Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix

We extend the KVB approach of Kiefer, Vogelsang, and Bunzel (2000, Econometrica) and Kiefer and Vogelsang (2002b, Econometric Theory) to construct a class of robust tests for over-identifying restrictions in the context of GMM. The proposed test does not require consistent estimation of the asymptotic covariance matrix but relies on kernel-based normalizing matrices to eliminate the nuisance parameters in the limit. Moreover, the proposed test is valid for any consistent GMM estimator, in contrast with the conventional test that requires the optimal GMM estimator, and hence is easy to implement. Our simulations show that the proposed test is properly sized and may even be more powerful than the conventional test computed with an inappropriate user-chosen parameter.generalized method of moments, kernel function, KVB approach, overidentifying restrictions, robust test

A New Test of the Martingale Difference Hypothesis

In this paper we propose a new class of tests for the martingale difference hypothesis based on the moment conditions derived by Bierens (1982). In contrast with the existing consistent tests, the proposed test has a standard limiting distribution and is easy to implement. Comparing with the commonly used autocorrelation- and spectrum-based tests, it has power against a much larger class of alternatives that may be serially correlated or uncorrelated. Moreover, this test does not rely on the assumption of conditional homoskedasticity and requires a weaker moment condition. Our simulations confirm that the proposed test is powerful against various linear and nonlinear alternatives and is quite robust to the failure of higher-order moments. Our empirical study on exchange rate returns also shows that the conclusion resulted from the proposed test is different from that of the conventional tests.autocorrelation-based test, Bierens’ equivalence result, martingale difference sequence, multivariate exponential distribution, spectrum-based test

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

Regulation of Skp2 Expression and Activity and Its Role in Cancer Progression

The regulation of cell cycle entry is critical for cell proliferation and tumorigenesis. One of the key players regulating cell cycle progression is the F-box protein Skp2. Skp2 forms a SCF complex with Skp1, Cul-1, and Rbx1 to constitute E3 ligase through its F-box domain. Skp2 protein levels are regulated during the cell cycle, and recent studies reveal that Skp2 stability, subcellular localization, and activity are regulated by its phosphorylation. Overexpression of Skp2 is associated with a variety of human cancers, indicating that Skp2 may contribute to the development of human cancers. The notion is supported by various genetic mouse models that demonstrate an oncogenic activity of Skp2 and its requirement in cancer progression, suggesting that Skp2 may be a novel and attractive therapeutic target for cancers

Ginseng essence, a medicinal and edible herbal formulation, ameliorates carbon tetrachloride-induced oxidative stress and liver injury in rats

AbstractBackgroundGinseng essence (GE) is a formulation comprising four medicinal and edible herbs including ginseng (Panax ginseng), American ginseng (Panax quinquefolius), lotus seed (Nelumbo nucifera), and lily bulb (Lilium longiflorum). This study was aimed at investigating the hepatoprotective effect of GE against carbon tetrachloride (CCl4)-induced liver injury in rats.MethodsWe treated Wistar rats daily with low, medium, and high [0.625 g/kg body weight (bw), 1.25 g/kg bw, and 3.125 g/kg bw, respectively] doses of GE for 9 wk. After the 1st wk of treatment, rats were administered 20% CCl4 (1.5 mL/kg bw) two times a week to induce liver damage until the treatment ended.ResultsSerum biochemical analysis indicated that GE ameliorated the elevation of aspartate aminotransferase and alanine aminotransferase and albumin decline in CCl4-treated rats. Moreover, CCl4-induced accumulation of hepatic total cholesterol and triglyceride was inhibited. The hepatoprotective effects of GE involved enhancing the hepatic antioxidant defense system including glutathione, glutathione peroxidase, glutathione reductase, glutathione S-transferase, superoxide dismutase, and catalase. In addition, histological analysis using hematoxylin and eosin and Masson's trichrome staining showed that GE inhibited CCl4-induced hepatic inflammation and fibrosis. Furthermore, immunohistochemical staining of alpha-smooth muscle actin indicated that CCl4-triggered activation of hepatic stellate cells was reduced.ConclusionThese findings demonstrate that GE improves CCl4-induced liver inflammation and fibrosis by attenuating oxidative stress. Therefore, GE could be a promising hepatoprotective herbal formulation for future development of phytotherapy