Stochastic battery bidding in real-time energy markets is a nuanced process,
with its efficacy depending on the accuracy of forecasts and the representative
scenarios chosen for optimization. In this paper, we introduce a pioneering
methodology that amalgamates Transformer-based forecasting with weighted
constrained Dynamic Time Warping (wcDTW) to refine scenario selection. Our
approach harnesses the predictive capabilities of Transformers to foresee
Energy prices, while wcDTW ensures the selection of pertinent historical
scenarios by maintaining the coherence between multiple uncertain products.
Through extensive simulations in the PJM market for July 2023, our method
exhibited a 10% increase in revenue compared to the conventional method,
highlighting its potential to revolutionize battery bidding strategies in
real-time markets.

我们引入了一种创新方法，将基于 Transformer 的预测与加权约束动态时间规整（wcDTW）相结合，以优化实时能源市场中随机电池竞标策略，并通过在 PJM 市场上进行的广泛模拟，展示了与传统方法相比，我们的方法可使收益增加 10％，突显其在实时市场中改变电池竞标策略的潜力。

使用 Transformer 遇到 wcDTW 以提高实时电池出价：一种新的情景选择方法

Transformer meets wcDTW to improve real-time battery bids: A new  approach to scenario selection

Task-based behavioral biometric authentication of users interacting in
virtual reality (VR) environments enables seamless continuous authentication by
using only the motion trajectories of the person's body as a unique signature.
Deep learning-based approaches for behavioral biometrics show high accuracy
when using complete or near complete portions of the user trajectory, but show
lower performance when using smaller segments from the start of the task. Thus,
any systems designed with existing techniques are vulnerable while waiting for
future segments of motion trajectories to become available. In this work, we
present the first approach that predicts future user behavior using
Transformer-based forecasting and using the forecasted trajectory to perform
user authentication. Our work leverages the notion that given the current
trajectory of a user in a task-based environment we can predict the future
trajectory of the user as they are unlikely to dramatically shift their
behavior since it would preclude the user from successfully completing their
task goal. Using the publicly available 41-subject ball throwing dataset of
Miller et al. we show improvement in user authentication when using forecasted
data. When compared to no forecasting, our approach reduces the authentication
equal error rate (EER) by an average of 23.85% and a maximum reduction of
36.14%.

该论文提出了一种基于任务行为生物特征认证的方法，利用个体的运动轨迹作为唯一签名，在虚拟现实环境中实现无缝的持续认证。通过使用深度学习方法进行行为生物特征认证，准确率较高，但在使用任务开始时的较小片段时性能较低。因此，现有技术设计的系统在等待未来运动轨迹片段可用时是易受攻击的。为了解决这个问题，该研究首次使用基于 Transformer 的预测方法，预测未来用户行为并用于用户认证。根据用户当前的任务环境下的轨迹，我们的方法可以预测用户未来的运动轨迹，因为用户不太可能在任务期间大幅改变其行为。我们使用 Miller 等人公开的 41 个被试的投掷球数据集来展示使用预测数据进行用户认证的改进效果。与不进行预测相比，我们的方法平均将认证等错误率（EER）降低了 23.85％，最大降低 36.14％。