Admissibility of linear predictor in the extended growth curve model

In the present paper, we first give the definition of the extended growth curve model, then according to the definition of admissible linear predictor and some matrix properties, obtain the necessary and sufficient conditions for a linear predictor to be admissible in the classes of homogenous and inhomogeneous linear predictors, respectively

Impact of Macroeconomic Variables on Stock Price Index: Evidence from Vietnam Stock Market

Vietnam stock market after nearly 20 years of operation has undergone many ups and downs. In addition to the contribution to the development of the national economy, the Vietnam Stock market still has some certain shortcomings. Goods on the market have increased in size, quantity, and categories but not much of diversity. The potential risks due to many different causes, always hurt investors. Therefore, the study of Macroeconomic factors affecting the volatility of stock indexes has been paid special attention by society. Research results show that, in the long term, the VNI stock price index is affected by the oil price, money supply, interest rate, SJC gold price, and exchange rate. In the short term, the stock price index has a causal relationship with oil prices, interest rates, and the M2 money supply. When there are impacts of market shocks, short-term corrections affect extremely slow to the long-term balance. Keywords: macroeconomic factors, VNI, Vietnam stock market, ECM, VAR DOI: 10.7176/RJFA/10-12-04 Publication date:June 30th 2019

Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.Comment: To appear in EMNLP 201

A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding

Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteristics; (2) directly incorporate multi-intent information for each token, which could lead to incorrect slot prediction due to the introduction of irrelevant intent. In this paper, we propose a framework termed DGIF, which first leverages the semantic information of labels to give the model additional signals and enriched priors. Then, a multi-grain interactive graph is constructed to model correlations between intents and slots. Specifically, we propose a novel approach to construct the interactive graph based on the injection of label semantics, which can automatically update the graph to better alleviate error propagation. Experimental results show that our framework significantly outperforms existing approaches, obtaining a relative improvement of 13.7% over the previous best model on the MixATIS dataset in overall accuracy.Comment: Submitted to ICASSP 202

Sirolimus increases tissue factor expression but not activity in cultured human vascular smooth muscle cells

BACKGROUND: Sirolimus-eluting stents (CYPHER stents) demonstrated remarkable efficacy in reducing restenosis rates in patients with coronary artery disease. There is a concern of sub-acute and late stent thrombosis. Tissue factor (TF) is critical in thrombosis. This study investigated the effect of sirolimus on TF expression and activity in cultured human vascular smooth muscle cells (SMCs). METHODS: SMCs were cultured from human saphenous veins and aortas. Quiescent cells were stimulated with sirolimus (0.1 – 20 ng/ml) over 24 hours. Cellular TF expression and activity released into culture medium were measured. The effect of sirolimus on activation of mammalian target of rapamycin (mTOR) was measured by phosphorylation of the substrate p70s6k at T389, and activation of RhoA was measured by pull-down assay. RESULTS: Sirolimus increased TF protein level in cultured human SMCs in a concentration and time-dependent manner (about 2-fold, p < 0.01) reaching maximal effect at 5 ng/ml. The stimulation of TF expression by sirolimus was associated with inhibition of basal activity of mTOR. No effects of sirolimus on RhoA or p38mapk activation that are positive regulators of TF in vascular wall cells were observed. The stimulation of TF expression by sirolimus (20 ng/ml) was prevented by the HMG-CoA reductase inhibitor fluvastatin (1 μmol/L). However, no increase in TF activity released from SMC into culture medium was observed after sirolimus treatment. CONCLUSION: Although sirolimus stimulates TF protein expression in human SMC associated with inhibition of mTOR, it does not enhance TF activity released from the cells, suggesting a relatively safe profile of CYPHER stents. The inhibition of TF expression by fluvastatin favors clinical use of statins in patients undergoing coronary stenting

Exploiting Prompt Caption for Video Grounding

Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the \emph{sparsity dilemma} in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. In this paper, we contend that exploiting easily available captions which describe general actions \ie, prompt captions (PC) defined in our paper, will significantly boost the performance. To this end, we propose a Prompt Caption Network (PCNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain prompt captions by Non-Prompt Caption Suppression (NPCS). To capture the potential information in prompt captions, we propose Caption Guided Attention (CGA) project the semantic relations between prompt captions and query sentences into temporal space and fuse them into visual representations. Considering the gap between prompt captions and ground truth, we propose Asymmetric Cross-modal Contrastive Learning (ACCL) for constructing more negative pairs to maximize cross-modal mutual information. Without bells and whistles, extensive experiments on three public datasets (\ie, ActivityNet Captions, TACoS and ActivityNet-CG) demonstrate that our method significantly outperforms state-of-the-art methods

G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory

The recent video grounding works attempt to introduce vanilla contrastive learning into video grounding. However, we claim that this naive solution is suboptimal. Contrastive learning requires two key properties: (1) \emph{alignment} of features of similar samples, and (2) \emph{uniformity} of the induced distribution of the normalized features on the hypersphere. Due to two annoying issues in video grounding: (1) the co-existence of some visual entities in both ground truth and other moments, \ie semantic overlapping; (2) only a few moments in the video are annotated, \ie sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations. Both characteristics lead to vanilla contrastive learning being unsuitable for video grounding. In this paper, we introduce Geodesic and Game Localization (G2L), a semantically aligned and uniform video grounding framework via geodesic and game theory. We quantify the correlations among moments leveraging the geodesic distance that guides the model to learn the correct cross-modal representations. Furthermore, from the novel perspective of game theory, we propose semantic Shapley interaction based on geodesic distance sampling to learn fine-grained semantic alignment in similar moments. Experiments on three benchmarks demonstrate the effectiveness of our method.Comment: ICCV202