Hyperspectral Image Restoration via Multi-mode and Double-weighted Tensor Nuclear Norm Minimization

Tensor nuclear norm (TNN) induced by tensor singular value decomposition plays an important role in hyperspectral image (HSI) restoration tasks. In this letter, we first consider three inconspicuous but crucial phenomenons in TNN. In the Fourier transform domain of HSIs, different frequency components contain different information; different singular values of each frequency component also represent different information. The two physical phenomenons lie not only in the spectral dimension but also in the spatial dimensions. Then, to improve the capability and flexibility of TNN for HSI restoration, we propose a multi-mode and double-weighted TNN based on the above three crucial phenomenons. It can adaptively shrink the frequency components and singular values according to their physical meanings in all modes of HSIs. In the framework of the alternating direction method of multipliers, we design an effective alternating iterative strategy to optimize our proposed model. Restoration experiments on both synthetic and real HSI datasets demonstrate their superiority against related methods

A numerical simulation of underwater shock-cavitation- structure interaction

Ph.DDOCTOR OF PHILOSOPH

Cell division promotes efficient retrotransposition in a stable L1 reporter cell line

Background: Long interspersed element type one (L1) actively modifies the human genome by inserting new copies of itself. This process, termed retrotransposition, requires the formation of an L1 ribonucleoprotein (RNP) complex, which must enter the nucleus before retrotransposition can proceed. Thus, the nuclear import of L1 RNP presents an opportunity for cells to regulate L1 retrotransposition post-translationally. The effect of cell division on L1 retrotransposition has been investigated by two previous studies, which observed varied degrees of inhibition in retrotransposition when primary cell strains or cancer cell lines were experimentally arrested in different stages of the cell cycle. However, seemingly divergent conclusions were reached. The role of cell division on retrotransposition remains highly debated. Findings: To monitor both L1 expression and retrotransposition quantitatively, we developed a stable dual-luciferase L1 reporter cell line, in which a bi-directional tetracycline-inducible promoter drives the expression of both a firefly luciferase-tagged L1 element and a Renilla luciferase, the latter indicative of the level of promoter induction. We observed an additional 10-fold reduction in retrotransposition in cell-cycle arrested cells even after retrotransposition had been normalized to Renilla luciferase or L1 ORF1 protein levels. In synchronized cells, cells undergoing two mitoses showed 2.6-fold higher retrotransposition than those undergoing one mitosis although L1 expression was induced for the same amount of time. Conclusions: Our data provide additional support for an important role of cell division in retrotransposition and argue that restricting the accessibility of L1 RNP to nuclear DNA could be a post-translational regulatory mechanism for retrotransposition

Environmental controls on coral skeletal δ13C in the northern South China Sea

In this paper, we investigate the relationship between seasonal climatic and environmental variables, and the skeletal δC of modern and mid-Holocene Porites lutea corals from the southern coast of Hainan Island in the northern South China Sea. No significant correlations were observed between δC in the modern coral and solar insolation or sea surface temperature. However, seasonal variability of δC in the modern coral covaries with rainfall on Hainan Island. Furthermore, the seasonal variations of δC in both the modern and mid-Holocene coral are synchronous with those of the coral ΔδO, which is a proxy for seawater δO and, in turn, largely controlled by local rainfall. These observations suggest that coral δC variations are closely associated with rainfall in this fregion. Given that river runoff contains dissolved inorganic carbon (DIC) with a negative δC, we suggest that periods of high rainfall on Hainan Island deliver increased amounts of C-depleted DIC to coastal seawater, resulting in an enhanced negative δC in the corals. Our findings, together with previous studies, appear to demonstrate that in coastal environments, coral skeletal δC levels are controlled mainly by terrestrial carbon input and are significantly influenced by terrestrial river runoff. Consequently, the geochemical interpretation of coral δC records may differ between coastal areas and offshore areas or the open ocean

Evaluation of annual resolution coral geochemical records as climate proxies in the Great Barrier Reef of Australia

Sampling of annually banded massive coral skeletons at annual (or higher) resolutions is increasingly being used to obtain replicate long-term time series of changing seawater conditions. However, few of these studies have compared and calibrated the lower annual resolution records based on coral geochemical tracers with the corresponding instrumental climate records, although some studies have inferred the climatic significance of annual coral series derived from averages of monthly or sub-annual records. Here, we present annual resolution analysis of coral records of elemental and stable isotopic composition that are approximately 70\ua0years long. These records were preserved in two coexisting colonies of Porites sp. from Arlington Reef, on the Great Barrier Reef in Australia, and are used to evaluate the climatic significance of annually resolved coral geochemical proxies. The geochemical records of coral sample “10AR2,” with its faster and relatively constant annual growth rate, appear to have been independent of skeletal growth rate and other vital effects. The annual resolution of Sr/Ca and ΔδO time series was shown to be a good proxy for annual sea surface temperature (SST; r\ua0=\ua0−0.67, n\ua0=\ua073, p\ua

Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB.Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th IEEE International Conference on Signal Processing (IEEE ICSP

BASIC STUDY FOR COAL MOISTURE CONTROL INTEGRATING PNEUMATIC CLASSIFICATION TECHNIQUE

A technique of coal moisture control integrating pneumatic classification with flue gas as heating medium was put forward. With this technique, refined coal moisture control can be realized accompanying classification in one process, and considerable high-quality energy can be saved in coking and milling procedure. In this paper, coal classification and moisture control behaviors was investigated at different conditions. Based on experimental results, the basic parameters for the technique were worked out accordingly

Dual blockage of STAT3 and ERK1/2 eliminates radioresistant GBM cells.

Radiotherapy (RT) is the major modality for control of glioblastoma multiforme (GBM), the most aggressive brain tumor in adults with poor prognosis and low patient survival rate. To improve the RT efficacy on GBM, the mechanism causing tumor adaptive radioresistance which leads to the failure of tumor control and lethal progression needs to be further elucidated. Here, we conducted a comparative analysis of RT-treated recurrent tumors versus primary counterparts in GBM patients, RT-treated orthotopic GBM tumors xenografts versus untreated tumors and radioresistant GBM cells versus wild type cells. The results reveal that activation of STAT3, a well-defined redox-sensitive transcriptional factor, is causally linked with GBM adaptive radioresistance. Database analysis also agrees with the worse prognosis in GBM patients due to the STAT3 expression-associated low RT responsiveness. However, although the radioresistant GBM cells can be resensitized by inhibition of STAT3, a fraction of radioresistant cells can still survive the RT combined with STAT3 inhibition or CRISPR/Cas9-mediated STAT3 knockout. A complementally enhanced activation of ERK1/2 by STAT3 inhibition is identified responsible for the survival of the remaining resistant tumor cells. Dual inhibition of ERK1/2 and STAT3 remarkably eliminates resistant GBM cells and inhibits tumor regrowth. These findings demonstrate a previously unknown feature ofSTAT3-mediated ERK1/2 regulation and an effective combination of two targets in resensitizing GBM to RT

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.Comment: Accepted by ACL2023 main conferenc