Optimal Fiscal Policy in an Economy Facing Socio-Political Instability

We present a model of optimal government policy when policy choices may exacerbate socio-political instability (SPI). We show that optimal policy that takes into account SPI transforms a standard concave growth model into a model with both a poverty trap and endogenous growth. The resulting equilibrium dynamics inherit the properties of government policies and need not be monotone. Indeed, for a broad set of conditions we demonstrate that government policy is unable to eliminate the poverty trap; when these conditions do not hold, "most" countries eventually reach a balanced growth path. The predictions of the model are tested by developing three new measures of SPI for a panel of 58 countries. Estimating optimal policies and the growth equation derived from the model reveals strong support for the theory. In particular, we show via simulations that optimal funding for public investment and the police cause a typical developing economy to expand on a quasi-linear growth path, with the baseline level of SPI determining whether growth is positive or negative.Socio-Political Instability, Endogenous Growth, Public Investment, Political Economy of Growth

ENTREPRENEURSHIP AND INNOVATION AND SOCIAL ENTREPRENEURSHIP IN VIETNAM: AN EXAMINATION FROM INSTITUTIONAL AND IDEOGRAPHIC LENS

This paper is an introduction to the special issue of Dalat University Journal of Science – Economics and Management on entrepreneurship in Vietnam. There are four papers in this special issue. The first paper examines the impact of institutions on entrepreneurship using data from the Provincial Competitive Index. The second paper utilizes a different set of institutional indicators from the World Bank’s Vietnam Enterprise Survey to assess the impacts of business environment on the development of SMEs. In both papers, the authors find that institutional factors such as entry barriers, lack of policy support systems, informal payment, provincial leadership, lack of access to finance, administrative and procedures, and tax inspections hindered the development of entrepreneurship in Vietnam. The third paper investigates the absence of medium-sized enterprises and the necessity for the development of such enterprises is critically important for Hochiminh City. Using primary and secondary data sources, the author presents a case study on two strategic sectors in the city. The result indicates that medium-sized enterprises are proven to be more effective than large-scale enterprises. The last paper focuses on social entrepreneurship in Vietnam. The authors use Hofstede’s measure of cultural differences to compare social ventures in Vietnam and the United States

Optimal Fiscal Policy in an Economy Facing Socio-Political Instability

We present a model of optimal government policy when policy choices may exacerbate socio-political instability (SPI). We show that optimal policy that takes into account SPI transforms a standard concave growth model into a model with both a poverty trap and endogenous growth. The resulting equilibrium dynamics inherit the properties of government policies and need not be monotone. Indeed, for a broad set of conditions we demonstrate that government policy is unable to eliminate the poverty trap; when these conditions do not hold, "most" countries eventually reach a balanced growth path. The predictions of the model are tested by developing three new measures of SPI for a panel of 58 countries. Estimating optimal policies and the growth equation derived from the model reveals strong support for the theory. In particular, we show via simulations that optimal funding for public investment and the police cause a typical developing economy to expand on a quasi-linear growth path, with the baseline level of SPI determining whether growth is positive or negative

EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers. Traditional recommendation algorithms with complexity dependent on the number of users and items make them difficult to adapt to the industrial environment. In this paper, we introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases. At the same time, the model gives a very good accuracy. We call this architecture EfficientRec with the implication of model compactness and the ability to scale to unlimited users and products.Comment: Published in 14th Asian Conference on Intelligent Information and Database Systems (ACIIDS), 202

Universality in odd-even harmonic generation and application in terahertz waveform sampling

Odd-even harmonics emitted from a laser-target system imprint rich, subtle information characterizing the system's dynamical asymmetry, which is desirable to decipher. In this Letter, we discover a simple universal relation between the odd-even harmonics and the asymmetry of the THz-assisted laser-atomic system -- atoms in a fundamental mid-IR laser pulse combined with a THz laser. First, we demonstrate numerically and then analytically formulize the harmonic even-to-odd ratio as a function of the THz electric field, the source of the system's asymmetry. Notably, we suggest a scaling that makes the obtained rule universal, independent of the parameters of both the fundamental pulse and atomic target. This universality facilitates us to propose a general pump-probe scheme for THz waveform sampling from the even-to-odd ratio, measurable within a conventional compact setup

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

EVALUATION OF STERCULIA FOETIDA L. GUM AS NATURAL BASED CONTROLLED RELEASE EXCIPIENT

Therefore, the physicochemical properties of Sterculia foetida L. gum, including solubility, scanning electron micrographs, melting point, swelling index, pH, viscosity, loss on drying were determined. Furthermore, compressed tablets were successfully prepared for in vitro studies at many different particle sizes, concentrations of gum, rotational speeds and media in order to evaluate the effects of these parameters on the rate of drug release. Besides, SFG tablets were compared with the tablets prepared with Hydroxymethylcellulose E15. The findings indicated that Sterculia foetida L. gum exhibited an excellent potential in managed release dosage forms.