We propose the integration of sentiment analysis and deep-reinforcement
learning ensemble algorithms for stock trading, and design a strategy capable
of dynamically altering its employed agent given concurrent market sentiment.
In particular, we create a simple-yet-effective method for extracting news
sentiment and combine this with general improvements upon existing works,
resulting in automated trading agents that effectively consider both
qualitative market factors and quantitative stock data. We show that our
approach results in a strategy that is profitable, robust, and risk-minimal --
outperforming the traditional ensemble strategy as well as single agent
algorithms and market metrics. Our findings determine that the conventional
practice of switching ensemble agents every fixed-number of months is
sub-optimal, and that a dynamic sentiment-based framework greatly unlocks
additional performance within these agents. Furthermore, as we have designed
our algorithm with simplicity and efficiency in mind, we hypothesize that the
transition of our method from historical evaluation towards real-time trading
with live data should be relatively simple.

我们提出了情感分析和深度强化学习集成算法用于股票交易，并设计了一种能够根据当前市场情绪动态改变其参与的交易代理的策略。我们展示了我们的方法导致了一个盈利的、稳健的和风险最小化的策略，其表现优于传统集成策略以及单一代理算法和市场指标。此外，我们设计的算法考虑了简洁性和效率，并且我们假设将我们的方法从历史评估向实时交易的转换应该相对简单。

学习市场：基于情绪的集成交易代理

Learning the Market: Sentiment-Based Ensemble Trading Agents

Quantum Machine Learning has the potential to improve traditional machine
learning methods and overcome some of the main limitations imposed by the
classical computing paradigm. However, the practical advantages of using
quantum resources to solve pattern recognition tasks are still to be
demonstrated.
This work proposes a universal, efficient framework that can reproduce the
output of a plethora of classical supervised machine learning algorithms
exploiting quantum computation's advantages. The proposed framework is named
Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine
multiple and diverse functions to solve typical supervised learning problems.
In its general formulation, MAQA can be potentially adopted as the quantum
counterpart of all those models falling into the scheme of aggregation of
multiple functions, such as ensemble algorithms and neural networks. From a
computational point of view, the proposed framework allows generating an
exponentially large number of different transformations of the input at the
cost of increasing the depth of the corresponding quantum circuit linearly.
Thus, MAQA produces a model with substantial descriptive power to broaden the
horizon of possible applications of quantum machine learning with a
computational advantage over classical methods. As a second meaningful
addition, we discuss the adoption of the proposed framework as hybrid
quantum-classical and fault-tolerant quantum algorithm.

本文提出了一个名为 MAQA 的通用，高效框架，可以重现利用量子计算优势解决典型监督学习问题的各种经典监督机器学习算法的输出，并允许生成大量的不同模型转换。MAQA 可作为所有聚合多个函数模型（如集成算法和神经网络）的量子对应物，并且可以作为混合量子 - 经典和容错量子算法的采用。

MAQA：监督学习的量子框架

MAQA: A Quantum Framework for Supervised Learning

Building robust and general dialogue models for spoken conversations is
challenging due to the gap in distributions of spoken and written data. This
paper presents our approach to build generalized models for the
Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations
Challenge of DSTC-10. In order to mitigate the discrepancies between spoken and
written text, we mainly employ extensive data augmentation strategies on
written data, including artificial error injection and round-trip text-speech
transformation. To train robust models for spoken conversations, we improve
pre-trained language models, and apply ensemble algorithms for each sub-task.
Typically, for the detection task, we fine-tune \roberta and ELECTRA, and run
an error-fixing ensemble algorithm. For the selection task, we adopt a
two-stage framework that consists of entity tracking and knowledge ranking, and
propose a multi-task learning method to learn multi-level semantic information
by domain classification and entity selection. For the generation task, we
adopt a cross-validation data process to improve pre-trained generative
language models, followed by a consensus decoding algorithm, which can add
arbitrary features like relative \rouge metric, and tune associated feature
weights toward \bleu directly. Our approach ranks third on the objective
evaluation and second on the final official human evaluation.

本文介绍了我们在 DSTC-10 上构建对话模型的方法。为缩小口头和书面数据之间的差距，我们采用了大量数据扩增策略，包括人工误差注入和文本转语音转换。我们改良了预训练语言模型，并对每个子任务应用了集成算法，以训练稳健的口头对话模型。我们的方法在官方评估中排名第三，在最终的正式人类评估中排名第二。