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

Punicalagin alleviates brain injury and inflammatory responses, and regulates HO-1/Nrf-2/ARE signaling in rats after experimental intracerebral haemorrhage

Purpose: To investigate the effect of punicalagin, an ellagitannin present in pomegranates, on intracerebral haemorrhage (ICH)-induced inflammatory responses and oxidative stress, and also unravel the underlying mechanism(s) of action. Methods: Collagenase type IV (0.2 U) was used to induce ICH in adult male Sprague-Dawley rats. Punicalagin was given to the rats at doses of 25, 50, and 75 mg/kg body weight via oral gavage for 15 days before ICH induction. The animals were sacrificed 24h following induction of ICH, and their brains were excised immediately and used for analysis. Histological changes were determined with Haematoxylin and Eosin (H&E) staining. Permeability to blood-brain barrier (BBB) was determined by quantifying the extent of extravasation of Evan Blue (EB). Protein expressions of HO-1/Nrf-2/ARE and NF-κB signaling were assayed using immunoblotting and RT-PCR. Levels of reactive oxygen species (ROS) and serum levels of cytokines were also determined. Results: Punicalagin treatment reduced inflammatory cell infiltration and cell damage, improved brain tissue architecture and BBB integrity. The punicalagin treatment increased the activities of antioxidant enzymes, and enhanced antioxidant status via activation of Nrf-2/ARE/HO-1 signaling pathway (p < 0.05). The treatment upregulated the expressions of HO-1 to 174 %, relative to 127 % in ICH control rats. Furthermore, it enhanced NF-κB levels and reversed the ICH injury-induced upregulations of IL-6, IL-18 and IL-1β. Conclusion: These findings indicate that punicalagin exerts neuroprotective effect in rats after experimental ICH through regulation of theHO-1/Nrf-2/ARE signaling pathway. Thus, punicalagin has therapeutic potential for ICH. Keywords: Brain injury, Haemoxygenase-1, Intracerebral haemorrhage, Inflammatory responses, Nrf2/ARE signalling, Punicalagi

Partition-based K-space Synthesis for Multi-contrast Parallel Imaging

Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore,utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve super reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Finally, the objective T2 is synthesized by extracting the sub-T2 data of each part. Experimental results showed that our combined technique can achieve comparable or better results than using traditional k-space parallel imaging(SAKE) that processes each contrast independently