Miracle-dz/UniFAR-FTUs huggingface dataset:
Green functions in the renormalized many-body perturbation theory for correlated and disordered electrons

The ways of introducing and handling renormalizations in the many-body perturbation theory are reviewed. We stress the indispensable role the technique of Green functions plays in extrapolating the weak-coupling perturbative approaches to intermediate and strong couplings. We separately discuss mass and charge renor- malizations. The former is incorporated in a self-consistent equation for the self-energy derived explicitly from generating Feynman diagrams within the Baym and Kadanoff approach. The latter amounts to self-consistent equations for two-particle irreducible vertices. We analyze the charge renormalization initiated by De Domi- nicis and Martin and demonstrate that its realization via the parquet approach may become a powerful and viable way of using the many-body diagrammatic approach reliably in non-perturbative regimes with coopera- tive phenomena induced by either strong interaction or strong static randomness. Electric and magnetic properties of solids are determined by the behavior of valence electrons weakly bound to the atoms forming the crystalline lattice. The behavior of the gas of valence electrons in solids is most profound in metals with an open Fermi surface. Conduction electrons in metals can be described in a number of situations and for various purposes quite accurately by Bloch waves, eigenstates of the Fermi-gas Hamiltonian. We know, however, that electrons even in metals are not a noninteracting gas. They are generally exposed to forces driving them out of the equilibrium state of the Fermi gas. Firstly, no metal is a pure crystal and hence the electrons do not move freely in space. They are scattered on randomly distributed impurities and lattice defects. Secondly, the electrons are neither free and their mutual repulsion cannot be neglected particularly for transition and heavy metals. It is the most important and long standing task of condensed-matter theorists to comprehend and qualitatively capture the deviations in the behavior of electrons in real metals from their idealized representation via Bloch waves. The problem with quantification of the behavior of electrons beyond the almost free-electron picture is in paucity of available tools for accomplishing this task. Even if we can reduce the description of the electron gas in metals to rather simple and generic models, we are unable to solve them exactly, except for a few limiting cases that mostly do not correspond to situations of physical interest. With the increasing power of modern computers the role of numerical solutions and numerical "brute force" approaches has increased. Numerical solutions are usually unbiased and can reach a rather high level of quantitative precision. In some aspects they substitute the missing exact analytic solutions. Numerical solutions are suitable and even indispensable in showing and displaying the trends and some global features of the models studied. They, however, fail in critical regions of phase transitions in the vicinity of singularities in correlation functions. In these
EMTE: An Enhanced Medical Terms Extractor Using Pattern Matching Rules

— Background : Downstream tasks, like clinical textual data classiﬁcation, perform best when given good quality datasets. Most of the existing clinical textual data preparation techniques rely on two main approaches, removing irrelevant data using cleansing techniques or extracting the valuable data using feature extraction tech- niques. Although both approaches showed promising results on experimental datasets, they still have limitations, mainly when applied to real-world datasets. The latter datasets are challenging because they might include incomplete, inconsistent and heterogeneous records. Therefore, understanding the nature of the data is vital in order to improve its quality before its transmission to the classiﬁcation model. Methods : This paper proposes a cleansing approach (called EMTE ) which extracts phrases (medical terms, abbreviations, and negations) using pattern-matching rules based on the linguistic processing of the clinical textual data. Without requiring any training, EMTE extracts the valuable medical data from clinical textual records even if they have different writing styles. Furthermore, since EMTE relies on dictionaries to store abbreviations and pattern-matching rules to detect the abbreviations, negations, and medical terms, it can be easily maintained and extended for industrial use. Experiments : To evaluate the performance of our approach, it was compared to three other techniques. All four cleansing techniques were applied to a large industrial imbalanced dataset, consisting of 2.21M samples from different specialties with 1,050 ICD-10 codes. The outputs of these cleansing techniques were fed to several Deep Neural Network (DNN) algorithms to solve the ICD-10 (International Classiﬁcation of Diseases, 10 th edition) multi-label classiﬁcation problem. In this problem, the clinical textual samples are the input data, and the ICD-10 codes are the actual labels. Results : The experimental results on several Deep Neural Network (DNN) algorithms showed that our cleansing approach signiﬁ- cantly improves the trained models’ performance when compared to the other tested
Correlating Intrusion Detection with Attack Graph on Virtual Computer Networkings

: Securing a computer networking system requires the ability to gather and organise information about potential vulnerabilities existing in the system. One way of utilising the information above is to generate an attack graph of all possible attack paths. Current attack graph generation methods reach scalability issue with the growth of network devices and links, and one solution is to correlate attack graph with intrusion detection systems. However, correlation techniques are rarely studied especially on generating attack graphs on virtual computer networks, as correlations are inflexible to be integrated to existing attack graph generators. Previously we proposed mAGG, an attack graph generation framework on virtual networkings; and LSAFID, an intrusion detection system based on doc-word. In this paper, we propose a new method for correlating intrusion detection algorithm for attack graph generation on virtual networkings. Our new proposed method is flexible in network architectures and functionalities, and shortens the scale of generated attack graph.
Collinear dual-color laser emission from a microfluidic dye laser

Collinear dual-color laser emission on a chip is obtained from a liquid dye mixture flowing through an optical microcavity. Soft lithography has been used to fabricate the microfluidic channels and integrate the optical resonator. The laser cavity, of nanoliter volume, is made of two parallel gold mirrors coated on the end faces of optical fibers and integrated into a polydimethylsiloxane chip. Such a design allows a simple but efficient coupling of the laser emission with an optical fiber. The amplification medium is composed of rhodamine 6G and sulforhodamine, dissolved in a common ethanol solution. When the dye mixture is optically pumped by a pulsed and frequency-doubled Nd:YAG laser, simultaneous laser emission at wavelengths 559nm and 597nm is observed, showing yellow and red colors with a complete spatial overlap within the single output beam. Moreover, the overall laser efficiency for the dye mixture system is significantly increased compared to that for the individual dyes.
Misspecified Moment Inequality Models: Inference and Diagnostics

This paper is concerned with possible model misspecification in moment inequality models. Two issues are addressed. First, standard tests and confidence sets for the true parameter in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. Second, specification tests have relatively low power against a range of misspecified models. Thus, failure to reject the null of correct specification does not necessarily provide evidence of correct specification. That is, model specification tests are subject to the problem that absence of evidence is not evidence of absence. This paper develops new diagnostics for model misspecification in moment inequality models that do not suffer from this problem.
Targeting Caspase-3 Gene in rCHO Cell Line by CRISPR/Cas9 Editing Tool and Its Effect on Protein Production in Manipulated Cell Line

Background Chinese hamster ovary (CHO) cells are the widely used mammalian cell host for biopharmaceutical manufacturing. During cell cultures, CHO cells lose viability mainly from apoptosis. Inhibiting cell death is useful because prolonging cell lifespans can direct to more productive cell culture systems for biotechnology requests. Objectives This study exploited a CRISPR/Cas9 technology to generate site-specific gene disruptions in the caspase-3 gene in the apoptosis pathway, which acts as an apoptotic regulator to extend cell viability in the CHO cell line. Methods The STRING database was used to identify the key pro-apoptotic genes to be modified by CRISPR/Cas9 system. The guide RNAs targeting the caspase-3 gene were designed, and vectors containing sgRNA and Cas9 were transfected into CHO cells that expressed erythropoietin as a heterologous protein. Indel formation was investigated by DNA sequencing. Caspase-3 expression was quantified by real-time PCR and western blot. The effect of editing the caspase-3 gene on the inhibition of apoptosis was also investigated by induction of apoptosis in manipulated cell lines by oleuropein. Finally, the erythropoietin production in the edited cells was compared to the control cells. Results The caspase-3 manipulation significantly prolongation of the cell viability and decreased the caspase-3 expression level of protein in manipulated CHO cells (more than 6-fold, P-value < 0.0001). Manipulated cells displayed higher threshold tolerance to apoptosis compared to the control cells when they were induced by oleuropein. They show a higher IC50 than the control ones (7271 µM/mL Vs. 5741 µM/mL). They also show a higher proliferation rate than the control cells in the presence of an apoptosis inducer (P-value < 0.0001). Furthermore, manipulated cell lines significantly produce more recombinant protein in the presence of 2,000 µM oleuropein compared to the control ones (P-value = 0.0021). Conclusions We understood that CRISPR/Cas9 could be effectively applied to suppress the expression of the caspase-3 gene and rescue CHO cells from apoptosis induced by cell stress and metabolites. The CRISPR/Cas9 system-assisted caspase-3 gene ablation can potentially increase erythropoietin yield in CHO cells.
Influence of Seasonal Post‐Depositional Processes on the Remanent Magnetization in Varved Sediments From Glacial Lake Ojibway (Canada)

The natural remanent magnetization (NRM) of high sedimentation rate sediments provides significant information about paleomagnetic secular variation of the Earth's magnetic field and can also potentially be used for stratigraphy. However, NRM acquisition depends on conditions inherent to the depositional environment. In addition to recording a precise annual chronology, varved sediments reflect marked annual sedimentary changes. The Earth's magnetic field does not vary significantly over such a short period, so magnetic changes recorded by varves are expected to reflect the influence of depositional parameters on the recording process. We focus here on a sequence of 27 ± 1 varves from the former proglacial Lake Ojibway (∼8.5 ka cal BP) from which individual cm‐thick summer and winter beds were sampled. Paleomagnetic, granulometric and geochemical analyses were conducted on each bed. A mean inclination shallowing of 24.3° is observed in winter beds, along with an 11.3° shallowing in summer beds. Magnetic declinations follow, on average, the expected field direction, but differences of up to 20° occur between successive beds. Summer beds are thicker than winter beds and have stronger magnetic susceptibility, higher Ca/Fe ratios and coarser sedimentary and magnetic grains. This grain size pattern reflects the input of coarser detrital particles during summer, while the finer fraction remained in suspension until it was deposited in winter. A combination of differential compaction between the winter and summer beds, seasonally varying physical and magnetic properties of sediments, and delayed NRM acquisition explains the variable and coercivity‐dependent inclination shallowing.
VR Virtual Prototyping Application for Airplane Cockpit: A Human-centred Design Validation

: The present study aimed to assess how professionals from the aviation industry perceived the usability of an application aimed at developing prototypes of airplane cockpits, in virtual reality, from a human-centred design perspective. 12 participants from the aeronautical industry took part in the study. An evaluation using the SUS (System Usability Scale) resulted in a final score of 81.3, while the results from the SAM (Self-Assessment Manikin) indicated a neutral-positive trend towards the application. From participant’s observations and comments, the application’s potential to improve airline security, pilot comfort, and cockpit design efforts, was recognized and appreciated. Despite the positive interactions, some aspects of the application were found to need further improvement, to better align with the expectations and needs of the professionals towards which the application is being geared to.
Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization

This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating solutions. Each solution explores its neighbourhood in a way similar to that of classical simulated annealing. Weights of the objectives, used for their local aggregation, are tuned in each iteration in order to assure a tendency for approaching the efficient solutions set while maintaining a uniform distribution of the generating solutions over this set. A computational experiment shows that the method is a better tool for approximating the efficient set than some previous proposals. © 1998 John Wiley & Sons, Ltd.
System for 3D Acquisition and 3D Reconstruction using Structured Light for Sewer Line Inspection

The assessment of sewer pipe systems is a highly important, but at the same time cumbersome and error-prone task. We introduce an innovative system based on single-shot structured light modules that facilitates the detection and classification of spatial defects like jutting intrusions, spallings, or misaligned joints. This system creates highly accurate 3D measurements with sub-millimeter resolution of pipe surfaces and fuses them into a holistic 3D model. The benefit of such a holistic 3D model is twofold: on the one hand, it facilitates the accurate manual sewer pipe assessment, on the other, it simplifies the detection of defects in downstream automatic systems as it endows the input with highly accurate depth information. In this work, we provide an extensive overview of the system and give valuable insights into our design choices.
Classification of benthic composition in a coral reef environment using spectral unmixing

Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.
Investigation of the electroluminescence spectrum shift of InGaN/GaN multiple quantum well light-emitting diodes under direct and pulsed currents

Electroluminescence (EL) spectra of InGaN/GaN multiple quantum well light emitting diodes with different piezoelectric polarization fields were investigated under pulsed and direct currents. We find a positive correlation between the piezoelectric polarization field and the thermally induced red-shift of the EL spectra at high direct currents above 25 A/cm2. Under pulsed current, when thermal effects are negligible, a non-uniform EL spectrum blue-shift rate as a function of injection level is observed and compared with numerical results obtained by both self-consistent and non-self-consistent K·P methods. We conclude that the screening effect is positively related to the piezoelectric polarization field, but the band filling-induced blue-shift is almost independent from the piezoelectric field. The electrostatic fields induced by free carriers in the quantum wells increase rapidly with current but tend to saturate at higher injection where the band filling effect becomes the dominant mechanism for the blu.
InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset

The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised learning approach requires a sufficiently large training dataset covering all the possible scenarios. And since most people exhibit facial expressions based upon their age group, gender, and ethnicity, a diverse facial expression dataset is needed. This becomes even more crucial while developing a FER system for the Indian subcontinent, which comprises of a diverse multi-ethnic population. In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10,200 images and 4,200 short videos of seven basic facial expressions. The dataset has posed expressions of 600 human subjects, and spontaneous/acted expressions of 6000 images crowd-sourced from the internet. To the best of our knowledge InFER is the first of its kind consisting of images from 600 subjects from very diverse ethnicity of the Indian Subcontinent. We also present the experimental results of baseline&deep FER methods on our dataset to substantiate its usability in real-world practical applications.
A hybrid implicit scheme for solving Navier–Stokes equations

A hybrid implicit scheme is developed in this study to solve 3D compressible Navier–Stokes equations on a hybrid unstructured mesh. An approximate evaluation technique for the residual term at a new time level is presented according to the analysis of the main physical mechanism introduced by the lower–upper symmetric Gauss–Seidel (LU‐SGS) relaxation‐based backward Euler implicit method. The residual evaluation method is computationally inexpensive because it does not require flux calculations for every cell surface. The approximated new time residual can be obtained without considerable computational effort; thus, a point‐implicit scheme with a multigrid property is designed to march a further time step after the previous LU‐SGS‐based backward Euler stage. This step leads to the principal ingredient of the presented two‐stage hybrid implicit scheme.
Random Quasi Intersections with Applications to Instant Machine Learning

: Random quasi intersections method was introduced. The number of such intersections grows exponentially with the increasing amount of pattern features, so that a non-polynomial problem in some machine learning applications emerges. However, the paper experimentally shows that randomness allows finding solutions to some visual machine learning tasks using a random quasi intersection-based fast procedure delivering 100% accuracy. Also, the paper discusses implementation of instant learning, which is, unlike deep learning, a non-iterative procedure. The inspiration comes from search methods and neuroscience. After decades of computing only one method was found able to deal efficiently with big data, - this is indexing, which is at the heart of both Google-search and large-scale DNA processing. On the other hand, it is known from neuroscience that the brain memorizes combinations of sensory inputs and interprets them as patterns. The paper discusses how to best index the combinations of pattern features, so that both encoding and decoding of patterns is robust and efficient.
A novel control strategy for dual active bridge bidirectional converter for electric vehicle application

Environmental issues and the depletion of fossil fuel resources cause increased demand for electric vehicles. An electric vehicle consists of two main components: a propulsion system and a battery charger. The design of an efficient, fast and economical charger is key to its success. Therefore, this paper presents a novel design of a dual active bridge‐based bidirectional converter with logical control for an electric vehicle application. The logical control of the converter enables the power flow between the grid and the electric vehicle and vice versa. The power flow is based on the single‐phase shift method. The converter selects the mode of operation based on the battery's state of charge and the user command using a binary switch. The power requirement influences the design of the circuit's parameters of the electric vehicle battery. A 50 V, 100 Ah, and 5 kW Li‐ion battery and AC power source of 230 V, 50 Hz are used in the simulation work. The circuit performance is verified in the SIMULINK/MATLAB environment. The analysis of the converter gives better control for various state‐of‐charge levels of electric vehicle batteries.