Corporate Social Responsibility and Likelihood of Financial Distress

Does doing good to society make firms less likely to have financial trouble? This paper looks at the benefit of corporate social responsibility (CSR) and examines whether firms’ CSR engagement affects their chance of falling into financial distress. After analyzing a broad U.S. database spanning 25 years from 1991 to 2015, we find that CSR engagement indeed reduces the likelihood of firms falling into financial distress, and the results are statistically robust and economically significant. Further, we find the impact of CSR on the likelihood of financial distress is more pronounced in economic downturns and for firms with high levels of international involvement. Collectively, our result suggests that CSR lowers financial distress risks by improving firmstakeholder relationships, which enhances our understanding of the stakeholder view of CSR with longitudinal approach and contextual consideration of firms

Pixel level optical-transfer-function design based on the surface-wave-interferometry aperture

The design of optical transfer function (OTF) is of significant importance for optical information processing in various imaging and vision systems. Typically, OTF design relies on sophisticated bulk optical arrangement in the light path of the optical systems. In this letter, we demonstrate a surface-wave-interferometry aperture (SWIA) that can be directly incorporated onto optical sensors to accomplish OTF design on the pixel level. The whole aperture design is based on the bull’s eye structure. It composes of a central hole (diameter of 300 nm) and periodic groove (period of 560 nm) on a 340 nm thick gold layer. We show, with both simulation and experiment, that different types of optical transfer functions (notch, highpass and lowpass filter) can be achieved by manipulating the interference between the direct transmission of the central hole and the surface wave (SW) component induced from the periodic groove. Pixel level OTF design provides a low-cost, ultra robust, highly compact method for numerous applications such as optofluidic microscopy, wavefront detection, darkfield imaging, and computational photography

A Regularized Opponent Model with Maximum Entropy Objective

In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference. We derive a variational lower bound of the likelihood of achieving the optimality and name it as Regularized Opponent Model with Maximum Entropy Objective (ROMMEO). From ROMMEO, we present a novel perspective on opponent modeling and show how it can improve the performance of training agents theoretically and empirically in cooperative games. To optimize ROMMEO, we first introduce a tabular Q-iteration method ROMMEO-Q with proof of convergence. We extend the exact algorithm to complex environments by proposing an approximate version, ROMMEO-AC. We evaluate these two algorithms on the challenging iterated matrix game and differential game respectively and show that they can outperform strong MARL baselines.Comment: Accepted to International Joint Conference on Artificial Intelligence (IJCA2019

Ultrasound-targeted microbubble destruction enhances AAV mediated gene transfection: human RPE cells in vitro and the rat retina in vivo

The present study was performed to investigate the efficacy and safety of Ultrasound-targeted microbubble destruction (UTMD) mediated rAAV2-EGFP to cultured human retinal pigment epithelium (RPE) cells _in vitro_ and the rat retina _in vivo_. _In vitro_ study, cultured human RPE cells were exposed to US under different conditions with or without microbubbles. Furthermore, the effect of UTMD to rAAV2-EGFP itself and the cells were evaluated. _In vivo_ study, gene transfer was examined by injecting rAAV2-EGFP into the subretinal space of the rats with or without microbubbles and then exposed to US. We investigated EGFP expression _in vivo_ via stereomicroscopy and performed quantitative analysis by Axiovision 3.1 software. HE staining and frozen sections were used to observe tissue damage and location of EGFP gene expression. _In vitro_ study, the transfection efficiency of rAAV2-EGFP increased 74.85% under the optimal UTMD conditions. Furthermore, there was almost no cytotoxicity to the cells and rAAV2-EGFP itself. _In vivo_ study, UTMD could be used safely to enhance and accelerate transgene expression of the retina. Fluorescence expression was mainly located in the layer of retina. UTMD is a promising method for gene delivery to the retina

Deep factorization for speech signal

Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper. The proposed framework infers speech factors in a sequential way, where factors previously inferred are used as conditional variables when inferring other factors. We will show that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with a high accuracy. This factorization and reconstruction approach provides potential values for many speech processing tasks, e.g., speaker recognition and emotion recognition, as will be demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap with arXiv:1706.0177