Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface fire spread model based on stochastic cellular automata

A surface fire spread model based on stochastic cellular automata is proposed and its dynamics is characterized and analyzed. The model attempts to model the dynamics of the fire surface spread in vegetation fires that occur under flat terrain and no-wind conditions. Each cell is characterized by one of the three states that are: vegetation cell, burning cell and burnt cell. The dynamics of fire spread is modeled as a stochastic event with an effective fire spread probability S which is a function of three probabilities: the proportion of vegetation cells across the lattice, the probability of a burning cell become burnt, and the probability of the fire spread from a burning cell to a neighbor vegetation cell. A set of simulation experiments are performed to analyze the effects of different values of the three probabilities on the final fire pattern.
Improving the Calibration of Long Term Predictions of Heart Failure Rehospitalizations using Medical Concept Embedding

‘Medical concept embedding’ aims to provide vector representations of International Statistical Classiﬁcation of Diseases (ICD) codes such that the relationship between two vectors mirrors the conceptual relationship between the two diagnoses or clinical interventions. Despite the growing interest in vector representations of clinical information in electronic health records (EHR), the utility of embedding methods has not been examined in the context of predicting individualized survival distributions (ISD). In this study, we apply ISD methods, speciﬁcally Cox-Proportional Hazards with Kalbﬂeisch-Prentice extension (CoxPH-KP) and Multi-task Logistic Regression (MTLR), to the task of predicting probability of Heart Failure (HF) rehospitalization or mortality, in a population-level database of 40,568 HF hospitalizations over the span of 8 years. Further, we compare performance of these ISD models with versus without code embeddings, that were learned in a temporally disjoint dataset of 229,359 all-cause hospitalizations. All our models show good discrimination in the validation dataset of 8,114 HF hospitalizations, with time-based concordance greater than 70% for every monthly intervals upto 8 years. Finally, we demonstrate that medical concept embedding does not always lead to improved model discrimination, but does improve model calibration, particularly over the longer time scales.
Rediscovering R-NET: An Improvement and In-Depth Analysis on SQUAD 2.0

Question-answering is a discipline within the fields of information retrieval (IR) and natural language processing (NLP) that is concerned with building systems that automatically answer questions posed by humans. In this project, we address the question-answering task by attempting to improve the R-NET model [1]. Specifically, our goals are to 1. reproduce R-NET and evaluate its performance on SQuAD v2.0 and 2. change certain features of the R-NET model to further improve its accuracy on SQUAD v2.0. We present an implementation of R-NET using LSTM’s instead of GRU’s, larger embedding and hidden dimensions, higher dropout, and more layers that achieves an improvement in performance from our baseline R-NET model.
DISTANCE AND PACKET DELIVERY RATIO ENHANCEMENT USING NEURO FUZZY LOGIC IN MANETS

----------------------------------------------------------------------------- Abstract : Data in the mobile ad hoc network transfers from one node to another node through an intermediate node. This intermediate node should be trusted node in order to avoid any failure in the system. Considering this fact, a new technique has proposed in this paper which uses the hybridization approach of neural network and Fuzzy logics referred as ANFIS. The proposed work evaluates the trust of each node in the network and the node formulating maximum trust node has chosen as the intermediate node for the transferring of data. Furthermore, the proposed technique optimizes the route by enhancing the number of factor which helps in route selection decision making efficiently and appropriately. The experimental analysis have been performed using proposed work and compared with the traditional technique to conclude the performance individually. Consequently, proposed technique outperforms the traditional technique in terms of each performance parameter such as Packet Delivery
Caribbean plate boundaries control on the tectonic duality in the back-arc of the Lesser Antilles subduction zone during the Eocene

The Eocene tectonic evolution of the easternmost Caribbean Plate (CP) boundary, i.e. the Lesser Antilles subduction zone (LASZ), is debated. Recents works shed light on a peculiar period of tectonic duality in the arc/back-arc regions. A compressive-to-transpressive regime occurred in the north, while rifting and seafloor spreading occurred in Grenada basin to the south. The mechanism for this strong spatial variation and its evolution through time has yet to be established. Here, using 3-D subduction mechanical models, we evaluate whether the change in the trench-curvature radius at the northeast corner of the CP could have modulated the duality. We assume asymmetrical CP boundaries at the north (from east to west: oblique subduction to strike-slip) and at the south (subduction-transform edge propagator-like behavior). Regardless of the imposed trench curvature, the southern half of our modeled CP always undergoes a NW-to-W-oriented extension due to the tendency of the southernmost part of the South-America slab to rollback. In contrast, the tectonic regime in the northeast corner of the CP depends on the trench-curvature radius. A low radius promotes transtension-to-transpression, with a NE-oriented compressive component of the principal stress. A high trench-curvature largely reduces the compressive component and promotes an extensive regime similar to that in the south. We thus propose that an initially low-curvature radius of the NE-LASZ triggered the tectonic N-S duality in the Eocene and led to an ephemeral period of transpression/compression at the north, although an additional mechanism might have been required to locally enhance compression.
Lightweight Convolutional Neural Networks for Camera-Based Object Detection on Mobile Devices

Nowadays, convolutional neural networks (CNNs) are widely used in various applications. Especially, tasks like speech and text recognition as well as object detection are very well suited for these kind of networks. The latter can be divided into two subtasks, namely finding objects in an image and determine their location. Of course, there are classical approaches, like pattern matching, to solve these tasks. Unfortunately, these approaches are often prone to error due to scaling and rotation of the objects to be detected. Furthermore, it is time consuming and computationally expensive to implement pattern matching for several different objects. On the other hand, CNNs are well suited for this task due to their generality and applicability for lots of different objects. Nevertheless, due to their high computational costs, these networks are typically operated on workstations with fast central processing units (CPUs) and, especially graphic cards. Anyhow, it is possible to train a CNN on a machine providing high computational power and utilize the trained network on mobile devices to perform the actual object detection. In this thesis the performance in terms of speed of lightweight CNNs for camera based object detection on mobile devices is investigated. For this purpose, three of the most popular state of the art lightweight object detection networks are examined. In addition, a Python script as well as an Android application capable of executing pretrained neural networks are presented. Based on this implementation the CNNs are compared in terms of speed measured in frames per second (FPS).
Identification of a Triangular Two Equation System Without Instruments

Abstract We show that a standard linear triangular two equation system can be point identified, without the use of instruments or any other side information. We find that the only case where the model is not point identified is when a latent variable that causes endogeneity is normally distributed. In this nonidentified case, we derive the sharp identified set. We apply our results to Acemoglu and Johnson’s model of life expectancy and GDP, obtaining point identification and comparable estimates to theirs, without using their (or any other) instrument.
Andrographis paniculata: A Review of its Anti-Cancer Potential

Since the prehistoric period medicinal plants have been manoeuvred for disease cure. Cancer is a global problem which is widely affecting the world. Plant derived natural products have gained much importance in the field of cancer chemotherapy during the recent past and the search for new drugs for cancer treatment still continues. Andrographis paniculata (Brum. F) Nees, a highly recognized plant of traditional Chinese and Indian healthcare system has gained the consideration of researchers for cancer cure. A. paniculata has multiple possible pharmacological actions. This review reports detailed information on major phytochemicals, traditional applications, pharmacology, and methodology of phytochemical purification from A. paniculata. Its major active phytochemical Andrographolide have shown significant anticancer properties. These characteristics of A. paniculata make it a strong candidate as a prospective plant derived anticancer drug.
Software Quality Assurance Research: Achievements, Challenges, Dreams

- Software engineering comprehends many disciplines dedicated to forestall and remedy malfunctions and to warrant adequate behavior. Testing, the topic of this paper, could be a widespread validation approach in trade, however it's still mostly unexpected, expensive, and erratically effective. Indeed, computer code testing could be a broad term encompassing a variety of activities on the event cycle and on the far side, geared toward completely different goals. Hence, computer code testing analysis faces a set of challenges. The same roadmap of the foremost relevant challenges to be self-addressed is here proposed. In it, the start line is deep-rooted by some important past achievements, whereas the destination consists of 4 known goals to that analysis ultimately tends, however that stay as unapproachable as dreams. The routes from the achievements to the dreams square measure sealed by the outstanding analysis challenges, that square measure mentioned within the paper at the side of attention-grabbing current work.
Robust QA System with xEDA: Final Report

Data augmentation is often used in machine learning to reduce overfitting. However, data augmentation has not been thoroughly explored in NLP in the context of improving robustness on shifts in data domains. We present xEDA: extended easy data augmentation techniques for boosting performance on question answering tasks. xEDA extends existing data augmentation techniques by drawing inspirations from techniques in computer vision. We evaluate its performance on out-of-domain question answering tasks and show that xEDA can improve performance and robustness to domain shifts when a small subset of the out-of-domain data is available at train time
Building a QA system (IID SQuAD track)

The ability of reading text and then answering questions about it, is a very challenging task for machines, because it is requiring both understanding of natural language and knowledge about the world as well as the context of the question. In this paper, we will build a QA system for the SQUAD 2.0 dataset, which extends the original dataset with unanswerable questions. We have already a fully-functional neural baseline, our goal is to improve this baseline, using new techniques and models used in high performing SQUAD models. We are also adding some changes to our models, to see if these changes increase the accuracy of our model. First, we will extend the baseline model to match the original BiDAF model by including a character-level embedding layer. Furthermore, we will replace the basic Attention Layer of our baseline by different dynamic attention layers such as a Dynamic Co-attention Layer, Self-Matching-Attention Layer as well as adapting transformers ideas to question answering. We also add another BiDAF layer performing a self attention process similar to the one between the context and the query. A correct implementation of these changes improves our model, such as the character-level embedding layer which allows our model to handle out-of-vocabulary words and extract information from words’ morphology.
Group Testing Large Populations for SARS-CoV-2

Group testing, the testing paradigm which combines multiple samples within a single test, was introduced in 1943 by Robert Dorfman. Since its original proposal for syphilis screening, group testing has been applied in domains such as fault identification in electrical and computer networks, machine learning, data mining, and cryptography. TheSARS-CoV-2 pandemic has led to proposals for using group testing in its original context of identifying infected individuals in a population with few tests. Studies suggest that non-adaptive group testing - in which all the tests are determined in advance - for SARS-CoV-2could help save 20% to 90% of tests depending on the prevalence. However, no systematic approach for comparing different non-adaptive group testing strategies currently exists. In this paper we develop a software platform for evaluating non-adaptive group testing strategies in both a noiseless setting and in the presence of realistic noise sources, modelled on published experimental observations, which makes them applicable to polymerase chain reaction (PCR) tests, the dominant type of tests for SARS-CoV-2. This modular platform can be used with a variety of group testing designs and decoding algorithms. We use it to evaluate the performance of near-doubly-regular designs and a decoding algorithm based on an integer linear programming formulation, both of which are known to be optimal in some regimes. We find savings between 40% and 91% of tests for prevalences up to 10% when a small error (below 5%) is allowed. We also find that the performance degrades gracefully with noise. We expect our modular, user-friendly, publicly available platform to facilitate empirical research into non-adaptive group testing for SARS-CoV-2.
Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-related Mental Disorders (MIND-Set): a cross-sectional comorbidity study from an RDoC perspective

Background: It is widely acknowledged that comorbidity between psychiatric disorders is common. Shared and diverse underpinnings of psychiatric disorders cannot be systematically understood on the basis of symptom-based categories of mental disorders, which map poorly onto pathophysiological mechanisms. In the MIND-Set study, we make use of current concepts of comorbidity that transcend the current diagnostic categories. We test this approach to psychiatric problems in patients with frequently occurring psychiatric disorders and their comorbidities (excluding psychosis). The main objective of the MIND-Set project is to determine the shared and specific mechanisms of neurodevelopmental and stress-related psychiatric disorders at different observational levels. Methods: This is an observational, cross-sectional study. Data from different observational levels as defined in the research domain criteria (RDoC; genetics, physiology, neuropsychology, system level neuroimaging, behavior, self-report and experimental neurocognitive paradigms) are collected over four time points. Included are adult ([≥] 18 years), non-psychotic, psychiatric patients with a clinical diagnosis of a stress-related disorder (mood disorder, anxiety disorder and/or addiction disorder) and/or a neurodevelopmental disorder (ASD and/or ADHD). Individuals with no current or past psychiatric diagnosis are included as controls. Data collection started in June 2016 with the aim to include a total of 650 patients and 150 healthy controls by 2021. The data collection procedure includes online questionnaires and three subsequent sessions with 1) Standardized clinical examination, physical examination, and blood sampling; 2) Psychological constructs, neuropsychological tests, and biological marker sampling; 3) Neuroimaging measures. Discussion: The MIND-Set study enables us to investigate the mechanistic underpinnings of non-psychotic psychiatric disorders transdiagnostically. We will identify both shared and disorder-specific markers at different observational levels that can be used as targets for future diagnostic and treatment approaches
Dermatomyositis

Background and Objectives Discoveries of dermatomyositis-specific antibodies (DMSAs) in patients with dermatomyositis raised awareness of various myopathologic features among antibody subtypes. However, only perifascicular atrophy and perifascicular myxovirus resistant protein A (MxA) overexpression were officially included as definitive pathologic criteria for dermatomyositis classification. We aimed to demonstrate myopathologic features in MxA-positive dermatomyositis to determine characteristic myopathologic features in different DMSA subtypes. Methods We performed a retrospective pathology review of muscle biopsies of patients with dermatomyositis diagnosed between January 2009 and December 2020 in a tertiary laboratory for muscle diseases. We included all muscle biopsies with sarcoplasmic expression for MxA and seropositivity for DMSAs. MxA-positive muscle biopsies that tested negative for all DMSAs were included as seronegative dermatomyositis. We evaluated histologic features stratified according to 4 pathology domains (muscle fiber, inflammatory, vascular, and connective tissue) and histologic features of interest by histochemistry, enzyme histochemistry, and immunohistochemical study commonly used in the diagnosis of inflammatory myopathy. We performed ultrastructural studies of 54 available specimens. Results A total of 256 patients were included. Of these, 249 patients were positive for 1 of the 5 DMSAs (seropositive patients: 87 anti–transcription intermediary factor 1-γ [TIF1-γ], 40 anti–complex nucleosome remodeling histone deacetylase [Mi-2], 29 anti–melanoma differentiation gene 5 [MDA5], 83 anti–nuclear matrix protein 2 [NXP-2], and 10 anti–small ubiquitin-like modifier-activating enzyme [SAE] dermatomyositis) and 7 patients were negative for all 5 DMSAs (seronegative patients). Characteristic myopathologic features in each DMSA subtype were as follows: anti-TIF1-γ with vacuolated/punched out fibers (64.7%; p < 0.001) and perifascicular enhancement in HLA-ABC stain (75.9%; p < 0.001); anti-Mi-2 with prominent muscle fiber damage (score 4.9 ± 2.1; p < 0.001), inflammatory cell infiltration (score 8.0 ± 3.0; p = 0.002), perifascicular atrophy (67.5%; p = 0.02), perifascicular necrosis (52.5%; p < 0.001), increased perimysial alkaline phosphatase activity (70.0%; p < 0.001), central necrotic peripheral regenerating fibers (45.0%; p = 0.002), and sarcolemmal membrane attack complex deposition (67.5%; p < 0.001); anti-MDA5 with scattered/diffuse staining pattern of MxA (65.5%; p < 0.001) with less muscle pathology and inflammatory features; anti-NXP-2 with microinfarction (26.5%; p < 0.001); and anti-SAE and seronegative dermatomyositis with HLA-DR expression (50.0%; p = 0.02 and 57.1%; p = 0.02, respectively). Discussion We describe a comprehensive serologic–pathologic correlation of dermatomyositis primarily using MxA expression as an inclusion criterion. In our study, DMSAs were associated with distinctive myopathologic features suggesting different underlying pathobiologic mechanisms in each subtype.
Automatic Detection of Diabetic Retinopathy in Fundus Image: A Survey

One of the major hitches generated by diabetes is diabetic retinopathy. Diabetic retinopathy is one of the grounds for impaired vision and blindness on a global scale. It occurs due to the impairment of blood vessels in the retina. It constitutes 5% of world blindness approximately 5 million. In the view that the prevalence rate of diabetes progressively increases there are high possibilities that a greater number of individuals are subjected to eye complications in the future. It is necessary for diabetic patients to undertake eye examinations from an ophthalmologist on an annual basis and to keep a track of their blood sugar levels, blood pressure, and cholesterol levels. If diabetic retinopathy is not treated properly at the preliminary stage, it may lead to permanent eye damage/blindness. To track down diabetic retinopathy at the early stage, the diabetic eye undergoes DR screening with the assistance of a computerized screening system. Diverse motivations and aspirations control human behaviour. Few of these acts are motivated by greater reasons, such as progressing humanity and improving people's lives, or alleviating human suffering. Among them, medical science inventions and discoveries have advanced to a new stage in terms of alleviating human suffering and improving life quality. Thousands of lives have been saved as a result of inventions and discoveries such as anaesthesia, medicines for smallpox, polio, plague, and penicillin. As a result, the motivation for this research is to alleviate human suffering. Throughout this paper, I have worked diligently to make the following assertion a reality: "Prevention is better than cure". This overview paper emphasis on general anatomy of the eye, retinal image acquisition, a concise overview of diabetic retinopathy, retinopathy, and measures of diagnostic accuracy. The literature survey evaluates the latest research over several approaches enabling the identification of DR in retinal fundus images comprehensively. The paper also addresses the challenges encountered in the detection of DR.
Socio-economic Benefits from Applying Innovative Technologies in the Tourism and Hospitality Industry in Asia

Technology has reshaped the nature of competition in the tourism industry. Hence, the whole system of technology management rapidly dispersed throughout the tourism industry and no player can escape its impact. Although the widespread adoption of technology has deeply influenced the tourism and technology industry, still we lack a clear understanding of the process by which partners in the technology and tourism industry can co-create, and share technology-enabled value. On the other hand, technology adoption has provided both challenges and opportunities for the tourism industry. This book chapter seeks to provide a framework for the utilization of innovative management methods in tourism by adopting a strategic perspective and socio-economic benefits from applying innovative technologies in the tourism industry.