Extracting Geospatial Information from Social Media Data for Hazard Mitigation, Typhoon Hato as Case Study (Short Paper)

With social media widely used for interpersonal communication, it has served as one important channel for information creation and propagation especially during hazard events. Users of social media in hazard-affected area can capture and upload hazard information more timely by portable and internet-connected electric devices such as smart phones or tablet computers equipped with (Global Positioning System) GPS devices and cameras. The information from social media(e.g. Twitter, facebook, sina-weibo, WebChat, etc.) contains a lot of hazard related information including texts, pictures, and videos. Most important thing is that a fair proportion of these crowd-sourcing information is valuable for the geospatial analysis in Geographic information system (GIS) during the hazard mitigation process. The geospatial information (position of observer, hazard-affected region, status of damages, etc) can be acquired and extracted from social media data. And hazard related information could also be used as the GIS attributes. But social media data obtained from crowd-sourcing is quite complex and fragmented on format or semantics. In this paper, we introduced the method how to acquire and extract fine-grained hazard damage geospatial information. According to the need of hazard relief, we classified the extracted information into eleven hazard loss categories and we also analyzed the public\u27s sentiment to the hazard. The 2017 typhoon "Hato" was selected as the case study to test the method introduced

Multi-Speaker Expressive Speech Synthesis via Semi-supervised Contrastive Learning

This paper aims to build an expressive TTS system for multi-speakers, synthesizing a target speaker's speech with multiple styles and emotions. To this end, we propose a novel contrastive learning-based TTS approach to transfer style and emotion across speakers. Specifically, we construct positive-negative sample pairs at both utterance and category (such as emotion-happy or style-poet or speaker A) levels and leverage contrastive learning to better extract disentangled style, emotion, and speaker representations from speech. Furthermore, we introduce a semi-supervised training strategy to the proposed approach to effectively leverage multi-domain data, including style-labeled data, emotion-labeled data, and unlabeled data. We integrate the learned representations into an improved VITS model, enabling it to synthesize expressive speech with diverse styles and emotions for a target speaker. Experiments on multi-domain data demonstrate the good design of our model.Comment: 5 pages, 3 figure

Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery

This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings

Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retaining the theoretical guarantees of regularization models. However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications. To tackle this challenge, we propose a self-supervised DL approach for accelerated MRI that is theoretically guaranteed and does not rely on fully sampled labels. Specifically, we achieve neural network structure regularization by exploiting the inherent structural low-rankness of the $k$-space data. Simultaneously, we constrain the network structure to resemble a nonexpansive mapping, ensuring the network's convergence to a fixed point. Thanks to this well-defined network structure, this fixed point can completely reconstruct the missing $k$-space data based on matrix completion theory, even in situations where full-sampled labels are unavailable. Experiments validate the effectiveness of our proposed method and demonstrate its superiority over existing self-supervised approaches and traditional regularization methods, achieving performance comparable to that of supervised learning methods in certain scenarios

Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems

Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit certain limitations in terms of interpretability and robustness. In response, we introduce Convex Latent-Optimized Adversarial Regularizers (CLEAR), a novel and interpretable data-driven paradigm. CLEAR represents a fusion of deep learning (DL) and variational regularization. Specifically, we employ a latent optimization technique to adversarially train an input convex neural network, and its set of minima can fully represent the real data manifold. We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold. Leveraging its inherent convexity, we have established the convergence of the projected subgradient descent algorithm for the CLEAR-informed regularization model. This convergence guarantees the attainment of a unique solution to the imaging inverse problem, subject to certain assumptions. Furthermore, we have demonstrated the robustness of our CLEAR-informed model, explicitly showcasing its capacity to achieve stable reconstruction even in the presence of measurement interference. Finally, we illustrate the superiority of our approach using MRI reconstruction as an example. Our method consistently outperforms conventional data-driven techniques and traditional regularization approaches, excelling in both reconstruction quality and robustness

Antibody-drug conjugates in urinary tumors: clinical application, challenge, and perspectives

Urinary tumors primarily consist of kidney, urothelial, and prostate malignancies, which pose significant treatment challenges, particularly in advanced stages. Antibody-drug conjugates (ADCs) have emerged as a promising therapeutic approach, combining monoclonal antibody specificity with cytotoxic chemotherapeutic payloads. This review highlights recent advancements, opportunities, and challenges in ADC application for urinary tumors. We discuss the FDA-approved ADCs and other novel ADCs under investigation, emphasizing their potential to improve patient outcomes. Furthermore, we explore strategies to address challenges, such as toxicity management, predictive biomarker identification, and resistance mechanisms. Additionally, we examine the integration of ADCs with other treatment modalities, including immune checkpoint inhibitors, targeted therapies, and radiation therapy. By addressing these challenges and exploring innovative approaches, the development of ADCs may significantly enhance therapeutic options and outcomes for patients with advanced urinary tumor

An Undersea Mining Microseism Source Location Algorithm Considering Wave Velocity Probability Distribution

The traditional mine microseism locating methods are mainly based on the assumption that the wave velocity is uniform through the space, which leads to some errors for the assumption goes against the laws of nature. In this paper, the wave velocity is regarded as a random variable, and the probability distribution information of the wave velocity is fused into the traditional locating method. This paper puts forwards the microseism source location method for the undersea mining on condition of the probability distribution of the wave velocity and comes up with the solving process of Monte Carlo. In addition, based on the simulated results of the Monte Carlo method, the space is divided into three areas: the most possible area (area I), the possible area (area II), and the small probability area (area III). Attached to corresponding mathematical formulations, spherical models and cylindrical models in different areas are, respectively, built according to whether the source is in the sensor arrays. Both the examples and the actual applications show that (1) the method of microseism source location in this paper can highly improve the accuracy of the microseism monitoring, especially for the source beyond the sensor arrays, and (2) the space-dividing method based on occurrence possibilities of the source can recognize and sweep the hidden dangers for it predicts the probable location range of the source efficiently, while the traditional method cannot