Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Prediction Mistakes in Observational Data*

Decision makers, such as doctors, judges, and managers, make consequential choices based on predictions of unknown outcomes. Do these decision makers make systematic prediction mistakes based on the available information? If so, in what ways are their predictions systematically biased? Uncovering systematic prediction mistakes is difficult as the preferences and information sets of decision makers are unknown to researchers. In this paper, I characterize behavioral and econometric assumptions under which systematic prediction mistakes can be identified in observational empirical settings such as hiring, medical testing, and pretrial release. I derive a statistical test for whether the decision maker makes systematic prediction mistakes under these assumptions and show how supervised machine learning based methods can be used to apply this test. I provide methods for conducting inference on the ways in which the decision maker’s predictions are systematically biased. As an illustration, I apply this econometric framework to analyze the pretrial release decisions of judges in New York City, and I estimate that at least 20% of judges make systematic prediction mistakes about failure to appear risk given defendant characteristics. *First version: June 2021. I am especially grateful to Isaiah Andrews, Sendhil Mullainathan, Neil Shephard, Elie Tamer and Jens Ludwig for their invaluable feedback, support, and advice. I thank Alex Albright, Nano Barahona, Laura Blattner, Iavor Bojinov, Raj Chetty, Bo Cowgill, Will Dobbie, Xavier Gabaix, Matthew Gentzkow, Ed Glaeser, Yannai Gonczarowski, Larry Katz, Daniel Martin, Ross Mattheis, Robert Minton, Ljubica Ristovska, Jonathan Roth, Suproteem Sarkar, Joshua Schwartzstein, Jesse Shapiro, Chris Walker, and participants at the Brookings Institution’s Artificial Intelligence Conference for many useful comments and suggestions. I also thank Hye Chang, Nicole Gillespie, Hays Golden, and Ellen Louise Dunn for assistance at the University of Chicago Crime Lab. All empirical results based on New York City pretrial data were originally reported in a University of Chicago Crime Lab technical report (Rambachan and Ludwig, 2021). I acknowledge financial support from the NSF Graduate Research Fellowship (Grant DGE1745303). All errors are my own. †Harvard University, Department of Economics: asheshr@g.harvard. edu
AUTOMATIC MOTION BASED PERSON ACTIVITY RECOGNITION IN VIDEO SURVEILLANCES

: Computer vision includes methods for acquiring, processing, analysing, and understanding images. Applications of this field include detecting events, controlling processes, navigation, modelling objects or environments, automatic inspection and many more. Activity recognition is one of the applications of computer vision that aims to recognize the actions and goals of one or more agents from a series of observations on the agent’s actions and the environmental conditions. The goal of the project is to train an algorithm to automate detection and recognition of human activities performed in the video data. The project can be utilized in scenarios such as surveillance systems, intelligent environment, sports play analysis and web based video retrieval. In the proposed system, actions like walking, running, bending, and side galloping and hand wave were recognized using a Temporal Template matching technique called Motion History Image (MHI) methodology. MHI method is extensively being used to represent the history of temporal changes involved in the execution of an activity. Here the intensity of the pixels of a scalar valued image is varied, depending upon the motion history.
Does Private Sector Credit gear up Private Investment in Bangladesh?

This paper examines whether private sector credit gears up private investment in Bangladesh or not by using the annual data from FY1976 to FY2016. It is found from the estimated result that the private sector credit variable is the most persuasive factor stimulating private investment in Bangladesh. It is therefore arguable that policy-makers in Bangladesh can capitalize on the private sector credit to influence private investment directly or indirectly. It is also found that public investment affects private investment positively. In other words, public investment crowds in private investment in the context of Bangladesh. This implies that public investment can enhance private investment in Bangladesh by increasing private returns through the provision of infrastructures (Communication, transports, energy, etc.). * Means Deputy General Managers and ** denotes Joint Directors in Bangladesh Bank. The views expressed here, however, are authors' own and do not necessarily reflect those of the central bank. Contact persons email address is omar.faruq@bb.org. bd.
Product Features and Customers Satisfaction of Patanjali Product: Evidence From Kathmandu, Nepal

Purpose: The objectives of this study are to identify the features of Patanjali products and it focuses to identify the satisfaction level and purchasing decision of consumer by the features of Patanjali products. Design/Methodology/Approarch: In this research, convenient sampling technique were used to get the sample, correlation and regression were used in order to get the result. Descriptive statistics is used to explain the respondents’ perception towards the features of Patanjali product. In this study, the data was collected through questionnaire from 300 respondents. This research used SPSS 23 for analysis. This study has been done on four attributes or determinants of Patanjali product, they are price, quality, availability and healthy (organic) of product. Findings: The result of this study indicates that there is positive relationship between the features of Patanjali product and satisfaction. It can be said that the independents variables have effect on consumer satisfaction of Patanjali products. The producer and marketing managers are recommended to focus on the product features in order to obtain loyal satisfied customers.
A Bioprinted Heart-on-a-Chip with Human Pluripotent Stem Cell-Derived Cardiomyocytes for Drug Evaluation

In this work, we show that valve-based bioprinting induces no measurable detrimental effects on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The aim of the current study was three-fold: first, to assess the response of hiPSC-CMs to several hydrogel formulations by measuring electrophysiological function; second, to customise a new microvalve-based cell printing mechanism in order to deliver hiPSC-CMs suspensions, and third, to compare the traditional manual pipetting cell-culture method and cardiomyocytes dispensed with the bioprinter. To achieve the first and third objectives, iCell2 (Cellular Dynamics International) hiPSC-CMs were used. The effects of well-known drugs were tested on iCell2 cultured by manual pipetting and bioprinting. Despite the results showing that hydrogels and their cross-linkers significantly reduced the electrophysiological performance of the cells compared with those cultured on fibronectin, the bio-ink droplets containing a liquid suspension of live cardiomyocytes proved to be an alternative to standard manual handling and could reduce the number of cells required for drug testing, with no significant differences in drug-sensitivity between both approaches. These results provide a basis for the development of a novel bioprinter with nanolitre resolution to decrease the required number of cells and to automate the cell plating process.
Effect of Different Piperacillin-Tazobactam Dosage Regimens on Synergy of the Combination with Tobramycin against Pseudomonas aeruginosa for the Pharmacokinetics of Critically Ill Patients in a Dynamic Infection Model

We evaluated piperacillin-tazobactam and tobramycin regimens against Pseudomonas aeruginosa isolates from critically ill patients. Static-concentration time-kill studies (SCTK) assessed piperacillin-tazobactam and tobramycin monotherapies and combinations against four isolates over 72 h. A 120 h-dynamic in vitro infection model (IVM) investigated isolates Pa1281 (MICpiperacillin 4 mg/L, MICtobramycin 0.5 mg/L) and CR380 (MICpiperacillin 32 mg/L, MICtobramycin 1 mg/L), simulating the pharmacokinetics of: (A) tobramycin 7 mg/kg q24 h (0.5 h-infusions, t1/2 = 3.1 h); (B) piperacillin 4 g q4 h (0.5 h-infusions, t1/2 = 1.5 h); (C) piperacillin 24 g/day, continuous infusion; A + B; A + C. Total and less-susceptible bacteria were determined. SCTK demonstrated synergy of the combination for all isolates. In the IVM, regimens A and B provided initial killing, followed by extensive regrowth by 72 h for both isolates. C provided >4 log10 CFU/mL killing, followed by regrowth close to initial inoculum by 96 h for Pa1281, and suppressed growth to <4 log10 CFU/mL for CR380. A and A + B initially suppressed counts of both isolates to <1 log10 CFU/mL, before regrowth to control or starting inoculum and resistance emergence by 72 h. Overall, the combination including intermittent piperacillin-tazobactam did not provide a benefit over tobramycin monotherapy. A + C, the combination regimen with continuous infusion of piperacillin-tazobactam, provided synergistic killing (counts <1 log10 CFU/mL) of Pa1281 and CR380, and suppressed regrowth to <2 and <4 log10 CFU/mL, respectively, and resistance emergence over 120 h. The shape of the concentration–time curve was important for synergy of the combination.
Blended Teaching Mode and Practice under the Background of “Internet +”: Case Study of Economics

: With the booming development of Internet technology and the increasing of online learning resources, the attraction of traditional teaching mode is gradually decreasing. How to design teaching mode to arouse students’ interest in learning is a major challenge for educators. This paper constructs a blended online-to-offline teaching mode based on SPOC, takes Economics as an example to explore the design and organization of blended online-to-offline teaching process and the assessment of learning results, in order to provide reference for blended teaching in universities. The paper gets the following enlightenment, the blended teaching mode can achieve learning effect as the traditional teaching mode, but it can improve students’ learning enthusiasm and initiative.
MULTIMODAL AUDIO AND IMAGE MUSIC TRANSCRIPTION

Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research ﬁelds which aim at obtaining a structured digital representation of the music content present in either a sheet music image or an acoustic recording, respectively. While these ﬁelds have historically evolved separately, the fact that both tasks share the same output representation poses the question of whether they could be combined in a multimodal framework that exploits the individual transcription advantages depicted by each modality in a synergistic manner. To assess this hypothesis, this work presents a proof-of-concept research piece that combines the predictions given by end-to-end AMT and OMR systems over a corpus of mono-phonic music pieces considering a local alignment approach. The results obtained, while showing a narrow improvement with respect to the best individual modality, validate our initial premise.
Exchange Rate Depreciation and Balance of Trade in Developing Countries: Evidence from Ghana

This study seek to explore the relationship between international trade and currency devaluations using quarterly time series data from Ghana from 2000Q1 to 2017Q4. The study employed the autoregressive distributed lag (ARDL) approach to establish both the long run and short run relationship. The study found that, real domestic income and real exchange rate have a positive relationship with imports in Ghana implying that, depreciation will increase exports whiles appreciation increase imports. Therefore, currency devaluations in Ghana will improve the Trade balance of Ghana. The study therefore recommends that, government and policy makers should pay more attention to stabilizing the real exchange rate given that, the Ghanaian economy is import driven and continuous devaluations will deteriorate the import sector thereby deteriorating the Economy as a whole since most sectors of the economy depends on import for production therefore causing economic growth.
Using apgarch/avgarch models Gaussian and non-Gaussian for modeling volatility exchange rate

This paper aims to measure the effect of the volatility on the daily closing price for the (Iraqi dinar against US dollar) from (21 July 2011) until (21 July 2021) using the models of asymmetric general autoregressive conditional heterogeneity (APGARCH and AVGARCH). The parameter estimated by Maximum Likelihood Estimation method and the error term assumed two distributional (General error distribution and Student’s t distribution), the results showed that the APGARCH(1,2) with error term distributed (Student’s t) distribution is the best model for the return series of the (IQ/USD) exchange rate to get the lowest value according to the information criteria for determining ranks (AIC, BIC) in addition to the presence of the asymmetric effect of the leverage, and this is evidence that negative shocks affect volatility more than positive shocks (the impact of the positive shocks is less than the impact of the negative shocks).
Using JST and CUSP Schemes in Aerodynamic Optimization Based on Discrete Adjoint Method in a Viscous All-Speed Solver Developed on Unstructured Grid

In this paper, using a preconditioning approach, a viscous all-speed flow solver has been developed based on JST and CUSP schemes and compared with Roe upwind scheme in unstructured database. The comparison between the preconditioning and non-preconditioning has been done. The results show that the nonpreconditioning compressible solvers hardly converged in low-speed regions while the preconditioned forms converged more rapidly. The results of all schemes were compared with experimental data which shows the all schemes have good agreement with experimental data, although, Roe upwind and CUSP schemes have more agreement to the data. Also, the CUSP scheme is faster than the other schemes. In the next step, aerodynamic optimization based on discrete adjoint method has been developed over the schemes. The verification of the methods done by comparing the results with finite difference method in both preconditioning and non-preconditioning approaches. The agreement of results is good. In general, it can be concluded that the CUSP algorithm is somewhat faster than the other two algorithms and is a good option for optimization.
Tracing Lisbon Metropolitan Area’s Foodscape: Insights from local initiatives and possibilities for future planning policies

In recent years, Lisbon Metropolitan Area (LMA) witnessed the occurrence of various initiatives, highlighting an increasing interest and entrepreneurship in transforming the food system, at both municipal and civil society levels. As sustainability demands become increasingly global, it becomes pivotal for planning policy to understand the mechanisms through which urban actors are pioneering these new ambitions. This paper describes how global goals are being materialized by local actors and practices, insofar as their scope aims to transform this region’s food system. Our work is based on a three-fold objective: to characterize such initiatives; study how they’re suggesting new food planning agendas in the LMA; and systematize what possible future planning policy can learn from such insights. After an introduction to the territory’s planning context and research rationale, a methodological account is provided before completing with a comparing scenario, taken from a 2014 questionnaire among planning officials, with the discourses of 80 initiatives of the public and civil society sectors enquired in 2019. Finally, while using an analytical framework grounded on the recommendations of the Food and Agriculture Organization (FAO) and the Resources Centres on Urban Agriculture and Food Security (RUAF), we flesh out some lessons for future planning policy aiming for long-term sustainability grounded on inter-municipal cooperation and mutual, continuous learning.
Behavior of High-plasticity Clay Stabilized with Lime and Rice Husk Ash

Soil is an important and fundamental element for building and road construction. However, poor properties of soil can affect the entire construction since the soil will resist the loads transferred from the upper structures. Additives such as cement, lime, and rice husk ash (RHA) can be used as stabilization materials to increase soil strength. This study examined the behavior of stabilized plastic clay mixed with cement, lime, and RHA. The clay stabilization success rate can be measured by the California Bearing Ratio (CBR), unconfined compression strength, and swelling potential. In this study, different mixtures were prepared as samples and tested under various loads: 1, 2, 3, 4, and 5 kPa. The results indicated that the addition of cement, lime, and RHA could effectively reduce the swelling potential of clay. The mixture variations of soil–cement composite 90% + RHA 10% and soil–cement composite 90% + lime 4% + RHA 6% showed the lowest swelling rate.
On the Unique Solvability of the Generalized Absolute Value Matrix Equation

The generalized absolute value matrix equation has application in a variety of optimization problems, its unique solvability is still on the way. In this note, the unique solvability of the generalized absolute value matrix equation is considered. A new unique solvability of generalized absolute value matrix equation is given. The obtained result can be regarded as an extension of the absolute value equation to the generalized absolute value matrix equation. As an application, new convergence of matrix multisplitting Picard-iterative method is presented.
A real-time forest fire and smoke detection system using deep learning

Large parts of the world’s forests are threatened by fires. These fires happen continuously every month around the globe. They are very costly to society and cause serious damage to the ecosystem. This raises the necessity to build a detection system to intervene early and take action. Fire and smoke have various colours, textures, and shapes, which are challenging to detect. In the modern world, neural networks are used extensively in most fields of human activities. For the detection of fire and smoke, we suggest a deep learning technology using transfer learning to extract features of forest fire and smoke. We used a pre-trained Inception-ResNet-v2 network on the ImageNet dataset to be trained on our dataset which consists of 1,102 images for each fire and smoke class. The classification accuracy, precision, recall, F1-Score, and specificity were 99.09%, 100%, 98.08%, 99.09%, and 98.30%, respectively. This model has been deployed on a Raspberry Pi device with a camera. For real-time detection, we used the Open CV library to read the camera stream frame by frame and predict the probability of fire or smoke.
EFFECT OF CORPORATE GOVERNANCE AND CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE ON CAPITAL STRUCTURE IN JAKARTA ISLAMIC INDEX Sihwahjoeni

This study aimed to analyze the influence of corporate governance and corporate social responsibility disclosure of the capital structure of companies included in the index Jakarta Islamic Index. This study is explanatory research. The number of samples in this study were 30 companies. The analysis technique used multiple regression analysis. The analysis showed that the corporate governance significant effect on the capital structure. Corporate social responsibility disclosure significant effect on the capital structure. This shows that the company's concern for the environment and the welfare of workers which is the company's assets are already quite high. Companies that have expressed social accountability information will have a positive image in the community generally and particularly among businesses because in addition to the interests of shareholders, also the interests of stakeholders and the environment. In addition, in carrying out operational activities, the company would need capital. Without capital, the company will be very difficult to achieve its objectives, namely to get huge profits.
Learning Discrete Neural Reaction Class to Improve Retrosynthesis Prediction

Computer-aided retrosynthesis accelerate and innovate the process of molecule and material design, allowing the discovery of new pathways and automating part of the overall development process for drugs and materials. Current machine-learning methods applied to retrosynthesis are limited by their lack of control when generating single-step reactions as they rely on sampling or beam search algorithm. In this work, we apply vector quantized representation learning [1] to learn reaction classes along with retrosynthetic predictions. We represent each reaction class with a vector allowing us to condition the retrosynthetic prediction. We show that learning reaction classes increases control as well as generating more diverse predictions than a baseline model. Our results are a significant step forward in the development of multistep retrosynthesis prediction.
The Effects of Running Shoes' Midsole Properties on Impact and Lower Extremity Joint's Dynamic Stability

Development and research on running shoes are continuously carried out in that the difference in running shoes' properties affects the impact delivered to the human body and the load to the lower extremity joint (Agresta, Kessler, Southern, Goulet, Zernicke & Zendler, 2018; Baltich, Maurer & Nigg, 2015; Ha, Ryu & Gil, 2020; Kulmala, Kosonen, Nurminen & Avela, 2018; Lam, Liu, Wu, Liu & Sun, 2019; Lam, Ng & Kong, 2017; Lewinson, Worobets & Stefanyshyn, 2016; Meardon, Willson, Kernozek, Duerst & Derrick, 2018; Nigg, Bahlsen, Luethi & Stokes, 1987). Even though the peak of vertical ground reaction force (PVGRF) was higher as the shoes were thicker, based on recent research that examined 12 types of shoes' mechanical characteristics and the correlation with impact absorption function (Ha et al., 2020), it was reported that the vertical loading rate (VLR) was smaller as the revelation time of PVGRF increased. In research that investigated the effects of shoe's cushioning on the impact of running (Meardon et al., 2018), it was reported that VLR was smaller in goods cushioning and soft (low hardness) shoes than hard (high hardness) shoes. Based on the above researches, it is judged that the thickness and properties of shoes critically affect impact absorption. Nonetheless, there is insufficient research examining the effects of shoes' thickness and properties on the stability and consistency of the lower extremity joint motions during running. Practical methods examining the stability and consistency of repetitive human body motions such as running are to look at variability with nonlinear timeseries methods (Buzzi, Stergiou, Kurz, Hageman & Heidel, 2003; Dingwell & Cusumano, 2000; Dingwell, Cusumano, Sternad & Cavanagh, 2000; Hausdorff, Forman, Ladin, Goldberger, Rigney & Wei, 1994; Ryu, 2006, 2007, 2008, 2009, 2014). Of the nonlinear methods, Lyapunov exponent (LyE) has been used in researches examining the dynamic stability and consistency of repetitive walking and running (Ryu, 2006, 2007, 2008, 2009, 2014), and it has an advantage that the sensitivity to local shaking Korean Journal of Sport Biomechanics 2021; 31(4): 290-296 http://dx.doi. org/10.5103/KJSB.2021. 31.4. 290 http://e-kjsb.org eISSN 2093-9752 ORIGINAL