Day-to-day Traffic Dynamics with Strategic Commuters

In the era of connected and automated mobility, commuters (connected drivers or automated vehicles) will possess strong computation capability and their travel decisions can be algorithmic and strategic. This paper investigates the day-to-day travel choice evolution of such strategic commuters who are capable of long-term planning and computation. We model the commute problem as a mean field game and examine the mean field equilibrium to derive the evolution of the network traffic flow pattern. The proposed model is general and can be tailored to various travel choices such as route or departure time. Under various conditions, we prove the existence and uniqueness of the day-to-day equilibrium traffic evolution pattern as well as its convergence to stationarity. Connection with traditional Wardropian equilibrium is established by examining the physical interpretation of the stationary solution

Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model

Generative Pre-Training (GPT) models like ChatGPT have demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. Although ChatGPT has been integrated into the overall workflow to boost efficiency in many domains, the lack of flexibility in the finetuning process hinders its applications in areas that demand extensive domain expertise and semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on the China National Medical Licensing Examination (CNMLE) and propose a novel approach to improve ChatGPT from two perspectives: integrating medical domain knowledge and enabling few-shot learning. By using a simple but effective retrieval method, medical background knowledge is extracted as semantic instructions to guide the inference of ChatGPT. Similarly, relevant medical questions are identified and fed as demonstrations to ChatGPT. Experimental results show that directly applying ChatGPT fails to qualify the CNMLE at a score of 51 (i.e., only 51\% of questions are answered correctly). While our knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not only passes the qualification but also surpasses the average score of humans (61). This research demonstrates the potential of knowledge-enhanced ChatGPT to serve as versatile medical assistants, capable of analyzing real-world medical problems in a more accessible, user-friendly, and adaptable manner

Entanglement as the cross-symmetric part of quantum discord

In this paper, we show that the minimal quantum discord over "cross-symmetric" state extensions is an entanglement monotone. In particular, we show that the minimal Bures distance of discord over cross-symmetric extensions is equivalent to the Bures distance of entanglement. At last, we refute a long-held but unstated convention that only contractive distances can be used to construct entanglement monotones by showing that the entanglement quantifier induced by the Hilbert-Schmidt distance, which is not contractive under quantum operations, is also an entanglement monotone.Comment: 9 pages, 1 figure. arXiv admin note: text overlap with arXiv:2012.0383

Autocorrelation of a class of quaternary sequences of period 2pm2p^m

Sequences with good randomness properties are quite important for stream ciphers. In this paper, a new class of quaternary sequences is constructed by using generalized cyclotomic classes of $\mathbb{Z}_{2p^m}$ $(m\geq1)$. The exact values of autocorrelation of these sequences are determined based on cyclotomic numbers of order $2$ with respect to $p^m$. Results show that the presented sequences have the autocorrelations with at most $4$ values

AutoKary2022: A Large-Scale Densely Annotated Dateset for Chromosome Instance Segmentation

Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i.e., karyotype analysis). However, it is still a challenging task due to lacking of densely annotated datasets and the complicated morphologies of chromosomes, e.g., dense distribution, arbitrary orientations, and wide range of lengths. To facilitate the development of this area, we take a big step forward and manually construct a large-scale densely annotated dataset named AutoKary2022, which contains over 27,000 chromosome instances in 612 microscopic images from 50 patients. Specifically, each instance is annotated with a polygonal mask and a class label to assist in precise chromosome detection and segmentation. On top of it, we systematically investigate representative methods on this dataset and obtain a number of interesting findings, which helps us have a deeper understanding of the fundamental problems in chromosome instance segmentation. We hope this dataset could advance research towards medical understanding. The dataset can be available at: https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset.Comment: Accepted by ICME 202

Shaping a subwavelength needle with ultra-long focal length by focusing azimuthally polarized light

10.1038/srep09977Scientific Reports