Design of a Satellite Cluster System in Distributed Simulation

This article presents the design and development of a satellite cluster system that supports an interfederation communication in High Level Architecture (HLA)-compliant distributed simulation. The interfederation communication enables the execution of a complex, large-scale cluster system of distributed satellites that share the dispersed data assets among satellite components collaboratively. After a brief review of the HLA bridge for interfederation communication, the authors discuss the design issues related to a satellite cluster system that provides cluster management, interfederation communication, and communication data management. They analyze system performance and scalability for centralized and decentralized configurations. The empirical results on the heterogeneous OS distributed system indicate that the satellite cluster system is effective and scalable due to the use of interfederation communication and the reduction of data transmission

Design of a Satellite Cluster System in Distributed Simulation

This article presents the design and development of a satellite cluster system that supports an interfederation communication in High Level Architecture (HLA)-compliant distributed simulation. The interfederation communication enables the execution of a complex, large-scale cluster system of distributed satellites that share the dispersed data assets among satellite components collaboratively. After a brief review of the HLA bridge for interfederation communication, the authors discuss the design issues related to a satellite cluster system that provides cluster management, interfederation communication, and communication data management. They analyze system performance and scalability for centralized and decentralized configurations. The empirical results on the heterogeneous OS distributed system indicate that the satellite cluster system is effective and scalable due to the use of interfederation communication and the reduction of data transmission

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions

Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations. In addition, we introduce Adaptive Weighting scheme for our contrastive learning approach in order to increase flexibility in optimization. Lastly, we propose Multimodal Interaction module to address the unalignment nature of multimodal data, thereby assisting the model in producing more reasonable multimodal representations. Experimental results show that our method outperforms prior baselines and achieves state-of-the-art results on two publicly available benchmark datasets for MRHP problem.Comment: Accepted to the main EMNLP 2022 conferenc

Photodynamic therapy and tumor imaging of hypericin-treated squamous cell carcinoma

BACKGROUND: Conventional cancer therapy including surgery, radiation, and chemotherapy often are physically debilitating and largely ineffective in previously treated patients with recurrent head and neck squamous cell carcinoma (SCC). A natural photochemical, hypericin, could be a less invasive method for laser photodynamic therapy (PDT) of these recurrent head and neck malignancies. Hypericin has powerful photo-oxidizing ability, tumor localization properties, and fluorescent imaging capabilities as well as minimal dark toxicity. The current study defined hypericin PDT in vitro with human SCC cells before the cells were grown as tumor transplants in nude mice and tested as a model for hypericin induced tumor fluorescence and PDT via laser fiberoptics. METHODS: SNU squamous carcinoma cells were grown in tissue culture, detached from monolayers with trypsin, and incubated with 0.1 μg to 10 μg/ml of hypericin before exposure to laser light at 514, 550, or 593 nm to define optimal dose, time, and wavelength for PDT of tumor cells. The SCC cells also were injected subcutaneously in nude mice and grown for 6–8 weeks to form tumors before hypericin injection and insertion of fiberoptics from a KTP532 surgical laser to assess the feasibility of this operating room instrument in stimulating fluorescence and PDT of tumors. RESULTS: In vitro testing revealed a hypericin dose of 0.2–0.5 μg/ml was needed for PDT of the SCC cells with an optimal tumoricidal response seen at the 593 nm light absorption maximum. In vivo tumor retention of injected hypericin was seen for 7 to10 days using KTP532 laser induced fluorescence and biweekly PDT via laser fiberoptics led to regression of SCC tumor transplants under 0.4 cm(2 )diameter, but resulted in progression of larger size tumors in the nude mice. CONCLUSION: In this preclinical study, hypericin was tested for 514–593 nm dye laser PDT of human SCC cells in vitro and for KTP532 surgical laser targeting of SCC tumors in mice. The results suggest hypericin is a potent tumor imaging agent using this surgical laser that may prove useful in defining tumor margins and possibly in sterilizing post-resection margins. Deeply penetrating pulsed infrared laser emissions will be needed for PDT of larger and more inaccessible tumors

Multiple Recurrent Acute Ischemic Strokes Treated by Thrombectomy in a Patient with Acute Pulmonary Embolism

BACKGROUND: Thrombectomy is recommended to treat for an acute ischemic stroke (AIS) patient with anterior large vessel occlusion. However, there were neither detailed guidelines nor systematic reviews of acute ischemic stroke patients having multiple times or re-occluded arteries. CASE REPORT: In our case report, we struggled a multiple (4-times) AIS patient underwent by one intravenous r-tpA and 3 remaining of endovascular treatment of thrombectomy. Especially, the finding of both pulmonary embolism and cerebral arteries occlusion in this patient made us difficult to decide the appropriate treatment plan. The patient was considered having multiple cardiac thrombi pumping out to the brain and pulmonary vessels even in treatment with NOAC (New Oral Anticoagulant). Our priority, normally, was to recanalize the brain vessels compared to the pulmonary arteries. CONCLUSION: In conclusion, based on this noticed case study, we want to share our experiences on the diagnosis of ischemic stroke, the strategy in treatment and prevention with anticoagulant therapy

Improving Neural Cross-Lingual Abstractive Summarization via Employing Optimal Transport Distance for Knowledge Distillation

Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary module and relies on the self-attention mechanism to attend among tokens in two languages. However, correlation learned by self-attention is often loose and implicit, inefficient in capturing crucial cross-lingual representations between languages. The matter worsens when performing on languages with separate morphological or structural features, making the cross-lingual alignment more challenging, resulting in the performance drop. To overcome this problem, we propose a novel Knowledge-Distillation-based framework for Cross-Lingual Summarization, seeking to explicitly construct cross-lingual correlation by distilling the knowledge of the monolingual summarization teacher into the cross-lingual summarization student. Since the representations of the teacher and the student lie on two different vector spaces, we further propose a Knowledge Distillation loss using Sinkhorn Divergence, an Optimal-Transport distance, to estimate the discrepancy between those teacher and student representations. Due to the intuitively geometric nature of Sinkhorn Divergence, the student model can productively learn to align its produced cross-lingual hidden states with monolingual hidden states, hence leading to a strong correlation between distant languages. Experiments on cross-lingual summarization datasets in pairs of distant languages demonstrate that our method outperforms state-of-the-art models under both high and low-resourced settings

Vision-and-Language Pretraining

With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V&L) tasks, scholars have introduced an abundance of deep learning models in this research domain. Furthermore, in recent years, transfer learning has also shown tremendous success in Computer Vision for tasks such as Image Classification, Object Detection, etc., and in Natural Language Processing for Question Answering, Machine Translation, etc. Inheriting the spirit of Transfer Learning, research works in V&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks. The aim of this article is to provide a comprehensive revision of contemporary V&L pretraining models. In particular, we categorize and delineate pretraining approaches, along with the summary of state-of-the-art vision-and-language pre-trained models. Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective on V&L pretraining. Lastly, we decided to take a further step to discuss numerous directions for future research.Comment: 35 pages, 3 figure

Minimizing Mission Risks through Emulating Space Communications Architectures

The goal of launching breakthrough missions with a minimal amount of risk at a reasonable cost is achievable regardless whether the mission is large, such as NASA’s International Space Station, or small, such as CHIPSat. To meet these goals, satellite missions must rely on new tools that detect any liabilities to the project during pre-launch testing. NASA/Glenn Research Center (GRC) is currently developing an emulation testbed to assist missions with validating requirements and resolving issues, whether science or communication, before moving to an operational status. The Space Communications Emulation Facility (SCEF) will serve as a nationally accessible NASA facility. In the testbed, mission managers can emulate complete missions under typical space-based scenarios or researchers can emulate specific components of a satellite mission. The goal of this paper is to explore SCEF by discussing the architecture of the hardware and software of the emulation testbed. In addition, the types of emulations and using SCEF to minimize risks will be highlighted. SCEF will provide missions with the tools that they can use to resolve issues earlier than traditional methods. The end result will be a realization of savings, in time and money, as they move from mission concepts to launch

Climate Justice Planning in Global South: Applying a Coupled Nature–Human Flood Risk Assessment Framework in a Case for Ho Chi Minh City, Vietnam

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods

InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling

Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks