Machine learning-based direct solver for one-to-many problems of temporal shaping of electron bunches

To control the temporal profile of a charged beam to meet requirements of various accelerator applications, a widely-used technique is bunch compression via 4-dipole chicanes that may sometimes have a one-to-many map. Current approaches based on stochastic optimization or supervised learning can be limited because of the one-to-many properties. Here we demonstrate how to construct a direct and real-time solver with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN), to solve one-to-many problems of temporal shaping. Unlike supervised learning that can only learn one-to-one maps, the CGAN solver can learn the one-to-many dynamics and accurately predict required longitudinal dispersion terms for a chicane to realize desired custom temporal profiles without any priori knowledge. Besides, the CGAN solver can simultaneously give multiple different solutions for a one-to-many problem, which breaks the limitation of stochastic optimization methods of finding one solution instead of many

Multi-access Coded Caching with Optimal Rate and Linear Subpacketization under PDA and Consecutive Cyclic Placement

This work considers the multi-access caching system proposed by Hachem et al., where each user has access to L neighboring caches in a cyclic wrap-around fashion. We first propose a placement strategy called the consecutive cyclic placement, which achieves the maximal local caching gain. Then under the consecutive cyclic placement, we derive the optimal coded caching gain from the perspective of Placement Delivery Array (PDA), thus obtaining a lower bound on the rate of PDA. Finally, under the consecutive cyclic placement, we construct a class of PDA, leading to a multi-access coded caching scheme with linear subpacketization, which achieves our derived lower bound for some parameters; while for other parameters, the achieved coded caching gain is only 1 less than the optimal one. Analytical and numerical comparisons of the proposed scheme with existing schemes are provided to validate the performance.Comment: 30 pages, 7 figure

Accelerating Transducers through Adjacent Token Merging

Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design is inefficient, particularly for long speech signals due to the quadratic computation of self-attention. To address this, we propose a new method, Adjacent Token Merging (A-ToMe), which gradually combines adjacent tokens with high similarity scores between their key values. In this way, the total time step could be reduced, and the inference of both the encoder and joint network is accelerated. Experiments on LibriSpeech show that our method can reduce 57% of tokens and improve the inference speed on GPU by 70% without any notable loss of accuracy. Additionally, we demonstrate that A-ToMe is also an effective solution to reduce tokens in long-form ASR, where the input speech consists of multiple utterances.Comment: Interspeech 202

Research on Development and Application of Low-Voltage and High-Speed Power Line Communication Technology

Low-voltage and high-speed power line communication (PLC) technology, as the main means of communication construction, enables the masses to obtain higher quality services and has attracted more and more public attention. This paper is divided into four parts: the introduction of PLC technology, the application significance of low-voltage and high-speed PLC communication technology, the characteristics of PLC channel and the application and comparison of high-speed PLC technology

Carbon disclosure, stock returns, institutional investors and the nature of corporate equity: An empirical study of Chinese market based on fixed effects model

Changes in the environment and climate have prompted countries to issue relevant policies to curb greenhouse gas emissions. Carbon disclosure policy is one of them. Carbon disclosure refers to the fact that the subject of greenhouse gas emissions can provide real and comprehensive carbon emission information in the form of regular reports or ad hoc reports. A behavior of social opening, as the main body of the market, the carbon disclosure of enterprises will have a certain impact on the capital market, and the behavior of investors may also change. The objective of this paper is to explore the impact of carbon disclosure on China's capital markets and investor behavior. The targets are as follows: 1. Relationship between stock yields and carbon disclosure. 2. The relationship between institutional investor shareholding ratio and carbon disclosure and stock return 3. What impact does institutional investment shareholding have on the relationship between carbon disclosure and stock return4. Due to the large number of state-owned enterprises in China's carbon emissions market, the paper also studies the impact of corporate nature on the relationship between carbon disclosure and stock returns This paper selects 100 listed companies from the sample stocks of the Shanghai 180 Carbon Efficiency Stock Exchange in China as a sample and selects relevant financial data of these companies for a total of five years from 2016-2020 2 for analysis. Then establish carbon disclosure assessment system to digitize the quality of carbon disclosure of enterprises. The next step in this paper is to use a fixed effect model to conduct regression analysis to test the hypothesis. According to the results, carbon disclosure is significantly positively correlated with stock returns. But institutional investor ownership does not significantly affect carbon disclosures or stock returns. The positive relationship between carbon disclosure and stock returns can be strengthened by the number of institutional investors. Lastly, this paper concludes that stock returns are more sensitive to changes in carbon disclosure in private firms than in state firms by aggregating firms in the sample into state and private firms. The conclusions of this paper can provide reference for some stakeholders; the government can better promote the implementation of corporate carbon disclosure policies; and investors in the market can also use some of the conclusions of this paper as a reference for investment portfolios. However, there are also shortcomings in this paper, such as the small number of sample data and the strong subjectivity of carbon disclosure indicators, which need to be improved