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

DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remaining ones. To resolve the issue, we propose an energy-based model framework to explicitly learn moment-query distributions. Moreover, we propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor to effectively encode moment-query inputs. Comprehensive experiments on four public temporal language grounding datasets showcase the superiority of our methods over the state-of-the-art baselines.Comment: Accepted at EMNLP 2023 (Findings

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, rationality, and diversity, which hampers document understanding. To overcome these challenges, we in this paper propose Transport Plan and Context-aware Hierarchical Topic Model (TraCo). Instead of early simple topic dependencies, we propose a transport plan dependency method. It constrains dependencies to ensure their sparsity and balance, and also regularizes topic hierarchy building with them. This improves affinity and diversity of hierarchies. We further propose a context-aware disentangled decoder. Rather than previously entangled decoding, it distributes different semantic granularity to topics at different levels by disentangled decoding. This facilitates the rationality of hierarchies. Experiments on benchmark datasets demonstrate that our method surpasses state-of-the-art baselines, effectively improving the affinity, rationality, and diversity of hierarchical topic modeling with better performance on downstream tasks.Comment: Accepted to AAAI2024 conference. Our code is available at https://github.com/bobxwu/TraC

READ-PVLA: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization. With a growing number of tasks and limited training data, such full fine-tuning approach leads to costly model storage and unstable training. To overcome these shortcomings, we introduce lightweight adapters to the pre-trained model and only update them at fine-tuning time. However, existing adapters fail to capture intrinsic temporal relations among video frames or textual words. Moreover, they neglect the preservation of critical task-related information that flows from the raw video-language input into the adapter's low-dimensional space. To address these issues, we first propose a novel REcurrent ADapter (READ) that employs recurrent computation to enable temporal modeling capability. Second, we propose Partial Video-Language Alignment (PVLA) objective via the use of partial optimal transport to maintain task-related information flowing into our READ modules. We validate our READ-PVLA framework through extensive experiments where READ-PVLA significantly outperforms all existing fine-tuning strategies on multiple low-resource temporal language grounding and video-language summarization benchmarks.Comment: Accepted at AAAI 202

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