Three-Dimensional Analysis of Wakefields Generated by Flat Electron Beams in Planar Dielectric-Loaded Structures

An electron bunch passing through dielectric-lined waveguide generates $\check{C}$erenkov radiation that can result in high-peak axial electric field suitable for acceleration of a subsequent bunch. Axial field beyond Gigavolt-per-meter are attainable in structures with sub-mm sizes depending on the achievement of suitable electron bunch parameters. A promising configuration consists of using planar dielectric structure driven by flat electron bunches. In this paper we present a three-dimensional analysis of wakefields produced by flat beams in planar dielectric structures thereby extending the work of Reference [A. Tremaine, J. Rosenzweig, and P. Schoessow, Phys. Rev. E 56, No. 6, 7204 (1997)] on the topic. We especially provide closed-form expressions for the normal frequencies and field amplitudes of the excited modes and benchmark these analytical results with finite-difference time-domain particle-in-cell numerical simulations. Finally, we implement a semi-analytical algorithm into a popular particle tracking program thereby enabling start-to-end high-fidelity modeling of linear accelerators based on dielectric-lined planar waveguides.Comment: 12 pages, 2 tables, 10 figure

Optofluidic ultrahigh-throughput detection of fluorescent drops

This paper describes an optofluidic droplet interrogation device capable of counting fluorescent drops at a throughput of 254000 drops per second. To our knowledge, this rate is the highest interrogation rate published thus far. Our device consists of 16 parallel microfluidic channels bonded directly to a filter-coated two-dimensional Complementary Metal-Oxide-Semiconductor (CMOS) sensor array. Fluorescence signals emitted from the drops are collected by the sensor that forms the bottom of the channel. The proximity of the drops to the sensor facilitates efficient collection of fluorescence emission from the drops, and overcomes the trade-off between light collection efficiency and field of view in conventional microscopy. The interrogation rate of our device is currently limited by the acquisition speed of CMOS sensor, and is expected to increase further as high-speed sensors become increasingly available

Certificateless public auditing with data privacy preserving for cloud-based smart grid data

As the promising next generation power system, smart grid can collect and analyze the grid information in real time, which greatly improves the reliability and efficiency of the grid. However, as smart grid coverage expands, more and more data is being collected. To store and manage the massive amount of smart grid data, the data owners choose to upload the grid data to the cloud for storage and regularly check the integrity of their data. However, traditional public auditing schemes are mostly based on Public Key Infrastructure (PKI) or Identity Based Cryptography (IBC) system, which will lead to complicated certificate management and inherent key escrow problems. We propose a certificateless public auditing scheme for cloud-based smart grid data, which can avoid the above two problems. In order to prevent the disclosure of the private data collected by the smart grid during the phase of auditing, we use the random masking technology to protect data privacy. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author