Flat bands promoted by Hund's rule coupling in the candidate double-layer high-temperature superconductor La3_3Ni2_2O7_7

We report strongly correlated electronic band structure calculations of the recently discovered double-layer high-temperature superconductor La$_3$Ni$_2$O$_7$ under pressure. Our calculations reveal dual nature of Ni-$d$ electrons with almost localized $d_{z^2}$ orbitals due to onsite Coulomb repulsion and very flat hybridization bands of Ni-$d_{x^2-y^2}$ and Ni-$d_{z^2}$ quasiparticles near the Fermi energy. We find that the quasiparticle effective mass are greatly enhanced by the Hund's rule coupling and their lifetimes are inversely proportional to the temperature, which explains the experimentally observed strange metal behavior in the normal state. We also find strong antiferromagnetic spin correlations of Ni-$d$ electrons, which may provide the pairing force of quasiparticles for the high-temperature superconductivity. These give a potential explanation of two key observations in experiment and connect the superconducting La$_3$Ni$_2$O$_7$ with cuprate high-temperature superconductors. The presence of flat bands and the interplay of orbital-selective Mott, Hund, and Kondo physics make La$_3$Ni$_2$O$_7$ a unique platform for exploring rich emergent quantum many-body phenomena in the future.Comment: 6 pages, 4 figure

Implicit government guarantees and bank risk

We develop a model on bank risk and implicit government guarantees. This model concerns the willingness and capacity of implicit government guarantees. Using the Option Pricing Theory, we derive a mathematical formulation of maximizing the bank’s net present value (NPV) with implicit government guarantees. Unlike previous work, both the loan portfolio and the bank’s NPV are regarded as a combination of options underlying the risky project. We conduct comparative static analyses and numerical examples to examine how implicit government guarantees and capital control affect bank risk and its asset scale. The main insight of our analysis is that implicit government guarantees have some unintended consequences: (a) Inefficient and excessive risk taking (including bank’s asset and overall risk); (b) Inefficient investment if there is no binding capacity constraint. We show that it is mainly due to the bank’s excessive reliance on contingent assets. In addition, we demonstrate the ineffectiveness of capital constraint on risk control under certain circumstances. Therefore, we suggest that the gradual withdrawal of implicit government guarantees should be accompanied by multiple combinations of regulatory measures and proper institutional reform to avoid risk surges

THE CURRENT SITUATION OF FAMILY EDUCATION AND SCHOOL AND KINDERGARTEN ADMISSION OF MIGRANT CHILDREN IN ZHEJIANG PROVINCE, CHINA: A SURVEY OF HANGZHOU, NINGBO AND JIAXING

Based on a survey of 322 parents of migrant children aged 3 to 10 in Hangzhou, Ningbo and Jiaxing, Zhejiang Province, China, the study found that nearly 70% of parents have been working in Zhejiang for 5-10 years, and 28% of them have worked for more than 10 years; 94% of them hold agricultural household registration and 6% of them hold non-agricultural household registration. Nearly 70% of these families have 2 to 3 children and 16% of them have 4 children. Those children who can get into the local public kindergartens and primary schools are all study in the local institutions and most school-aged children and the majority of pre-school children who do not meet the admission requirements for local public kindergartens and primary schools return to their hometown due to the pressure of tuition fees. At the same time, family education is not efficient, more than half of the children’s spare time is occupied by television and video games, the proportion of reading and sports activities is only a little more than 10%. Most parents have little time to read, do homework and physical exercise with their children, because of their heavy work or low level of education. This paper puts forward some relevant countermeasures and suggestions, hoping to change this situation.  Article visualizations

Uber-in-light: Unobtrusive visible light communication leveraging complementary color channel

Abstract: Recently, Visible Light Communication (VLC) over a screen-camera channel has drawn considerable attention to unobtrusive design. It overcomes the distractive nature of traditional coded image approaches (e.g., barcodes). Previous unobtrusive methods fall into two categories: (1) utilizing alpha channel, a well known concept in computer graphics, to encode bits into the pixel translucency change with off-the-shelf smart devices; and (2) leveraging the spatial-temporal flicker-fusion property of human vision system with the fast frame rate of modern displays. However, these approaches heavily rely on high-end devices to achieve both unobtrusive and high accuracy screen-camera-based data communication without affecting video-viewing experience. Unlike previous approaches, we propose Uber-in-light, a novel unobtrusive and accurate VLC system, that enables real-time screen-camera communication, applicable to any screen and camera. The proposed system encodes the data as complementary intensity changes over Red, Green, and Blue (RGB) color channels that could be successfully decoded by camera while leaving the human visual perception unaffected. We design a MFSK modulation scheme with dedicated frame synchronization signal embedded in an orthogonal color channel to achieve high throughput. Furthermore, together with the complementary color intensity, an enhanced MUSIC-based demodulation scheme is developed to ensure highly accurate data transmission. Our user experience experiments confirmed the effectiveness of delivering unobtrusive data across different types of video content and resolutions. Extensive real-time performance evaluations are conducted using our prototype implementation to demonstrate the efficiency and reliability of the proposed system under diverse wireless environments

Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search

Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose an automatic and effective adaptive learning method to optimize end-to-end cascaded multi-task models based on Neural Architecture Search (NAS) framework. The candidate adaptive operations on each specific module consist of frozen, inserting an adapter and fine-tuning. We further add a penalty item on the loss to limit the learned structure which takes the amount of trainable parameters into account. The penalty item successfully restrict the searched architecture and the proposed approach is able to search similar tuning scheme with hand-craft, compressing the optimizing parameters to 8.7% corresponding to full fine-tuning on SLURP with an even better performance

Ti:Sapphire micro-structures by femtosecond laser inscription: Guiding and luminescence properties

We report on the fabrication of buried cladding waveguides with different diameters in a Ti:Sapphire crystal by femtosecond laser inscription. The propagation properties are studied, showing that the cladding waveguides could support near- to mid-infrared waveguiding at both TE and TM polarizations. Confocal micro-photoluminescence experiments reveal that the original fluorescence properties in the waveguide region are very well preserved, while it suffers from a strong quenching at the centers of laser induced filaments. Broadband waveguide fluorescence emissions with high efficiency are realized, indicating the application of the cladding waveguides in Ti:Sapphire as compact broadband luminescence sources in biomedical fields.This work was supported by the National Natural Science Foundation of China (No. 11404194 and No. 11274203) and Junta de Castilla y León (Project SA086A12-2). J. Yang acknowledges the support from the National Natural Science Foundation of China (No. 11405098). The authors thank D. Jaque for m-PL measurement of the sample

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network

The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due to the existing lack of awareness within static speech enhancement frameworks regarding the expected speech in downstream modules. These limitations impede the effectiveness of static speech enhancement approaches in achieving optimal performance for a range of speech processing tasks, thereby challenging the notion of universal applicability. The fundamental issue in achieving universal speech enhancement lies in effectively informing the speech enhancement module about the features of downstream modules. In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement. We found the role of deciding whether to employ data augmentation techniques as crucial downstream training information. This decision significantly impacts the expected speech and the performance of the speech enhancement module. Moreover, we introduce a novel speech enhancement network, the Plugin Speech Enhancement (Plugin-SE). The Plugin-SE is a dynamic neural network that includes the speech enhancement module, gate module, and weight prediction module. Experimental results demonstrate that the proposed Plugin-SE approach is competitive or superior to other joint training methods across various downstream tasks