金融/语音/音频处理学术速递[8.23]

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q-fin金融，共计4篇

cs.SD语音，共计2篇

eess.AS音频处理，共计3篇

1.q-fin金融:

【1】 Sensitivity of Optimal Retirement Problem to Liquidity Constraints 标题：最优退休问题对流动性约束的敏感性 链接：https://arxiv.org/abs/2108.09035

作者：Guodong Ding,Daniele Marazzina 机构： Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci , I-, Milano, Italy 摘要：在这项工作中，我们解析地解决了一个最优退休问题，在该问题中，代理通过对偶方法优化配置风险投资、消费和休闲率，以最大化以消费和休闲的幂效用函数为特征的收益函数。我们在不同的时间跨度上施加不同的流动性约束，并进行敏感性分析，以发现这种约束的影响。 摘要：In this work we analytically solve an optimal retirement problem, in which the agent optimally allocates the risky investment, consumption and leisure rate to maximise a gain function characterised by a power utility function of consumption and leisure, through the duality method. We impose different liquidity constraints over different time spans and conduct a sensitivity analysis to discover the effect of this kind of constraint.

【2】 Assessment of waterfront office redevelopment plan on optimal building energy demand and rooftop photovoltaics for urban decarbonization 标题：城市脱碳最优建筑能源需求和屋顶光伏的滨水写字楼重建方案评估 链接：https://arxiv.org/abs/2108.09029

作者：Younghun Choi,Takuro Kobashi,Yoshiki Yamagata,Akito Murayama 机构：Center for Global Environment Research, National Institute for Environment Studies,-, Onogawa, Tsukuba, Ibaraki ,- , Japan, Department of Urban Engineering, School of Engineering, The University of Tokyo; ,-,-, Hongo, Bunkyo-ku, Tokyo ,- 备注：29 pages 摘要：滨水区重新开发的设计通常侧重于吸引力、休闲和美观，从而形成各种类型的建筑和街区形状，对环境方面的考虑有限。然而，日益加剧的气候变化影响要求这些建筑具有可持续性、弹性和零二氧化碳排放。通过制作五个具有固定楼层面积的场景（加上现有建筑），我们调查了在东京滨水重建背景下，建筑和区域形式与建筑集成光伏（BIPV）如何影响能源消耗和生产、自给自足、二氧化碳排放和能源成本。根据建筑物每小时的预计电力需求，对2018年和2030年屋顶光伏系统进行了技术经济分析，屋顶光伏系统的成本不断下降。我们发现，在东京，屋顶光伏系统的环保建筑设计越来越经济，二氧化碳排放量减少2-9%，这取决于屋顶的大小。投资回收期从2018年的14年下降到2030年的6年。为了在2050年实现二氧化碳净零排放，有必要立即采取行动，在现有和新建筑上安装屋顶PVs，提高建筑业和建筑业主的能效。为了促进这些行动，国家和地方政府需要采取适当的政策。 摘要：Designing waterfront redevelopment generally focuses on attractiveness, leisure, and beauty, resulting in various types of building and block shapes with limited considerations on environmental aspects. However, increasing climate change impacts necessitate these buildings to be sustainable, resilient, and zero CO2 emissions. By producing five scenarios (plus existing buildings) with constant floor areas, we investigated how building and district form with building integrated photovoltaics (BIPV) affect energy consumption and production, self-sufficiency, CO2 emission, and energy costs in the context of waterfront redevelopment in Tokyo. From estimated hourly electricity demands of the buildings, techno-economic analyses are conducted for rooftop PV systems for 2018 and 2030 with declining costs of rooftop PV systems. We found that environmental building designs with rooftop PV system are increasingly economical in Tokyo with CO2 emission reduction of 2-9% that depends on rooftop sizes. Payback periods drop from 14 years in 2018 to 6 years in 2030. Toward net-zero CO2 emissions by 2050, immediate actions are necessary to install rooftop PVs on existing and new buildings with energy efficiency improvements by construction industry and building owners. To facilitate such actions, national and local governments need to adopt appropriate policies.

【3】 Bermudan option pricing by quantum amplitude estimation and Chebyshev interpolation 标题：基于量子振幅估计和切比雪夫插值的百慕大期权定价 链接：https://arxiv.org/abs/2108.09014

作者：Koichi Miyamoto 机构：Center for Quantum Information and Quantum Biology, Osaka University, -, Machikaneyama, Toyonaka, Osaka,-, Japan 备注：14 pages, no figure 摘要：金融衍生品的定价，特别是早期可行权期权，如百慕大期权，是金融机构一项重要但繁重的数字任务，其加速将带来巨大的业务影响。最近，人们开始研究量子计算在金融问题上的应用。在本文中，我们首先提出了百慕大期权定价的量子算法。该方法通过切比雪夫插值，使用量子振幅估计估计的插值节点处的值，对连续值进行近似，连续值是百慕大期权定价的关键部分。在该方法中，调用oracle以生成基础资产价格路径的次数按$\widetilde{O}（\epsilon^{-1}）$的比例递增，其中$\epsilon$是期权价格的容错性。这意味着与经典的基于蒙特卡罗的方法（如最小二乘蒙特卡罗）相比，二次加速，其中oracle调用号为$\widetilde{O}（\epsilon^{-2}）$。 摘要：Pricing of financial derivatives, in particular early exercisable options such as Bermudan options, is an important but heavy numerical task in financial institutions, and its speed-up will provide a large business impact. Recently, applications of quantum computing to financial problems have been started to be investigated. In this paper, we first propose a quantum algorithm for Bermudan option pricing. This method performs the approximation of the continuation value, which is a crucial part of Bermudan option pricing, by Chebyshev interpolation, using the values at interpolation nodes estimated by quantum amplitude estimation. In this method, the number of calls to the oracle to generate underlying asset price paths scales as $\widetilde{O}(\epsilon^{-1})$, where $\epsilon$ is the error tolerance of the option price. This means the quadratic speed-up compared with classical Monte Carlo-based methods such as least-squares Monte Carlo, in which the oracle call number is $\widetilde{O}(\epsilon^{-2})$.

【4】 Deep Sequence Modeling: Development and Applications in Asset Pricing 标题：深序列建模：发展及其在资产定价中的应用 链接：https://arxiv.org/abs/2108.08999

作者：Lin William Cong,Ke Tang,Jingyuan Wang,Yang Zhang 机构：Finance at the Johnson Graduate School of Management at Cornell University in Ithaca, NY., at Beihang University in Beijing, China. 摘要：我们使用人工智能的一项重要技术——深度序列建模来预测资产收益和衡量风险溢价。由于资产收益往往表现出传统时间序列模型无法有效捕获的序列依赖性，序列建模以其数据驱动的方法和优异的性能提供了一条有前途的道路。在本文中，我们首先概述了深序列模型的发展，介绍了它们在资产定价中的应用，并讨论了它们的优点和局限性。然后，我们使用美国股票数据对这些方法进行比较分析。我们展示了序列建模如何通过合并复杂的历史路径依赖而使投资者总体受益，并且基于长期和短期记忆（LSTM）的模型往往具有最佳的样本外性能。 摘要：We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a promising path with its data-driven approach and superior performance. In this paper, we first overview the development of deep sequence models, introduce their applications in asset pricing, and discuss their advantages and limitations. We then perform a comparative analysis of these methods using data on U.S. equities. We demonstrate how sequence modeling benefits investors in general through incorporating complex historical path dependence, and that Long- and Short-term Memory (LSTM) based models tend to have the best out-of-sample performance.

2.cs.SD语音:

【1】 Parsing Birdsong with Deep Audio Embeddings 标题：基于深度音频嵌入的鸟鸣句法分析 链接：https://arxiv.org/abs/2108.09203

作者：Irina Tolkova,Brian Chu,Marcel Hedman,Stefan Kahl,Holger Klinck 机构：School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 备注：IJCAI 2021 Artificial Intelligence for Social Good (AI4SG) Workshop 摘要：鸟类种群监测在保护工作和了解生物多样性丧失方面发挥了至关重要的作用。传感技术（如被动声学监测）和伴随的分析工具（如深度学习）促进了这一过程的自动化。然而，机器学习模型通常难以推广到训练数据中未遇到的示例。在我们的工作中，我们提出了一种半监督方法来识别特征呼叫和环境噪声。我们使用几种方法来学习音频样本的潜在表示，包括卷积自动编码器和两个预先训练的网络，并将生成的嵌入进行分组，以便领域专家识别聚类标签。我们表明，我们的方法可以提高分类精度，并提供洞察环境声学数据集的潜在结构。 摘要：Monitoring of bird populations has played a vital role in conservation efforts and in understanding biodiversity loss. The automation of this process has been facilitated by both sensing technologies, such as passive acoustic monitoring, and accompanying analytical tools, such as deep learning. However, machine learning models frequently have difficulty generalizing to examples not encountered in the training data. In our work, we present a semi-supervised approach to identify characteristic calls and environmental noise. We utilize several methods to learn a latent representation of audio samples, including a convolutional autoencoder and two pre-trained networks, and group the resulting embeddings for a domain expert to identify cluster labels. We show that our approach can improve classification precision and provide insight into the latent structure of environmental acoustic datasets.

【2】 Estimation of Playable Piano Fingering by Pitch-difference Fingering Matching Model 标题：用音高差指法匹配模型估计可演奏钢琴指法 链接：https://arxiv.org/abs/2108.09058

作者：Haoyue Zhao,Xin Guan,Qiang Li 机构：School of Microelectronics, Tianjin University, Tianjin , China 备注：31 pages,12 figures 摘要：现有的钢琴指法标记统计模型通常考虑手指之间的限制以及指法和音符之间的相关性，很少直接包含音符之间的关系。有限的学习手指转移规则通常会导致手指的某些部分实际上无法播放。而传统的模型往往采用原始音符，这无助于探索音高和指法之间的映射性质。受直接从基音差中获取指法知识的手工标注的启发，我们提出了一种基音差序列和指法（PdF）匹配模型。为了获得可玩的指法，需要学习手指转移规则，特别是将先前的手指转移知识结合到模型中。为了表征该模型的可玩性，我们还提出了一个新的评价指标，即无能力执行指进率（IFR）。综合实验结果表明，与现有的最先进的三阶隐马尔可夫标记模型相比，该模型的总体匹配率和最高匹配率分别提高了3%和1.6%，所有分数的指法都可以播放。 摘要：The existing piano fingering labeling statistical models usually consider the constraints among the fingers and the correlation between fingering and notes, and rarely include the relationship among the notes directly. The limited learned finger-transfer rules often cause that some parts of the fingering cannot be playable in fact. And traditional models often adopt the original notes, which cannot help to explore the mapping nature between the pitches and fingering. Inspired from manual-ly annotation which acquire the fingering knowledge directly from pitch-difference, we proposed a pitch-difference sequence and fingering (PdF) matching model. And to get playable fingering, be-sides learned finger-transfer rules, prior finger-transfer knowledge is especially combined into the model. In order to characterize the playable performance of the model, we also presented a new evaluation index named incapable-performing fingering rate (IFR). Comprehensive experimental re-sults show that compared with the existing state-of-the-art third-order hidden Markov labeling model, the general and the highest matching rate of our model increases by 3% and 1.6% respective-ly, and the fingering for all scores can be playable.

3.eess.AS音频处理:

【1】 Investigation of the Assessment of Infant Vocalizations by Laypersons 标题：外行人对婴幼儿发声评定的调查研究 链接：https://arxiv.org/abs/2108.09205

作者：Franz Anders,Mario Hlawitschka,Mirco Fuchs 机构：Leipzig University of Applied Sciences, Germany, Laboratory for Biosignal Processing, Eilenburger Straße , Leipzig, Germany, Gustav-Freytag-Straße ,a , Leipzig, Germany 摘要：本次调查的目的是由非专业人士对婴儿的发声进行评估。更具体地说，目标是确定（1）婴儿发声的一组最显著的类别，（2）它们之间的关系以及与情感评级的关系，以及（3）基于这些标签和关系的分类方案建议。非专业人士的评估行为尚未被调查，因为当前的婴儿发声分类方案旨在专业和科学应用。该研究方法基于奈梅根协议，在该协议中，参与者根据声学类别标签、连续情感量表、配价、紧张唤醒和能量唤醒对发声记录进行评分。我们根据参与者评分确定一致性刺激评分以及刺激相似性。我们的主要发现是：（1）我们确定了9个显著标签，（2）价与标签评分的总体关联性最大，（3）在负价空间中，标签与价评分之间有很强的关联性，但中性标签的关联性较低，（4）将标签分为3-5类时，刺激可分性最高。最后，我们根据这些发现提出了两种分类方案。 摘要：The goal of this investigation was the assessment of acoustic infant vocalizations by laypersons. More specifically, the goal was to identify (1) the set of most salient classes for infant vocalizations, (2) their relationship to each other and to affective ratings, and (3) proposals for classification schemes based on these labels and relationships. The assessment behavior of laypersons has not yet been investigated, as current infant vocalization classification schemes have been aimed at professional and scientific applications. The study methodology was based on the Nijmegen protocol, in which participants rated vocalization recordings regarding acoustic class labels, and continuous affective scales valence, tense arousal and energetic arousal. We determined consensus stimuli ratings as well as stimuli similarities based on participant ratings. Our main findings are: (1) we identified 9 salient labels, (2) valence has the overall greatest association to label ratings, (3) there is a strong association between label and valence ratings in the negative valence space, but low association for neutral labels, and (4) stimuli separability is highest when grouping labels into 3 - 5 classes. We finally propose two classification schemes based on these findings.

【2】 Sparse Array Capon Beamformer Design Availing Deep Learning 标题：基于深度学习的稀疏阵列Capon波束形成器设计 链接：https://arxiv.org/abs/2108.08962

作者：Syed A. Hamza,Moeness G. Amin 机构： Hamza is with the School of Engineering, Widener University, Villanova University 摘要：本文考虑了实现最大信干噪比（MaxSINR）的接收波束形成稀疏阵列设计。我们开发了一种基于监督神经网络的设计方法，其中类标签是使用有效的稀疏波束形成器频谱分析（SBSA）方法生成的。SBSA使用未知窄带干扰环境的显式信息来训练网络，其性能与使用枚举的训练非常接近，即穷举搜索，这在计算上对大型阵列是禁止的。所采用的DNN有效地逼近了从输入-接收数据空间相关性到稀疏配置输出的未知映射，并具有有效的干扰抑制能力。该问题被提出为一个多标签分类问题，其中实现MaxSINR的选定天线位置由DNN的输出层指示。除了从分类精度方面评估DNN的性能外，我们还从分类稀疏阵列抑制干扰和最大化信号功率的能力方面评估了DNN的性能。结果表明，即使在未命中分类的情况下，当至少一个传感器位置与最佳位置不匹配时，DNN也能有效地学习具有理想SINR特性的次优稀疏配置。这显示了DNN学习所提出的优化算法的能力，从而为高效的实时实现铺平了道路。 摘要：The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using enumerations, i.e., exhaustive search which is computationally prohibitive for large arrays. The employed DNN effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a multi-label classification problem where the selected antenna locations achieving MaxSINR are indicated by the output layer of DNN. In addition to evaluating the performance of the DNN in terms of the classification accuracy, we evaluate the performance in terms of the the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that even in the case of miss-classification, where at least one sensor location doesn't match the optimal locations, the DNN effectively learns the sub-optimal sparse configuration which has desirable SINR characteristics. This shows the ability of the DNN to learn the proposed optimization algorithms, hence paving the way for efficient real-time implementation.

【3】 Parsing Birdsong with Deep Audio Embeddings 标题：基于深度音频嵌入的鸟鸣句法分析 链接：https://arxiv.org/abs/2108.09203

作者：Irina Tolkova,Brian Chu,Marcel Hedman,Stefan Kahl,Holger Klinck 机构：School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 备注：IJCAI 2021 Artificial Intelligence for Social Good (AI4SG) Workshop 摘要：鸟类种群监测在保护工作和了解生物多样性丧失方面发挥了至关重要的作用。传感技术（如被动声学监测）和伴随的分析工具（如深度学习）促进了这一过程的自动化。然而，机器学习模型通常难以推广到训练数据中未遇到的示例。在我们的工作中，我们提出了一种半监督方法来识别特征呼叫和环境噪声。我们使用几种方法来学习音频样本的潜在表示，包括卷积自动编码器和两个预先训练的网络，并将生成的嵌入进行分组，以便领域专家识别聚类标签。我们表明，我们的方法可以提高分类精度，并提供洞察环境声学数据集的潜在结构。 摘要：Monitoring of bird populations has played a vital role in conservation efforts and in understanding biodiversity loss. The automation of this process has been facilitated by both sensing technologies, such as passive acoustic monitoring, and accompanying analytical tools, such as deep learning. However, machine learning models frequently have difficulty generalizing to examples not encountered in the training data. In our work, we present a semi-supervised approach to identify characteristic calls and environmental noise. We utilize several methods to learn a latent representation of audio samples, including a convolutional autoencoder and two pre-trained networks, and group the resulting embeddings for a domain expert to identify cluster labels. We show that our approach can improve classification precision and provide insight into the latent structure of environmental acoustic datasets.