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Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following $\mu $ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects’ number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In such a computing system, cloud servers have no energy limitations, since they have unlimited energy resources. Edge computing systems, however, are resource-constrained, and the energy consumption is thus expensive, which requires an energy-efficient method for RNN job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement. Based on the QoS requirements, the runtime manager dynamically assigns RNN inference tasks to edge and cloud computing systems and performs energy optimization on edge systems using dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud system indicate that in edge systems, our method can reduce the energy up to 45% compared with the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel's size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN's problem: that it is difficult to fully extract the sEMG's temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%. [ABSTRACT FROM AUTHOR]

Aiming at the problem of floor failure in deep mining nowadays, taking a certain working face of a deep mine in Shandong, China as the engineering background, the method of numerical simulation and similar simulation are used to study the failure law of floor in the mining process. The numerical simulation results show that the vertical stress increasing area of deep mining is "bubble-shaped"; shearing stress is generally "butterfly-shaped"; plastic zone distribution is "rectangular-shaped", shearing failure is dominant in the surrounding and middle parts, the range of plastic zone decreases with the increase of the depth from the coal seam; when the working face is advanced to 100 m, the floor failure depth reaches 30 m and tends to be stable state no longer increase. The similar simulation results show that that with the increase of floor rock depth, the stress value decreases, and the stress propagation of floor rock mass to deep depth has a lag effect, and the lag time is longer with the increase of depth. Finally, the depth of floor failure in working face is measured by the method of subsection water injection in the downhole. The measured result shows that the maximum floor failure depth is 31.6 m, which is similar to the result of numerical simulation. The two results were verified each other. The numerical simulation can provide a reference for the study of floor failure law in similar mines, and it can also provide some guidance for the prevention and control of water inrush in deep mines. [ABSTRACT FROM AUTHOR]

The narrow therapeutic time window and risk of intracranial hemorrhage largely restrict the clinical application of thrombolysis in acute ischemic stroke. Adjunctive treatments added to rt-PA may be beneficial to improve the capacity of neural cell to withstand ischemia, and to reduce the hemorrhage risk as well. This review aims to evaluate the neuroprotective effects of adjunctive treatments in combination with thrombolytic therapy for acute ischemic stroke. Relevant studies were searched in the PubMed, Web of Science and EMBASE database. In this review, we first interpret the potential role of adjunctive treatments to thrombolytic therapy in acute ischemic stroke. Furthermore, we summarize the current clinical evidence for the combination of intravenous recombinant tissue plasminogen activator and various adjunctive therapies in acute ischemic stroke, either pharmacological or non-pharmacological therapy, and discuss the mechanisms of some promising treatments, including uric acid, fingolimod, minocycline, remote ischemic conditioning, hypothermia and transcranial laser therapy. Even though fingolimod, minocycline, hypothermia and remote ischemic conditioning have yielded promising results, they still need to be rigorously investigated in further clinical trials. Further trials should also focus on neuroprotective approach with pleiotropic effects or combined agents with multiple protective mechanisms. [ABSTRACT FROM AUTHOR]

The evidence we uncover suggests that the practice of profit and risk sharing among keiretsu firms reduces the firm level idiosyncratic risk. However, rather than eliminating firm-level risk, it is being transformed into market-level risk. Since market-level risk is priced, this actually destroys shareholders’ wealth. Additionally, the heightened correlation among keiretsu firms essentially diminishes the diversification efficacy of a portfolio of keiretsu firms. In summary, our results suggest that the practice of profit and risk sharing, which on the surface seems to be a blessing, may actually have a detrimental effect on the value of keiretsu firms. [ABSTRACT FROM AUTHOR]

This paper investigates Black–Scholes call and put option thetas, and derives upper and lower bounds for thetas as a function of underlying asset value. It is well known that the maximum time premium of an option occurs when the underlying asset value equals the exercise price. However, we show that the maximum option theta does not occur at that point, but instead occurs when the asset value is somewhat above the exercise price. We also show that option theta is not monotonic in any of the parameters in the Black–Scholes option-pricing model, including time to maturity. We further explain why the implications of these findings are important for trading and hedging strategies that are affected by the decay in an option’s time premium. [ABSTRACT FROM AUTHOR]

A knowledge flow is invisible but it plays an important role in ordering knowledge exchange when working in a team. It can help achieve effective team knowledge management by modeling, optimizing, monitoring, and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving force behind the formation of an autonomous knowledge flow network, and explores the underlying principles. Knowing these principles helps teams and the support systems improve cooperation by monitoring the knowledge energy of nodes, by evaluating and adjusting knowledge flows, and by adopting appropriate strategies. A knowledge flow network management mechanism can help improve the efficiency of knowledge-intensive distributed teamwork. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A casual inspection of a graph of volatility indexes over time indicates that volatility has undergone infrequent, but significant, shifts in its average level. The purpose of this article is to test for multiple structural breaks in the mean level of market volatility measured by the VIX and VXO, and to identify statistically the dates of these mean shifts. We find evidence of three distinct periods: pre-1992, 1992–1997, and post-1997. We find that the mean volatility, as well as its standard deviation, was lowest during 1992–1997. Our findings provide statistical evidence consistent with popular beliefs that market volatility changes over time. [ABSTRACT FROM AUTHOR]

We use a simultaneous equation model which treats firm value, investments and management ownership as endogenous to the firm. Our results show a feedback relation between corporate value and management ownership, i.e., corporate value is positively impacted by management ownership, which in turn is positively impacted by corporate value. Corporate value also affects investments made by the firm. We also find that the effect of the main bank on corporate value is positive but only up to a certain point; then, it turns negative. Supporting the argument that keiretsu firms have lower agency cost, we find that firms belonging to a keiretsu have higher valuations during the sample period. Finally, we find that management ownership increases as the ownership of the main bank, ownership of institutional holders and cross-holdings decreases, suggesting a substitution effect among these monitoring forces. Our results indicate that ignoring the web of these relationships leads to incorrect inferences. [ABSTRACT FROM AUTHOR]