Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian estimation and comparison of conditional moment models

We consider the Bayesian analysis of models in which the unknown distribution of the outcomes is specified up to a set of conditional moment restrictions. The non‐parametric exponentially tilted empirical likelihood function is constructed to satisfy a sequence of unconditional moments based on an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). For any given sample size, results are robust to the number of expanded moments. We derive Bernstein–von Mises theorems for the behaviour of the posterior distribution under both correct and incorrect specification of the conditional moments, subject to growth rate conditions (slower under misspecification) on the number of approximating functions. A large‐sample theory for comparing different conditional moment models is also developed. The central result is that the marginal likelihood criterion selects the model that is less misspecified. We also introduce sparsity‐based model search for high‐dimensional conditioning variables, and provide efficient Markov chain Monte Carlo computations for high‐dimensional parameters. Along with clarifying examples, the framework is illustrated with real data applications to risk‐factor determination in finance, and causal inference under conditional ignorability.
A CNV STUDY AN EXTENSION TO THE CNV STUDY AND AN EVALUATION

The author has recently reported an experiment in which an electrical brain wave called the contingent negative variation (CNV) was examined for evidence of paranormally derived information about a future event (Hartwell, 1978). No significant results were obtained in that study which derived its subjects mainly from laboratory personnel and from the family and friends of the experimenters. In the present experiment a very similar procedure was used with some subjects recruited according to the degree of perceptual defensiveness they showed on a previous Defense Mechanism Test. This test has been used as a predictor of performance on an ESP test in several previous investigations (for a summary see Johnson, 1977). In addition to these subjects, two others were selected because of their interest and their previous histories of spontaneous psychic experiences. One of these had been a subject in the earlier CNV experiment, providing results that supported the hypothesis. These latter two subjects were aquainted with the nature and purpose of the experiment. The subjects recruited from those taking the Defense Mechanism Test were not told about the parapsychological hypothesis being tested. They were told only that they have been selected because of their performance on the earlier subliminal test and that the warning stimuli used here provided varying levels of information, some of them being very difficult to see.
PErCEivED iNEquAliTy AND gENErAlizED TruST.*

This study examines the relationship between perceived inequality and generalized trust. An important part of the literature on inequality and trust argues that inequality increases the social distance between people and fuels status anxiety. This raises the question whether it is the perception of inequality, rather than actual income inequality, that affects generalized trust. This chapter addresses this question empirically by studying the relation between perceived, as opposed to actual, inequality and generalized trust. We hypothesize that higher perceived inequality is associated with lower generalized trust, even when controlling for relevant resources. Our empirical strategy is twofold. First, we link aggregate inequality perceptions in 26 countries to individual trust data obtained from the European Values Study. A model with perceived inequality as the only country characteristic shows support for our hypothesis. However, when controlling for national wealth, there is no significant association between perceived inequality and trust. Second, we use individual level data from the German ALLBUS survey. On the individual level, we do not find an association between inequality perceptions and individual trust. We conclude by discussing three possible explanations for why we found little support for the hypothesized association. CHAPTEr FivE PErCEivED iNEquAliTy AND gENErAlizED TruST. * * This chapter is the result of a collaboration with B. Lancee. Parts of this chapter are based on: Steijn, S. R. and Lancee, B. (2011). Does Income Inequality Negatively Affect General Trust? Examining three potential problems with the inequalitytrust hypothesis, Gini Discussion Paper (20) CHAPTEr FivE – Perceived inequality and generalized trust 119
Expression of MMP-9 and TIMP-1 and Its Correlation with Lymph Node Metastasis in Esophageal Squamous Carcinoma

Objective To study the expression of matrix metalloproteinase-9(MMP-9) and tissue inhibitor of metalloproteinase-1(TIMP-1), and its correlation with lymph node metastasis in esophageal squamous carcinoma(ESC). Methods The expressions of MMP-9 and TIMP-1 were detected in 78 cases of esophageal squamous carcinomas by using immunohistochemical SP method. Results The expression level of MMP-9 in high differentiated (grade Ⅰ～Ⅱ) and early stage (stageⅠ～Ⅱ) ESC was significantly lower than that in low differentiated(grade Ⅲ～Ⅳ) and late stage (stage Ⅲ～Ⅳ) ESC(P0.05). However, the expression level of TIMP-1 in high differentiated and early stage ESC was significantly higher than that in low differentiated and late stage ESC (P0.05). The expression level of MMP-9 in ESC was positively correlated with histological grading and lymph node metastasis(P0.05), but the expression level of TIMP-1 in ESC was negatively correlated with those(P0.05). There was a negative correlation between MMP-9 and TIMP-1 expression(P0.01). Conclusion MMP-9 might be a biomarker for evaluating the malignant biological behavior of ESC, and the expression levels of both MMP-9 and TIMP-1 were related with the invasion and lymph node metastasis of ESC.
FEDERAL RESERVE statistical release WEEKLY CONSOLIDATED CONDITION REPORT OF LARGE COMMERCIAL BANKS AND DOMESTIC SUBSIDIARIES

U . S . T R E A S U R Y S E C U R I T I E S T O T A L T R A D I N G ACCOUNT I N V E S T M E N T ACCOUNT MATURING I N : ONE YEAR OR L E S S OVER ONE THROUGH F I V E YEARS OVER F I V E Y E A R S OTHER S E C U R I T I E S T O T A L T R A D I N G ACCOUNT I N V E S T M E N T ACCCUNT U . S . GOVERNMENT A G E N C I E S AND C O R P O R A T I O N S S T A T E S AND P O L I T I C A L S U B D I V I S I O N S MATURING I N : — " ONE YEAR OR L E S S OVER ONE YEAR OTHER B O N D S , C O R P O R A T E S T O C K S AND S E C U R I T I E S F E D E R A L F U N D S S O L D 1 / TO COMMERCIAL B A N K S TO NONBANK B R O K E R S AND D E A L E R S I N S E C U R I T I E S TO O T H E R S OTHER L O A N S , G R O S S COMMERCIAL AND I N D U S T R I A L B A N K E R S ' A C C E P T A N C E S AND COMMERCIAL P A P E R ALL OTHER U . S . A D D R E S S E E S N O N U . S . A D D R E S S E E S REAL E S T A T E L O A N S L O A N S TO I N D I V I D U A L S FOR P E R S O N A L E X P E N D I T U R E S LOANS TO F I N A N C I A L I N S T I T U T I O N S T O T A L COMMERCIAL B A N K S I N THE U . S . B A N K S I N F O R E I G N C O U N T R I E S S A L E S F I N A N C E , P E R S O N A L F I N A N C E C O M P A N I E S , E T C . OTHER F I N A N C I A L I N S T I T U T I O N S L O A N S TO NONBANK B R O K E R S AND D E A L E R S I N S E C U R I T I E S LOANS TO O T H E R S FOR P U R C H A S I N G AND C A R R Y I N G S E C U R I T I E S 2 / L O A N S TO F I N A N C E A G R I C U L T U R A L P R O D U C T I O N ALL OTHER L O A N S L E S S : U N E A R N E D I N C O M E LOAN L O S S R E S E R V E OTHER L O A N S , NET L E A S E F I N A N C I N G R E C E I V A B L E S ALL OTHER A S S E T S T O T A L A S S E T S ( I N M I L L I O N S OF D O L L A R S ) ( I N M I L L I O N S OF D O L L A R S )
Intent-Aware Human Motion Prediction using Deep Generative Neural Networks

A critical capability required for the wide adoption of mobile robots into society is the ability to navigate safely around pedestrians. One important component to enable safe navigation is to accurately predict the motion of pedestrians in the scene. The main objective of this research is to develop novel techniques that accurately predict human motion by using past motion and intent as a prior for making the prediction. In this study, we develop neural network architectures that are capable of learning environment-agnostic embeddings that serve as a prior for prediction. We combine these embeddings with contextual information including desired velocity and a probability distribution describing the intent to make predictions. We compare the average displacement error and final displacement error with state-of-the-art published results and show evidence that combining contextual information results in more accurate prediction of future motion.
Detecting and Fixing Flaky Tests

My research interests are in Software Engineering, with a focus on Software Testing. Software has become a crucial part of our daily lives, and software testing is widely used to check the quality of software. Developers frequently make changes to improve software and then perform regression testing by rerunning their tests to check that changes do not break existing functionality. Despite its widespread use, regression testing still has two key challenges: (1) test suites have flaky tests that nondeterministically pass and fail independently of the developers’ changes, misleading developers that their changes broke existing functionality, and (2) regression testing takes a long time to run, wasting developer time and compute resources. These challenges are faced by both openand closed-source communities, including companies such as Facebook, Google, and Microsoft. My research so far has focused on tackling the two key challenges of regression testing: flaky tests and long regression testing time. More specifically, I developed techniques and tools for (1) making regression testing more reliable by detecting flaky tests and mitigating their effects [5, 6, 12, 19, 21, 24], (2) optimizing test suites for faster run times through better test placement and through removing redundant tests [1, 20, 22, 25, 26], and (3) speeding up regression testing by rerunning only the tests that can be affected by code changes [13,14,23,27,31]. I also evaluated test-suite quality through mutation testing [3, 4, 7, 8, 9, 10]. As long as people write software, there will be bugs, and software testing will remain highly relevant. In the future, I plan to develop testing techniques for emerging software domains where testing is still not mature enough, such as machine learning, probabilistic programming, or Internet of Things. These domains are inherently nondeterministic, leading to challenges in testing that I look forward to overcoming. I also plan to improve testing of code for computational science [2, 11, 18, 28], where reproducibility is currently a big challenge that can be alleviated by software testing techniques.
Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving

With the growth of 3D sensing technologies, one can now use a large number of 3D points to precisely represent objects’ surfaces and surrounding environments. We call those 3D points a 3D point cloud; it has a growing impact on various applications, including autonomous driving, drones, robotics, virtual reality and preservation of historical artifacts [4]. For example, a self-driving car could use multiple sensors to observe the world, such as LiDARs, cameras and RADARs [2]. Among those, LiDARs produce two types of 3D point clouds: real-time LiDAR sweeps and high-deﬁnition maps. Both types of 3D point clouds provides accurate range information for self-driving cars, which are critical to localization and perception systems. We consider these point clouds large-scale point clouds because they contain a large number of 3D points and record outdoor, open areas
Schedule of the Total Sum of Loads from Electrical Brooders with the Two-Position Temperature Regulators

The time heating constants for various means of heating (such as infrared lamps, ordinary umbrella brooders, electrically heated concrete floors, etc.) are very different from each other. Due to the different inertia of the electrical schedules, the heaters of the two types are completely different. Despite the significant discrepancy between the work schedules of the heaters of various types, the total schedule of their work can be described in general form, if we operate not with the actual heater on and off times, but with their relative values and take the pulse duty cycle from 0 to 1.The expected value, variance, and standard deviation are expressing the nature of the total load at a given time. In order to correctly predict the graph over time, in addition to the specified parameters, it is also necessary to know the speed (pace) of the change in the implementation of the graph. For this, the load graph is usually examined from the standpoint of the theory of random functions, according to which the load graph is considered as a random function.The results of the calculations coincide fairly accurately with the data obtained from the register of brooders.The considered calculation example shows the possibility of determining the average temporal characteristics of the total electrical load curve from a group of electric heaters with two-position temperature regulators operating independently of each other, from the dynamics of temperature control even in the absence of an experimental load curve from one brooder.
Prediction and Implementation of Graphene and Other Two-Dimensional Material Based Superconductors: A Review

This article comprehensively reviews the theoretical attempts toward prediction and subsequent realization of superconductivity in graphene and other two-dimensional (2-D) elemental materials and their compounds. From the large family of 2-D materials, a potential set of candidates were categorized by the researchers employing first principle study, mostly with density functional theory as a tool. The effect of different external chemical and physical parameters such as charge doping (through intercalation, substitution, electric field, etc.), pressure, and strain on superconducting transition of these materials was also summarized. Due to severe difficulty in the implementation techniques, only a handful of experimental authentications were reported by researchers so far. Experimental investigations on the effect of influencing external parameters mentioned above were also in a nascent state. Researchers are in search of “unconventional superconductivity” and “Ising effect” in graphene and other 2-D materials, which can eventually lead to higher critical temperature and higher limit of in-plane magnetic field. However, the implementation across the whole spectrum is still a crucial challenge.
Edge-Preserving Stereo Matching Using Minimum Spanning Tree

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.
A Strategic Routing Analysis for Agro Sensor Communications in Mobile Ad hoc Networks

Recently, agro sensor communications are getting popularity with the advent of wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs). Infrastructure less MANETs are considered as a potential solution for agricultural domains to provide robust and effective operations in wireless networks by enabling routing characteristics at each mobile node. The efficient use of routing protocol is pivotal for network management in MANETs. In this research, we demonstrate a strategic routing analysis to evaluate the performance of different routing protocols, e.g., proactive and reactive in terms of various network quality of service (QoS) parameters for multiple data rate enabled agro sensor mobile nodes. Two different network scenarios (low-node and high-node) are simulated for various network parameters. Simulation results indicate that OLSR protocol provides higher throughput than others for all nodes. This investigation also shows that GRP has the highest network load capacity than other protocols for agro sensor applications. Another finding from this research i s t hat proactive protocols have the lower average end to end delay and MAC delay than reactive protocols.
Case Study on Design and Evaluation of a Multi-Soft-Core Processor

Modern FPGAs (Field Programmable Gate Arrays) are becoming increasingly important when it comes to embedded system development. Within these FPGAs, soft-core processors are often used to solve a wide range of different tasks. Soft-core processors are a cost-effective and time-efficient way to realize embedded systems. The trend for soft-core processors, as well as mainstream CPUs (central processing units), leads to multi-core architectures. Both the necessary memory architectures and the compilers play an important role in this process. In this paper, a novel method that aims at minimizing the necessary memory resources on the FPGA while maximizing the processing speed of any given algorithm is described. In the first step, an application-specializable multi-soft-core processor architecture is presented that is capable of solving problems while adhering to hard real-time deadlines. Its special architecture and other necessary features are discussed. Furthermore, a method for the generation of optimized machine code for each processor core as well as hard real-time compatible deadlock handling mechanisms are presented. Selected algorithms are implemented to demonstrate the functionality and efficiency of the realized approach for different configurations of the multi-soft-core processor architecture.
The Pretreatment of Thiopental Sodium for Reducing Injection Pain of Propofol

Background: The incidence of pain on injection of propofol varies between 30 and 100%. A variety of pretreatments have been tried to alleviate this problem such as a local anesthetics, cooling and opioids. However, none of these pharmacological maneuvers were satisfactory yet. In a recent study, subhypnotic doses of both thiopental sodium and propofol decrease the acute pain. We report a comparison of thiopental sodium, lidocaine and placebo on the incidence and severity of pain on injection of propofol. Method: A controlied, double-blind study was performed to compare the prior administration of intravenous saline 2 ml(n 30, group S), lidocaine 20 mg(n-30, group L) and thiopental sodium 50 mg(n 30, group T) in alleviating the pain by propofol. Injection pain was assessed with the four-point verbal categorical scoring system. Result: The incidence of injection pain during induction was lower in group L(30%) and T(17%) than group S(77%). The incidence of injection pain was lower in group T(17%) than group L(30%), but not significant statistically. The pain scores for recall of pain in the recovery room was simlar to those pain during propofol induction. Conclusion: The pretreatment of thiopental sodium can be effective in reducing both incidence and severity of propofol injection pain and has similar effect to lidocaine to prevent propofol injection pain.
A General SVM-Based Multi-Objective Optimization Methodology for Axial Flux Motor Design: YASA Motor of an Electric Vehicle as a Case Study

Axial flux motor design is normally depended on a designer’s experience to adjust design parameters, which is vague and complex, for example, torque density and torque ripple are two key factors of a motor to restrain its development, since torque density dominates a motor’s volume and weight, while torque ripple determines its stability. Therefore, a general optimizations methodology is required in its design process. To realize this purpose, this paper proposes a general multi-objective optimization methodology for practical motor design. In detail, this methodology is based on Support Vector Machine-Chaotic Cultural Differential Evolution(SVM-CCDE) algorithm for both maximizing the torque density and minimizing the torque ripple, and Yokeless And Segmented Armature(YASA) motor of an electric vehicle(EV) considering practical constraints is presented as a typical example since it is a special topology of axial flux motor(AFM). A comparative analysis is presented in the paper to demonstrate the proposed method’s advanced features. Finally, the effectiveness of the optimization method is verified by finite element analysis (FEA) via Ansys software, the results well agree the analyses and further validate the proposed method. The proposed method is a potential feasible solution to improve the EV’s motor design in the coming future.
Hybrid Plasmonic Waveguide based 3×3 Multimode Interferometer for Compact 120° Optical Hybrid

Based on hybrid plasmonic waveguide (HPW), we propose a compact 3×3 multimode interferometer (MMI) for 120° optical hybrid. Also, tapered HPW sections are implemented to improve the coupling efficiencies for lights coupled into/out of the multimode section. The simulation results show that at a wavelength of 1550 nm, the transmission rate of the 3×3 HPW-MMI is 0.692 (1.61 dB loss), the maximum transmission imbalance is 0.43 dB, and the phase error is 4.66°.
Investigations on Driver and Layout for Paralleled GaN HEMTs in Low Voltage Application

Gallium nitride is becoming more popular in low-voltage applications. Gallium nitride (GaN) high electron mobility transistors (HEMTs) has positive temperature feature. It makes parallel application feasible for GaN HEMTs. Meanwhile, paralleled GaN HEMTs will increase the power handling capability and the efficiency of the converter. The parasitic parameters of both driving and power loop layout are critical which need to be equalized and minimized in GaN HEMT parallel operation. The parameters mismatch of the parallel branch will lead to significant effect on the dynamic characteristics and bring thermal problem of the GaN HEMTs. This paper focuses on the design of paralleled low-voltage enhancement GaN HEMT, and evaluates the effect of the parasitic inductances in both driver and power loops. The LT-spice and ANASYS Q3D Extractor simulation are carried out to analysis the effect of the unbalance parameters. The design guidelines for driver and PCB design are summarized as well. Finally, a 300W isolated DC-DC converter is built to verify the analysis and simulation on parallel operation.