Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

Recommender systems are essential for online applications, and sequential recommendation has enjoyed significant prevalence due to its expressive ability to capture dynamic user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason for this issue is that language models often lack an understanding of domain-specific knowledge and item-related textual content. To address this issue, we adopt a new sequential recommendation paradigm and propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations. We demonstrate the effectiveness of our approach through experiments on several benchmark datasets, showing promising results and providing valuable insights into the influence of our model on sequential recommendation tasks. Furthermore, our experimental codes are publicly available

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work, we propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon, in which the core idea is to model the dependency between different categories of entity recognition. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. The former part automatically assigns adaptive weights across each task to achieve optimal recognition accuracy in the multi-layer network. The latter module employs the attention operation to model the dependency between different entities. In this way, our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities. Extensive experiments on public datasets verify the effectiveness of our method. Besides, we also perform ablation analyses to deeply understand our methods

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility. However, the integration of UAM into existing transportation systems is a complex task that requires a thorough understanding of its impact on traffic flow and capacity. In this paper, we conduct a survey to investigate the current state of research on UAM in metropolitan-scale traffic using simulation techniques. We identify key challenges and opportunities for the integration of UAM into urban transportation systems, including impacts on existing traffic patterns and congestion; safety analysis and risk assessment; potential economic and environmental benefits; and the development of shared infrastructure and routes for UAM and ground-based transportation. We also discuss the potential benefits of UAM, such as reduced travel times and improved accessibility for underserved areas. Our survey provides a comprehensive overview of the current state of research on UAM in metropolitan-scale traffic using simulation and highlights key areas for future research and development

Salmon Calcitonin Exerts an Antidepressant Effect by Activating Amylin Receptors

Depressive disorder is defined as a psychiatric disease characterized by the core symptoms of anhedonia and learned helplessness. Currently, the treatment of depression still calls for medications with high effectiveness, rapid action, and few side effects, although many drugs, including fluoxetine and ketamine, have been approved for clinical usage by the Food and Drug Administration (FDA). In this study, we focused on calcitonin as an amylin receptor polypeptide, of which the antidepressant effect has not been reported, even if calcitonin gene-related peptides have been previously demonstrated to improve depressive-like behaviors in rodents. Here, the antidepressant potential of salmon calcitonin (sCT) was first evaluated in a chronic restraint stress (CRS) mouse model of depression. We observed that the immobility duration in CRS mice was significantly increased during the tail suspension test and forced swimming test. Furthermore, a single administration of sCT was found to successfully rescue depressive-like behaviors in CRS mice. Lastly, AC187 as a potent amylin receptor antagonist was applied to investigate the roles of amylin receptors in depression. We found that AC187 significantly eliminated the antidepressant effects of sCT. Taken together, our data revealed that sCT could ameliorate a depressive-like phenotype probably via the amylin signaling pathway. sCT should be considered as a potential therapeutic candidate for depressive disorder in the future

A Microfabricated Bandpass Filter with Coarse-Tuning and Fine-Tuning Ability Based on IPD Process and PCB Artwork

In this paper, a bandpass filter (BPF) was developed utilizing GaAs-based integrated passive device technology which comprises an asymmetrical spiral inductor and an interleaved array capacitor, possessing two tuning modes: coarse-tuning and fine-tuning. By altering the number of layers and radius of the GaAs substrate metal spheres, capacitance variation from 0.071 to 0.106 pF for coarse-tuning, and of 0.0015 pF for fine-tuning, can be achieved. Five air bridges were employed in the asymmetrical spiral inductor to save space, contributing to a compact chip area of 0.015λ0 × 0.018λ0. The BPF chip was installed on the printed circuit board artwork with Au bonding wire and attached to a die sink. Measured results demonstrate an insertion loss of 0.38 dB and a return loss of 21.5 dB at the center frequency of 2.147 GHz. Furthermore, under coarse-tuning mode, variation in the center frequency from 1.956 to 2.147 GHz and transmission zero frequency from 4.721 to 5.225 GHz can be achieved. Under fine-tuning mode, the minimum tuning value and the average tuning value of the proposed BPF can be accurate to 1.0 MHz and 4.7 MHz for the center frequency and 1.0 MHz and 12.8 MHz for the transmission zero frequency, respectively

Spatiotemporal Evaluation of Blue and Green Water in Xinjiang River Basin Based on SWAT Model

Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil and Water Assessment Tool) model, the spatiotemporal distribution and evaluation of the blue and green water during the period (1982–2016) in the basin were explored by the Mann–Kendall test, precipitation anomaly percentage, and scenario simulation. It is found that the SWAT model presents a satisfactory performance in runoff simulation of the basin. The multi-year average blue water in the Xinjiang River basin is 1138 mm, and the green water is 829 mm, with a green water coefficient of 0.42. The amount of blue water in wet years is about 1.5 times that in normal years and 2.4 times that in dry years. Compared with the green water, the blue water of the basin is more sensitive to the variations in precipitation. In spatial distribution, the blue and green water in the middle of the basin is obviously more than those in other parts of the basin. During the study period, the blue water in the basin shows a slight decreasing trend, and the green water shows a significant decreasing trend. It is also found that climatic factors have a greater influence on the trend of blue and green water than land use, and the decrease in precipitation is the dominant cause for the trend of blue and green water

Spatiotemporal Evaluation of Blue and Green Water in Xinjiang River Basin Based on SWAT Model

Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil and Water Assessment Tool) model, the spatiotemporal distribution and evaluation of the blue and green water during the period (1982–2016) in the basin were explored by the Mann–Kendall test, precipitation anomaly percentage, and scenario simulation. It is found that the SWAT model presents a satisfactory performance in runoff simulation of the basin. The multi-year average blue water in the Xinjiang River basin is 1138 mm, and the green water is 829 mm, with a green water coefficient of 0.42. The amount of blue water in wet years is about 1.5 times that in normal years and 2.4 times that in dry years. Compared with the green water, the blue water of the basin is more sensitive to the variations in precipitation. In spatial distribution, the blue and green water in the middle of the basin is obviously more than those in other parts of the basin. During the study period, the blue water in the basin shows a slight decreasing trend, and the green water shows a significant decreasing trend. It is also found that climatic factors have a greater influence on the trend of blue and green water than land use, and the decrease in precipitation is the dominant cause for the trend of blue and green water