Social Inclusion and Foreigner Support in the Post-COVID-19 Era: An Interview-based Survey of Highly Educated Foreigners

departmental bulletin pape

Social Inclusion and Foreigner Support in the Post-COVID-19 Era: An Interview-based Survey of Highly Educated Foreigners

departmental bulletin pape

Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools

This paper introduces a high-throughput software tool framework called {\it sam2bam} that enables users to significantly speedup pre-processing for next-generation sequencing data. The sam2bam is especially efficient on single-node multi-core large-memory systems. It can reduce the runtime of data pre-processing in marking duplicate reads on a single node system by 156-186x compared with de facto standard tools. The sam2bam consists of parallel software components that can fully utilize the multiple processors, available memory, high-bandwidth of storage, and hardware compression accelerators if available. The sam2bam provides file format conversion between well-known genome file formats, from SAM to BAM, as a basic feature. Additional features such as analyzing, filtering, and converting the input data are provided by {\it plug-in} tools, e.g., duplicate marking, which can be attached to sam2bam at runtime. We demonstrated that sam2bam could significantly reduce the runtime of NGS data pre-processing from about two hours to about one minute for a whole-exome data set on a 16-core single-node system using up to 130 GB of memory. The sam2bam could reduce the runtime for whole-genome sequencing data from about 20 hours to about nine minutes on the same system using up to 711 GB of memory

Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog datase

L vector spaces and L fields

We construct in ZFC an L topological vector space -- a topological vector space that is an L space -- and an L field -- a topological field that is an L space. This generalizes results in [5] and [8].Comment: It has been accepted for publication in SCIENCE CHINA Mathematic

MAω1(S)[S]_{\omega_1}(S)[S] does not imply K2\mathcal{K}_2

We construct a model in which MA$_{\omega_1}$(S)[S] holds and $\mathcal{K}_2$ fails. This shows that MA$_{\omega_1}$(S)[S] does not imply $\mathcal{K}_2$ and answers an old question of Larson and Todorcevic in [3]. We also investigate different strong colorings in models of MA$_{\omega_1}$(S)[S]

The Difference of Between Event Marketing and Activity Marketing

In the modern commodity economy society, Event marketing and Activity marketing have long been regarded as golden opportunities in marketing management by companies, therefore a large number of enterprises have fit them into the system of marketing strategy. But in recent years, scholars and companies simply confuse these two concepts with each other. They hold the opinion that event marketing is activity marketing and vice versa, besides the way to call them, no essential difference lying in. It’s hardly realized that the opinion stated above mixed the attributes of these two. This concept is not only unfavourable to the realization of intended marketing goals, but also go ill with the control of different business opportunities in Event marketing and Activity marketing

Out-of-domain Detection for Natural Language Understanding in Dialog Systems

Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the acceptance of the OOD input that is unsupported by the current system may lead to catastrophic failure. However, most existing OOD detection methods rely heavily on manually labeled OOD samples and cannot take full advantage of unlabeled data. This limits the feasibility of these models in practical applications. In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances, and thereby improves the performance of OOD detection. To this end, an autoencoder is trained to map an input utterance into a latent code. and the codes of IND and OOD samples are trained to be indistinguishable by utilizing a generative adversarial network. To provide more supervision signals, an auxiliary classifier is introduced to regularize the generated OOD samples to have indistinguishable intent labels. Experiments show that these pseudo OOD samples generated by our model can be used to effectively improve OOD detection in NLU. Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.Comment: Accepted by TALS

Abstracts: Challenges of Comprehensive Disaster Assistance for Foreigners Based on Recent Disaster Experience

departmental bulletin pape