Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of sleeper’s impact on the deep-water pipeline lateral global buckling

Pipelines are the most important transportation way for offshore oil and gas, and the lateral buckling is the main global buckling form for deep-water pipelines. The sleeper is an economic and efficient device to trigger the lateral buckling in preset location. This paper analyzed the lateral buckling features for on-bottom pipeline and pipeline with sleeper. The stress and strain variation during buckling process is shown to reveal the impact of sleeper on buckling.
Elements of Modern Signal Processing Lecture 11 — February 18 , 2021

Last Time: In modeling the problem of X-ray tomography, we introduced the Radon transform, which outputs a collection of line integrals of an input function. The procedure of backprojection was a first attempt at recovering a signal from its Radon transform; however, we saw that it did not return exact reconstruction, but rather a smoothed version of the original signal. Through the projection-slice theorem, we established a relation between the Radon and the Fourier transforms. This relation allowed us to derive a closed-form expression for the actual inverse Radon transform, which we call the filtered backprojection formula.
A dual split Bregman method for fast ℓ1 minimization

In this paper we propose a new algorithm for fast l1 minimization as frequently arising in compressed sensing. Our method is based on a split Bregman algorithm applied to the dual of the problem of minimizing ∥u∥1 + 1 2α ∥u∥2 such that u solves the under determined linear system Au = f , which was recently investigated in the context of linearized Bregman methods. Furthermore, we provide a convergence analysis for split Bregman methods in general and show with our compressed sensing example that a split Bregman approach to the primal energy can lead to a different type of convergence than split Bregman applied to the dual, thus making the analysis of different ways to minimize the same energy interesting for a wide variety of optimization problems.
Revisiting Knowledge Distillation via Label Smoothing Regularization — Supplementary

where q is the distribution of ground truth, p is the output distribution of student model, H(, ) is cross-entropy loss function and DKL is KL divergence, and pτ is the output distribution of teacher model soften by temperature τ . The temperature τ and weight α are hyper-parameters. The temperature τ and weight α for Reverse KD and Normal KD are given in Tab. 1; τ and α for De-KD are given in Tab. 2. For fair comparisons, the hyper-parameters of Normal KD, ReKD and De-KD are grid searched from 70 training epochs (200 epochs in total), and 8 GPUs are used to search these hyper-paramwters (4 NVIDIA V, 4 NVIDIA X). All experiments are conducted with the same experiment settings.
Some remarks about Dependent Type Theory

The goal of this paper is to describe a calculus designed in 84/85 [19] and later presented in [28]. This calculus was obtained by applying the ideas introduced by N.G. de Bruijn [30, 31] for AUTOMATH to some functional systems created by J.-Y. Girard [40]. There was also strong connections with the work of P. Martin-Löf [64, 66]. This calculus provided quite simple uniform notations for proofs and (functional) programs. Because of this simplicity and uniformity, it was possible to use it for analysing logical problems such as impredicativity [81, 82], paradoxes [20, 26, 23, 7], but also notions of computer science such as parametricity [9, 3]. It could also be used as the basis of implementations of proof and functional systems [27], arguably simpler, or at least competitive, with the ones that were available at the time [46, 17, 76]. One main theme of this work is the importance of notations in mathematics and computer science: new questions were asked and solved only because of the use of AUTOMATH notation, itself a variation of λ-notation introduced by A. Church [14, 58] for representing functions.
GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison

 We then apply principal component analysis (PCA) to GFVs of all of the PSNs, in order to compare PSNs (see Figure 4). Methodology Results • Early methods for protein structural comparisons were sequence-based. • Amino acids that are distant in the sequence can be close in the 3-dimensional (3D) structure. • 3D contact approaches can complement sequence approaches. • Traditional 3D contact approaches study 3D structures directly. • 3D structures can be modeled as protein structure networks (PSNs) (see Figure 1). GRAFENE: graphlet-based alignment-free network approach that integrates 3D structural and sequence data to improve protein structural comparison
Cryptography and network security - principles and practice (2. ed.)

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. The Principles and Practice of Cryptography and Network Security Stallings’ Cryptography and Network Security, Seventh Edition, introduces the reader to the compelling and evolving field of cryptography and network security. In an age of viruses and hackers, electronic eavesdropping, and electronic fraud on a global scale, security is paramount. The purpose of this book is to provide a practical survey of both the principles and practice of cryptography and network security. In the first part of the book, the basic issues to be addressed by a network security capability are explored by providing a tutorial and survey of cryptography and network security technology. The latter part of the book deals with the practice of network security: practical applications that have been implemented and are in use to provide network security. The Seventh Edition streamlines subject matter with new and updated material — including Sage, one of the most important features of the book. Sage is an open-source, multiplatform, freeware package that implements a very powerful, flexible, and easily learned mathematics and computer algebra system. It provides hands-on experience with cryptographic algorithms and supporting homework assignments. With Sage, the reader learns a powerful tool that can be used for virtually any mathematical application. The book also provides an unparalleled degree of support for the reader to ensure a successful learning experience.
Cryptography and network security - principles and practice (3. ed.)

This revised third edition presents the subject with the help of learning objectives (LO) guided by Bloom's Taxonomy and supports outcome-based learning. It discusses concepts from elementary to advanced levels with focus on mathematical preliminaries. Numerous solved examples, algorithms, illustrations & usage of fictitious characters make the text interesting and simple to read. Salient Features: Dedicated section on Elementary Mathematics Pseudo codes used to illustrate implementation of algorithm Includes new topics on Shannon's theory and Perfect Secrecy, Unicity Distance and Redundancy of Language Interesting elements introduced through QR codes Solutions to select chapter-end problems (End of every chapter) 19 Proofs of theorems (Appendix Q) Secured Electronic Transaction (Appendix R) Enhanced Pedagogical Features: Solved Examples: 260 Exercises: 400 Review Questions: 200 Illustration: 400
Bioinformatics models in drug abuse and Neuro-AIDS: Using and developing databases

The magnitude of the problems of drug abuse and Neuro-AIDS warrants the development of novel approaches for testing hypotheses in diagnosis and treatment ranging from cell culture models to developing databases. In this study, cultured neurons were treated with/without HIV-TAT, ENV, or cocaine in a 2x2x2 expression study design. RNA was purified, labeled, and expression data were produced and analyzed using ANOVA. Thus, we identified 35 genes that were significantly expressed across treatment conditions. A diagram is presented showing examples of molecular relationships involving a significantly expressed gene in the current study (SOX2). Also, we use this information to discuss examples of gene expression interactions as a means to portray significance and complexity of gene expression studies in Drug Abuse and Neuro-AIDS. Furthermore, we discuss here that critical interactions remain undetected, which may be unravelled by developing robust database systems containing large datasets and gleaned information from collaborating scientists . Hence, we are developing a public domain database we named The Agora database , that will served as a shared infrastructure to query, deposit, and review information related to drug abuse and dementias including Neuro-AIDS. A workflow of this database is also outlined in this paper.
Hydrological modelling using artificial neural networks

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
The Dual-tree Complex Wavelet Transform

[A coherent framework for multiscale signal and image processing] T he dual-tree complex wavelet transform (CWT) is a relatively recent enhancement to the discrete wavelet transform (DWT), with important additional properties: It is nearly shift invariant and directionally selective in two and higher dimensions. It achieves this with a redundancy factor of only 2d for d-dimensional signals, which is substantially lower than the undecimated DWT. The multidimensional (M-D) dual-tree CWT is nonseparable but is based on a computationally efficient, separable filter bank (FB). This tutorial discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing. We use the complex number symbol C in CWT to
Fostering diffusion of scientific contents of National Society Cardiovascular Journals: the new ESC search engine

Editors’ Network Members (Editors-in-Chiefs of National Society Cardiovascular Journals): Loizos Antoniades (Editor-in-Chief, Cyprus Heart Journal), Eduard Apetrei (Editor-in-Chief, Romanian Journal of Cardiology), Michael Aschermann (Editor-in-Chief, Cor et Vasa), Leonardo Bolognese (Editor-in-Chief, Giornale Italiano Di Cardiologia), Mirza DiliÊ (Editor-in-Chief, Medical Journal), Istvan Edes (Editor-in-Chief, Cardiologia Hungarica), Krzysztof J. Filipiak (Editor-in-Chief, Kardiologia Polska), Faig Guliyev (Editor-in-Chief, Azerbaijan Cardiology Journal), Habib Haouala (Editor-in-Chief, Cardiologie Tunisienne), Mahmoud Mohamed Hassanein (Editor-in-Chief, Egyptian Heart Journal), Magda Heras (Editor-in-Chief, Revista Espanola de CardiologÌa), Christer Höglund (Editor-in-Chief, Svensk Cardiologi), Ivan Hulin, (Editor-in-Chief, Cardiology Letters), Kurt Huber (Editor-in-Chief, Journal für Kardiologie), Mario Ivanuπa (Editor-in-Chief, Cardiologia Croatica), Germanas Marinskis (Editor-in-Chief, Seminars in Cardiovascular Medicine), Izet MaπiÊ (Editor-in-Chief, Medical Archives), Miodrag OstojiÊ (Editor-in-Chief, Heart and Blood Vessels), Otmar Pachinger (Editor-in-Chief, Wiener Klinische Wochenschrift — the Central European Journal of Medicine), Dimitar Raev (Editor-in-Chief, Bulgarian Journal Cardiology), Mamanti Rogava (Editor-in-Chief, Cardiology and Internal Medicine XXI), Olaf Rodevand (Editor-in-Chief, Hjerteforum), Vedat Sansoy (Editor-in-Chief, Archives of the Turkish Society of Cardiology), Evgeny Shlyakhto (Editor-in-Chief, Russian Journal of Cardiology), Valentin A Shumakov (Editor-in-Chief, Ukrainian Journal of Cardiology), Ernst Van der Wall (Editor-in-Chief, Netherlands Heart Journal), Jorgen Videbak (Editor-in-Chief, Cardiologisk Forum), Thomas F Lüscher (Editor-in-Chief, Cardiovascular Medicine).
Computer immunology

from dangerous foreign pathogens, including bacteria, viruses, parasites, and toxins. Their role in the body is analogous to that of computer security systems in computing. Although there are many differences between living organisms and computers, the similarities are compelling and could point the way to improved computer security.1 Improvements can be achieved by designing computer immune systems with some of the important properties of natural immune systems, including multilayered protection; highly distributed detector, effector, and memory systems; diversity of detection ability across individuals; inexact matching strategies; and sensitivity to most new foreign patterns. Some of these properties are well known but seldom implemented successfully; other properties are less well known. The immune system provides a persuasive example of how they might be implemented in a coherent system. The immune system comprises cells and molecules.2 Recognition of foreign protein, called antigen, occurs when immune system detectors, including T cells, B cells, and antibodies, bind to antigen. Binding between detector and antigen is determined by the physical and chemical properties of their binding regions. Binding is highly specific, so each detector recognizes only a limited set of structurally related antigen. When a detector and antigen bind, a com-
From Videos to Verbs : Mining Videos for Activities using a cascade of dynamical systems ( Supplemental Material )

A useful test for a representational model is to synthesize from it, and see how well the synthesized samples resemble real-world phenomenon. In this section, we show a few synthesis results obtained using the learnt models. In the first experiment, we used one walk sequence from the USF gait gallery data to learn one walk pattern. We modeled the entire walk sequence using just one LTI model. Then, we used the learnt model to generate the sequence. A few frames from the generated sequence are shown in figure 1.
The HP Auto RAID hierarchical storage system

The technology we describe in this paper, known as HP Auto RAID, automatically and transparently manages migration of data blocks between these two levels as access patterns change. The result is a fully redundant storage system that is extremely easy to use, is suitable for a wide variety of workloads, is largely insensitive to dynamic workload changes, and performs much better than disk arrays with comparable numbers of spindles and much larger amounts of front-end RAM cache. Because the implementation of the HP Auto RAID technology is almost entirely in software, the additional hardware cost for these benefits is very small.
Reducing language context confusion for end-to-end code-switching automatic speech recognition

Code-switching is about dealing with alternative languages in the communication process. Training end-to-end (E2E) automatic speech recognition (ASR) systems for code-switching is known to be a challenging problem because of the lack of data compounded by the increased language context confusion due to the presence of more than one language. In this paper, we propose a languagerelated attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model based on the Equivalence Constraint Theory (EC). The linguistic theory requires that any monolingual fragment that occurs in the code-switching sentence must occur in one of the monolingual sentences. It establishes a bridge between monolingual data and code-switching data. By calculating the respective attention of multiple languages, our method can efficiently transfer language knowledge from rich monolingual data. We evaluate our method on ASRU 2019 Mandarin-English code-switching challenge dataset. Compared with the baseline model, the proposed method achieves 11.37% relative mix error rate reduction.
Concept Mapping in Agile Usability : A Case Study

In this paper we report on the experience of using our concept mapping approach on an agile software project to assess its fitness. Participants used our novel concept mapping approach over a four week period during the development of a software tool for a local nonprofit agency. Results indicate that our concept mapping approach has value as a visual tool in agile usability environments.
A patent search strategy based on machine learning for the emerging field of service robotics by

Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents as concerning service robots. We introduce a synergy of a traditional technology identification process, namely keyword extraction and verification by an expert community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert bias regarding vested interests on lexical query methods, (2) avoids problems with citational approaches, and (3) facilitates evolutionary changes. Based upon a small core set of worldwide service robotics patent applications we derive apt n-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level of 85%.
IBIS: Interposed Big-data I/O Scheduler

Big-data systems are increasingly shared by diverse, data-intensive applications from different domains. However, existing systems lack the support for I/O management, and the performance of big-data applications degrades in unpredictable ways when they contend for I/Os. To address this challenge, this paper proposes IBIS, an Interposed Big-data I/O Scheduler, to provide I/O performance differentiation for competing applications in a shared big-data system. IBIS transparently intercepts, isolates, and schedules an application's different phases of I/Os via an I/O interposition layer on every datanode of the big-data system. It provides a new proportional-share I/O scheduler, SFQ(D2), to allow applications to share the I/O service of each datanode with good fairness and resource utilization. It enables the distributed I/O schedulers to coordinate with one another and to achieve proportional sharing of the big-data system's total I/O service in a scalable manner. Finally, it supports the shared use of big-data resources by diverse frameworks and manages the I/Os from different types of big-data workloads (e.g., batch jobs vs. queries) across these frameworks. The prototype of IBIS is implemented in Hadoop/YARN, a widely used big-data system. Experiments based on a variety of representative applications (WordCount, TeraSort, Facebook, TPC-H) show that IBIS achieves good total-service proportional sharing with low overhead in both application performance and resource usages. IBIS is also shown to support various performance policies: it can deliver stronger performance isolation than native Hadoop/YARN (99% better for WordCount and 15% better for TPC-H queries) with good resource utilization; and it can also achieve perfect proportional slowdown with better application performance (30% better than native Hadoop).
A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest X-Rays and clinical variables

BACKGROUND Patients with COVID-19 could develop severe disease requiring admission to the Intensive Care Unit (ICU). This manuscript presents a novel method that predicts whether a patient will need admission to the ICU and assess the risk of in-hospital mortality by training a deep learning model that combines a set of clinical variables and features in the Chest-X-Rays. METHODS This was a prospective diagnostic test study. Patients with confirmed SARS-CoV-2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for Chest-X-ray images using an artificial intelligence (AI) tool and a Random Forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models. RESULTS A total of 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, the fraction of inspired oxygen - FiO2 on admission, dyspnoea on admission, and obesity. Moreover, the variables associated with hospital mortality were age, the fraction of inspired oxygen - FiO2 on admission, and dyspnoea. When implementing the AI model to interpret the Chest-X-rays and the clinical variable identified by random forest, we developed a model that accurately predicts ICU admission (AUC:0.92±0.04) and hospital mortality (AUC:0.81±0.06) in patients with confirmed COVID-19. CONCLUSIONS This automated Chest-X-ray interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 that might require admission to the ICU.
Presenting an empirical model for determining the sugar beet evapotranspiration by GDD parameter (Case study: Torbat-Jam, Iran)

So far, various methods have been presented for determining reference crop evapotranspiration in different parts of the world. The most popular and prestigious of them are probably the combined methods of Penman family, modified Blany-Criddle, Hargreaves - Samani and thornthwaite. Due to lack of lysimeter data in many parts of Iran, presenting an equation according to the regional condition with high precision is very paramount. The purpose of this paper is to present a method with higher precision for determining the evapotranspiration of sugar beet by using the meteorology and GDD (Growing Degree Day) parameters in Torbat_Jam, Iran. Determined methods were evaluated with FAO- Penman- Monteith method as a standard model. RMSE, R2 and Nash-Sutcliffe, and NS indexes were used for comparing fitness indexes. Results showed that equation 4 is appropriate for calculating crop water requirements, also R2, RMSE, and Nash-Sutcliffe coefficient were 0.683, 1.117 and 0.99, for equation 4, respectively.