Miracle-dz/UniFAR-FTUs huggingface dataset:
Donald Trump, Global Warming, and Public Philosophy of Science

Global warming is at best a controversial contemporary scientific, economic, social, and political issue, at least from the perspective of public media and persona. In other words, it is a wicked problem, which requires public attention and response. Unfortunately, the public’s perspective of the controversy is often (mis)informed through the media and its star persona. Abstract Volume 2, Number 1 Spring 2019 DOI: 10.25335/PPJ.2. 1-5
Fuzzy Multi-criteria Decision Making Method for Machine Selection

The selection of appropriate machines is one of the most crucial decisions for a manu- facturing company to develop an efficient production environment. The aim of this study is to propose a fuzzy approach for selecting the best machine. This paper is based on a fuzzy exten- sion of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, the ratings of various alternatives versus various subjective criteria and the weights of all criteria are assessed in linguistic variables represented by fuzzy numbers. Fuzzy numbers try to resolve the ambiguity of concepts that are associated with human being's judgments. To determine the order of the alternatives, closeness coefficient is defined by calculating the dis- tances to the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). By using fuzzy TOPSIS, uncertainty and vagueness from subjective perception and the experiences of decision maker can be effectively represented and reached to a more effective decision.
Adaptive Bayesian Inference for Discontinuous Inverse Problems, Application to Hyperbolic Conservation Laws

Various works from the literature aimed at accelerating Bayesian inference in inverse problems. Stochastic spectral methods have been recently proposed as sur- rogate approximations of the forward uncertainty propagationmodel over the support of the prior distribution. These representations are efficient because they allow afford- able simulation of a large number of samples from the posterior distribution. Unfor- tunately, they do not perform well when the forward model exhibits strong nonlinear behavior with respect to its input. In this work, we first relate the fast (exponential) L 2 -convergence of the forward approximation to the fast (exponential) convergence (in terms of Kullback-Leibler di- vergence) of the approximate posterior. In particular, we prove that in case the prior distribution is uniform, the posterior is at least twice as fast as the convergence rate of the forward model in those norms. The Bayesian inference strategy is developed in the framework of a stochastic spectral projection method. The predictedconvergence rates are then demonstrated for simple nonlinear inverse problems of varying smoothness. We then propose an efficient numerical approach for the Bayesian solution of in- verse problems presenting strongly nonlinear or discontinuous system responses. This comes with the improvement of the forward model that is adaptively approximated by an iterative generalized Polynomial Chaos-based representation. The numerical ap- proximations and predicted convergence rates of the former approach are compared to the new iterative numerical method for nonlinear time-dependent test cases of vary- ing dimension and complexity, which are relevant regarding our hydrodynamics mo- tivations and therefore regarding hyperbolic conservation laws and the apparition of discontinuities in finite time. AMS subject classifications: 52B10, 65D18, 68U05, 68U07
On the Diameter of Unitary Cayley Graphs of Rings

Abstract The unitary Cayley graph of a ring $R$ , denoted $\Gamma \left( R \right)$ , is the simple graph defined on all elements of $R$ , and where two vertices $x$ and $y$ are adjacent if and only if $x\,-\,y$ is a unit in $R$ . The largest distance between all pairs of vertices of a graph $G$ is called the diameter of $G$ and is denoted by $\text{diam}\left( G \right)$ . It is proved that for each integer $n\,\ge \,1$ , there exists a ring $R$ such that $\text{diam}\left( \Gamma \left( R \right) \right)=n$ . We also show that $\text{diam}\left( \Gamma \left( R \right) \right)\in \left\{ 1,2,3,\infty \right\}$ for a ring $R$ with ${R}/{J\left( R \right)}\;$ self-injective and classify all those rings with $\text{diam}\left( \Gamma \left( R \right) \right)\,=\,1,\,2,\,3$ , and $\infty$ , respectively.
Approach to Pancreatic Head Mass in the Background of Chronic Pancreatitis

Chronic pancreatitis (CP) is a known risk factor for pancreatic cancer. CP may present with an inflammatory mass, and differentiation from pancreatic cancer is often difficult. Clinical suspicion of malignancy dictates a need for further evaluation for underlying pancreatic cancer. Imaging modalities remain the mainstay of evaluation for a mass in background CP; however, they have their shortcomings. Endoscopic ultrasound (EUS) has become the go-to investigation. Adjunct modalities such as contrast-harmonic EUS and EUS elastography, as well as EUS-guided sampling using newer-generation needles are useful in differentiating inflammatory from malignant masses in the pancreas. Paraduodenal pancreatitis and autoimmune pancreatitis often masquerade as pancreatic cancer. In this narrative review, we discuss the various modalities used to differentiate inflammatory from malignant masses of the pancreas.
Proportional hazard modeling for hierarchical systems with multi-level information aggregation

Reliability modeling of hierarchical systems is crucial for their health management in many mission-critical industries. Conventional statistical modeling methodologies are constrained by the limited availability of reliability test data, especially when the system-level reliability tests of such systems are expensive and/or time-consuming. This article presents a semi-parametric approach to modeling system-level reliability by systematically and explicitly aggregating lower-level information of system elements; i.e., components and/or subsystems. An innovative Bayesian inference framework is proposed to implement information aggregation based on the known multi-level structure of hierarchical systems and interaction relationships among their composing elements. Numerical case study results demonstrate the effectiveness of the proposed method.
Immunization uptake in rural Bangladesh : a multilevel analysis

This study examines the effect of the immunization programme in four rural areas of Bangladesh. Using a multilevel discrete time hazards model, this paper identifies the demographic and socioeconomic characteristics which influence the uptake of immunization. However, even after controlling for these observed factors, a considerable amount of variation between households remains due to unobserved characteristics. There is also a large amount of geographical variation in the uptake of immunization with evidence of village level variation within intervention areas.
Unsupervised Defect Segmentation via Forgetting-Inputting-Based Feature Fusion and Multiple Hierarchical Feature Difference

In the field of surface defect detection, there is a significant imbalance between the number of positive and negative samples, which has led to a growing interest in positive-samples-based anomaly detection methods. Reconstruction-based methods are currently the most commonly used approach, but they often struggle to repair abnormal foregrounds and reconstruct clear backgrounds simultaneously. To address this issue, we propose a new approach called the forgetting-inputting-based feature fusion and multiple hierarchical feature difference network (FIM-Net). The FIM-Net method incorporates a novel latent feature repair module (LFRM), which combines encoding and memory-encoding obtained by memory-augmented module (MAM) via a novel forgetting-inputting-based feature fusion module (FIFFM) to repair abnormal foregrounds while preserving clear backgrounds. Additionally, we introduce a manual defect generation algorithm (MDGA) to simulate realistic and feature-rich anomalies. Finally, we use a multiple hierarchical feature difference (MHFD) for defect segmentation to achieve more accurate defect location. Our extensive comparison experiments demonstrate that the FIM-Net method achieves the state-of-the-art detection accuracy and shows great potential for industrial applications.
Contextualizing Hypertext: Its Literary Status

Hypertext refers to the electronic links of texts. In the traditional texts, readers have to follow the linearity, and fixity of the book they are reading. Now computers give them a chance to escape this sort of restriction. Computer is a medium for the creation and the reading of texts. Hypertext or digital text, as we know, takes the form of a series of links. These links are adaptable, flexible, and interactive. They question the unity of the text, and the roles of both the reader and the author. Hence, hypertext requires redefining the concepts of author, reader, text, and structure--concepts of great importance to postmodernists. In addition, hypertext consists of fragments, pastiches, different styles and genres. It ignores boundaries, and addresses the mass. In brief, hypertext develops and brings to live many postmodern concepts such as decentering, intertextuality, writerly text and rejection of linearity and fixity of linear narrative. My paper is about hypertext in the light of postmodern theory.
Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization

Computer vision algorithms implementations, especially for real-time applications, are present in a variety of devices that we are currently using (from smartphones or automotive applications to monitoring/security applications) and pose specific challenges, memory bandwidth or energy consumption (e.g., for mobility) being the most notable ones. This paper aims at providing a solution to improve the overall quality of real-time object detection computer vision algorithms using a hybrid hardware–software implementation. To this end, we explore the methods for a proper allocation of algorithm components towards hardware (as IP Cores) and the interfacing between hardware and software. Addressing specific design constraints, the relationship between the above components allows embedded artificial intelligence to select the operating hardware blocks (IP cores)—in the configuration phase—and to dynamically change the parameters of the aggregated hardware resources—in the instantiation phase, similar to the concretization of a class into a software object. The conclusions show the benefits of using hybrid hardware–software implementations, as well as major gains from using IP Cores, managed by artificial intelligence, for an object detection use-case, implemented on a FPGA demonstrator built around a Xilinx Zynq-7000 SoC Mini-ITX sub-system.
Effect of Inter-Repetition Rest on Kinetic and Kinematic Variables in the Power Clean

The purpose of this investigation was to examine the effect of IRR on kinetic and kinematic variables in the power clean during a multiple set exercise protocol. 10 male, recreational weightlifters participated in 4 testing sessions. Subjects performed 3 sets of 6 repetitions at 80% 1RM with 0 (P0), 20 (P20), or 40 seconds (P40) IRR with 3 min. rest between sets. Peak power, force, and velocity significantly decreased to a greater extent during P0, whereas were maintained during P20 and P40. In all variables, P0 was significantly different from P20 and P40. Significant differences were found in horizontal displacement (bar path) for all 3 sets of P0. No significant differences were found in horizontal displacement for all 3 sets of P20. Significant differences were found in horizontal displacement for 2 sets of P40. The addition of IRR periods may allow for a greater volume of training while reinforcing proper movement mechanics.
Oxidative Cyclization at ortho-Position of Phenol: Improved Total Synthesis of 3-(Phenethylamino)demethyl(oxy)aaptamine

A shorter synthesis of the demethyl(oxy)aaptamine skeleton was developed via oxidative intramolecular cyclization of 1-(2-azidoethyl)-6-methoxyisoquinolin-7-ol followed by dehydrogenation with a hypervalent iodine reagent. This is the first example of oxidative cyclization at the ortho-position of phenol that does not involve spiro-cyclization, resulting in the improved total synthesis of 3-(phenethylamino)demethyl(oxy)aaptamine, a potent anti-dormant mycobacterial agent.
Yer dalgası yayılımının zaman ve frekans domeninde sayısal modellemesi

Ground wave propagation problem has been a subject of interest from the beginning of the 20 th century. The model environment used in this study is a spherical earth, which may have various ground characteristics above which exists a radially inhomogeneous atmosphere. Using this canonical model we pose ourselves the following problem: Determine the ground wave propagation characteristics between two points, which may be selected anywhere on or above the ground. The problem is very complex and neither a full-wave numerically computable analytical solution, nor a three-dimensional (3D), generally applicable numerical solution has yet appeared. Therefore analytical approximate solutions or two-dimensional (2D) numerical approaches have so far been used. Here, first we consider ray – mode solutions and Split Step Parabolic Equation (SSPE) method, and then introduce a novel pure numerical method, which is based on the FiniteDifference Time Domain (FDTD) technique and applicable for a broad range of propagation problems. Since the propagation region is always larger than the available FDTD space, the propagating pulse is traced within a sliding window and this method is named as Time-Domain Wave-Propagator (TDWP). The results obtained via TDWP are compared with the data obtained via ray – mode solutions and/or SSPE
The Patient Journey of Schizophrenia in Mental Health Services: Results from a Co-Designed Survey by Clinicians, Expert Patients and Caregivers

Background: The Patient Journey Project aims to collect real-world experiences on schizophrenia management in clinical practice throughout all the phases of the disorder, highlighting virtuous paths, challenges and unmet needs. Methods: A 60-item survey was co-designed with all the stakeholders (clinicians, expert patients and caregivers) involved in the patient’s journey, focusing on three areas: early detection and management, acute phase management and long-term management/continuity of care. For each statement, the respondents expressed their consensus on the importance and the degree of implementation in clinical practice. The respondents included heads of the Mental Health Services (MHSs) in the Lombardy region, Italy. Results: For early diagnosis and management, a strong consensus was found; however, the implementation degree was moderate-to-good. For acute phase management, a strong consensus and a good level of implementation were found. For long-term management/continuity of care, a strong consensus was found, but the implementation level was slightly above the cut-off, with 44.4% of the statements being rated as only moderately implemented. Overall, the survey showed a strong consensus and a good level of implementation. Conclusions: The survey offered an updated evaluation of the priority intervention areas for MHSs and highlighted the current limitations. Particularly, early phases and chronicity management should be further implemented to improve the patient journey of schizophrenia patients.
Multiple Solutions of an Unsteady Stagnation-Point Flow with Melting Heat Transfer in a Darcy–Brinkman Porous Medium

Abstract The characteristics of the unsteady boundary layer flow with melting heat transfer near a stagnation-point towards a flat plate embedded in a DarcyBrinkman porous medium with thermal radiation are investigated. The governing partial differential equations are transformed into self-similar ordinary differential equations by similarity transformations. The transformed self-similar equations are solved numerically using bvp4c from Matlab for several values of the flow parameters. The study reveals that the multiple solutions exist for the decelerating (A < 0) flow, whereas for the accelerating (A ≥ 0) flow, the solution is unique. The results also indicate that the melting phenomenon increases the rate of heat transfer and delays the boundary layer separation. To validate the current numerical results, comparison with available results is made and found to be in a good agreement.
Solder Joint Inspection on Printed Circuit Boards: A Survey and a Dataset

Surface mount technology (SMT) is a procedure for mounting electronic components to the surface of printed circuit boards (PCBs). Although the SMT procedure is more reliable than the conventional through-hole mounting, many errors may occur in SMT lines. In this article, we surveyed methods presented for the optical inspection of solder joints on PCBs. The methods are grouped by problem-solving approach: reference-based, machine learning/computer vision, deep learning, and 3-D reconstruction. We compare and discuss these approaches according to their advantages and disadvantages, with a focus on the more recent deep learning and 3-D reconstruction techniques, and list the public datasets for solder joint inspection. Since defective samples rarely occur in the SMT manufacturing lines, public datasets have few defective samples that are essential for the inspection task. To fill this gap, we publish a public dataset with both normal and different types of SMT errors. Our dataset can be used as a benchmark to compare different algorithms. The dataset is available at https://github.com/furkanulger/solder-joint-dataset.
Association of Plasma Dopamine and Norepinephrine Levels and Cannabis Use among Male Cannabis Smokers

Background: Many studies have employed both direct and indirect methods of measuring the brain dopaminergic activity in relation to drug dependence, internet addiction and other disorders in which dopamine signalling has been implicated. However, only a few scientific reports have found a relationship between plasma dopamine and/or norepinephrine levels and addiction. Objective: The present work was aimed at determining the association between plasma dopamine level and consumption of cannabis as the most widely used illicit drug in the world. Methods: One hundred and six participants with cannabis use disorder based on International Classification of Diseases version 10 (ICD-10) were recruited for the study. Screening for current cannabis use disorder was done using Cannabis Use Disorder Identification Test (CUDIT). Cannabis dependence was assessed using the Severity of Dependence Scale (SDS). Venous blood samples were taken from the subjects to assay for the plasma dopamine by enzyme-linked immunosorbent assay (ELISA) method. Results: There was a high correlation between plasma dopamine and SDS scores with plasma dopamine accounting for more than 31% of the variance in SDS scores. However, the relationship between plasma dopamine and current cannabis use disorder was not strong as it accounts for just more than 9% of the variance in CUDIT scores. There was a relationship found between plasma norepinephrine and SDS scores. Plasma norepinephrine only accounts for about 5% of the variance in SDS score while there was virtually no association between plasma norepinephrine and CUDIT scores. Conclusion: Higher plasma dopamine level was found to be significantly associated with cannabis use and dependence as measured by SDS but less associated with current cannabis use disorder as measured by CUDIT.
The Fixing Number of a Graph

The fixing number of a graph is the order of the smallest subset of its vertex set such that assigning distinct labels to all of the vertices in that subset results in the trivial automorphism; this is a recently introduced parameter that provides a measure of the non-rigidity of a graph. We provide a survey of elementary results about fixing numbers. We examine known algorithms for computing the fixing numbers of graphs in general and algorithms which are applied only to trees. We also present and prove the correctness of new algorithms for both of those cases. We examine the distribution of fixing numbers of various classifications of graphs. 3 Executive Summary This project began as an expansion on the work by Gibbons and Laison in [1]. In that paper, they defined the fixing number of a graph as the minimum number of vertices necessary to label so as to remove all automorphisms from that graph. A variety of open problems were proposed in that paper and the initial goals of this project were to solve as many of them as possible. Gibbons and Laison proposed a greedy fixing algorithm for the computation of the fixing number of a graph and the first open question of their paper was whether the output of the algorithm was well defined for a given graph and further if that output would always equal the actual fixing number. We provide a counterexample to this algorithm to show that its output is not well defined for all possible input graphs. Additionally, we provide and prove the correctness of a new algorithm for the computation of fixing number of graphs in general. In addition to this algorithm we examine several existing algorithms for the computation of fixing numbers of trees and provide an enhancement of one of those algorithms that decreases its computational complexity. In the final section of this paper, we examine the distribution of fixing numbers of several classes of graphs, i.e. determining the probability distribution of fixing numbers of graphs of a given order. We pay special attention to the fixing numbers of trees and of various sub-classifications of trees.
Linear Models for Multivariate Repeated Measures Data

We study the general linear model (GLM) with doubly exchangeable distributed error for m observed random variables. The doubly exchangeable linear model (DEGLM) arises when the midimensional error vectors are \doubly exchangeable" (de¯ned later), jointly normally distributed, which is much weaker assumption than the independent and identically distributed error vectors as in the case of GLM or classical GLM (CGLM). We estimate the parameters in the model and also ¯nd their distributions.
Self-Reinforcement Attention Mechanism For Tabular Learning

Apart from the high accuracy of machine learning models, what interests many researchers in real-life problems (e.g., fraud detection, credit scoring) is to find hidden patterns in data; particularly when dealing with their challenging imbalanced characteristics. Interpretability is also a key requirement that needs to accompany the used machine learning model. In this concern, often, intrinsically interpretable models are preferred to complex ones, which are in most cases black-box models. Also, linear models are used in some high-risk fields to handle tabular data, even if performance must be sacrificed. In this paper, we introduce Self-Reinforcement Attention (SRA), a novel attention mechanism that provides a relevance of features as a weight vector which is used to learn an intelligible representation. This weight is then used to reinforce or reduce some components of the raw input through element-wise vector multiplication. Our results on synthetic and real-world imbalanced data show that our proposed SRA block is effective in end-to-end combination with baseline models.
Choose your Colour: Tree Interpolation for Quantified Formulas in SMT

We present a generic tree-interpolation algorithm in the SMT context with quantifiers. The algorithm takes a proof of unsatisfiability using resolution and quantifier instantiation and computes interpolants (which may contain quantifiers). Arbitrary SMT theories are supported, as long as each theory itself supports tree interpolation for its lemmas. In particular, we show this for the theory combination of equality with uninterpreted functions and linear arithmetic. The interpolants can be tweaked by virtually assigning each literal in the proof to interpolation partitions (colouring the literals) in arbitrary ways. The algorithm is implemented in SMTInterpol.