Miracle-dz/UniFAR-FTUs huggingface dataset:
V ARIATIONAL M ODEL -B ASED I MITATION L EARNING IN H IGH -D IMENSIONAL O BSERVATION S PACES

We consider the problem setting of imitation learning where the agent is provided a fixed dataset of demonstrations. While the agent can interact with the environment for exploration, it is oblivious to the reward function used by the demonstrator. This setting is representative of many applications in robotics where task demonstrations may be straightforward while reward shaping or conveying stylistic aspects of human motion may be difficult. For this setting, we develop a variational model-based imitation learning algorithm (VMIL) that is capable of learning policies from visual observations. Through experiments, we find that VMIL is more sample efficient compared to prior algorithms in several challenging visionbased locomotion and manipulation tasks, including a high-dimensional in-hand dexterous manipulation task. All results including videos can be found online at https://sites.google. com/view/vim.
Random probabilistic circuits

Density estimation could be viewed as a core component in machine learning, since a good estimator could be used to solve many tasks such as clas-siﬁcation, regression, and imputing missing values. The main challenge of density estimation is balancing the model expressiveness and its learning and inference complexity. Probabilistic circuits (PCs) model a probability distribution as a computational graph. By imposing speciﬁc structural properties on such models many inference tasks become tractable. However, learning PCs usually relies on greedy and time consuming algorithms. In this paper we propose a new uniﬁed approach to efﬁciently learn PCs having several structural properties. We introduce extremely randomized PCs (XPCs), PCs with a random structure. We show their advantage on standard density estimation benchmarks when compared to other density estimators.
Novel efficient approach for frequent data set mining of uncertain databases

: In sensor monitoring systems, location-based services, integration of data etc., the handling of data is basically imprecise. In this research paper, mining of recurrent item sets from uncertain database which is also indeterminate in nature is concentrated. The efficiency of frequent mining of datasets from uncertain database is accomplished by utilizing Adaboost algorithm. The algorithm is utilized to meet the challenges of exponential number of possible worlds with minimum threshold; the algorithm aims to detect the frequent item sets among uncertain database along with filling the missing values. The proposed algorithm will fill the missing values using Adaboost algorithm and also to find frequent item sets.
Prevalence of A. baumannii as a nosocomial pathogen in the ICU patients of Surat City

Immuno-compromised or hospitalized for longer duration increase the rate of infection of A. baumannii, a nosocomial pathogen. There are various routes and site of infection for this microorganism. It was seen that patients with pneumonitis, urinary tract infections, blood infection, post surgical patients are more prone to get infected with A. baumannii. Studies have that certain A. baumannii strains are multidrug resistance (MDR) which make it difficult to treat. If one can know the chances of infection with A. baumannii in particular types of disease than it will be helpful in determination of precautionary measures. Here a study is carried out to determine the prevalence of A. baumannii in the ICU patients of Surat city with objective of identify most susceptible condition for infection. For this study, samples from 198 patients in form of respiratory fluid, blood, urine and pus were collected Microbes were isolated on blood agar, MacConkey’s agar and Nutrient agar. Selected strains were further processed and identified by Vitek GN2 ID card. Microorganisms identified as A. baumannii was consider for determination of prevalence in various conditions including age, gender, type of samples and duration of hospitalization. Results of the study have shown that patient with endotracheal tube and catheter are more prone to get infected as compare to other mode of infection.
The possibility to apply the Frenet trihedron and formulas for the complex movement of a point on a plane with the predefined plane displacement

Material particles interact with the working moving surfaces of machines in various technological processes. Mechanics considers a technique to describe the movement of a point and decompose the speed and acceleration into single unit vectors of the accompanying trajectory trihedron for simple movement. The shape of the spatial curve uniquely sets the movement of the accompanying Frenet trihedral as a solid body. This paper has considered the relative movement of a material particle in the static plane of the accompanying Frenet trihedron, which moves along a flat curve with variable curvature. Frenet formulas were used to build a system of differential equations of relative particle movement. In contrast to the conventional approach, the chosen independent variable was not the time but the length of the arc of the guide curve along which the trihedron moves. The system of equations has been built in the projections onto the unit vectors of the moving trihedron; it has been solved by numerical methods. The use of the accompanying curve trihedron as a moving coordinate system makes it possible to solve the problems of the complex movement of a point. The shape of the curve guide assigned by parametric equations in its length function determines the portable movement of the trihedron and makes it possible to use Frenet formulas to describe the relative movement of a point in the trihedron system. This approach enables setting the portable movement of the trihedron osculating plane along a curve with variable curvature, thereby revealing additional possibilities for solving problems on a complex movement of a point at which rotational motion around a fixed axis is a partial case. The proposed approach has been considered using an example of the relative movement of cargo in the body of a truck moving along the road with a curvilinear axis of variable curvature. The charts of the relative trajectory of cargo slip and the relative speed for the predefined speed of the truck have been constructed
The impact of mental health and substance use issues on COVID-19 vaccine readiness: a cross sectional community-based survey in Ontario, Canada

Background: COVID-19 vaccines have been approved for use in Canada since December 2020. However, data about factors associated with vaccine hesitancy and the impact of mental health and/or substance use (MHSU) issues on vaccine uptake are currently not available. The goal of this study was to explore factors, particularly MHSU factors, that impact COVID-19 vaccination intentions in Ontario, Canada. Methods: A community-based cross-sectional survey with recruitment based on age, gender, and geographical location (to ensure a representative population of Ontario), was conducted in February 2021. Multinomial logistic regression was used to test the relationship between COVID-19 vaccination status and plans and sociodemographic background, social support, anxiety about contracting COVID-19, and MHSU concerns. Results: Of the total sample of 2528 respondents, 1932 (76.4%) were vaccine ready, 381 (15.1%) were hesitant, and 181 (7.1%) were resistant. Significant independent predictors of vaccine hesitancy compared with vaccine readiness included younger age (OR=2.11, 95%CI=1.62-2.74), female gender (OR=1.36, 95%CI=1.06-1.74), Black ethnicity (OR=2.11, 95%CI=1.19-3.75), lower education (OR=1.69, 95%CI=1.30-2.20), lower SES status (OR=.88, 95%CI=.84-.93), lower anxiety about self or someone close contracting COVID-19 (OR=2.06, 95%CI=1.50-2.82), and lower depression score (OR=.90, 95%CI=.82-.98). Significant independent predictors of vaccine resistance compared with readiness included younger age (OR=1.72, 95%CI=1.19-2.50), female gender (OR=1.57, 95%CI=1.10-2.24), being married (OR=1.50, 95%CI=1.04-2.16), lower SES (OR=.80, 95%CI=.74-.86), lower satisfaction with social support (OR=.78, 95%CI=.70-.88), lower anxiety about contracting COVID-19 (OR=7.51, 95%CI=5.18-10.91), and lower depression score (OR=.85, 95%CI=.76-.96). Interpretation: COVID-19 vaccination intention is affected by sociodemographic factors, anxiety about contracting COVID-19, and select mental health issues.
Online Learning of Feed-Forward Models for Variable Impedance Control in Manipulation Tasks

— While performing a new manipulation task, humans tend to be stiffer in the initial trials to improve task accuracy and counter any unforeseen disturbances. After a sufﬁcient number of repetitions, humans are able to perform the task with lower stiffness without causing any signiﬁcant reduction in task performance. Existing literature in human and animal motor control indicates that learned internal models of the manipulation task and the environment are used to predict the state in response to particular control actions, adapting stiffness on the ﬂy to minimise energy usage. Inspired by these ﬁndings, we describe a framework that, in a signiﬁcant departure from existing work, supports online learning of a time-independent forward model of any given manipulation task in the task space from a small number of samples. Errors in the predictions of the forward model are used to dynamically revise the model and adapt the impedance parameters of the feedback controller. Furthermore, a hybrid force-motion controller supports compliance in certain directions while adapting the stiffness in other directions. We evaluate our framework’s capabilities in the context of continuous-contact manipulation tasks with varying external forces.
Cloud Based EHR System for Secure Medical Data and Privacy Protection in Cloud Environment

: Today, health data stored in the cloud is considered a highly confidential record, which must be hidden to prevent unauthorized access to protect patient information. Therefore, security issues related to moving medical data to the cloud have attracted the attention of researchers and scientists. A hybrid of data encryption models used to store diagnostic data in medical images. The proposed hybrid encryption scheme is based on the integration of Blowfish and encryption algorithms. our proposed framework applies a set of security constraints and access control that guarantee privacy and protection of medical information. We believe that the proposed framework paves the way for a new generation of lower cost, more efficient healthcare systems.
Technology readiness and UTAUT2 in e-wallet adoption in a developing country

Background: An e-wallet is a digital equivalent of a physical wallet which plays an essential role in payment system transformation. To embrace the concept of a cashless society, the Malaysian Government and central bank have taken various steps to encourage the adoption of e-wallets. Despite the seamless services offered by the e-wallet, it is yet to reach high-scale adoption in Malaysia. This study aims to investigate Malaysians’ readiness towards the e-wallet and their perceptions of it by employing the UTAUT2 model. Methods: A total of 309 valid data were gathered and analysed with partial least squares structural equation modelling (PLS-SEM). Results: The findings revealed that the respondents were confident about the new technology and tended to believe that e-wallet was somehow useful for them. The results also disclosed that e-wallet adoption intention was significantly influenced by performance expectancy, price value, facilitating conditions, and followed closely by social influence. Nonetheless, insecurity did not present significant impact on both performance expectancy and effort expectancy of e-wallet. Conclusions: This study provides a substantial contribution to the knowledge domain by combining system-specific and individual-specific models in an e-wallet context. The outcomes of this study would also benefit e-wallet service providers and policymakers by delivering holistic insight into Malaysians’ readiness and adoption behaviour of the e-wallet.
An Investigation of the Maximum Specimen Thickness for Differential Phase Contrast Lorentz Microscopy

Examination of magnetic domain structure in the transmission electron microscope is generally confined to very thin foils, where the specimen approximates to a pure phase object, and is achieved by the long established methods of Fresnel or Foucault contrast Lorentz microscopy, or by differential phase contrast (DPC) imaging in a scanning transmission electron micro sco pe (STEM) . If no quantitative interpretation of the image is required then magnetic contrast can be observed from thicker foils, and in this paper we describe an attempt to determine experimentally the range of foil thickness over which this is possible. To this end we have examined electropolished foils of single crystal Incalloy using an extended VG HB501 STEM to produce both DPC and Fresnel contrast images of the sa me area. The foil thicknes s at points along the domain walls was measured from the change in the Lorentz deflec tion angle as the STEM probe was moved across the domain wall, and this led to an estimate of 700nm for the limiting thicknes s at which domain contrast was still visible in the DPC image s. This value is obviously influenced by a number of factors, including the degree of inelastic scattering and the saturation magnetisation of the material, but it is sufficiently high that there might exist a range of thickness over which both tran smission and scanning electron micro sco pes could be used to study the domain structure in the same areas of specimen.
Forward Guidance with Unanchored Expectations

We study zero interest-rate policy in response to a large negative demand shock when long-run expectations can fall over time. Because falling expectations make monetary policy less effective by raising real interest rates, the optimal forward guidance policy makes large front-loaded promises to stabilize expectations. Policy is too stimulatory in the event of transitory shocks, but provides insurance against persistent shocks. The optimal policy is well-approximated by a constant calendar-based forward guidance, independent of the shock’s realised persistence. The insurance property distinguishes our paper from other bounded rationality papers that solve the forward guidance puzzle and generates important quantitative differences.
It Takes Two to Tango: Interlockings and Partial Equity Ownership

We study the relationship between the acquisition of partial equity ownership and interlocking directorates among rival companies. Partial equity ownership between rivals in the product market is convenient, even in the case of passive participation, since, internalizing competition, raises the profits of both companies. The price of the acquisition, however, is affected by the marginal cost of the target company. When this cost is private information, the bidder has to elicit the true value of the equity stake from the target through a proper design of the offer in the context of asymmetric information. One possible alternative is for the bidder to propose an interlocking directorate to observe the private cost: to achieve this goal, the bidder has to convince the target to host one of his executives on the board. We build a novel framework to analyze the choice to interlock together with the acquisition of a minority equity stake and study when the two events are observed at equilibrium. We suggest that interlocking directorates may be ancillary to a minority acquisition when the value of the target is private information.
Did Financial Consumers Benefit from the Digital Transformation? An Empirical Investigation and a Case Study

This study aims to document the inter-linkages between two on-going global trends through an empirical investigation – (1) the rapid advancement of the ICT-driven innovations in delivering various types of financial service, and (2) the growing legal and regulatory efforts to protect consumers in the financial markets, after the recent financial crisis in particular. To that end, we compile a set of macro-level indicators on the aggregate outcomes of the financial service sector in Korea during the last three decades, and conduct a regression analysis to gauge the effects of the digital transformation (DT) in the country on those indicators. Using an ARDL-ECM as our empirical model, our results show that: Over time, the unit cost for financial intermediation in the country tends to move in tandem with the growth of economic output, although the profit portion of the cost did not exhibit a long-term relationship with the GDP trend; The long-term effect of the DT trend is negative (i.e., costsaving) for labor input and capital expenditure, which are shown to be statistically significant, and, as a consequence, its impact on the total intermediation cost is also positive and statistically significant. Based on the above outcomes, we elaborate their implications in designing a set of policy instruments to protect financial consumers, and also discuss the Vietnamese case as an illustration to extend this type of macro-level analysis for the purpose of coming up with a policy regime for financial consumers in an emerging-market context.
Home-country Export Regulations, Credit Markets, and Corruption: Implications for Different Types of Internationalization

Direct exporting activities and outward foreign direct investment (OFDI) are two types of internationalization that differ in firms’ opportunities, resources and risks. We study home-country institutional factors for internationalization and empirically investigate the direct and joint effects of export regulations, credit markets and corruption in explaining exporting and OFDI from a country. Using country-level data from 96 developed and developing countries between 2000 and 2018, we test a series of hypotheses and examine nonlinearity in the relationships. The results of the study suggest that export regulations partially affect exporting but do not affect OFDI. Access to financial resources can be critical in parts for both exports and OFDI. The findings also show that corruption can have different implications for exports and OFDI. The interactions of corruption with export regulations and credit markets reveal some unexpected and counter-intuitive results, highlighting the importance of distinguishing between the direct and indirect (joint) effects of business environment factors and corruption on exports and OFDI. The results of the study contain important information for policymakers.
Dark Side of Financial Statement Comparability: Evidence from Stock Price Synchronicity in a Culturally Tight Country

I document evidence that financial statement comparability curbs the incorporation of firm-specific information into stock prices, resulting in higher stock price synchronicity in China. The positive comparability-synchronicity relationship is more pronounced when firms operate in culturally tight provinces, are less innovative, have poor financial performances, have high disclosure opacity, and have weak corporate governance mechanisms. Using the effect of China’s 2007 accounting standards as a regulatory shock, I find that an exogenous increase in comparability leads to an increase in synchronicity. Finally, I find that comparability increases future systematic risks and decreases future firm values. In contrast to the extant literature, the findings of this study show that financial statement comparability adversely affects information environments in a culturally tight country.
Do Monetary Policy Measures Affect Foreign Exchange Rates during the COVID-19 Pandemic? Evidence from Turkey

English Abstract: The study examines how foreign exchange (FX) rates in Turkey are affected by the pandemic considering the impacts of monetary policy responses to the pandemic. Selected FX rates are examined by using 10 independent variables containing monetary policy indicators and the pandemic figures. In this context, daily data from February 1, 2019 to August 31, 2020 that consists of the pre-pandemic and the pandemic periods are considered and machine learning algorithms are applied. The findings reveal that the pandemic and monetary policy indicators have a statistically significant and high effect on the FX rates, and the influence of independent factors on the FX rates vary according to the periods. According to the results of the study, it is emphasized the importance of the pandemic and monetary policy measures on the FX rates because monetary policy indicators have a statistically significant and high impact on the FX rates in Turkey for the pandemic period. Turkish Abstract: Çalışma, pandemiye yönelik para politikası tedbirlerini dikkate alarak Türkiye’de döviz kurlarının pandemiden nasıl etkilendiğini incelemektedir. Seçilmiş döviz kurları, para politikası göstergeleri ve pandemi rakamlarını içeren 10 bağımsız değişken kullanılarak incelenmiştir. Bu kapsamda, pandemi öncesi ve pandemi dönemlerinden oluşan 1 Şubat 2019 ve 31 Ağustos 2020 arasındaki günlük veriler dikkate alınmış ve makine öğrenmesi algoritmaları uygulanmıştır. Bulgular, pandemi ve para politikası tedbirlerinin döviz kurları üzerinde istatistiksel olarak anlamlı ve yüksek bir etkiye sahip olduğunu ve bağımsız değişkenlerin döviz kurları üzerindeki etkisinin dönemlere göre farklılık gösterdiğini ortaya koymaktadır. Çalışmanın sonuçlarına göre, para politikası tedbirlerinin pandemi döneminde için Türkiye’deki döviz kurları üzerinde istatistiksel olarak anlamlı ve yüksek bir etkiye sahip olması nedeniyle, pandemi ve para politikası tedbirlerinin döviz kurları üzerindeki önemi vurgulanmaktadır.
A Count Data Model with Dual Endogenous Binary Variables

Count regression models are widely used in many scientific disciplines. Nevertheless, some binary explanatory variables could be endogenous during the process of generating the count dependent variable, thus simultaneous equations bias arises. Except for some numerically intensive simulation-based approaches, existing studies can only handle one endogenous binary variable. We extend this strand of the literature by estimating a Poisson model with dual endogenous binary regressors using a two-stage quasi-maximum likelihood (2SQML) approach. Our method is fast, easily implemented, and has promising finite sample performance under a Poisson model with two-way fixed effects specification consisting of hundreds of parameters. We also take our approach to trade data and find that signing a preferential trade agreement (PTA) has a strong positive impact on bilateral trade flows. In contrast, a bilateral investment treaty (BIT) has a negative effect on trade when the endogeneity of these two binary variables is taken into account.
Does Herding Behaviour and Overconfidence Drive the Investor’s Decision Making During the Covid-19 Pandemic

This study is intended to analyze the effect of herding behavior and overconfidence in encouraging investment decision-making by investors on the Indonesia Stock Exchange in the Jakarta area. The population of this research is investors who invest in investment instruments listed on the Indonesia Stock Exchange in the Jakarta area with an unknown population, and the number of samples in this study is 100 investors. Data analysis in this study used an alternative method of Structural Equation Modeling (SEM) with Wrap PLS 5.0 data processing tools. The findings of this study is herding has a positive and significant effect on investment decisions by investors in the Jakarta area. Overconfidence has a positive and significant effect on investment decisions by investors in the Jakarta area. This means that the increasing herding and overconfidence of investors can drive the investor’s decisions making in the Jakarta area.