Insulator-to-metal phase transition in Yb-based Kondo insulators

The periodic Anderson lattice model for the crystalline electric field (CEF)split 4f quartet states is used to describe the Yb-based Kondo insulators/semiconductors. In the slave-boson mean-field approximation, we derive the hybridized quasiparticle bands, and find that decreasing the hybridization difference of the two CEF quartets may induce an insulator-to-metal phase transition. The resulting metallic phase has a hole and an electron Fermi pockets. Such a phase transition may be realized experimentally by applying pressure, reducing the difference in hybridization of the two CEF quartets.Comment: 5 pages, 3 figure

Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis

Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.Comment: Accepted by the IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biolog

Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation

In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medical data. Currently, most FL models employ random initialization, which has been proven effective in various instances. However, given the unique challenges posed by non-IID (independently and identically distributed) data in FL, we propose a novel perspective: exploring the impact of using the foundation model with enormous pre-trained knowledge, such as the Segment Anything Model (SAM), as an instructive teacher for FL model initialization in medical image segmentation task. This work for the first time attempts to utilize the foundation model as an instructive teacher for initialization in FL, assessing its impact on the performance of FL models, especially in non-IID data scenarios. Our empirical evaluation on chest x-ray lung segmentation showcases that FL with foundation model instructed initialization not only achieves faster convergence but also improves performance in complex data contexts. These findings offer a new perspective for model initialization in FL

2-(2-Hy­droxy-3-meth­oxy­phen­yl)-1H-benzimidazol-3-ium perchlorate

In the title mol­ecular salt, C14H13N2O2 +·ClO4 −, the ring systems in the cation are almost coplanar [dihedral angle = 5.53 (13)°]. Intra­molecular N—H⋯O and O—H⋯O hydrogen bonds generate S(6) and S(5) rings, respectively. In the crystal, the two H atoms involved in the intra­molecular hydrogen bonds also participate in inter­molecular links to acceptor O atoms of the perchlorate anions. A simple inter­molecular N—H⋯O bond also occurs. Together, these form a double-chain structure along [101]

A Multi-mode, Multi-class Dynamic Network Model With Queues For Advanced Transportation Information Systems

In this paper we propose a composite Variational Inequality formulation for modeling multimode, multi-class stochastic dynamic user equilibrium problem in recurrent congestion networks with queues. The modes typically refer to different vehicle types such as passenger cars, trucks, and buses sharing the same road space. Each vehicle type has its own characteristics, such as free flow speed, vehicle size. We extend single mode deterministic point model to multimode deterministic point model for modeling the asymmetric interactions among various modes. Meanwhile, each mode of travelers is classified into two classes. Class 1 is equipped travelers following stochastic dynamic user-equilibrium with less uncertainty of travel cost, class 2 is unequipped travelers following stochastic dynamic user-equilibrium with more uncertainty of travel cost. A solution algorithm based on stochastic dynamic network loading for logit-based simultaneous route and departure time choices is adopted. Finally a numerical example is presented in a simple network

Synthesis and Characterization of Phase Change Material Microcapsules Using Different Emulsifiers

In this study, a series of phase change material microcapsules (MicroPCMs) were synthesized by a core-shell-like emulsion polymerization method using different emulsifiers. The interactions between core material, shell material and dispersed medium were investigated in detail. The copolymer (PS-MAA) of styrene and methylacrylic acid and paraffin were used as wall materials and core materials respectively. High temperature interfacial tensiometer was employed to measure the interactions between different materials. Fourier transformed infrared spectroscopy (FT-IR), Scanning electron microscopy (SEM), Thermogravimetry (TG), Differential scanning calorimeter (DSC) and laser particle size analyzer were used to characterize the chemical structure, morphology, thermal properties, particles size and size distribution of the MicroPCMs. The results indicated that alkylphenol polyoxyethylene (OP-10) is the optimal emulsifier in this method. The MicroPCMs prepared by using OP-10 as emulsifier displayed smooth and compact surface, the productivity was as high as 93.35%. The melting enthalpy and crystallization enthalpy were -84.6J/g and 83.5J/g, respectively. The mean particle size was 16.33μm