H. Li

J. R. González, É. O. Rodrigues, C. P. Damião et al.

Thyroid cancer is said to be the second most common type of cancer in female individuals and the third in males by 2030, according to projections. In general, detecting cancer in its early stages improves the chance of survival of the individual. Thermography is a diagnostic tool that has been increasingly used to detect cancer and abnormalities, including that of thyroid. Various methods to segment and detect hot regions in thermograms and, consequently, to detect suspicious tissues present in these images have been proposed. It is well known that medical diagnosis yields a great deal of information. Thus, physicians have to comprehensively analyse and evaluate this information in a short period of time, which is infeasible in most cases. In this work, we perform a general review of thermography , focusing on the thyroid analysis. We propose protocols for image acquisiton and an autonomous registration for thyroid images. We also perform analyses of the image data, which include feature extraction, image processing, and a possible approach for classification of healthy or unhealthy patients. In summary, this work presents a pilot project for detection of tumors in our university hospital, which is part of an effort to support preventive medical actions in our endocrinology department. Under some future adjustments, this project will be submitted for approval by the ethics and research committee of Hospital Universitário Antonio Pedro at Universidade Federal Fluminense (HUAP-UFF) and to the Brazilian Ministry of Health Ethical committee under the name: Evaluation of the importance of thermography to aid diagnosis of thyroid nodules of patients in HUAP-UFF (in Portuguese: Avaliação da importância da termografia no auxílio à investigação diagnóstica de nódulos tireoidianos em pacientes acompanhados no HUAP-UFF).

X. Chen, S. Chen, T. Xu et al.

Traditional online map tiles, widely used on the Internet such as Google Map and Baidu Map, are rendered from vector data. Timely updating online map tiles from vector data, of which the generating is time-consuming, is a difficult mission. It is a shortcut to generate map tiles in time from remote sensing images, which can be acquired timely without vector data. However, this mission used to be challenging or even impossible. Inspired by image-to-image translation (img2img) techniques based on generative adversarial networks (GAN), we proposed a semi-supervised Generation of styled map Tiles based on Generative Adversarial Network (SMAPGAN) model to generate styled map tiles directly from remote sensing images. In this model, we designed a semi-supervised learning strategy to pre-train SMAPGAN on rich unpaired samples and fine-tune it on limited paired samples in reality. We also designed image gradient L1 loss and image gradient structure loss to generate a styled map tile with global topological relationships and detailed edge curves of objects, which are important in cartography. Moreover, we proposed edge structural similarity index (ESSI) as a metric to evaluate the quality of topological consistency between generated map tiles and ground truths. Experimental results present that SMAPGAN outperforms state-of-the-art (SOTA) works according to mean squared error, structural similarity index, and ESSI. Also, SMAPGAN won more approval than SOTA in the human perceptual test on the visual realism of cartography. Our work shows that SMAPGAN is potentially a new paradigm to produce styled map tiles. Our implementation of the SMAPGAN is available at https://github.com/imcsq/SMAPGAN.

H. Li, J. Sun, Z. Zhang

We consider operator learning for efficiently solving parametric non-selfadjoint eigenvalue problems. To overcome the spectral instability and mode switching inherent in non-selfadjoint operators, we introduce a hybrid framework that learns the stable invariant eigensubspace mapping rather than individual eigenfunctions. We proposed a Deep Eigenspace Network (DEN) architecture integrating Fourier Neural Operators, geometry-adaptive POD bases, and explicit banded cross-mode mixing mechanisms to capture complex spectral dependencies on unstructured meshes. We apply DEN to the parametric non-selfadjoint Steklov eigenvalue problem and provide theoretical proofs for the Lipschitz continuity of the eigensubspace with respect to the parameters. In addition, we derive error bounds for the reconstruction of the eigenspace. Numerical experiments validate DEN's high accuracy and zero-shot generalization capabilities across different discretizations.

D. Y. Rao, X. J. Wu, H. Li et al.

In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach.

H. Li, B. Alarcon, S. M. Espinosa et al.

GrabYourWallet, #boycottNRA and other online boycott campaigns have attracted substantial public interest in recent months. However, a number of significant challenges are preventing online boycotts from reaching their potential. In particular, complex webs of brands and subsidiaries can make it difficult for participants to conform to the goals of a boycott. Similarly, participants and organizers have limited visibility into a boycott's progress. This affects their ability to use sociotechnical innovations from social computing to incentivize participation. To address these challenges, this paper makes a system contribution: a new boycott tool called Out of Site. Out of Site uses lightweight automation to remove obstacles to successful online boycotts. We describe the design challenges associated with Out of Site and report results from two phases of deployment with the GrabYourWallet and Stop Animal Testing boycott communities. Our findings highlight the potential of boycott-assisting technologies and inform the design of this new class of technologies. Finally, like is the case for many systems in social computing, while we designed Out of Site for pro-social uses, there are a number of easily predictable ways in which the system can be leveraged for anti-social purposes (e.g. exacerbating filter bubble issues, empowering boycotts of businesses owned by racial, ethnic, and religious minorities). As such, we developed for this project a new, very straightforward design approach that treats preventing these anti-social uses as a top-tier design concern. This approach stands in contrast to the status quo of ignoring potential anti-social uses and/or considering them to be a secondary design priority. We discuss how our simple approach may help other research projects reduce their potential negative impacts with minimal burden.

G. Zhang, H. Li

Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization. The key step in SNNs is to properly scale the exponential linear unit (referred to as SELU) to inherently incorporate normalization based on central limit theory. SELU is a monotonically increasing function, where it has an approximately constant negative output for large negative input. In this work, we propose a new activation function to break the monotonicity property of SELU while still preserving the self-normalizing property. Differently from SELU, the new function introduces a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function, which is referred to as a scaled exponentially-regularized linear unit (SERLU). The bump-shaped function has approximately zero response to large negative input while being able to push the output of SERLU towards zero mean statistically. To effectively combat over-fitting, we develop a so-called shift-dropout for SERLU, which includes standard dropout as a special case. Experimental results on MNIST, CIFAR10 and CIFAR100 show that SERLU-based neural networks provide consistently promising results in comparison to other 5 activation functions including ELU, SELU, Swish, Leakly ReLU and ReLU.