Intervention study of finger-movement exercises and finger weight-lift training for improvement of handgrip strength among the very elderly

AbstractObjectivesTo examine the effects of finger-movement exercises and finger weight-lift training on handgrip strength and Activities of Daily Living Scale (ADLS) values.MethodsA total of 80 very elderly adults (aged ≥80 years) were assigned to either an intervention group (n = 40) or a control group (n = 40). Subjects in the intervention group performed finger-movement exercises and weight-lift training for a period of 3 months, while subjects in the control group received no intervention, and were unaware of the interventions received in the other group.ResultsAfter completing 3 months of finger-movement exercises and weight-lift training, the average handgrip strength of the 40 participants in the intervention group had increased by 2.1 kg, whereas that in the control group decreased by 0.27 kg (P < 0.05). After receiving intervention, the number of subjects in the intervention group with an ADLS score >22 points decreased by 7.5% (P < 0.05, vs. pre-intervention).ConclusionsThe combined use intervention with finger-movement exercises and proper finger weight-lift training improved the handgrip strength and ADLS values of very elderly individuals. These rehabilitation exercises may be used to help the elderly maintain their self-care abilities

Why did some firms perform better in the global financial crisis?

We explore what firm and macroeconomic factors assisted Chinese firms to resist the global financial crisis. We find that firms with higher top ten shareholder ratios or firms that are older exhibited saliently higher performance during the crisis, but performed poorly during the non-crisis period. Firm size has a notably negative impact on firm performance. Firms audited by the Big Four accounting firms have a significantly negative correlation with performance. During the crisis, stock markets became less efficient in incorporating firm-specific information into stock prices, signifying that the determinants of firm performance vary across non-crisis and crisis periods

Online Knowledge Distillation with Diverse Peers

Distillation is an effective knowledge-transfer technique that uses predicted distributions of a powerful teacher model as soft targets to train a less-parameterized student model. A pre-trained high capacity teacher, however, is not always available. Recently proposed online variants use the aggregated intermediate predictions of multiple student models as targets to train each student model. Although group-derived targets give a good recipe for teacher-free distillation, group members are homogenized quickly with simple aggregation functions, leading to early saturated solutions. In this work, we propose Online Knowledge Distillation with Diverse peers (OKDDip), which performs two-level distillation during training with multiple auxiliary peers and one group leader. In the first-level distillation, each auxiliary peer holds an individual set of aggregation weights generated with an attention-based mechanism to derive its own targets from predictions of other auxiliary peers. Learning from distinct target distributions helps to boost peer diversity for effectiveness of group-based distillation. The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i.e., the model used for inference. Experimental results show that the proposed framework consistently gives better performance than state-of-the-art approaches without sacrificing training or inference complexity, demonstrating the effectiveness of the proposed two-level distillation framework.Comment: Accepted to AAAI-202

Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal where attacks threaten lives, important data, or mission success. But can cybersecurity be done proactively? Our research capitalizes on the Germination Period—the time lag between hacker communities discussing software flaw types and flaws actually being exploited—where proactive measures can be taken. We argue for a novel proactive approach, utilizing big data, for (I) identifying potential attacks before they come to fruition; and based on this identification, (II) developing preventive counter-measures. The big data approach resulted in our vision of the Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools to a wide variety of unstructured data sources to predict vulnerabilities and develop countermeasures. Our exploratory study is the first to show the promise of this novel proactive approach and illuminates challenges that need to be addressed

Did the S.A.R.S. epidemic weaken the integration of Asian stock markets? Evidence from smooth time-varying cointegration analysis

The purpose of this study is to examine the effect of the Severe Acute Respiratory Syndrome (S.A.R.S.) epidemic on the long-run relationship between China and four Asian stock markets. To this end, we first employ the advanced smooth time-varying cointegration model to investigate the existence of a time-varying cointegration relation among these markets and then employ the difference-indifferences approach to analyse whether or not the S.A.R.S. epidemic impacted the long-run relation between China and these four markets during the period 1998–2008, covering 5 years before and after the S.A.R.S. outbreak. Our results support the existence of a time-varying cointegration relation in the aggregate stock price indices, and that the S.A.R.S. epidemic did weaken the long-run relationship between China and the four markets. Therefore, stockholders and policy makers should be concerned about the influence of catastrophic epidemic diseases on the financial integration of stock market in Asia

Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g. particle size and shape, sample packing and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum, and hence mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical component in the same sample mixture. Based on this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e. near-infrared total diffuse transmittance spectra of four-component suspension samples and near infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology