107 research outputs found
Mergers between regulated firms with unknown efficiency gains
In an industry where regulated firms interact with unregulated suppliers, we investigate the welfare effects of a merger between regulated firms when cost synergies are uncertain before the merger and their realization becomes private information of the merged firm. The optimal merger policy trades off potential cost savings against regulatory distortions from informational problems. We show that, as a consequence of this trade-off, more intense competition in unregulated segments of the market induces a more lenient merger policy. The regulated firms' diversification into a competitive segment of the market can lead to a softer merger policy when competition is weaker
Regulatory risk, vertical integration, and upstream investment
We investigate the impact of regulatory risk on vertical integration and upstream investment by a regulated firm that provides an essential input to downstream competitors. Regulatory risk reflects uncertainty about the regulator's commitment to a regulatory policy that promotes the regulated firm's unobservable investment effort. We show that, when the regulator sets the regulatory policy after the vertical industry structure has been established, some degree of regulatory risk is ex ante socially beneficial. Regulatory risk makes vertical integration profitable and stimulates upstream investment at a lower social cost. This occurs for moderate costs of investment effort and firm small risk aversion. Our analysis sheds new light on some relevant empirical patterns in vertically related markets
ANSWER : generating information dissemination network on campus
Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG
Graduate employment prediction with bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*
DEFINE: friendship detection based on node enhancement
Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.E
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social
consequences such as drunkenness and suicide. In addition to academic
performance, unconscious biases can become one key obstacle for hunting jobs
for graduating students. Thus, it is necessary to understand these unconscious
biases so that we can help these students at an early stage with more
personalized intervention. In this paper, we develop a framework, i.e., MAYA
(Multi-mAjor emploYment stAtus) to predict students' employment status while
considering biases. The framework consists of four major components. Firstly,
we solve the heterogeneity of student courses by embedding academic performance
into a unified space. Then, we apply a generative adversarial network (GAN) to
overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory
(LSTM) with a novel dropout mechanism to comprehensively capture sequential
information among semesters. Finally, we design a bias-based regularization to
capture the job market biases. We conduct extensive experiments on a
large-scale educational dataset and the results demonstrate the effectiveness
of our prediction framework
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
Building scalable vision-language models to learn from diverse, multimodal
data remains an open challenge. In this paper, we introduce an Efficient
Vision-languagE foundation model, namely EVE, which is one unified multimodal
Transformer pre-trained solely by one unified pre-training task. Specifically,
EVE encodes both vision and language within a shared Transformer network
integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which
capture modality-specific information by selectively switching to different
experts. To unify pre-training tasks of vision and language, EVE performs
masked signal modeling on image-text pairs to reconstruct masked signals, i.e.,
image pixels and text tokens, given visible signals. This simple yet effective
pre-training objective accelerates training by 3.5x compared to the model
pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing
to the combination of the unified architecture and pre-training task, EVE is
easy to scale up, enabling better downstream performance with fewer resources
and faster training speed. Despite its simplicity, EVE achieves
state-of-the-art performance on various vision-language downstream tasks,
including visual question answering, visual reasoning, and image-text
retrieval.Comment: Accepted by AAAI 202
Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks
Facial appearance matters in social networks. Individuals frequently make
trait judgments from facial clues. Although these face-based impressions lack
the evidence to determine validity, they are of vital importance, because they
may relate to human network-based social behavior, such as seeking certain
individuals for help, advice, dating, and cooperation, and thus they may relate
to centrality in social networks. However, little to no work has investigated
the apparent facial traits that influence network centrality, despite the large
amount of research on attributions of the central position including
personality and behavior. In this paper, we examine whether perceived traits
based on facial appearance affect network centrality by exploring the initial
stage of social network formation in a first-year college residential area. We
took face photos of participants who are freshmen living in the same
residential area, and we asked them to nominate community members linking to
different networks. We then collected facial perception data by requiring other
participants to rate facial images for three main attributions: dominance,
trustworthiness, and attractiveness. Meanwhile, we proposed a framework to
discover how facial appearance affects social networks. Our results revealed
that perceived facial traits were correlated with the network centrality and
that they were indicative to predict the centrality of people in different
networks. Our findings provide psychological evidence regarding the interaction
between faces and network centrality. Our findings also offer insights in to a
combination of psychological and social network techniques, and they highlight
the function of facial bias in cuing and signaling social traits. To the best
of our knowledge, we are the first to explore the influence of facial
perception on centrality in social networks.Comment: 11 pages, 8 figure
Unprotected quadratic band crossing points and quantum anomalous Hall effect in FeB2 monolayer
Quadratic band crossing points (QBCPs) and quantum anomalous Hall effect
(QAHE) have attracted the attention of both theoretical and experimental
researchers in recent years. Based on first-principle calculations, we find
that the FeB monolayer is a nonmagnetic semimetal with QBCPs at .
Through symmetry analysis and invariant theory, we
find that the QBCP is not protected by rotation symmetry and consists of two
Dirac points with same chirality (Berry phase of ). Once introducing
Coulomb interactions, we find that there is a
spontaneous-time-reversal-breaking instability of the spinful QBCPs, which
gives rise to a QAH insulator with orbital moment ordering
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