Regional Railway Hub in Pakistan and China's Role

[Considering the gifted geographical position that Pakistan assumes, the development of a regional railway hub in the country will enhance overland connectivity between Central, West, South Asia and Western China to a great extent. It may help China's Western regions particularly Xinjiang trade more conveniently with the Middle East and beyond. Such a railway hub in Pakistan and particularly the linkage with China will actually ensure the realization of a very significant segment of the trans-Asian railways. China should join hands with Pakistan, both in terms of financial and technical cooperation, in this respect to reap immense bilateral as well regional gains in the years ahead.

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization.Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets.Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses.Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets

Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization

Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations. However, the empirical results of CAD's OOD generalization are not as efficient as anticipated. In this study, we attribute the inefficiency to the myopia phenomenon caused by CAD: language models only focus on causal features that are edited in the augmentation operation and exclude other non-edited causal features. Therefore, the potential of CAD is not fully exploited. To address this issue, we analyze the myopia phenomenon in feature space from the perspective of Fisher's Linear Discriminant, then we introduce two additional constraints based on CAD's structural properties (dataset-level and sentence-level) to help language models extract more complete causal features in CAD, thereby mitigating the myopia phenomenon and improving OOD generalization capability. We evaluate our method on two tasks: Sentiment Analysis and Natural Language Inference, and the experimental results demonstrate that our method could unlock the potential of CAD and improve the OOD generalization performance of language models by 1.0% to 5.9%.Comment: Expert Systems With Applications 2023. arXiv admin note: text overlap with arXiv:2302.0934

Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding

Chain-of-Thought (CoT) is a technique that guides Large Language Models (LLMs) to decompose complex tasks into multi-step reasoning through intermediate steps in natural language form. Briefly, CoT enables LLMs to think step by step. However, although many Natural Language Understanding (NLU) tasks also require thinking step by step, LLMs perform less well than small-scale Masked Language Models (MLMs). To migrate CoT from LLMs to MLMs, we propose Chain-of-Thought Tuning (CoTT), a two-step reasoning framework based on prompt tuning, to implement step-by-step thinking for MLMs on NLU tasks. From the perspective of CoT, CoTT's two-step framework enables MLMs to implement task decomposition; CoTT's prompt tuning allows intermediate steps to be used in natural language form. Thereby, the success of CoT can be extended to NLU tasks through MLMs. To verify the effectiveness of CoTT, we conduct experiments on two NLU tasks: hierarchical classification and relation extraction, and the results show that CoTT outperforms baselines and achieves state-of-the-art performance.Comment: EMNLP2023 Main Conferenc

Children's use of prosody and word order to indicate information status in English noun phrase conjuncts

Our study investigates the influence of information status on word order and prosody in children and adults. Using an elicited production task, we examine the ordering and intonation of noun phrases in phrasal conjuncts in 3-5-year-old and adult speakers of English. Findings show that English-speaking children are less likely to employ the "old-before-new" order than adults and are also not adult-like in using prosody to mark information status. Our study suggests that even though intonation and word order are linguistic devices that are acquired early, their use to mark information status is still developing at age four