CEO Compensation and Disclosure Policy

This paper examines the relationship between CEO compensation and disclosure policy related to corporate governance information within S&P 500 index. Our sample consists of 456 companies for the period from 2005 to 2015. Most previous researchers mainly put their attention on various corporate governance characteristics such as board size, board independence, and executive ownership when analysing CEO compensation. Our paper extends the previous study by dividing corporate governance into two aspects: governance transparency and governance characteristics. We find a significant relationship between CEO total compensation and governance transparency. In addition, a significant positive relationship exists between CEO salary and governance transparency with year and industry fixed effect. The higher transparency, the less option compensation the CEO receives. As for governance characteristic measure, we choose CEO ownership and board independence as independent variables. We find that more CEO ownership leads to less total compensation, salary, and more option awards. However, no significant evidence shows the impact of board independence. The results show that governance with higher transparency can serve as an alternative mechanism for pay-for-performance. When governance transparency is relatively high, the board is able to monitor the CEO better and hence is able to tilt the compensation towards fixed-salary and less pay-for-performance

Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem

Long-term Effects of the Great Recession on Household Investment Behavior: a PSID perspective

HonorsEconomicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162605/1/cocozwj.pd

Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against the compared MIPL and partial-label learning approaches.Comment: Accepted at NeurIPS 202

Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each other. However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class. To solve the above problems, we propose a novel backbone for WSOD based on our tailored Vision Transformer named Weakly Supervised Transformer Detection Network (WSTDN). Our algorithm is not only the first to demonstrate that self-attention modules that consider inter-instance relationships are effective backbones for WSOD, but also we introduce a novel bounding box mining method (BBM) integrated with a memory transfer refinement (MTR) procedure to utilize the instance dependencies for facilitating instance refinements. Experimental results on PASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of our proposed WSTDN and modified instance refinement modules

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201

Large zeta sums

In this article, we investigate the behaviour of values of zeta sums $\sum_{n\le x}n^{it}$ when $t$ is large. We show some asymptotic behaviour and Omega results of zeta sums, which are analogous to previous results of large character sums $\sum_{n\le x}\chi(n)$.Comment: 11 pages

Parental migration and self-reported health status of adolescents in China: a cross-sectional study

Background: Over 100 million children are parented by migrant workers in China. The aim of this study was to investigate how self-reported adolescent physical and mental health are associated with parental migration. Methods: Based on cross-sectional data of 13996 students in 112 schools drawn from a nationally representative sample of middle school students in China, this study used self-reported measures for adolescent physical and mental health. Ordered logistic regression was used for the analysis of self-reported physical health, and linear regression was used for the analysis of self-reported mental health, both adjusting for socio-economic covariates and school fixed effects, to determine how adolescent health is associated with parental migration. Findings: In urban areas, migrant adolescents were physically healthier (OR=1.19, 95% CI: 1.03–1.36), and similarly mentally healthy (b=-0.07, 95% CI: -0.37–0.23), compared to urban adolescents from intact families; in rural areas, left-behind adolescents were less physically (OR=0.84, 95% CI: 0.76–0.94) and mentally (b=0.45, 95% CI: 0.24–0.66) healthy than rural-intact adolescents, holding other variables constant. Left-behind adolescents had less close parent-adolescent relationships than rural-intact adolescents with both father (OR=0.63, 95% CI: 0.56–0.71) and mother (OR=0.62, 95% CI: 0.54–0.70). Interpretation: Our study highlights a great need for health interventions aimed at left-behind adolescents in China and globally, and the important roles of parent-adolescent relationships in addressing the health needs of left-behind adolescents