Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

A COMPARISON OF BALANCE CONTROL BETWEEN JAVELIN THROWERS AND BASEBALL PITCHERS

This study evaluated the balance control in javelin throwers and baseball pitchers. Twenty-four athletes took part in the study and for each we measured their static and dynamic balance by Biodex Balance System, AMTl AccuSway force plate and Y balance test. The paired t-test was conducted to compare all the variables within subjects. The independent t-test was used to compare trail leg (TL) of baseball pitchers with propulsive leg (PL) of javelin throwers and stride leg (SL) of baseball pitchers with braking leg (BL) of javelin throwers. The results showed that there was no difference between the PL and BL in dynamic and static balance in javelin throwers. However, the dynamic balance in the TL was significantly better than the SL in baseball pitchers. The static balance in the PL in javelin throwers was also better than the TL in baseball pitchers. The research improves the understanding of balance control in elite athletes

Volatility of the Utilities Industry: Its Causal Relationship to Other Nine Industries

The goal of this study is to investigate the causality relationship between the Utilities industry and the nine other industries. Previous literatures show that volatility of stock prices is informative; Granger causality is applied in this research by using of a leveraged bootstrap test developed by Hacker and Hatemi-J (2006) to examine the behavior of the volatility. The results indicate that causality of the volatility of the Utilities industry on the volatility of seven other industries, except the Information Technology and Telecommunication Services industries. The data also suggest that Financials industry has impact on the Utilities industry

Regulation and Function of the Caspase-1 in an Inflammatory Microenvironment.

The inflammasome is a complex of proteins that has a critical role in mounting an inflammatory response in reply to a harmful stimulus that compromises the homeostatic state of the tissue. The NLRP3 inflammasome, which is found in a wound-like environment, is comprised of three components: the NLRP3, the adaptor protein ASC and caspase-1. Interestingly, although ASC levels do not fluctuate, caspase-1 levels are elevated in both physiological and pathological conditions. Despite the observation that merely raising caspase-1 levels is sufficient to induce inflammation, the crucial question regarding the mechanism governing its expression is unexplored. We found that, in an inflammatory microenvironment, caspase-1 is regulated by NF-κB. Consistent with this association, the inhibition of caspase-1 activity parallels the effects on wound healing caused by the abrogation of NF-κB activation. Surprisingly, not only does inhibition of the NF-κB/caspase-1 axis disrupt the inflammatory phase of the wound-healing program, but it also impairs the stimulation of cutaneous epithelial stem cells of the proliferative phase. These data provide a mechanistic basis for the complex interplay between different phases of the wound-healing response in which the downstream signaling activity of immune cells can kindle the amplification of local stem cells to advance tissue repair

Preserving User Preferences in Document-Category Management: An Ontology-based Evolution Approach

Preserving the user’s preference in document-category management is essential because it affects his/her search efficiency, cognitive processing load, and satisfaction. Prior research has investigated automated document category evolution by using lexicon-based documentcategory evolution techniques which take into account the document categories previously created by the user. However, comparing documents at the lexical level cannot solve word mismatch or ambiguity problems effectively. To address such problems inherent to the lexicon-based approach, we propose an ONtology-based Category Evolution (ONCE) technique, which uses an appropriate ontology to support document-category evolution at the conceptual level rather than at the lexical level. Specifically, we develop an Ontology Enrichment (OE) technique for automatic leaning of concept descriptors in the adopted ontology. We empirically evaluate the effectiveness of the proposed ONCE technique, using a lexicon-based document-category evolution technique (i.e., CE2) and the hierarchical agglomerative clustering (HAC) technique for benchmark purposes. According to our empirical results, ONCE appears more effective than CE2 and HAC, and achieves higher clustering recall and precision

Optimal Power Flow in Hybrid AC and Multi-terminal HVDC Networks with Offshore Wind Farm Integration Based on Semidefinite Programming

Multi-terminal high voltage direct current (MTHVDC) technology is a promising technology for the offshore wind farm integration, which requires the new control and operation scheme. Therefore, the optimal power flow problem for this system is important to achieve the optimal economic operation. In this paper, convex relaxation model based on semidefinite programming for the MT-HVDC system considering DC/DC converters is proposed to solve the optimal power flow problem. A hybrid AC and MT-HVDC system for offshore wind farm integration is used for the test. The simulation results validate the effectiveness of the proposed model and guarantee that the global optimum solution is achieved.Comment: Accepted in IEEE/PES ISGT Asia 2019 conference (May, 2019), Chengdu, Chin

Sport Sentiments and Stock Returns: Example of FIFA World Cups

This paper investigates the relation between sport sentiments and excess stock returns, and our data is from the 17th to 20th FIFA World Cups. Many studies find the relation between sport sentiments and stock market returns. In contrast to the previous studies, this paper considers seven conditions: (1) Considering stock markets are efficient markets, this paper uses not only close price but also open price to estimate excess stock returns which is affected by game results; (2) This paper further considers that sport sentiments affect sponsors’ excess stock returns (this point is seldom discussed by the past literature); (3) This paper further considers a time-lagged effect between sport sentiments and excess stock returns. (4) This paper further considers the persistent effect of previous games result. (5) This paper further employs the samples by not only for all participant countries but also for the each of participant countries. (6) This paper further considers the conditions of extreme wins and extreme loses. (7) This paper further considers the samples of championship games to exam the relation between sport sentiments and excess stock returns. Our results find that sport sentiments does not affect stock market returns, the reason is that investors are rational in dealing with sport sentiments (FIFA World Cup) and the stock trading decisions. This paper further finds Sponsors effect hypothesis: A significant positive/ negative effect on sponsors’ excess stock returns after wins/ loses in the championship games, and this effect only occurs on the open price of the next trading day. These empirical results can offer an important information for the investors of sport sponsor stocks

A Review of Western and Traditional Chinese Medical Approaches to Managing Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a disease of attention because of increase in prevalence from 20% to 41%. The clinical and pathological conditions in patients with NAFLD range from steatosis alone to nonalcoholic steatohepatitis (NASH) with or without fibrosis to hepatic cancer. In the United States, NAFLD was the second-leading indication for liver transplant between 2004 and 2013. Although imaging studies such as magnetic resonance elastography and the use of diagnostic panels and scoring systems can provide a fairly accurate diagnosis of NAFLD, there are few treatment options for patients with mild to moderate disease other than lifestyle modification. Many of the currently used medical treatments have been shown to cause severe side effects and some have been shown to be associated with increased risk for certain types of cancer. In recent years, a number of traditional Chinese herbal treatments have been examined for their potential uses as treatment for NAFLD. In this review, we provide a general overview of NAFLD and a survey of Western pharmacologic drugs currently used to treat the disease as well as the results of recent studies on the effectiveness of traditional Chinese herbal remedies for managing nonalcoholic fatty liver disease

Learning a Complete Image Indexing Pipeline

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding