Miracle-dz/UniFAR-FTUs huggingface dataset:
An Econometric Analysis of the Macroeconomic Determinants Impact of Gross Domestic Product (GDP) in Bangladesh

<b>English abstract:</b> The research shows that Bangladesh's Gross Domestic Product (GDP) has a causal relationship with exports, imports, and inflation. The study also reveals that the issue of export, import, and inflation has become a vital weapon of Gross Domestic Product (GDP) growth for Bangladesh bringing in technological development as well as capital investment and human capital knowledge alike needed for economic growth. Bangladesh is a developing country, and its economy has been hit by the trade deficit since its inception. According to this view, this paper purposes to study the relationship to export, import inflation, and economic growth by the cointegration and the Vector Autoregressive Model (VAR) test. The empirical analysis has been conducted by using annual secondary data for the year 1982-2019. The results obtained concluded that the series was present and that the regression model was significant. Based on the results, exports had a positive but not significant relationship to GDP. Imports, on the other hand, had an insignificant and negative relationship to GDP. Inflation is a significant and positive relationship to GDP. <br><br><b>Spanish abstract:</b> La investigación muestra que el Producto Interno Bruto (PIB) de Bangladesh tiene una relacióncausal con las exportaciones, las importaciones y la inflación. El estudio también revela que el tema de las exportaciones, las importaciones y la inflación se ha convertido en un arma vital para el crecimiento del Producto Interno Bruto (PIB) de Bangladesh, aportando tanto desarrollo tecnológico como inversión de capital y conocimiento del capital humano necesarios para el crecimiento económico. Bangladesh es un país en desarrollo y su economía se ha visto afectada por el déficit comercial desde sus inicios. De acuerdo con esta visión, este trabajo tiene como objetivo estudiar la relación entre las exportaciones, la inflación de las importaciones y el crecimiento económico mediante la prueba de cointegración y del Modelo Vector Autorregresivo (VAR). El análisis empírico se ha realizado utilizando datos secundarios anuales para el año 1982-2019. Los resultados obtenidos concluyeron que la serie estaba presente y que el modelo de regresión era significativo. Según los resultados, las exportaciones tuvieron una relación positiva pero no significativa con el PIB. Las importaciones, por otro lado, tenían una relación insignificante y negativa con el PIB. La inflación es una relación significativa y positiva con el PIB.
Investor Orientations and Endowment Portfolio Investments in German Foundations

Germany benefits from an especially lively philanthropic sector, with over twenty three thousand active charitable foundations. An empirical assessment of the portfolio preferences of German foundations yields fundamental intragroup differences in their approach to asset allocation. We build on entrepreneurial orientation theory to explore these differences in behavior, motives, rationales and portfolio investment preferences and extract a typology along a commercial and mission orientation in charitable foundations’ investment orientation. We suggest an explanatory model that improves our understanding of the observed differences foundations exhibit in their investment orientation and enables a more structured understanding of the sources of capital for differing commercial and social endeavors.
Endogeneity Bias and Other Problems in the Estimation of Production Functions

The possible endogeneity of labor and capital in production functions, and the consequent bias of the estimated elasticities, have been discussed and addressed in the literature in different ways since the 1940s. This paper revisits an argument first outlined in the 1950s, which questioned production function estimations. This argument is that output, capital and employment, are linked through a distribution accounting identity, a key point that the recent literature has overlooked. This identity can be rewritten as a form that resembles a production function (Cobb-Douglas, CES, translog). We show that this happens because the data used in empirical exercises are value (monetary) data, not physical quantities. The argument has clear predictions about the size of the factor elasticities and about what is commonly interpreted as the bias of the estimated elasticities. To test these predictions, we estimate a typical Cobb-Douglas function using five estimators and show that: (i) the identity is responsible for the fact that the elasticities must be the factor shares; (ii) the bias of the estimated elasticities (i.e., departure from the factor shares) is, in reality, caused by the omission of a term in the identity; and (iii) the estimation method is a second-order issue. Estimation methods that theoretically deal with endogeneity, including the most recent ones, cannot solve this problem. We conclude that the use of monetary values rather than physical data poses an insoluble problem for the estimation of production functions. This is, consequently, far more serious than any supposed endogeneity problems. JEL Codes: C18, C81, C82
Magnezyum Fosfit Uygulamalarının Kuru İncir Meyve Kalitesine Etkileri

Dried fig is produced in Turkey mainly from sun drying of Sarilop (Ficus carica L.) variety and nearly 90 % of the production is exported. Dried fig pests create important problems therefore fumigation with Methyl Bromide (MeBr) became compulsory for the sector. According to the Montreal Protocol, Methyl Bromide (MeBr) is banned for use in Turkey since January 1, 2007 except laboratory, quarantine and pre-shipment. A research was carried out on the effect of Magnesium Phosphide (MgPH3), an alternative to Methyl Bromide, on dried fig quality. Quality of fruits treated with phosphine at 600 ppm for 3 or 5 days and at 1000 ppm for 5 days under tarpaulin was monitored. The tested concentrations and exposure periods provided 100 % mortality in test insects. Fumigation with phosphine for 3 to 5 days at 600 ppm is recommended for the dried fig sector since no negative effects were
Diabetic Retinopathy Detection from Retinal Images using Convolutional Neural Network

Diabetic retinopathy (DR) is a popular problem of diabetes and the major reasons of loss of sight in the active inhabitants. Many of the problems of DR can be avoided by blood glucose manage and regular remedy. Since the kinds and the difficulties of DR, it is actually hard for DR detection in the time-consuming regular analysis. This document is to effort towards discovering an automated method to sort out a provided set of fundus images. We provide convolutional neural networks (CNNs) power to DR detection that consists of 3 major hard challenges: classification, segmentation and detection. Combined with transfer learning and hyper-parameter tuning, we follow AlexNet, VggNet, GoogleNet, ResNet, and analyze exactly how well these types do together with the DR image classification. We utilize publicly available Kaggle platform for training these versions. The greatest classification accuracy is 95.68% and the outcomes have confirmed the better accuracy of CNNs and transfer learning on DR image classification.
Effect of date syrup as a substitute for sugar on the physicochemical and sensory properties of soft ice cream

Abstract In this study the possibility of utilizing date syrup as a sugar replacer in soft ice-cream was investigated. For this purpose control sample was formulated based on Iranian ice- cream standard. Data syrup which produced from third grade Kabkab dates, replaced sucrose in four levels (25, 50, 75 and 100%). Physical properties including overrun, viscosity and freezing point (drawing temperature) were measured for each treatment. Sensory properties including texture, flavor, odor and total acceptance were judged using 15 panelists. Data of physical properties were analyzed as a completely randomized design in triplicate and for analyzing sensory properties completely randomized block design was used to eliminate differences of panelists. Comparison between means was done with Duncan's test at level P=0.05. Results showed that replacement affected all above properties significantly (P 0.05). However some properties at 75% replacement also had no significant difference with comparison to control sample. Sample with 100% replacement was rejected by panelists. Key words: Ice Cream, Sugar Replacement, Date Syrup, Physical and Sensory Properties
How Do Firms Manage Earnings Following Natural Disasters?

We examine the timing and magnitude of earnings management following natural disasters to provide new insights into how and when firms manage earnings. Using difference-in-difference regressions and a sample of U.S. publicly listed firms from 1980 to 2017, we find that natural disasters reduce the magnitude of corporate earnings management by 19.5% in the two-year, post-disaster period. Natural disasters attract the attention of investors and intensify external monitoring, which results in reduced levels of corporate earnings management. In addition, we find increased information disclosure quality following disasters. Our findings of reduced earnings management following negative, external shocks contradict the predictions of big bath accounting practices.
Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data

The assessment of laboratory animal behavior is of central interest in modern neuroscience research. Behavior is typically studied in terms of pose changes, which are ideally captured in three dimensions. This requires triangulation over a multi-camera system which view the animal from different angles. However, this is challenging in realistic laboratory setups due to occlusions and other technical constrains. Here we propose the usage of lift-pose models that allow for robust 3D pose estimation of freely moving rodents from a single view camera view. To obtain high-quality training data for the pose-lifting, we first perform geometric calibration in a camera setup involving bottom as well as side views of the behaving animal. We then evaluate the performance of two previously proposed model architectures under given inference perspectives and conclude that reliable 3D pose inference can be obtained using temporal convolutions. With this work we would like to contribute to a more robust and diverse behavior tracking of freely moving rodents for a wide range of experiments and setups in the neuroscience community.
FitVid: Overfitting in Pixel-Level Video Prediction

An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain. There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks while having similar parameter count as the current state-of-the-art models. We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques. As a result, FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links

We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96×WDM, DP-16QAM transmission over 450km of TWC fiber. The new equalizer outperforms previous NN-based approaches and a 3-steps-per-span DBP.
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection

With the rise of machine learning and deep learning based applications in practice, monitoring, i.e. verifying that these operate within specification, has become an important practical problem. An important aspect of this monitoring is to check whether the inputs (or intermediates) have strayed from the distribution they were validated for, which can void the performance assurances obtained during testing. There are two common approaches for this. The, perhaps, more classical one is outlier detection or novelty detection, where, for a single input we ask whether it is an outlier, i.e. exceedingly unlikely to have originated from a reference distribution. The second, perhaps more recent approach, is to consider a larger number of inputs and compare its distribution to a reference distribution (e.g. sampled during testing). This is done under the label drift detection. In this work, we bridge the gap between outlier detection and drift detection through comparing a given number of inputs to an automatically chosen part of the reference distribution.
Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These “non-standard” data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models, networks, and domain specific problems such as sequence alignments and reconstructions, pedigrees, and phylogenies. In principle, Bayesian inference should be particularly wellsuited in such scenarios, as the Bayesian paradigm provides a principled way to obtain confidence assessment for random variables of any type. However, much of the recent work on making Bayesian analysis more accessible and computationally efficient has focused on inference in Euclidean spaces. In this paper, we introduce Blang, a domain specific language and library aimed at bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types while using a declarative syntax similar to the popular family of probabilistic programming languages, BUGS. Blang is augmented with intuitive language additions to create data types of the user’s choosing. To perform inference at scale on such arbitrary state spaces, Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov chain Monte Carlo methods.
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-range dependencies. However, intricate temporal patterns of the long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease. Code is available at this repository: \url{https://github.com/thuml/Autoformer}.
Driver-centric Risk Object Identification

—A massive number of trafﬁc fatalities are due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in urgent need. Risky situations are generally deﬁned based on collision prediction in existing research. However, collisions are only one type of risk in trafﬁc scenarios. We believe a more generic deﬁnition is required. In this work, we propose a novel driver-centric deﬁnition of risk, i.e., risky objects inﬂuence driver behavior. Based on this deﬁnition, a new task called risk object identiﬁcation is introduced. We formulate the task as a cause-effect problem and present a novel two-stage risk object identiﬁcation framework, taking inspiration from models of situation awareness and causal inference. A driver-centric Risk Object Identiﬁcation (ROI) dataset is curated to evaluate the proposed system. We demonstrate state-of-the-art risk object identiﬁcation performance compared with strong baselines on the ROI dataset. In addition, we conduct extensive ablative studies to justify our design choices.
Hamiltonian kinetic-Hall magnetohydrodynamics with fluid and kinetic ions in the current and pressure coupling schemes

We present two generalized hybrid kinetic-Hall magnetohydrodynamics (MHD) models describing the interaction of a two-fluid bulk plasma, which consists of thermal ions and electrons, with energetic, suprathermal ion populations described by Vlasov dynamics. The dynamics of the thermal components are governed by standard fluid equations in the Hall MHD limit with the electron momentum equation providing an Ohm's law with Hall and electron pressure terms involving a gyrotropic electron pressure tensor. The coupling of the bulk, low-energy plasma with the energetic particle dynamics is accomplished through the current density (current coupling scheme; CCS) and the ion pressure tensor appearing in the momentum equation (pressure coupling scheme; PCS) in the first and the second model, respectively. The CCS is a generalization of two well-known models, because in the limit of vanishing energetic and thermal ion densities, we recover the standard Hall MHD and the hybrid kinetic-ions/fluid-electron model, respectively. This provides us with the capability to study in a continuous manner, the global impact of the energetic particles in a regime extending from vanishing to dominant energetic particle densities. The noncanonical Hamiltonian structures of the CCS and PCS, which can be exploited to study equilibrium and stability properties through the energy-Casimir variational principle, are identified. As a first application here, we derive a generalized Hall MHD Grad–Shafranov–Bernoulli system for translationally symmetric equilibria with anisotropic electron pressure and kinetic effects owing to the presence of energetic particles using the PCS.
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras

The record-breaking achievements of deep neural networks (DNNs) in image classification and detection tasks resulted in a surge of new computer vision applications during the past years. However, their computational complexity is restricting their deployment to powerful stationary or complex dedicated processing hardware, limiting their use in smart edge processing applications. We propose an automated deployment framework for DNN acceleration at the edge on field-programmable gate array (FPGA)-based cameras. The framework automatically converts an arbitrary-sized and quantized trained network into an efficient streaming-processing IP block that is instantiated within a generic adapter block in the FPGA. In contrast to prior work, the accelerator is purely logic and thus supports end-to-end processing on FPGAs without on-chip microprocessors. Our mapping tool features automatic translation from a trained Caffe network, arbitrary layer-wise fixed-point precision for both weights and activations, an efficient XNOR implementation for fully binary layers as well as a balancing mechanism for effective allocation of computational resources in the streaming dataflow. To present the performance of the system we employ this tool to implement two CNN edge processing networks on an FPGA-based high-speed camera with various precision settings showing computational throughputs of up to 337GOPS in low-latency streaming mode (no batching), running entirely on the camera.
A Revisit to the ABS H2 Equation

In this paper we revisit the Adler-Bobenko-Suris H2 equation. H2 equation is linearly related to S and S variables in the Cauchy matrix scheme. We elaborate the coupled quad-system of S and S in terms of their 3-dimensional consistency, Lax pair, bilinear form and continuum limits. It is shown that S itself satisfies a 9-point lattice equation and in continuum limit S is related to the eigenfunction in the Lax pair of the Kortewegde Vries equation.
Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings<3 and>3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (\chi^2) testing were performed to demonstrate the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. For benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, \chi^2=105.04) and (OR=7.06, \chi^2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, \chi^2=159.02) and (OR=2.29, \chi^2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers.