Validation and Application of SMAP SSS Observation in Chinese Coastal Seas

Using sea surface salinity (SSS) from the Soil Moisture Active Passive (SMAP) mission from September 2015 to August 2016, the spatial distribution and seasonal variation in SSS in the Chinese coastal seas were investigated. First, in situ salinity observation over Chinese East Sea was used to validate SMAP observation. Then, the SSS signature of the Yangtze River fresh water was analyzed using SMAP data and the river discharge data. The SSS around the Yangtze River estuary in the Chinese East Sea, the Bohai Sea and the Yellow Sea is significantly lower than that of the open ocean. The SSS of Chinese coastal seas shows significant seasonal variation, and the seasonal variation in the adjacent waters of the Yangtze River estuary is the most obvious, followed by that of the Pearl River estuary. The minimum value of SSS appears in summer while maximum in winter. The root-mean-squared difference of daily SSS between SMAP observation and in situ observation is around 3 psu in both summer and winter, which is much lower than the annual range of SSS variation. The path of fresh water from SMAP and in situ observation is consistent during summer time

A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.Comment: Accepted to ACL 2023. The repository is available at https://github.com/lupantech/dl4mat

DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT.Comment: 12 pages, 3figures, conference:ICONI

SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT

De acá a la China: análisis de la imagen estereotipada que tienen de Argentina los estudiantes universitarios chinos

A raíz de la profundización de las relaciones entre Argentina y China, y el impulso del aprendizaje del idioma español en la educación superior del país oriental, los estudiantes universitarios chinos se han convertido en participantes activos, comunicadores y promotores de esas relaciones. Este artículo presta atención a la imagen estereotipada que estos universitarios se han hecho de Argentina. Para ello se utilizan los resultados de un cuestionario aplicado a 412 estudiantes universitarios chinos. El análisis evidencia que en dicho grupo se han formado estereotipos en cinco dimensiones y se determina el impacto entre los encuestados de las relativas a la simpatía y el interés por la nación sudamericana y su población