The Development of the Offshore Reinsurance Market in Singapore and Its Experiences for India

In March 2017, China Insurance Regulatory Commission issued Notice on Matters Related to the Provision of Guarantee Measures for Offshore Reinsurers, and announced the formal establishment of a deposit system for Offshore reinsurers.This measure improves China’s reinsurance regulatory system. It can not only prevent the cross-border transfer of foreign financial risks through reinsurance exchanges but also promote the smooth and healthy development of Chinese reinsurance market. The establishment of Offshore reinsurance margin system provides institutional guarantee for the construction of international Offshore reinsurance in China. India, as a major shipping country in the world, has superior geographical location and mature shipping business. The establishment of international reinsurance center plays an important role in promoting the development of India’s insurance market and shipping market. Offshore reinsurance business accounts for greater proportion in business structure of the international reinsurance center. The establishment and development of Offshore reinsurance center in India need to compete with many mature Offshore reinsurance companies in the world. Singapore is not only Asia’s largest international financial center but also one of Asia’s largest international reinsurance centers. Its Offshore reinsurance market is more mature. In the past 10 years, it has maintained a steady growth trend, which can provide a reference for the development of Offshore reinsurance business in India. This paper begins with the development conditions and market data of Singapore Offshore reinsurance market, analyzes the development of Singapore’s Offshore reinsurance market and the development of the domestic insurance industry in India, combines with the analysis of the advantages and disadvantages of Mumbai, Kandla Port and other places, and puts forward suggestions for the development of Offshore reinsurance business in India

Multi-Scenario Ranking with Adaptive Feature Learning

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.Further analysis also validates the rationality of adaptive feature learning under a multi-scenario scheme. Moreover, our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.Comment: 10 pages

X-linked inhibitor of apoptosis positive nuclear labeling: a new independent prognostic biomarker of breast invasive ductal carcinoma

<p>Abstract</p> <p>Background</p> <p>It's well recognized that X-linked inhibitor of apoptosis (XIAP) was the most potent caspase inhibitor and second mitochondria-derived activator of caspase (Smac) was the antagonist of XIAP. Experiments in vitro identified that down regulation of XIAP expression or applying Smac mimics could sensitize breast cancer cells to chemotherapeutics and promote apoptosis. However, expression status and biologic or prognostic significance of XIAP/Smac in breast invasive ductal carcinoma (IDC) were not clear. The present study aimed to investigate relationship among expression status of XIAP/Smac, apoptosis index (AI), clinicopathologic parameters and prognosis in IDC.</p> <p>Methods</p> <p>Immunohistochemistry and TUNEL experiment were performed to detect expression of XIAP, Smac, ER, PR, HER2 and AI in 102 cases of paraffin-embedded IDC samples respectively. Expression of XIAP/Smac were also detected in limited 8 cases of fresh IDC specimens with Western blot.</p> <p>Results</p> <p>Positive ratio and immunoscore of XIAP was markedly higher than Smac in IDC (<it>P </it>< 0.0001). It was noteworthy that 44 cases of IDC were positive in nuclear for XIAP, but none was for Smac. Expression status of Smac was more prevalent in HER2 positive group than negative group (<it>P </it>< 0.0001) and AI was positively correlated with HER2 protein expression (r_s= 0.265, <it>P </it>= 0.017). The present study first revealed that XIAP positive nuclear labeling (XIAP-N), but not cytoplasmic staining (XIAP-C), was the apoptotic marker correlated significantly with patients' shortened overall survival (<it>P </it>= 0.039). Survival analysis demonstrated that XIAP-N was a new independent prognostic factor except for patient age and lymph node status.</p> <p>Conclusion</p> <p>Disturbed balance of expression between XIAP and Smac probably contributed to carcinogenesis and XIAP positive nuclear labeling was a new independent prognostic biomarker of breast IDC.</p

The (Too Many) Problems of Analogical Reasoning with Word Vectors

This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such “linguistic regularities” as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity

Guiding the Training of Distributed Text Representation with Supervised Weighting Scheme for Sentiment Analysis

Abstract With the rapid growth of social media, sentiment analysis has received growing attention from both academic and industrial fields. One line of researches for sentiment analysis is to feed bag-of-words (BOW) text representation into classifiers. Usually, raw BOW requires weighting schemes to obtain better performance, where important words are given more weights while unimportant ones are given less weights. Another line of researches focuses on neural models, where distributed text representations are learned from raw texts automatically. In this paper, we take advantages of techniques in both lines of researches. We use words’ weights to guide neural models to focus on important words. Various supervised weighting schemes are explored in this work. We discover that better text features are learned for sentiment analysis when suitable weighting schemes are applied upon neural models

Correction to: Guiding the Training of Distributed Text Representation with Supervised Weighting Scheme for Sentiment Analysis

Abstract In the originally published article, the acknowledgment section is missing. Please find it as follows

Neural Bag-of-Ngrams

Bag-of-ngrams (BoN) models are commonly used for representing text. One of the main drawbacks of traditional BoN is the ignorance of n-gram's semantics. In this paper, we introduce the concept of Neural Bag-of-ngrams (Neural-BoN), which replaces sparse one-hot n-gram representation in traditional BoN with dense and rich-semantic n-gram representations. We first propose context guided n-gram representation by adding n-grams to word embeddings model. However, the context guided learning strategy of word embeddings is likely to miss some semantics for text-level tasks. Text guided n-gram representation and label guided n-gram representation are proposed to capture more semantics like topic or sentiment tendencies. Neural-BoN with the latter two n-gram representations achieve state-of-the-art results on 4 document-level classification datasets and 6 semantic relatedness categories. They are also on par with some sophisticated DNNs on 3 sentence-level classification datasets. Similar to traditional BoN, Neural-BoN is efficient, robust and easy to implement. We expect it to be a strong baseline and be used in more real-world applications

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( ${R}^{{2}}\text {)}$ and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario