A Learning Curve of the Market: Chasing Alpha of Socially Responsible Firms

This paper explores stock market reactions to corporate social performance. We find that a value-weighted portfolio based on the list of “100 Best CSR companies in the world”, published by Reputation Institute, yields statistically significant annual abnormal returns of 1.63% and 1.26%, by controlling for Carhart four factors and Fama-French five factors, respectively (2.39% and 1.84% respectively for an equal-weighted portfolio). Moreover, such abnormal returns decrease as time goes, especially after the inaugural publication of the CSR lists in 2013. The paper also indicates that companies with better social performance are more likely to have positive earnings surprises, and that their returns are more sensitive to earnings surprises. The results of this paper have three implications: firstly, CSR reputation contributes positively to a firm’s short-term superior equity performance; secondly, the CSR lists facilitate market correction of mispricing intangibles such as CSR reputation - abnormal returns decrease as the market gradually learns about the value of firms’ social performance; lastly, the paper contributes to the socially responsible investing (SRI) screens and provides guidance for investors who would like to do well financially by doing good socially

Atomic decoration for improving the efficiency of field electron emission of carbon nanotubes

The field electron emission from the single-walled carbon nanotubes with their open ends terminated by -BH, -NH, and -O has been simulated. The apex-vacuum barrier and the emission current have been calculated. It has been found that -BH and -NH suppress the apex-vacuum barrier significantly and lead to higher emission current in contrast to the -O terminated structure in the same applied field. The calculated binding energy implies that the carbon nanotubes terminated with -BH and -NH are more stable than those saturated by oxygen atoms or by hydrogen atoms.Comment: 8 pages, 9 figures, LaTeX; content changed, typos corrected, references adde

Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN

Endbulb synaptic depression within the range of presynaptic spontaneous firing and its impact on the firing reliability of cochlear nucleus bushy neurons

The majority of auditory nerve fibers exhibit prominent spontaneous activity in the absence of sound. More than half of all auditory nerve fibers in CBA mice have spontaneous firing rates higher than spikes/sec, and some fibers exceeding 100 spikes/sec. We tested whether and to what extent endbulb synapses are depressed by activity between 10 and 100 Hz, within the spontaneous firing rates of auditory nerve fibers. In contrast to rate-dependent depression seen at rates >100 Hz, we found that the extent of depression was essentially rate-independent (~35%) between 10 and 100 Hz. Neither cyclothiazide nor γ-D-glutamylglycine altered the rate-independent depression, arguing against receptor desensitization and/or vesicle depletion as major contributors for the depression. When endbulb synaptic transmission was more than half-blocked with the P/Q Ca2+ channel blocker ω-agatoxin IVA, depression during 25 and 100 Hz trains was significantly attenuated, indicating P/Q Ca2+ channel inactivation may contribute to low frequency synaptic depression. Following conditioning with a 100 Hz Poisson train, the EPSC paired pulse ratio was increased, suggesting a reduced release probability. This in turn should reduce subsequent depletion-based synaptic depression at higher activation rates. To probe whether this conditioning of the synapse improves the reliability of postsynaptic responses, we tested the firing reliability of bushy neurons to 200 Hz stimulation after conditioning the endbulb with a 25 Hz or 100 Hz stimulus train. Although immediately following the conditioning train, bushy cells responded to minimal suprathreshold stimulation less reliably, the firing reliability eventually settled to the same level (<50%) regardless of the presence or absence of the preconditioning. However, when multiple presynaptic fibers were activated simultaneously, the postsynaptic response reliability did not drop significantly below 90%. These results suggest that single endbulb terminals do not reliably trigger action potentials in bushy cells under “normal” operating conditions. We conclude that the endbulb synapses are chronically depressed even by low rates of spontaneous activity, and are more resistant to further depression when challenged with a higher rate of activity. However, there seems to be no beneficial effect as assessed by the firing reliability of postsynaptic neurons for transmitting information about higher rates of activity

Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202