunauer–Emmett–Teller/Barrett–Joyner–Halenda (BET/BJH) presented a similar surface area for both particles, and the pore size was 2.4 nm. Ultra-Small-Angle X-ray Scattering (USAXS)/ Small-Angle X-ray Scattering (SAXS) showed the size of mSiO2/Pt-5.0/SiO2 and Ring-mSiO2/Pt-5.0/SiO2 were 420 nm and 272 nm, respectively. It also showed that the ring structure was 30 nm above the silica core. Using high-resolution Transmission Electron Microscopy (TEM), it was found that the platinum nanoparticles are loaded evenly on the surface of the silica. In situ SAXS heating experiments and Thermogravimetric Analysis (TGA) indicated that the mSiO2/Pt-5.0/SiO2 were more stable during the high temperature, while the Ring-mSiO2/Pt-5.0/SiO2 had more change in the particle

Synthesis and Characterization of Mesoporous Silica Nanoparticles Loaded with Pt Catalysts

Coating the catalyst with a nanoporous layer has been demonstrated to be an effective approach to improve catalyst stability. Herein, we systematically investigate two types of core-shell mesoporous silica nanoparticles with a platinum nanocatalyst using a variety of characterization methods. One of the mesoporous particles has a unique amine ring structure in the middle of a shell (Ring-mSiO2/Pt-5.0/SiO2), and the other one has no ring structure (mSiO2/Pt-5.0/SiO2). Brunauer–Emmett–Teller/Barrett–Joyner–Halenda (BET/BJH) presented a similar surface area for both particles, and the pore size was 2.4 nm. Ultra-Small-Angle X-ray Scattering (USAXS)/ Small-Angle X-ray Scattering (SAXS) showed the size of mSiO2/Pt-5.0/SiO2 and Ring-mSiO2/Pt-5.0/SiO2 were 420 nm and 272 nm, respectively. It also showed that the ring structure was 30 nm above the silica core. Using high-resolution Transmission Electron Microscopy (TEM), it was found that the platinum nanoparticles are loaded evenly on the surface of the silica. In situ SAXS heating experiments and Thermogravimetric Analysis (TGA) indicated that the mSiO2/Pt-5.0/SiO2 were more stable during the high temperature, while the Ring-mSiO2/Pt-5.0/SiO2 had more change in the particle.This article is published as Lyu, Xingyi, Xun Wu, Yuzi Liu, Wenyu Huang, Byeongdu Lee, and Tao Li. "Synthesis and characterization of mesoporous silica nanoparticles loaded with Pt catalysts." Catalysts 12, no. 2 (2022): 183. DOI: 10.3390/catal12020183. Copyright 2022 by the authors. Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC02-07CH11358; AC02-06CH1135

Solvation Structure of Methanol-in-Salt Electrolyte Revealed by Small-Angle X‑ray Scattering and Simulations

The solvation structure of water-in-salt electrolytes was thoroughly studied, and two competing structuresanion solvated structure and anion networkwere well-defined in recent publications. To further reveal the solvation structure in those highly concentrated electrolytes, particularly the influence of solvent, methanol was chosen as the solvent for this proposed study. In this work, small-angle X-ray scattering, small-angle neutron scattering, Fourier-transform infrared spectroscopy, and Raman spectroscopy were utilized to obtain the global and local structural information. With the concentration increment, the anion network formed by TFSI– became the dominant structure. Meanwhile, the hydrogen bonds among methanol were interrupted by the TFSI– anion and formed a new connection with them. Molecular dynamic simulations with two different force fields (GAFF and OPLS-AA) are tested, and GAFF agreed with synchrotron small-angle X-ray scattering/wide-angle X-ray scattering (SAXS/WAXS) results well and provided insightful information about molecular/ion scale solvation structure. This article not only deepens the understanding of the solvation structure in highly concentrated solutions, but more importantly, it provides additional strong evidence for utilizing SAXS/WAXS to validate molecular dynamics simulations

Single-neuron representation of learned complex sounds in the auditory cortex

The sensory responses of cortical neuronal populations following training have been extensively studied. However, the spike firing properties of individual cortical neurons following training remain unknown. Here, we have combined two-photon Ca2+ imaging and single-cell electrophysiology in awake behaving mice following auditory associative training. We find a sparse set (~5%) of layer 2/3 neurons in the primary auditory cortex, each of which reliably exhibits high-rate prolonged burst firing responses to the trained sound. Such bursts are largely absent in the auditory cortex of untrained mice. Strikingly, in mice trained with different multitone chords, we discover distinct subsets of neurons that exhibit bursting responses specifically to a chord but neither to any constituent tone nor to the other chord. Thus, our results demonstrate an integrated representation of learned complex sounds in a small subset of cortical neurons