Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network

Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, we propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA is devised. The multi-head attention (MHA) based network and the ResNet-152 are employed to deal with texts and images, respectively. The integration of MHA and capsule network aims to capture the interaction among the multimodal inputs. In addition to the targeted aspect, the information from the context and the image is also incorporated for sentiment delivered. We evaluate the proposed model on two manually annotated datasets. the experimental results demonstrate the effectiveness of our proposed model for this new task

Inhibition of Proteasomal Degradation of Rpn4 Impairs Nonhomologous End-Joining Repair of DNA Double-Strand Breaks

BACKGROUND: The proteasome homeostasis in Saccharomyces cerevisiae is regulated by a negative feedback circuit in which the transcription factor Rpn4 induces the proteasome genes and is rapidly degraded by the assembled proteasome. The integrity of the Rpn4-proteasome feedback loop is critical for cell viability under stressed conditions. We have demonstrated that inhibition of Rpn4 degradation sensitizes cells to DNA damage, particularly in response to high doses of DNA damaging agents. The underlying mechanism, however, remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: Using yeast genetics and biochemical approach we show that inhibition of Rpn4 degradation displays a synthetic growth defect with deletion of the MEC1 checkpoint gene and sensitizes several checkpoint mutants to DNA damage. In addition, inhibition of Rpn4 degradation leads to a defect in repair of double-strand breaks (DSBs) by nonhomologous end-joining (NHEJ). The expression levels of several key NHEJ genes are downregulated and the recruitment of Yku70 to a DSB is reduced by inhibition of Rpn4 degradation. We find that Rpn4 and the proteasome are recruited to a DSB, suggesting their direct participation in NHEJ. Inhibition of Rpn4 degradation may result in a concomitant delay of release of Rpn4 and the proteasome from a DSB. CONCLUSION/SIGNIFICANCE: This study provides the first evidence for the role of proteasomal degradation of Rpn4 in NHEJ

Tailoring hierarchical microstructures and nanoprecipitates in additive-manufactured Al-Zn-Mg-Cu-Nb alloys for simultaneously enhancing strength and ductility

Additive manufacturing provides an efficient way of producing metallic components with complex geometries. Their microstructure is substantially different to those from conventional processing, creating opportunities for manipulating the final microstructure and properties via heat treatment. Here, we demonstrate that as-built heterostructures in an Al-Zn-Mg-Cu-Nb alloy produced during the solidification of molten pools provide a driving force and additional Zener pinning sources for recrystallization. This creates a bimodal grain structure after solution treatment, causing additional hetero-deformation-induced strengthening and hardening. Coarse grains are found to promote work hardening and blunt the propagate of cracks during deformation, increasing ductility. Together with precipitation strengthening from a high number density nanoprecipitates, the simultaneous improvement of strength and ductility in a highly alloyed Al-Zn-Mg-Cu-Nb alloy is achieved. These results provide a simple strategy for the development of additively manufactured age-hardening alloys with improved strength and ductility for high performance structural applications