Numerical study of solitary wave propagating through vegetation

Ph.DDOCTOR OF PHILOSOPH

Domain Generalization with Small Data

In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the distribution over distributions (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods

The Various Components of the Circulation in the Singapore Strait Region: Tidal, Wind and Eddy-driven Circulations and Their Relative Importance

To obtain a better understanding of environment-related physical oceanography in Singapore Strait Region, numerical experiments are implemented to study the circulation in SSR. The three important components, tidal, wind and eddy-driven circulations are identified. It is shown that the tidal circulation is dominant in the region. Even though the wind and eddy circulations are relatively small, they may have significant effect on the local circulation and material transport.Singapore-MIT Alliance. Center for Environmental Sensing and MonitoringSingapore. National Research Foundatio

Induction of Reproductive Diapause in \u3ci\u3eHabrobracon hebetor\u3c/i\u3e (Hymenoptera: Braconidae) When Reared at Different Photoperiods at Low Temperatures

Development of the parasitoid Habrobracon hebetor (Say) (Hymenoptera: Braconidae) at low temperatures was determined to identify rearing conditions that might result in adults that were in reproductive diapause. Diapausing adults would be expected to survive cold storage longer than nondiapausing adults for use in biological control programs. Only a few eggs were found in the ovaries when H. hebetor females were reared during the immature stages at 17.5 and 20°C with a 16-h photoperiod, and the ovaries were poorly developed and contained no eggs when females were reared with a 10-h photoperiod in these low temperatures. Rearing H. hebetor at 17.5 and 20°C did not result in diapause of immature stages, but did appear to result in possible adult reproductive diapause when the immature stages were reared with a 10-h photoperiod. Females reared during the immature stages at 17.5°C with a 10-h photoperiod lived longer and took longer to lay their first eggs and to lay 50% of their eggs than those females reared at 17.5°C with a 16-h photoperiod. Females reared during the immature stages at 20°C with a 10-h photoperiod took longer to lay their first eggs and to lay 50% of their eggs, and they had a lower respiration rate, than those females reared at 20°C with a 16-h photoperiod. Females that were reared in conditions that appeared to induce reproductive diapause resumed oviposition and their respiration rate increased soon after being transferred to a higher temperature (27.5°C). Thus, females reared at a 10-h photoperiod at 17.5 and 20°C appear to enter reproductive diapause

Improving Continual Relation Extraction through Prototypical Contrastive Learning

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance

Domain-invariant Feature Exploration for Domain Generalization

Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains. We then propose DIFEX for Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation framework to capture the high-level Fourier phase as the internally-invariant features and learn cross-domain correlation alignment as the mutually-invariant features. We further design an exploration loss to increase the feature diversity for better generalization. Extensive experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.Comment: Accepted by Transactions on Machine Learning Research (TMLR) 2022; 20 pages; code: https://github.com/jindongwang/transferlearning/tree/master/code/DeepD

A New Algorithm to Classify the Homogeneity of ERS-2 Wave Mode SAR Imagette

A new classification parameter is developed using 1535 ERS-2 wave mode synthetic aperture radar (SAR) test imagettes to better differentiate homogeneous and inhomogeneous imagettes. The comparison between the new parameter (Min) and the previous one (Inhomo) (Schulz-Stellenfleth and Lehner, 2004) was done under varied threshold values of Inhomo. It is concluded that the performance of ‘Min’ is much better than ‘Inhomo’ when applying to the 1535 test imagettes. Furthermore, both Min and Inhomo are applied to nearly 1 million imagettes collected for the period from 1 September 1998 to 30 November 2000. The comparisons of the global inhomogeneous distribution between ‘Min’ and ‘Inhomo’ reveal that both the areas and percentage of inhomogeneity calculated by ‘Min’ are larger than that calculated by ‘Inhomo’. By analyzing the low wind speed distribution of HOAPS data, we found that low wind speed over the ocean is one of the key reasons for the inhomogeneity of SAR imagettes

Effects of metastasis-associated in colon cancer 1 inhibition by small hairpin RNA on ovarian carcinoma OVCAR-3 cells

<p>Abstract</p> <p>Background</p> <p>Metastasis-associated in colon cancer 1 (MACC1) is demonstrated to be up-regulated in several types of cancer, and can serve as biomarker for cancer invasion and metastasis. To investigate the relations between MACC1 and biological processes of ovarian cancer, MACC1 specific small hairpin RNA (shRNA) expression plasmids were used to investigate the effects of MACC1 inhibition on ovarian carcinoma OVCAR-3 cells.</p> <p>Methods</p> <p>Expressions of MACC1 were detected in different ovarian tissues by immunohistochemistry. MACC1 specific shRNA expression plasmids were constructed and transfected into OVCAR-3 cells. Then, expressions of MACC1 were examined by reverse transcription polymerase chain reaction (RT-PCR) and Western blot. Cell proliferation was observed by MTT and monoplast colony formation assay. Flow cytometry and TUNEL assay were used to measure cell apoptosis. Cell migration was assessed by wound healing and transwell migration assay. Matrigel invasion and xenograft model assay were performed to analyze the potential of cell invasion. Activities of Met, MEK1/2, ERK1/2, Akt, cyclinD1, caspase3 and MMP2 protein were measured by Western blot.</p> <p>Results</p> <p>Overexpressions of MACC1 were detected in ovarian cancer tissues. Expression of MACC1 in OVCAR-3 cells was significantly down-regulated by MACC1 specific small hairpin RNA. In OVCAR-3 cells, down-regulation of MACC1 resulted in significant inhibition of cell proliferation, migration and invasion, meanwhile obvious enhancement of apoptosis. As a consequence of MACC1 knockdown, expressions of Met, p-MEK1/2, p-ERK1/2, cyclinD1 and MMP2 protein decreased, level of cleaved capase3 was increased.</p> <p>Conclusions</p> <p>RNA interference (RNAi) against MACC1 could serve as a promising intervention strategy for gene therapy of ovarian carcinoma, and the antitumor effects of MACC1 knockdown might involve in the inhibition of HGF/Met and MEK/ERK pathways.</p