Compensation negotiation and corporate governance: the evidence from China

This paper examines CEO pay dispersion for the listed companies in China. We apply a two-tier stochastic frontier model to the CEO compensation framework where asymmetric information generates a surplus between the minimum wage that CEOs accept and the maximum payment that firms offer. This surplus leads to CEO pay dispersion coming from the negotiation power between the CEO and the firm. We generate the surplus extracted by each CEO-firm pair and analyze how corporate governance affects them. An empirical analysis finds that: (1) On average, CEOs are paid 23.26% more than the benchmark; (2) additionally, we examine the bargaining power in state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). We find that CEOs in SOEs have less bargaining power due to compensation regulations. We then examine compensation for new CEOs hired externally and find that CEOs hired externally have less bargaining power on average; and (3) corporate governance has a significant effect on the salary bargaining power of each agent. More specifically, the CEO-Chairman dummy has a significant positive effect on the bargaining power of firms and CEOs, but the latter is larger. Board size has a negative effect on both. Independent directors help improve the bargaining power of the firms and board meeting times help enhance the bargaining power of the CEOs. Equity concentration has a significant negative effect on both sides

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Holistic Visual-Textual Sentiment Analysis with Prior Models

Visual-textual sentiment analysis aims to predict sentiment with the input of a pair of image and text, which poses a challenge in learning effective features for diverse input images. To address this, we propose a holistic method that achieves robust visual-textual sentiment analysis by exploiting a rich set of powerful pre-trained visual and textual prior models. The proposed method consists of four parts: (1) a visual-textual branch to learn features directly from data for sentiment analysis, (2) a visual expert branch with a set of pre-trained "expert" encoders to extract selected semantic visual features, (3) a CLIP branch to implicitly model visual-textual correspondence, and (4) a multimodal feature fusion network based on BERT to fuse multimodal features and make sentiment predictions. Extensive experiments on three datasets show that our method produces better visual-textual sentiment analysis performance than existing methods.Comment: Published in MIPR 202

Thermal strain induced large electrocaloric effect of relaxor thin film on LaNiO3/Pt composite electrode with the coexistence of nanoscale antiferroelectric and ferroelectric phases in a broad temperature range

Ferroelectric/antiferroelectric thin/thick films with large electrocaloric (EC) effect in a broad operational temperature range are very attractive in solid-state cooling devices. We demonstrated that a large positive electrocaloric (EC) effect (maximum ΔT ~ 20.7 K) in a broad temperature range (~ 110 K) was realized in Pb0.97La0.02(Zr0.65Sn0.3Ti0.05)O3 (PLZST) relaxor antiferroelectric (AFE) thin film prepared using a sol-gel method. The large positive EC effect may be ascribed to the in-plane residual thermal tensile stress during the layer-by-layer annealing process, and the high-quality film structure owing to the utilization of the LaNiO3/Pt composite bottom electrode. The broad EC temperature range may be ascribed to the great dielectric relaxor dispersion around the dielectric peak because of the coexistence of nanoscale multiple FE and AFE phases. Moreover, a large pyroelectric energy density (6.10 Jcm−3) was harvested by using an Olsen cycle, which is much larger than those (usually less than 10− Jcm−3) obtained by using direct thermal-electrical, Stirling and Carnot cycles, etc. These breakthroughs enable the PLZST thin film an attractive multifunctional material for applications in modern solid-state cooling and energy harvesting

Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study

Rate of Decline in Serum PFOA Concentrations after Granular Activated Carbon Filtration at Two Public Water Systems in Ohio and West Virginia

Drinking water in multiple water districts in the Mid-Ohio Valley has been contaminated with perfluorooctanoic acid (PFOA), which was released by a nearby DuPont chemical plant. Two highly contaminated water districts began granular activated carbon filtration in 2007.To determine the rate of decline in serum PFOA, and its corresponding half-life, during the first year after filtration.Up to six blood samples were collected from each of 200 participants from May 2007 until August 2008. The primary source of drinking water varied over time for some participants; our analyses were grouped according to water source at baseline in May-June 2007.For Lubeck Public Service District customers, the average decrease in serum PFOA concentrations between May-June 2007 and May-August 2008 was 32 ng/mL (26%) for those primarily consuming public water at home (n = 130), and 16 ng/mL (28%) for those primarily consuming bottled water at home (n = 17). For Little Hocking Water Association customers, the average decrease in serum PFOA concentrations between November-December 2007 and May-June 2008 was 39 ng/mL (11%) for consumers of public water (n = 39) and 28 ng/mL (20%) for consumers of bottled water (n = 11). The covariate-adjusted average rate of decrease in serum PFOA concentration after water filtration was 26% per year (95% confidence interval, 2528% per year).The observed data are consistent with first-order elimination and a median serum PFOA half-life of 2.3 years. Ongoing follow-up will lead to improved half-life estimation

The SMILES Mid-Infrared Survey

The Mid-Infrared Instrument (MIRI) for JWST is supplied with a suite of imaging bandpass filters optimized for full spectral coverage in eight intermediate-width bands from 5 to 26 microns and a narrower one at 11.3 microns. This contrasts with previous infrared space telescopes, which generally have provided only two broad bands, one near 10 microns and the other near 20 microns. The expanded MIRI spectral capability provides new possibilities for detailed interpretation of survey results. This is an important feature of the instrument, on top of its great increase in sensitivity and angular resolution over any previous mission. The Systematic Mid-infrared Instrument Legacy Extragalactic Survey (SMILES) was designed to take full advantage of this capability. This paper briefly describes the history of infrared surveys that paved the way for MIRI on JWST and for our approach to designng SMILES. It illustrates the use of the observations for a broad range of science programs, and concludes with a brief summary of the need for additional surveys with JWST/MIRI.Comment: submitted to ApJ to accompany a paper on the SMILES data release by Alberts et a