CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis

As an extensive research in the field of Natural language processing (NLP), aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack of sufficient annotation resources, thus more and more recent researchers focus on cross-lingual aspect-based sentiment analysis (XABSA). However, most recent researches only concentrate on cross-lingual data alignment instead of model alignment. To this end, we propose a novel framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based Sentiment Analysis. Specifically, we design two contrastive strategies, token level contrastive learning of token embeddings (TL-CTE) and sentiment level contrastive learning of token embeddings (SL-CTE), to regularize the semantic space of source and target language to be more uniform. Since our framework can receive datasets in multiple languages during training, our framework can be adapted not only for XABSA task, but also for multilingual aspect-based sentiment analysis (MABSA). To further improve the performance of our model, we perform knowledge distillation technology leveraging data from unlabeled target language. In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset). The results demonstrate that the proposed method has a certain improvement in the three tasks of XABSA, distillation XABSA and MABSA. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/CL-XABSA

Simple Synthesis and Growth Mechanism of Core/Shell CdSe/ SiO

Core-shell-structured CdSe/SiOx nanowires were synthesized on an equilateral triangle Si (111) substrate through a simple one-step thermal evaporation process. SEM, TEM, and XRD investigations confirmed the core-shell structure; that is, the core zone is single crystalline CdSe and the shell zone is SiOx amorphous layer and CdSe core was grown along (001) direction. Two-stage growth process was present to explain the growth mechanism of the core/shell nanwires. The silicon substrate of designed equilateral triangle providing the silicon source is the key factor to form the core-shell nanowires, which is significant for fabrication of nanowire-core sheathed with a silica system. The PL of the product studied at room temperature showed two emission bands around 715 and 560 nm, which originate from the band-band transition of CdSe cores and the amorphous SiOx shells, respectively

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark

A Phos-Tag-Based Approach Reveals the Extent of Physiological Endoplasmic Reticulum Stress

Cellular response to endoplasmic reticulum (ER) stress or unfolded protein response (UPR) is a key defense mechanism associated with many human diseases. Despite its basic and clinical importance, the extent of ER stress inflicted by physiological and pathophysiological conditions remains difficult to quantitate, posing a huge obstacle that has hindered our further understanding of physiological UPR and its future therapeutic potential. Here we have optimized a Phos-tag-based system to detect the activation status of two proximal UPR sensors at the ER membrane. This method allowed for a quantitative assessment of the level of stress in the ER. Our data revealed quantitatively the extent of tissue-specific basal ER stress as well as ER stress caused by the accumulation of misfolded proteins and the fasting-refeeding cycle. Our study may pave the foundation for future studies on physiological UPR, aid in the diagnosis of ER-associated diseases and improve and facilitate therapeutic strategies targeting UPR in vivo

Towards Malay named entity recognition: an open-source dataset and a multi-task framework

Named entity recognition (NER) is a key component of many natural language processing (NLP) applications. The majority of advanced research, however, has not been widely applied to low-resource languages represented by Malay due to the data-hungry problem. In this paper, we present a system for building a Malay NER dataset (MS-NER) of 20,146 sentences through labelled datasets of homologous languages and iterative optimisation. Additionally, we propose a Multi-Task framework, namely MTBR, to integrate boundary information more effectively for NER. Specifically, boundary detection is treated as an auxiliary task and an enhanced Bidirectional Revision module with a gated ignoring mechanism is proposed to undertake conditional label transfer. This can reduce error propagation by the auxiliary task. We conduct extensive experiments on Malay, Indonesian, and English. Experimental results show that MTBR could achieve competitive performance and tends to outperform multiple baselines. The constructed dataset and model would be made available to the public as a new, reliable benchmark for Malay NER

The Relationship between Blood Lipids and Risk of Atrial Fibrillation: Univariable and Multivariable Mendelian Randomization Analysis

We performed univariable and multivariable Mendelian randomization (MR) analysis to evaluate the association between blood lipids and risk of atrial fibrillation (AF), including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), Apolipoprotein A1, and Apolipoprotein B. Methods: Data on the single nucleotide polymorphisms (SNPs) related to blood lipids were obtained from the UK Biobank study with more than 300,000 subjects of White British European ancestry, and data for AF were from the latest meta-analysis of Genome-wide association study (GWASs) with six independent cohorts with more than 1,000,000 subjects of European ancestry. The univariable MR analysis was conducted to explore whether genetic evidence of individual lipid-related traits was significantly associated with AF risks and multivariable MR analysis with three models was performed to assess the independent effects of lipid-related traits. Results: The IVW estimate showed that genetically predicted LDL-C (OR: 1.016, 95% CI: 0.962–1.073, p = 0.560), HDL-C (OR: 0.951, 95% CI: 0.895–1.010, p = 0.102), TG (OR: 0.961, 95% CI: 0.889–1.038, p = 0.313), Apolipoprotein A1 (OR: 0.978, 95% CI: 0.933–1.025, p = 0.356), and Apolipoprotein B (OR: 1.008, 95% CI: 0.959–1.070, p = 0.794) were not causally associated with the risk of AF. Sample mode (OR: 0.852, 95% CI: 0.731–0.993, p = 0.043) and weighted mode (OR: 0.907, 95% CI: 0.841–0.979, p = 0.013) showed that a 1-unit increase in TG (mmol/L) was causally associated with a 14.8% and 9.3% relative decrease in AF risk, respectively. The multivariable MR analysis with model 1, 2, and 3 indicated that TG, LDL-C, HDL-C, Apolipoprotein A1, and Apolipoprotein B were not associated with the lower risk for AF. Conclusions: Our multivariable Mendelian randomization analysis (MVMR) finding suggested no genetic evidence of lipid traits was significantly associated with AF risk. Furthermore, more work is warranted to confirm the potential association between lipid traits and AF risks