Does engaging in government initiated corporate social responsibility activities improve corporate innovation? Evidence from China

The Chinese government initiated a nationwide poverty alleviation campaign in 2016. Many Chinese listed firms engaged in poverty alleviation campaign and made significant contributions. In this study, we investigate the relationship between firms’ poverty alleviation campaign and firm innovation performance. Using a large sample of 3140 Chinese A-share listed firms on Shenzhen and Shanghai Stock Exchange from 2016 to 2019, this study demonstrates that firms’ poverty alleviation campaign contribute to the improvement of firm innovation performance. These results are more pronounced for firms with higher internet search volume. Overall, our findings provide important support for listed firms to engage in CSR activities initiated by governmen

Wavelength dependencies of the Kerr rotation and ellipticity for the magneto-optical recording media

Here we present wavelength dependence measurements of Co/Pd and Co/Pt superlattice samples with different compositions. We explore the relationship between the composition and the magneto-optical spectra. The induced magnetization in the Pt of Co/Pt or in the Pd of Co/Pd samples plays an important role in the magneto-optical activity, and is discussed for the samples measured. The experimental set-up and the samples used are described. The measurement results of one Co/Pt sample and a series of Co/Pd samples are discussed

Probing WL′WHW^\prime_L WH and WR′WHW^\prime_R W H Interaction at LHC

Many new physics models predict the existence of TeV-scale charged gauge boson $W^\prime$ together with Higgs boson(s). We study the $W^\prime WH$ interaction and explore the angular distribution of charged lepton to distinguish $W_R^\prime WH$ from $W_L^\prime WH$ in $pp\to HW\to b \bar b l \nu$ process at the LHC. It is found that a new type forward-backward asymmetry($A_{FB}$) relating to the angle between the direction of the charged lepton in $W$ rest frame and that of the reconstructed $W^\prime$ in laboratory frame is useful to investigate the properties of $W^\prime W H$ interaction. We analyze the Standard Model backgrounds and develop a set of cuts to highlight the signal and suppress the backgrounds at LHC. We find that $A_{FB}$ can reach 0.03(-0.07) for $W_R^\prime$($W_L^\prime$) production at $\sqrt{S}=14$ TeV

Graphene-based spintronic components

A major challenge of spintronics is in generating, controlling and detecting spin-polarized current. Manipulation of spin-polarized current, in particular, is difficult. We demonstrate here, based on calculated transport properties of graphene nanoribbons, that nearly +-100% spin-polarized current can be generated in zigzag graphene nanoribbons (ZGNRs) and tuned by a source-drain voltage in the bipolar spin diode, in addition to magnetic configurations of the electrodes. This unusual transport property is attributed to the intrinsic transmission selection rule of the spin subbands near the Fermi level in ZGNRs. The simultaneous control of spin current by the bias voltage and the magnetic configurations of the electrodes provides an opportunity to implement a whole range of spintronics devices. We propose theoretical designs for a complete set of basic spintronic devices, including bipolar spin diode, transistor and logic gates, based on ZGNRs.Comment: 14 pages, 4 figure

Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles

There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major challenges. First, in order to deal with the sparsely-labeled physiological data, we first decomposed the raw physiological data using signal spectrum analysis, based on which we extracted both complexity and energy features. Such a procedure helped reduce noise and improve feature extraction effectiveness. Second, in order to improve the explainability of the machine learning models in emotion recognition with physiological data, we proposed Light Gradient Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP) for emotion prediction and model explanation, respectively. The LightGBM model outperformed the eXtreme Gradient Boosting (XGBoost) model on the public Database for Emotion Analysis using Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for binary classification of valence, arousal, and liking, respectively, with cross-subject validation using eight peripheral physiological signals. Furthermore, the SHAP model was able to identify the most important features in emotion recognition, and revealed the relationships between the predictor variables and the response variables in terms of their main effects and interaction effects. Therefore, the results of the proposed model not only had good performance using peripheral physiological data, but also gave more insights into the underlying mechanisms in recognizing emotions