Comparison of muscle synergies elicited from transcranial meganetic stimulation (tms) and voluntary movements

A key question in motor control is the redundancy of musculoskeletal elements involved. This problem refers to as the degree of freedom problem. The Muscle Synergy Hypothesis is one of the hypotheses that aim to resolve the problem which defines that a muscle synergy is a combination of a small set of muscles activated at different levels, serving as a building block that constructs motor behaviors. A recent study (Overduin et al. 2012) demonstrated that muscle synergies decomposed by Nonnegative Matrix Factorization (NMF) from EMG patterns evoked by intra-cortical microsimulation (ICMS) in the monkey remarkably matched ones observed in naturalistic reach-and-grasp behaviors. Another study (Ajiboye et al. 2009) showed that synergies elicited from a small number of hand postures can allow prediction of hand postures in general. Inspired by aforementioned studies, the aim of this study was to investigate whether Transcranial Magnetic Stimulation (TMS) can elicit muscle synergies matching ones observed in voluntary movements in healthy human subjects and whether these synergies can serve as frameworks to predict EMG patterns evoked by either TMS or voluntary movements. Five healthy right-handed subjects participated in the study. 8 hand muscles were recorded to capture either TMS-evoked motor evoked potential (MEP) and electromyography (EMG) resulted from subjects’ shaping American Sign Language (ASL) letters and numbers. NMF was utilized to extract synergies from both MEP and EMG data. We observed 5 or 6 synergies can capture 90% of variance of original and matched synergies of two classes. The reconstructions of the original datasets (VTMS: MEP data; Vvol: EMG data; Vrand: Random data as control) from synergies (Hvol synergies elicited from ASL tasks; HTMS synergies elicited from TMS) was done by the nonnegative least-square algorithm, and Proportion of Variance Accounted for (PAV) served as a measure to quantify the quality of the estimation, giving results Hvol -\u3e Vvol: 0.92±0.02; HTMS -\u3e VTMS: 0.94±0.02; Hvol -\u3e Vrand: 0.53±0.03; HTMS -\u3e Vrand: 0.53±0.07; HTMS -\u3e Vvol: 0.70±0.06; Hvol -\u3e VTMS: 0.79±0.06. In conclusion, we argue that cortical components may involve in encoding synergies and we also demonstrate the possibility of synergies serving as frameworks in predicting and explaining human hand postures in general

Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Large Language Models (LLMs) have shown remarkable success in various tasks, but concerns about their safety and the potential for generating malicious content have emerged. In this paper, we explore the power of In-Context Learning (ICL) in manipulating the alignment ability of LLMs. We find that by providing just few in-context demonstrations without fine-tuning, LLMs can be manipulated to increase or decrease the probability of jailbreaking, i.e. answering malicious prompts. Based on these observations, we propose In-Context Attack (ICA) and In-Context Defense (ICD) methods for jailbreaking and guarding aligned language model purposes. ICA crafts malicious contexts to guide models in generating harmful outputs, while ICD enhances model robustness by demonstrations of rejecting to answer harmful prompts. Our experiments show the effectiveness of ICA and ICD in increasing or reducing the success rate of adversarial jailbreaking attacks. Overall, we shed light on the potential of ICL to influence LLM behavior and provide a new perspective for enhancing the safety and alignment of LLMs

Odd Yao-Yao Graphs are Not Spanners

It is a long standing open problem whether Yao-Yao graphs YY_{k} are all spanners [Li et al. 2002]. Bauer and Damian [Bauer and Damian, 2012] showed that all YY_{6k} for k >= 6 are spanners. Li and Zhan [Li and Zhan, 2016] generalized their result and proved that all even Yao-Yao graphs YY_{2k} are spanners (for k >= 42). However, their technique cannot be extended to odd Yao-Yao graphs, and whether they are spanners are still elusive. In this paper, we show that, surprisingly, for any integer k >= 1, there exist odd Yao-Yao graph YY_{2k+1} instances, which are not spanners

CEO tenure and environmental fraud for listed family firms

This paper examines CEO tenure's impact on the environmental fraud committed by listed Chinese family firms from the year 2012 to 2019. Using a bivariate probit model, we provide robust evidence that CEO tenure is positively related to the environmental fraud commission, indicating that longer-serving CEOs are more likely to violate environmental regulations and to commit fraud in the family firms. Besides, when there is a higher proportion of independent directors, CEO tenure is negatively related to the environmental fraud commission but positively related to fraud detection. Moreover, when there is a greater proportion of family members in senior positions, CEOs with longer tenure are more likely to commit environmental fraud. Our additional analysis finds that compared to non-family professional CEOs, family CEOs are more likely to commit environmental fraud. Overall, our results call for the introduction of CEO tenure limits and the increased recruitment of independent directors in family firms

High-Brightness Image Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications

Female analysts and COVID-19 corporate donation

This paper examines the impact of female analysts' coverage on firm's philanthropic activities amidst the outbreak of the COVID-19 pandemic in China. Using a hand-collected dataset of corporate philanthropy, the paper provides robust evidence that firms covered by female analysts are more likely to contribute actively to the well-being of societies by increasing corporate donation. This positive relationship is more pronounced if the company is privately controlled or covered by female analysts with more working experience, or located in more infectious provinces. Overall, our findings call for more female analyst recruitment, yielding benefits of pressuring firms to engage in philanthropy.</p