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The impact of architectural form on physiological stress: a systematic review 建筑形式对生理压力的影响:系统综述
IF 2.6 Q2 Computer Science Pub Date : 2024-01-04 DOI: 10.3389/fcomp.2023.1237531
Cleo Valentine
Technological advancements in physiological body sensor networks (i.e., biometric tracking wearables) and simulated environments (i.e., VR) have led to increased research in the field of neuroarchitecture, specifically investigating the effects of architectural forms, defined here as subtle variations in the shape or configuration of the interior built environment, on neurological responses. While this research field is still in its nascent stages, early findings suggest that certain architectural forms may impact physiological stress responses. Physiological stress has, in turn, been implicated in the development of certain diseases, including cardiovascular disease, cancer, chronic kidney disease, non-alcoholic fatty liver disease and autoimmune and neurodegenerative disorders. To aid future research, particularly into the relationship between media architecture and physiological stress, this paper conducts a systematic review following PRISMA-P guidelines on studies that evaluated physiological stress responses to architectural form using clinical biomarkers. The review identifies the specific clinical biomarkers used to evaluate physiological stress responses to architectural forms and the distinct categories of architectural forms that have, to date, been correlated with elevated stress responses: curvature, enclosure and proportion. Although these studies' findings imply that the identified architectural forms influence physiological stress, their generalisability is arguably constrained by several factors. These constraints include the paucity of research in this area, the lack of uniformity in the definition and measurement of these architectural forms, the varying contextual settings, the unisensory approach of research methodologies, and the duration of exposure under evaluation. The review concludes that clinical biomarkers may be used to measure the impact of architectural form on physiological stress; however, future research should strive for standardized approaches in defining and measuring architectural forms in order to increase the transferability and robustness of results.
人体生理传感器网络(即生物特征跟踪可穿戴设备)和模拟环境(即 VR)方面的技术进步促进了神经建筑领域的研究,特别是研究建筑形式对神经反应的影响,这里的建筑形式是指室内建筑环境的形状或配置的微妙变化。虽然这一研究领域仍处于起步阶段,但早期研究结果表明,某些建筑形式可能会影响生理压力反应。生理压力反过来又与某些疾病的发生有关,包括心血管疾病、癌症、慢性肾病、非酒精性脂肪肝以及自身免疫和神经退行性疾病。为了帮助未来的研究,特别是研究媒体建筑与生理压力之间的关系,本文按照 PRISMA-P 指南,对使用临床生物标记评估建筑形式的生理压力反应的研究进行了系统综述。综述确定了用于评估建筑形式生理压力反应的特定临床生物标志物,以及迄今为止与压力反应升高相关的不同建筑形式类别:弧度、围合和比例。尽管这些研究结果表明,已确定的建筑形式会对生理压力产生影响,但它们的普遍性可能会受到一些因素的制约。这些限制因素包括:该领域的研究较少,对这些建筑形式的定义和测量缺乏统一性,背景环境各不相同,研究方法采用单一感官方法,以及所评估的暴露时间长短。综述得出结论,临床生物标志物可用于测量建筑形式对生理压力的影响;然而,未来的研究应努力采用标准化方法来定义和测量建筑形式,以提高结果的可转移性和稳健性。
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引用次数: 0
Lived experience in human-building interaction (HBI): an initial framework 人类建筑互动(HBI)中的生活经验:初步框架
IF 2.6 Q2 Computer Science Pub Date : 2024-01-04 DOI: 10.3389/fcomp.2023.1233904
Eléni Economidou, Alina Itzlinger, Christopher Frauenberger
The emerging field of human-building interaction (HBI) has its roots in the historical trends of the development of architecture and human-computer interaction (HCI). Advancements in building information modelling (BIM), sensing, and actuation technologies as well as the commodification and miniaturisation of microprocessors over the past two decades are transforming what once were quixotic visions of a cybernetic architecture into reality. This new reality which integrates computation with architecture opens up different kinds of engagements in the ways we design, use, and inhabit our built environments. A question that follows this new reality is: how can we conceptualise human experience in such environments? Thus far, the lived human experience of such interactions has been an overlooked aspect in HBI-related research. In this article, we provide an initial experience framework for HBI underpinned by existing literature from the HCI and architecture domains on the subjective, lived-in experience of architecture and findings derived from a case study of a field-deployed HBI interface. The research objective of our framework is to outline aspects of HBI lived experiences that can be used as guiding lenses for HBI designers and practitioners who wish to design for and assess such experiences.
新兴的人机交互(HBI)领域源于建筑和人机交互(HCI)发展的历史趋势。过去二十年来,建筑信息模型(BIM)、传感和执行技术的进步,以及微处理器的商品化和微型化,正在把过去对控制论建筑的幻想变成现实。这种将计算与建筑融为一体的新现实为我们设计、使用和居住建筑环境的方式开辟了不同的途径。随之而来的一个问题是:我们如何才能将人类在这种环境中的体验概念化?迄今为止,人类在此类互动中的生活体验一直是人机交互相关研究中被忽视的一个方面。在本文中,我们将根据人机交互和建筑领域现有的关于建筑的主观、生活体验的文献,以及实地部署的人机交互界面的案例研究结果,为人机交互提供一个初步的体验框架。我们的框架的研究目标是概述人机交互生活体验的各个方面,这些方面可以作为人机交互设计师和从业人员设计和评估此类体验的指导视角。
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引用次数: 0
Care-full data, care-less systems: making sense of self-care technologies for mental health with humanistic practitioners in the United Kingdom 充满关爱的数据,没有关爱的系统:与英国的人文实践者一起了解心理健康的自我保健技术
IF 2.6 Q2 Computer Science Pub Date : 2023-12-22 DOI: 10.3389/fcomp.2023.1230284
Velvet Spors, Martin Flintham, Pat Brundell, David Murphy
The days of dusty couches in therapists' offices behind closed doors are long gone. Now, personalized mood tracking, therapy appointments and breathing exercises are just mere clicks (or taps) away: Technologies for self-care (SCTs) that focus on mental health are both a flourishing industry and an academic field of interest. As societal, and cultural artifacts, SCTs for mental health are imbued with values, worldviews, and assumptions about these concepts by their designers and developers. Here, current SCTs tend to lean toward a more medical(ised) approach due to being shaped by dominant views of mental health as an individualized issue. However, this approach is only one of many potential pedagogies and approaches. As an alternative, we explore what SCTs for mental health could be like, from a humanistic, person-centered standpoint: We conceptualize mental health in holistic terms, as an experiential quality of everyday life.To this end, we report on two engagements with humanistic practitioners and the person-centered approach as a guiding principle: First, we ran a workshop informed by the Rogerian “encounter group”. This approach is focused on providing the space to meaningfully meet and relate to people. Inspired by this concept, we brought together humanistic practitioners to openly explore what technology for (self-)care means for them. Second, we build on the insights from the aforementioned study by organizing an asynchronous, online whiteboard for humanistic practitioners—counselors, students-in-training, therapists, and researchers—to explore their utopian, realistic and dystopian visions of SCTs.Through thematic analysis and affinity-clustering these engagements, we construct an understanding that technology within a person-centered, humanistic context is a constrained, ambiguous undertaking, yet also one full of potential.We conclude the paper by sketching out three design opportunities for how the person-centered approach, and humanistic psychology in general could be integrated into caring technologies.
在治疗师的办公室里关起门来,坐在布满灰尘的沙发上的日子一去不复返了。现在,只需点击(或轻点)鼠标,就能实现个性化的情绪跟踪、治疗预约和呼吸练习:专注于心理健康的自我保健技术(SCT)既是一个蓬勃发展的产业,也是一个备受关注的学术领域。作为社会和文化的产物,心理健康的 SCT 被其设计者和开发者注入了价值观、世界观和对这些概念的假设。在这里,由于心理健康是一个个体化的问题这一主流观点的影响,目前的小班教学倾向于采用一种更加医学化的方法。然而,这种方法只是众多潜在教学法和方法中的一种。作为一种替代方案,我们从人文主义和以人为本的角度出发,探讨了针对心理健康的小班教 学可能是什么样的:为此,我们报告了与人本主义实践者的两次合作,并将以人为本的方法作为指导原则:首先,我们举办了一个以罗杰 "相遇小组 "为基础的研讨会。这种方法的重点是提供有意义地与人会面和交往的空间。在这一理念的启发下,我们召集了人文实践者,让他们公开探讨用于(自我)护理的技术对他们意味着什么。其次,我们以上述研究的见解为基础,为人本主义实践者--咨询师、受训学生、治疗师和研究人员--组织了一个异步在线白板会,探讨他们对小班教学的乌托邦式、现实式和乌托邦式愿景。通过对这些参与的主题分析和亲和力聚类,我们构建了这样一种认识,即在以人为本的人文背景下,技术是一项受限的、模糊的工作,但同时也是一项充满潜力的工作。最后,我们勾勒出三个设计机会,以说明如何将以人为本的方法和人文心理学整合到护理技术中。
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引用次数: 0
Wearable systems without experiential disruptions: exploring the impact of device feedback changes on explicit awareness, physiological synchrony, sense of agency, and device-body ownership 无体验干扰的可穿戴系统:探索设备反馈变化对明确意识、生理同步、代入感和设备-身体所有权的影响
IF 2.6 Q2 Computer Science Pub Date : 2023-12-21 DOI: 10.3389/fcomp.2023.1289869
Caitlin Morris, Valdemar Danry, Pattie Maes
Technologies on the body that require explicit awareness to be operated or monitored often risk disrupting human awareness and induce stress and excessive cognitive load. With the increasing interest in body-centric technologies, it is thus essential to understand how to build technologies that interface with human awareness without disrupting or requiring too many cognitive resources. In this paper, we build and evaluate a wearable system that uses different feedback types to alter human awareness (of the device). We further demonstrate how this awareness impacts cognitive load, sense of body-ownership, and sense of agency, which are often essential antecedents to successful and continued use. Moreover, we further investigate physiological signals, such as physiological synchrony, as well as qualitative reports in a multimodal analysis. Our results show that devices that provide feedback that deviate from expected behavior tend to generate higher amounts of explicit awareness, and that such increased awareness correlates with increased cognitive load, lower sense of agency and lower sense of body-ownership. Moreover, we find that interoceptive acuity correlates with diminished sense of agency. We discuss their implications for designing wearable body-centric systems that induce or disrupt different levels of awareness to deliver or diminish a sense of body-ownership and agency over the system.
需要明确意识才能操作或监控的人体技术往往有可能扰乱人类意识,造成压力和过度认知负荷。因此,随着人们对以身体为中心的技术的兴趣与日俱增,了解如何在不干扰或不需要过多认知资源的情况下构建与人的意识相联系的技术至关重要。在本文中,我们构建并评估了一个可穿戴系统,该系统使用不同的反馈类型来改变人类(对设备)的认知。我们进一步证明了这种意识如何影响认知负荷、身体拥有感和代理感,而这些往往是成功和持续使用的基本前提。此外,我们还进一步研究了生理同步等生理信号以及多模态分析中的定性报告。我们的研究结果表明,提供偏离预期行为反馈的设备往往会产生更多的明确意识,而这种意识的增加与认知负荷的增加、代理感的降低和身体所有权感的降低相关。此外,我们还发现感知间的敏锐度与代理感的减弱相关。我们讨论了这些研究对设计以身体为中心的可穿戴系统的影响,这些系统可以诱导或破坏不同程度的意识,从而提供或减少身体所有权感和对系统的代理感。
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引用次数: 0
Emotion recognition from MIDI musical file using Enhanced Residual Gated Recurrent Unit architecture 利用增强型残差门控递归单元架构从 MIDI 音乐文件中识别情感
IF 2.6 Q2 Computer Science Pub Date : 2023-12-21 DOI: 10.3389/fcomp.2023.1305413
V. Bhuvana Kumar, M. Kathiravan
The complex synthesis of emotions seen in music is meticulously composed using a wide range of aural components. Given the expanding soundscape and abundance of online music resources, creating a music recommendation system is significant. The area of music file emotion recognition is particularly fascinating. The RGRU (Enhanced Residual Gated Recurrent Unit), a complex architecture, is used in our study to look at MIDI (Musical Instrument and Digital Interface) compositions for detecting emotions. This involves extracting diverse features from the MIDI dataset, encompassing harmony, rhythm, dynamics, and statistical attributes. These extracted features subsequently serve as input to our emotion recognition model for emotion detection. We use an improved RGRU version to identify emotions and the Adaptive Red Fox Algorithm (ARFA) to optimize the RGRU hyperparameters. Our suggested model offers a sophisticated classification framework that effectively divides emotional content into four separate quadrants: positive-high, positive-low, negative-high, and negative-low. The Python programming environment is used to implement our suggested approach. We use the EMOPIA dataset to compare its performance to the traditional approach and assess its effectiveness experimentally. The trial results show better performance compared to traditional methods, with higher accuracy, recall, F-measure, and precision.
音乐中复杂的情感综合体是由各种听觉成分精心构成的。随着声音范围的扩大和在线音乐资源的丰富,创建一个音乐推荐系统意义重大。音乐文件情感识别领域尤其引人入胜。在我们的研究中,使用了 RGRU(增强型残差门控循环单元)这一复杂结构来研究 MIDI(乐器和数字接口)作品,以检测情感。这涉及从 MIDI 数据集中提取各种特征,包括和声、节奏、动态和统计属性。这些提取的特征随后将作为情感识别模型的输入,用于情感检测。我们使用改进的 RGRU 版本来识别情感,并使用自适应红狐算法 (ARFA) 来优化 RGRU 的超参数。我们建议的模型提供了一个复杂的分类框架,可有效地将情感内容分为四个独立的象限:正-高、正-低、负-高和负-低。我们使用 Python 编程环境来实现我们建议的方法。我们使用 EMOPIA 数据集将其性能与传统方法进行比较,并通过实验评估其有效性。试验结果表明,与传统方法相比,该方法具有更高的准确率、召回率、F-measure 和精确度。
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引用次数: 0
Exploring fMRI RDMs: enhancing model robustness through neurobiological data 探索 fMRI RDM:通过神经生物学数据增强模型稳健性
IF 2.6 Q2 Computer Science Pub Date : 2023-12-19 DOI: 10.3389/fcomp.2023.1275026
W. Pickard, Kelsey Sikes, Huma Jamil, Nicholas Chaffee, Nathaniel Blanchard, Michael Kirby, Christopher Peterson
Artificial neural networks (ANNs) are sensitive to perturbations and adversarial attacks. One hypothesized solution to adversarial robustness is to align manifolds in the embedded space of neural networks with biologically grounded manifolds. Recent state-of-the-art works that emphasize learning robust neural representations, rather than optimizing for a specific target task like classification, support the idea that researchers should investigate this hypothesis. While works have shown that fine-tuning ANNs to coincide with biological vision does increase robustness to both perturbations and adversarial attacks, these works have relied on proprietary datasets—the lack of publicly available biological benchmarks makes it difficult to evaluate the efficacy of these claims. Here, we deliver a curated dataset consisting of biological representations of images taken from two commonly used computer vision datasets, ImageNet and COCO, that can be easily integrated into model training and evaluation. Specifically, we take a large functional magnetic resonance imaging (fMRI) dataset (BOLD5000), preprocess it into representational dissimilarity matrices (RDMs), and establish an infrastructure that anyone can use to train models with biologically grounded representations. Using this infrastructure, we investigate the representations of several popular neural networks and find that as networks have been optimized for tasks, their correspondence with biological fidelity has decreased. Additionally, we use a previously unexplored graph-based technique, Fiedler partitioning, to showcase the viability of the biological data, and the potential to extend these analyses by extending RDMs into Laplacian matrices. Overall, our findings demonstrate the potential of utilizing our new biological benchmark to effectively enhance the robustness of models.
人工神经网络(ANN)对扰动和对抗性攻击非常敏感。对抗性鲁棒性的一个假设解决方案是将神经网络嵌入空间中的流形与生物流形相一致。最近的先进研究强调学习鲁棒性神经表征,而不是针对特定的目标任务(如分类)进行优化,这支持了研究人员应研究这一假设的想法。虽然有研究表明,微调人工神经网络使其与生物视觉相吻合确实能提高对扰动和对抗性攻击的鲁棒性,但这些研究都依赖于专有数据集--由于缺乏公开可用的生物基准,因此很难评估这些说法的有效性。在这里,我们提供了一个精心策划的数据集,该数据集由两个常用计算机视觉数据集(ImageNet 和 COCO)中的图像生物表示组成,可以轻松集成到模型训练和评估中。具体来说,我们采用了一个大型功能性磁共振成像(fMRI)数据集(BOLD5000),将其预处理为表征异质性矩阵(RDM),并建立了一个任何人都可以使用的基础设施,利用生物表征训练模型。利用这一基础架构,我们研究了几种流行神经网络的表征,发现随着网络针对任务的优化,它们与生物保真度的对应关系有所下降。此外,我们还使用了一种以前未曾探索过的基于图的技术--费德勒分区,以展示生物数据的可行性,以及通过将 RDM 扩展到拉普拉卡矩阵来扩展这些分析的潜力。总之,我们的研究结果证明了利用我们的新生物基准有效增强模型稳健性的潜力。
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引用次数: 0
Smart sensing enabled dynamic spectrum management for cognitive radio networks 认知无线电网络的智能传感动态频谱管理
IF 2.6 Q2 Computer Science Pub Date : 2023-12-19 DOI: 10.3389/fcomp.2023.1271899
Muhammad Fraz, M. Muslam, Mudassar Hussain, Rashid Amin, Jiang Xie
Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.
认知无线电网络(CRN)开创了无线通信的变革时代,重塑了无线电频谱利用和管理的格局。认知无线电网络的核心是动态感知无线电频谱和调整传输参数的关键能力,以预先解决干扰和优化频谱利用。本文探讨了 CRN 中与服务质量(QoS)管理相关的不断升级的挑战,这些挑战因 CRN 的动态性质而加剧,尤其是在以高流动性为特征的场景中。同时,鉴于能源效率对网络运营成本和可持续性的直接影响,文章强调了能源效率的重要意义。为了有效驾驭客户关系网络中服务质量和能源管理之间错综复杂的相互作用,我们提出了智能传感动态频谱管理方案(SSDSM)。在 SSDSM 框架内,认知用户的能量经过智能感知,而 QoS 则通过动态频谱管理进行管理。建议的方案通过完善基于模糊的控制器来优化服务响应时间,并通过预定义规则触发的定期感测来减少能耗。在集中式模式下,整个网络由一个中央控制节点监管,其任务是利用 SSDSM 方案制定最佳信道列表,并将其分配给认知用户。通过使用 MATLAB 进行严格测试,评估和验证了拟议方案的功效。结果显示,系统效率有了明显提高,包括吞吐量最大化、切换率降低和服务响应延迟最小化。这项研究有助于推动认知无线电网络性能指标的持续讨论,以追求可靠和可持续的无线通信服务。
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引用次数: 0
Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment 在 FOG 环境中使用基于动态 K-means 聚类算法的模糊逻辑进行机器学习驱动的任务调度
IF 2.6 Q2 Computer Science Pub Date : 2023-12-14 DOI: 10.3389/fcomp.2023.1293209
Muhammad Saad Sheikh, Rabia Noor Enam, R. Qureshi
Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.
雾计算已成为在网络边缘实现低延迟、情境感知和高效计算的关键技术。有效的任务调度在优化雾计算系统性能方面发挥着至关重要的作用。传统的任务调度算法主要是为集中式云环境设计的,往往无法满足雾节点动态、异构和资源受限的特性。为了克服这些局限性,我们引入了一种复杂的机器学习驱动方法,它能使任务分配适应不断变化的雾环境条件。我们的方法将 K-Means 聚类算法与模糊逻辑(一种稳健的无监督学习技术)相结合,根据雾节点的资源特征和工作负载模式对其进行有效分组。所提出的方法结合了 K-means 的聚类能力和模糊逻辑的适应性,可动态地为雾节点分配任务。通过利用机器学习技术,我们展示了如何将任务智能地分配给雾节点,从而减少执行时间、响应时间和网络使用。通过大量实验,我们展示了我们提出的方法在动态雾环境中的有效性和适应性。事实证明,聚类是一种省时高效的方法,可有效识别每个虚拟机(VM)的作业群组。为了对我们提出的方法进行建模和评估,我们使用了 iFogSim。仿真结果证实了我们的调度技术的有效性,与 iFogSim 框架内现有的基于机器学习和非机器学习的调度方法相比,我们的调度技术在缩短执行时间、最小化网络利用率和改善响应时间方面都有显著提升。
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引用次数: 0
Editorial: Technology for the greater good? The influence of (ir)responsible systems on human emotions, thinking and behavior 社论:科技造福人类?不负责任的系统对人类情感、思维和行为的影响
IF 2.6 Q2 Computer Science Pub Date : 2023-12-13 DOI: 10.3389/fcomp.2023.1341692
Andrea Szymkowiak, Lynsay A. Shepherd, Jason R. C. Nurse, P. Brauner, Martina Ziefle
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引用次数: 0
Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer 利用 D 波量子退火器的黑盒优化技术探索新型化学材料
IF 2.6 Q2 Computer Science Pub Date : 2023-12-12 DOI: 10.3389/fcomp.2023.1286226
Mikiya Doi, Yoshihiro Nakao, Takuro Tanaka, Masami Sako, Masayuki Ohzeki
In materials informatics, searching for chemical materials with desired properties is challenging due to the vastness of the chemical space. Moreover, the high cost of evaluating properties necessitates a search with a few clues. In practice, there is also a demand for proposing compositions that are easily synthesizable. In the real world, such as in the exploration of chemical materials, it is common to encounter problems targeting black-box objective functions where formalizing the objective function in explicit form is challenging, and the evaluation cost is high. In recent research, a Bayesian optimization method has been proposed to formulate the quadratic unconstrained binary optimization (QUBO) problem as a surrogate model for black-box objective functions with discrete variables. Regarding this method, studies have been conducted using the D-Wave quantum annealer to optimize the acquisition function, which is based on the surrogate model and determines the next exploration point for the black-box objective function. In this paper, we address optimizing a black-box objective function containing discrete variables in the context of actual chemical material exploration. In this optimization problem, we demonstrate results obtaining parameters of the acquisition function by sampling from a probability distribution with variance can explore the solution space more extensively than in the case of no variance. As a result, we found combinations of substituents in compositions with the desired properties, which could only be discovered when we set an appropriate variance.
在材料信息学中,由于化学空间浩瀚无边,要搜索具有所需特性的化学材料是一项挑战。此外,由于评估特性的成本较高,因此必须利用少量线索进行搜索。在实践中,还需要提出易于合成的成分。在现实世界中,例如在化学材料的探索中,经常会遇到以黑箱目标函数为目标的问题,在这种情况下,以明确的形式形式化目标函数具有挑战性,而且评估成本很高。最近的研究提出了一种贝叶斯优化方法,将二次无约束二元优化(QUBO)问题表述为具有离散变量的黑箱目标函数的替代模型。关于该方法,已有研究使用 D-Wave 量子退火器来优化获取函数,该方法基于代理模型并确定黑箱目标函数的下一个探索点。在本文中,我们以实际化学材料勘探为背景,探讨优化包含离散变量的黑盒目标函数。在这一优化问题中,我们证明了通过从有方差的概率分布中采样获取获取函数参数的结果,与无方差的情况相比,可以更广泛地探索解空间。因此,我们发现了具有所需特性的成分中的取代基组合,而只有设置适当的方差才能发现这些组合。
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引用次数: 0
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