A stable iterative method for refining discriminative gene clusters

<p>Abstract</p> <p>Background</p> <p>Microarray technology is often used to identify the genes that are differentially expressed between two biological conditions. On the other hand, since microarray datasets contain a small number of samples and a large number of genes, it is usually desirable to identify small gene subsets with distinct pattern between sample classes. Such gene subsets are highly discriminative in phenotype classification because of their tightly coupling features. Unfortunately, such identified classifiers usually tend to have poor generalization properties on the test samples due to overfitting problem.</p> <p>Results</p> <p>We propose a novel approach combining both supervised learning with unsupervised learning techniques to generate increasingly discriminative gene clusters in an iterative manner. Our experiments on both simulated and real datasets show that our method can produce a series of robust gene clusters with good classification performance compared with existing approaches.</p> <p>Conclusion</p> <p>This backward approach for refining a series of highly discriminative gene clusters for classification purpose proves to be very consistent and stable when applied to various types of training samples.</p

Motivations and the Intent to Study Abroad Among US, French and Chinese Students

This paper analyzes the relationship between students’ motivations and their intention to participate in study abroad programs using a model based on expectancy theory. We surveyed US, Chinese and French business students who studied in their home countries. Results suggest that certain motivations are common among students from the three countries. We found that the direction of the relationship between motivations and the intent to study abroad varied among the three countries, that nationality moderates all of the relationships, and that different levels of the barriers moderate the relationship between motivations and the intention to study abroad

Pre-training Language Models for Comparative Reasoning

Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While there have been approaches for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks. Our approach introduces a novel method of collecting scalable data for text-based entity comparison, which leverages both structured and unstructured data. Moreover, we present a framework of pre-training language models via three novel objectives on comparative reasoning. Evaluation on downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions. This work also releases the first integrated benchmark for comparative reasoning.Comment: EMNLP 2023 - Camera Ready. Typos fixe

Do values or goals better explain intent? A cross-national comparison

Among six major lines of inquiry on motivations, two theories are especially pertinent to consumer behavior studies: values and goals. Several studies show that consumer behavior can be predicted on the basis of values or goals; this study examines which are the stronger predictors by presenting a cross-cultural comparison of the values and goals that may influence the behavioral intentions of U.S., Chinese, and French students to study abroad. As a service, study abroad has financial implications, represents a choice, and competes with other educational products. Therefore, goals appear to explain behavioral intentions better than do values, except among U.S. students. The goals and values associated with behavioral intentions differ across cultures and have different perceived dimensions, such that items cluster on those dimensions with specific meanings, depending on the culture. The different influences of values and goals on behavioral intentions may help marketing managers design more efficient marketing strategies in international markets. This paper thus contributes to the marketing literature by suggesting that national cultures moderate the effect of values and goals on consumer intentions

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.Comment: Accepted to EACL 2023 as regular long pape

Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm's effectiveness is verified through simulation experiments