ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Genre: Doctoral Dissertation
- Creators: Shen, Wei
- Creators: Yang, Yezhou
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
本研究聚焦冷链物流产业园金融服务助力冷链物流企业发展问题,主要研究内容包括:第一,基于产融结合理论,梳理冷链物流企业与产业园之间关系,从供需两侧探索冷链物流企业和产业园的金融服务的范围、类型和特点。第二,基于平台理论,构建冷链物流企业采纳产业园金融服务的研究模型,探索金融服务影响冷链物流企业的经营因素,分析冷链物流企业采纳产业园金融服务的因素和途径。第三,基于信息不对称理论,关切信息技术支持和知识分享在冷链物流企业采纳产业园提供金融服务过程中的调节作用。同时,梳理产业园提供金融服务可能面临哪些风险,制订冷链物流企业入驻园区的标准,防范风险。
本文运用实证研究方法,通过对国内18家冷链物流相关的产业园、物流园、冷链物流、商贸流通、金融等企业实地考察和专家访谈基础上,拟定问卷并对268家企业进行调查收集数据,使用结构方程模型进行假设检验。研究发现:金融服务的有形性、可靠性、移情性、经济性对冷链物流企业采纳产业园金融服务影响显著,而响应性的影响不显著。同时
信息技术支持和知识共享的调节作用不显著。最后,针对产业园吸引冷链物流企业提供金融服务、冷链物流企业采纳产业园金融服务的风险,提出防范策略措施。
在复杂多变的商业环境中,企业传统的人力资源管理已经难以应对日益频发的员工职业倦怠、人际间矛盾冲突、频繁跳槽等局面与问题。企业员工工作的价值与意义早已不再是传统的雇佣模式下,通过出卖劳动力或智力从而获得工资以实现“养家糊口”的目的那么单纯与简单,员工也希望通过辛勤的工作,以获得个体的幸福感、荣誉感与认同感等。对于现代企业的管理者而言,员工追求事业的提升、个人价值的实现,不仅体现在薪酬、福利待遇的提升,更重要的是员工个人的成长以及潜能和竞争力的提升。
随着组织行为学和心理学的不断发展与演变,与员工幸福感相关的研究备受关注。对现代企业而言,管理者借助制度设计对员工幸福积极管理,可以最大限度地发挥员工的积极性、主动性与创造性,实现员工与企业之间的利益相趋同,从而更为高效地实现组织的目标。基于此,本文以民营企业员工工作幸福感作为研究的切入点,借助理论分析、问卷调查和实证分析相结合的研究方法,系统深入地研究我国民营企业员工工作幸福感的构成、可控前因和绩效后果等问题。
本文研究发现:
第一,员工薪酬的提高有助于员工工作幸福感的提升,薪资对基层员工幸福感的影响显著高于其对高层员工幸福感的影响;
第二,完善的晋升机制对于中层员工而言更能提升其幸福感,完善的晋升机制更有利于中层员工;
第三,公平性的提高有助于提高员工工作幸福感,而且这种正效应更多体现在基层员工群体之中;
第四,高层员工更注重自我价值的实现,高层员工的工作挑战性越高,其自我实现需求获得的满足感则约高,但是对于基层员工和中层员工而言,其效果则恰恰相反,基础员工和高层员工更多地将工作挑战性和压力看作是一种负面的因素;
第五,员工幸福感的确会给企业带来正向的绩效。
本文的研究框架和实证结论不仅可以丰富学术界有关员工工作幸福感的研究,而且为企业管理者进行绩效管理以及员工工作质量的提升提供理论和实证借鉴。
本文首先认真总结分析了有关上市企业治理结构和盈余管理等方面的历史文献资料,依托当前资本市场上普遍运用的委托代理、内部人控制和契约等理论,系统研究了我国民企上市公司在自身治理结构方面的突出特征以及其对盈余管理方面所构成影响的深层次原理。在此基础上,本文通过2015-2017年我国上市企业数据,基于截面Jones模型对民营企业和非民营企业盈余管理程度进行测算和比较分析,发现民营企业盈余管理程度更高;从四个层面系统研究民企公司自身的治理结构突出特点,设立回归模型论证了民营企业独特的公司治理结构特征对盈余管理程度确实会产生影响;最后,本文进一步利用修正的费尔萨姆一奥尔森估价模型对民营上市公司盈余管理有公司价值的关系进行了验证,发现两者具有显著相关性。