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  • To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

    Yang, Zhuo (2022-06) [Dissertation]
    Advisor: Zhang, Xiangliang
    Committee members: Wang, Di; Moshkov, Mikhail; Feng, Zhuo
    Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. Understanding and exploiting the Label Dependency (LD) is well accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack. From the perspective of generalization on clean data, previous works have proved the advantage of exploiting LD in multi-label classification. To further verify the positive role of LD in multi-label classification and address previous limitations, we originally propose an approach named Prototypical Networks for Multi- Label Learning (PNML). Specially, PNML addresses multi-label classification from the angle of estimating the positive and negative class distribution of each label in a shared nonlinear embedding space. PNML achieves the State-Of-The-Art (SOTA) classification performance on clean data. From the perspective of robustness under evasion attack, as a pioneer, we firstly define the attackability of an multi-label classifier as the expected maximum number of flipped decision outputs by injecting budgeted perturbations to the feature distribution of data. Denote the attackability of a multi-label classifier as C∗, and the empirical evaluation of C∗ is an NP-hard problem. We thus develop a method named Greedy Attack Space Exploration (GASE) to estimate C∗ efficiently. More interestingly, we derive an information-theoretic upper bound for the adversarial risk faced by multi-label classifiers. The bound unveils the key factors determining the attackability of multi-label classifiers and points out the negative role of LD in multi-label classifiers’ adversarial robustness, i.e. LD helps the transfer of attack across labels, which makes multi-label classifiers more attackable. One step forward, inspired by the derived bound, we propose a Soft Attackability Estimator (SAE) and further develop Adversarial Robust Multi-label learning with regularized SAE (ARM-SAE) to improve the adversarial robustness of multi-label classifiers. This work gives a more comprehensive understanding of LD in multi-label learning. The exploiting of LD should be encouraged since its positive role in models’ generalization on clean data, but be restricted because of its negative role in models’ adversarial robustness.
  • Libraries’ Role in Improving an Institution’s Scholarly Communication Impact

    Baessa, Mohamed A.; Tomic, Nevena; Grenz, Daryl M. (2022-05-18) [Presentation]
    Scholarly communication services are an essential part of a university library. These services help scholars navigate shifting publishing, intellectual property, copyright, and information policy landscapes in ways that promote research dissemination, accessibility, and impact. In this workshop, we will begin by discussing a range of scholarly communication services offered by academic and research libraries, tools and skills required by librarians to be able to offer these services to their community, as well as the open access movement and different approaches to supporting open access.
  • Reconfigurable Intelligent Surface Enabled Interference Nulling and Signal Power Maximization in mmWave bands

    Ye, Jia; Kammoun, Abla; Alouini, Mohamed-Slim (IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-17) [Article]
    Reconfigurable intelligent surface (RIS) has emerged as a promising mean to enhance wireless transmission. The effective reflected paths provided by RIS are able to alleviate the susceptibility to blockage effects, especially in high-frequency band communications, where signals experience severe path loss and high directivity. This paper is concerned with an RIS-assisted system over the millimeter wave (mmWave) channel characterized by sparse propagation paths. A base station tries to connect with the desired user through an RIS, while the undesired user can also receive the signal transmitted from BS unavoidably, which is treated as the interference signal. All terminals are assumed to be equipped with a single antenna for the sake of simplicity. The paper aims to propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power transmitted from the base station (BS) at the desired user, while nulling the received interference signal power at the undesired user. The proposed reflecting design relies on the decomposition of the reflecting beamforming vectors and all channel path vectors into Kronecker product of factors being uni-modulus vectors. By exploiting characteristics of Kronecker mixed products, different factors of the reflecting are designed for either nulling the interference signal at the undesired user, or coherently combining data paths at the desired user. Furthermore, a channel estimation strategy is proposed to enable the proposed reflecting beamforming design. The magnitude, azimuth, and elevation arrival and departure angles of desired and undesired paths are estimated by an efficient 2-dimension (2-D) line spectrum optimization technique based on the atomic norm minimization (ANM) framework. The performance of the reflecting designs and channel estimation scheme is analyzed and demonstrated by simulation results.
  • Ergodic Capacity Analysis of UAV-based FSO Links over Foggy Channels

    Jung, Kug-Jin; Nam, Sung Sik; Alouini, Mohamed-Slim; Ko, Young-Chai (IEEE Wireless Communications Letters, Institute of Electrical and Electronics Engineers (IEEE), 2022-05-17) [Article]
    In this paper, we investigate the ergodic capacity of unmanned aerial vehicle (UAV)-based free space optics (FSO) links over random foggy channel. More specifically, we derive composite probability density function (PDF) and close approximation for the moments of the composite PDF using the statistical model of a UAV-based 3D pointing error and a random foggy channel. With it, we obtain upper bound and asymptotic approximation of the ergodic capacity for the two possible detection techniques of intensity modulation/direct detection (IM/DD) and heterodyne detection at high and low signal-to-noise ratio (SNR) regimes. The numerical results confirm all the presented analytic results via computer-based Monte-Carlo simulations.
  • Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections

    Salvaña, Mary Lai O.; Lenzi, Amanda; Genton, Marc G. (Journal of the American Statistical Association, Informa UK Limited, 2022-05-17) [Article]
    When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special property is observed: the covariances and cross-covariances are stronger in certain directions. This property is attributed to the presence of natural forces, such as wind, which cause the transport and dispersion of these variables. This spatio-temporal dynamics prompted the use of the Lagrangian reference frame alongside any Gaussian spatio-temporal geostatistical model. Under this modeling framework, a whole new class was birthed and was known as the class of spatio-temporal covariance functions under the Lagrangian framework, with several developments already established in the univariate setting, in both stationary and nonstationary formulations, but less so in the multivariate case. Despite the many advances in this modeling approach, efforts have yet to be directed to probing the case for the use of multiple advections, especially when several variables are involved. Accounting for multiple advections would make the Lagrangian framework a more viable approach in modeling realistic multivariate transport scenarios. In this work, we establish a class of Lagrangian spatio-temporal cross-covariance functions with multiple advections, study its properties, and demonstrate its use on a bivariate pollutant dataset of particulate matter in Saudi Arabia.

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