Efforts to predict stock market outcomes have yielded limited success due to
the inherently stochastic nature of the market, influenced by numerous
unpredictable factors. Many existing prediction approaches focus on
single-point predictions, lacking the depth needed for effective
decision-making and often overlooking market risk. To bridge this gap, we
propose a novel model, RAGIC, which introduces sequence generation for stock
interval prediction to quantify uncertainty more effectively. Our approach
leverages a Generative Adversarial Network (GAN) to produce future price
sequences infused with randomness inherent in financial markets. RAGIC's
generator includes a risk module, capturing the risk perception of informed
investors, and a temporal module, accounting for historical price trends and
seasonality. This multi-faceted generator informs the creation of
risk-sensitive intervals through statistical inference, incorporating
horizon-wise insights. The interval's width is carefully adjusted to reflect
market volatility. Importantly, our approach relies solely on publicly
available data and incurs only low computational overhead. RAGIC's evaluation
across globally recognized broad-based indices demonstrates its balanced
performance, offering both accuracy and informativeness. Achieving a consistent
95% coverage, RAGIC maintains a narrow interval width. This promising outcome
suggests that our approach effectively addresses the challenges of stock market
prediction while incorporating vital risk considerations.

我们提出了一种新的模型 RAGIC，通过引入序列生成进行股票间隔预测以更有效地量化不确定性，从而弥补了现有单点预测方法的不足。RAGIC 的生成器包括风险模块和时间模块，通过统计推断生成风险敏感的区间，以反映市场波动性，同时考虑到历史价格趋势和季节性，实现了平衡的性能和准确性，并成功解决了股票市场预测中的挑战。

RAGIC：用于股票区间构建的风险感知生成对抗模型

RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval  Construction

Natural disasters such as hurricanes are increasing and causing widespread
devastation. People's decisions and actions regarding whether to evacuate or
not are critical and have a large impact on emergency planning and response.
Our interest lies in computationally modeling complex relationships among
various factors influencing evacuation decisions. We conducted a study on the
evacuation of Hurricane Irma of the 2017 Atlantic hurricane season. The study
was guided by the Protection motivation theory (PMT), a widely-used framework
to understand people's responses to potential threats. Graphical models were
constructed to represent the complex relationships among the factors involved
and the evacuation decision. We evaluated different graphical structures based
on conditional independence tests using Irma data. The final model largely
aligns with PMT. It shows that both risk perception (threat appraisal) and
difficulties in evacuation (coping appraisal) influence evacuation decisions
directly and independently. Certain information received from media was found
to influence risk perception, and through it influence evacuation behaviors
indirectly. In addition, several variables were found to influence both risk
perception and evacuation behaviors directly, including family and friends'
suggestions, neighbors' evacuation behaviors, and evacuation notices from
officials.

通过研究带有图形模型的其他因素与撤离决策的复杂关系，研究表明自然灾害、保护动机理论和风险感知是影响撤离决策的关键因素。

飓风疏散行为的图模型

A Graphical Model of Hurricane Evacuation Behaviors

Considering the functionality of situational awareness in safety-critical
automation systems, the perception of risk in driving scenes and its
explainability is of particular importance for autonomous and cooperative
driving. Toward this goal, this paper proposes a new research direction of
joint risk localization in driving scenes and its risk explanation as a natural
language description. Due to the lack of standard benchmarks, we collected a
large-scale dataset, DRAMA (Driving Risk Assessment Mechanism with A captioning
module), which consists of 17,785 interactive driving scenarios collected in
Tokyo, Japan. Our DRAMA dataset accommodates video- and object-level questions
on driving risks with associated important objects to achieve the goal of
visual captioning as a free-form language description utilizing closed and
open-ended responses for multi-level questions, which can be used to evaluate a
range of visual captioning capabilities in driving scenarios. We make this data
available to the community for further research. Using DRAMA, we explore
multiple facets of joint risk localization and captioning in interactive
driving scenarios. In particular, we benchmark various multi-task prediction
architectures and provide a detailed analysis of joint risk localization and
risk captioning. The data set is available at this https URL

提出了一种联合风险定位和风险解释的研究方向，使用 DRAMA 数据集探索了交互式驾驶场景中的多方面联合风险定位和字幕，其中包括多任务预测架构的基准测试和详细分析。