Miracle-dz/UniFAR-FTUs huggingface dataset:
Multioutput Image Classification to Support Postearthquake Reconnaissance

: After hazard events, large numbers of images are collected by reconnaissance teams to document the post-event state of structures, and to assess their performance and improve design procedures and codes. The majority of these data are captured as images and manually labeled. This highly repetitive task requires considerable domain expertise and time. Advances in deep learning have enabled researchers to rapidly classify reconnaissance images. Thus far, these classification methods are limited to a simple classification schema in which the classes are all either mutually exclusive or independent. To date, an efficient classification system of a complex schema containing many classes arranged in a multi-level hierarchical structure is not available to support earthquake reconnaissance. To address this gap, this paper introduces a comprehensive classification schema and a multi-output deep convolutional neural network (DCNN) model for rapid postearthquake image classification. In contrast to past work, herein a single multi-output DCNN classification model with a hierarchy-aware prediction was trained to enable the rapid organization of images. The performance of the proposed multi-output model was validated through comparisons with multi-label and multi-class models using an F1-score. As result, the multi-output model outperformed other models. Then, the multi-output model was deployed to a web-based platform called the Automated Reconnaissance Image Organizer, which can be used to easily organize earthquake reconnaissance images. DOI: 10.1061/(ASCE)CF.1943-5509.0001755. © 2022 American Society of Civil Engineers.
Parental Reminder Strategies and the Cost Implication for Improved Immunisation Outcomes: A Systematic Review and Meta-Analysis

Getting children vaccinated amidst prevailing barriers to immunisation has been challenging in both developed and developing countries. To address these problems, studies on parental reminder strategies were conducted to improve immunisation outcomes in children. These led to the development of different parental reminder interventions. This review systematically reviews different parental interventions and their cost implication for improved immunisations. Five online databases; Medline Complete, the Cumulative Index for Nursing and Allied Health Literature [CINAHL], Academic search premier, SPORTDiscus, and Health Source Nursing/Academic were searched using search terms. A total of 24 articles that met the inclusion criteria were included in this review. Studies that provided sufficient information were included for meta-analysis using Comprehensive Meta-Analysis version three, while narrative synthesis was used for the other studies. Results indicate that a heterogeneous and low-quality certainty of evidence on parental voice calls (OR 4.752, 95% CI 1.846–12.231, p = 0.001) exists in improving immunisation coverage. Regarding immunisation timeliness, a high-quality certainty of evidence on Short Message Services (SMS)-delivered health education messages (OR 2.711 95% CI 1.387–5.299, p = 0.004) had more effect on timely immunisation uptake. The average cost of SMS-delivered parental reminder interventions for improved immunisation outcomes was USD 0.50. The study concludes that mobile technology is a promising, cost-effective strategy for improved immunisation outcomes.
MAGNETO-ROTATIONAL NEUTRON STAR EVOLUTION: THE ROLE OF CORE VORTEX PINNING

We consider the pinning of superfluid (neutron) vortices to magnetic fluxtubes associated with a type II (proton) superconductor in neutron star cores. We demonstrate that core pinning affects the spin-down of the system significantly and discuss implications for regular radio pulsars and magnetars. We find that magnetars are likely to be in the pinning regime, whereas most radio pulsars are not. This suggests that the currently inferred magnetic field for magnetars may be overestimated. We also obtain a new timescale for the magnetic field evolution which could be associated with the observed activity in magnetars, provided that the field has a strong toroidal component.
Higher-order Markov Graph based Bug Detection in Cloud-based Deployments

Detecting execution anomalies is an integral part of building and protecting modern large-scale distributed systems. These systems generate a large volume of system logs to record system state and significant events, which provide a valuable resource to help debug system failures and perform root cause analysis. However, detecting anomalies in log sequences remains a challenge due to reasons including the imbalance of the data, the complexity of relationships between events, and the high dimensionality of log events. Traditional graph-based models may lose important higher-order sequence patterns and result in undetectable higher-order anomalies because they use first-order or fixed-order networks to represent the underlying log data. In this paper, we propose a novel unsupervised graph-based anomaly detection method, called GraphLog, which utilizes a variable high-order network representation. This variable representation enables GraphLog to efficiently learn log patterns from normal logs and detect first-order and higher-order log patterns that deviate from normal data. We demonstrate that the proposed graph-based log anomaly detection algorithm is effective, and it outperforms other baseline methods when trained using two real-world datasets.
Nonclassical light from an incoherently pumped quantum dot in a microcavity

Semiconductor microcavities with artificial single-photon emitters have become one of the backbones of semiconductor quantum optics. In many cases however, technical and physical issues limit the study of optical fields to incoherently excited systems. We analyze the model of a two-level system in a single-mode cavity, where the former is incoherently driven. The specific structure of the applied master equation yields a recurrence relation for the steady-state values of correlations of the intracavity field and the emitter. We provide boundary conditions, that permit a systematical, easy to implement solution, which is numerically less demanding than standard methods. Different cavity systems from previous experiments are analyzed. The derived boundary conditions also allow us direct analytical statements about the overall quantum state and its higher order moments. With this we can give very good approximations for the full quantum state of the field and show, that for every physically reasonable set of system parameters, the state of the intracavity field is nonclassical.
THE TIES THAT BIND? GALACTIC MAGNETIC FIELDS AND RAM PRESSURE STRIPPING

One process affecting gas-rich cluster galaxies is ram pressure stripping (RPS), i.e., the removal of galactic gas through direct interaction with the intracluster medium (ICM). Galactic magnetic fields may have an important impact on the stripping rate and tail structure. We run the first magnetohydrodynamic (MHD) simulations of RPS that include a galactic magnetic field, using 159 pc resolution throughout our entire domain in order to resolve mixing throughout the tail. We find very little difference in the total amount of gas removed from the unmagnetized and magnetized galaxies, although a magnetic field with a radial component will initially accelerate stripped gas more quickly. In general, we find that magnetic fields in the disk lead to slower velocities in the stripped gas near the disk and faster velocities farther from the disk. We also find that magnetic fields in the galactic gas lead to larger unmixed structures in the tail. Finally, we discuss whether ram pressure stripped tails can magnetize the ICM. We find that the total magnetic energy density grows as the tail lengthens, likely through turbulence. There are μG-strength fields in the tail in all of our MHD runs, which survive to at least 100 kpc from the disk (the edge of our simulated region), indicating that the area-filling factor of magnetized tails in a cluster could be large.
Formulation and evaluation of maline based solid super-saturable self-emulsifying drug delivery system for risperidone

Objective: Solid super-saturable self-emulsifying drug delivery system of risperidone was formulated utilizing maline; a type of a deep eutectic choline based ionic liquid (DEIL), as oil phase in order to enhance the drug's solubility, dissolution, and intestinal penetration, and therefore its bioavailability. Method: Maline was prepared using 1:1 molar ratio choline chloride and malonic acid. The SEDDs was formulated using maline as oil phase, tween80 or tween 20 as surfactant, and PEG600 or PG as co-surfactant in comparsion to conventional one using oleic acid as oil phase. Soluplus was utilized as drug precipitation inhibitor. For SEDDs solidification Aerosil 200 adsorbant was used as a carrier. Results: The mean droplet size of the optimized formulation 210.3 nm with polydispersity of 0.357, zeta potential -3.47, drug content 100.21±0.47%. The optimum solid supersaturable SEDDs formula (SSM) showed excellent flow properties with Carr’s Index of 8.77%, Hausner ratio of 1.09, and angle of repose at 38.72○. The FTIR, DSC and XRD results showed a good drug excipient compatability.Ex vivo permeation test on isolated sheap intestine revealed an inhancement in the risperidone amount permeated from the optimum formulation comparable to that of the pure drug and marketed tablet and even the conventional solid S-SEDDs formula. Conclusion: The developed Maline based solid super-saturable self-emulsifying drug delivery system of risperidone can be considered as an innovative, economically feasible alternative to existing risperidone formulations and a promising method for enhancing the oral bioavailability of poor soluble drugs.
A Survey and Comparison of Time-Delay Estimation Methods in Linear Systems

In this thesis the problem of time-delay estimation (TDE) in linear dynamic systems is treated. The TDE is studied for signal-to-noise ratios, input signals, and systems that are common in process industry. This also implies that both open-loop and closed-loop cases are of interest. The true time-delay is estimated, which may be different from the time-delay giving the best model approximation of the true system. Time-delays which are not a multiple of the sampling interval are also of interest to estimate.In this thesis, a review and a classification according to underlying principles of TDE methods in the literature are made. The main classes are: 1) Time-Delay Approximation Methods: The time-delay is estimated from a relation (a model) between the input and output signals expressed in a certain basis. The time-delay is not an explicit parameter in the model. 2) Explicit Time-Delay Parameter Methods: The time-delay is an explicit parameter in the model. 3) Area and Moment Methods: The time-delay is estimated from certain integrals of the impulse and step responses. 4) Higher Order Statistics Methods.Some new methods and variants of old ones are suggested and evaluated, some of which have good estimation performance and some poor performance. Properties of TDE methods are analyzed, both theoretically and experimentally. Recommendations are given on how to choose estimation method and input signal. Generally, prediction error methods where the time-delay parameter is explicit and is optimized simultaneously with the other model parameters give good estimation quality.Most evaluations have been conducted with factorial experiments using Monte Carlo simulations in open and closed loop. Some statistical analysis methods have been utilized: The RMS error of the time-delay estimates gives an absolute measure of the performance. ANOVA (ANalysis Of VAriance) and confidence intervals give conclusions with a certain level of confidence.
Learning health system, positive deviance analysis, and electronic health records: Synergy for a learning health system

Over the past decade, numerous efforts have encouraged the realization of the learning health system (LHS) in the United States. Despite these efforts, and promising aims of the LHS, the full potential and value of research conducted within LHSs have yet to be realized. New technology coupled with a catalyzing global pandemic have spurred momentum. In addition, the LHS has lacked a consistent framework within which “best evidence” can be identified. Positive deviance analysis, itself reinvigorated by recent advances in health information technology (IT) and ubiquitous adoption of electronic health records (EHRs), may finally provide a framework through which LHSs can be operationalized and optimized.
Encoding simplicial quantum geometry in group field theories

An extended group field theory formalism for quantum gravity, based on a field that is a function of both group variables, interpreted as discretized connection, and Lie algebra variables, interpreted as discretized triads, has been proposed recently as an attempt to define models with a clearer link with simplicial geometry. In the context of such a formalism, we introduce a new symmetry requirement on the field. This leads, in 3D, to Feynman amplitudes interpreted as simplicial path integrals based on the Regge action, to a proper relation between the discrete connection and the triad vectors appearing in the Regge action, and to a much more satisfactory and transparent encoding of simplicial geometry already at the level of the group field theory action.
Occurrence of urinary schistosomiasis and associated bacteria in parts of Ondo State, Nigeria

Schistosomiasis is a parasitic disease caused by blood flukes (trematode worms) of the genus Schistosoma and is common among the rural community dwellers that have occupation or recreation activities that link them with infected water bodies. The disease wreak a lot of havoc in the victims which range from anaemia, increase risk of liver fibrosis and bladder cancer, enlarged liver, difficult and painful urination, infertility etc. Nigeria has been reported to be the most endemic country in the world for schistosomiasis with about 29 million infected cases. However, people with urinary schistosomiasis are vulnerable to secondary infections caused by bacteria as a result of the break down in the mucosa barrier occasioned by the wear and tear of the spiny eggs of the schistosomes. Meanwhile, the control measures instituted by various agencies against schistosomiasis paid little attention to its co-infection with bacteria. This study was designed to evaluate the occurrence of urinary schistosomiasis and co-infection with bacteria in parts of Ondo State, Nigeria. Ethical approval was obtained from the Ethical Committee of the Ondo State Ministry of Health, Akure. Macroscopic and microscopic examinations, and microbiological analysis of the urine samples collected were performed using standard techniques. Of the five hundred and nine (509) urine collected, ova of Schistosoma haematobium were detected in one hundred and twenty one 121 (23.77%), significant bacteriuria was detected in 104 (20.43%) and co-infection was detected in thirty six 36 (29.75%) of schistosomiasis cases. There was a positive association between urinary schistosomiasis and bacteriuria (X2(1) = 8.481, p = 0.004). This study revealed a high occurrence of urinary schistosomiasis and significant bacteriuria in the study areas which suggests that bacterial presence may be a potent complication in the management of urinary schistosomiasis.
Simulation-Based Multiobjective Optimization of Open-Pit Mine Haulage System: A Modified-NBI Method and Meta Modeling Approach

A large number of engineering problems involve several conflicting objectives, which today are often solved through expensive simulation calculations. Methods based on meta-models are one of the approaches to solving this group of problems. In this paper, multiobjective optimization in the extraction system of a copper open-pit mine complex is presented by the modified-NBI optimization method and regression meta-model. For this purpose, two objective functions of maximizing the amount of total extraction, which is the sum of the extraction of sulfide, oxide, low-grade ores, and waste in this mine, and minimizing the transport time of haulage according to the limitation of its storage capacity, transport equipment, and budget are considered. The Central Composite Design (CCD) method is used to build the Design of Experiments (DOE) for the design variables. The considered design variables are the number of trucks of 120 tons, 240 tons, 35 tons, and 100 tons. The number of targets considered in each design combination is considered the response surface. The suitable meta-model to maximize the total extraction rate and minimize the transport time of the haulage, two modified functions of nonlinear regression have been determined. The accuracy of the models for selection has been done using PRESS and R 2 statistics. The most common PRESS error has also been used to validate the meta-models. Then the multiobjective optimization problem was solved using the modified-NBI method. Finally, Pareto and optimal solutions using the proposed approach were presented and discussed.
Adversarial Attacks on Deep-learning Models in Natural Language Processing

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples against DNNs in Computer Vision (CV), research efforts on attacking DNNs for Natural Language Processing (NLP) applications have emerged in recent years. However, the intrinsic difference between image (CV) and text (NLP) renders challenges to directly apply attacking methods in CV to NLP. Various methods are proposed addressing this difference and attack a wide range of NLP applications. In this article, we present a systematic survey on these works. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze 40 representative works in a comprehensive way. To make the article self-contained, we cover preliminary knowledge of NLP and discuss related seminal works in computer vision. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and more robust adversarial attacks on NLP DNNs.
UNKNOWN INPUT OBSERVERS DESIGN FOR A CLASS OF NONLINEAR SYSTEMS - APPLICATION TO BIOCHEMICAL PROCESSES

Abstract A general theoretical framework that allows a simultaneous estimation of the reaction rates and the non measured component concentrations in biochemical reactors is proposed. Two main features of the proposed approach are worth to be mentioned. The first one lies in the fact that the reaction rates are treated as unknown inputs and they are simultaneously estimated with the component concentrations (the state variables). The second one is related to the gain of the proposed observers which involves a design function that has to satisfy a mild condition which is given. Different expressions of such a function are proposed. Of particular interest, it is shown that high gain like observers and sliding mode like observers can be derived by considering particular expressions of the design function. The performance of the proposed observers is highlighted in simulation through an example dealing with a chemical reactor.
Lived in Experience following Coronary Artery Bypass Grafting in selected Hospital Chengalpatttu.

Methodology The study approach were qualitative descriptive design focusing on the lived experience following CABG (coronary artery bypass graft) patients ( n = 15). Data were collected using semi-structured in depth interview conducted for 15 days 1 month after hospital discharge. The transcripts of the interview were analyzed using descriptive thematic analysis.
Ferri-taramite, a new member of the amphibole supergroup, from the Jakobsberg Mn–Fe deposit, Värmland, Sweden

Abstract. Ferri-taramite (IMA CNMNC 2021-046), ideally ANaB(CaNa)C(Mg3Fe23+)(Si6Al2)O22W(OH)2, occurs in skarn from the Jakobsberg manganese mine, Värmland, Sweden. Associated minerals are celsian, phlogopite, aegirine-augite, andradite, hancockite, melanotekite, microcline (var. hyalophane), calcite, baryte, prehnite, macedonite and oxyplumboroméite. Conditions of formation, close to peak metamorphism (at circa 650 ∘C and 0.4 GPa), include silica undersaturation, a slightly peralkaline character and relatively high oxygen fugacities. Ferri-taramite forms poikiloblastic crystals up to 5 mm and is dark brownish black with a yellowish grey streak. The amphibole is brittle with an uneven to splintery fracture. Cleavage parallel to {110} is good. Hardness (Mohs) is ∼ 6, and Dcalc=3.227(5) g cm−3. Holotype ferri-taramite has the experimental unit formula A(Na0.79K0.16Pb0.01)Σ0. 96B(Ca1.26Na0.72Mn0.022+)Σ2C(Mg2.66Mn0.582+Fe0.162+Zn0.02Fe1.263+ Al0.26Ti0.06)Σ5. 00T(Al1.86Si6.14)Σ8O22W(OH)2, based on chemical analyses (EDS, laser-ablation ICP-MS) and spectroscopic (Mössbauer, infrared) and single-crystal X-ray diffraction data. The mineral is optically biaxial (–), with α=1.670(5), β=1.680(5) and γ=1.685(5) in white light and 2Vmeas=70(10)∘ and 2Vcalc=70.2∘. Ferri-taramite is distinctly pleochroic in transmitted light, with X pale yellow, Y dark brown, Z yellowish brown and absorption Y>Z>X. The eight strongest reflections in the X-ray powder pattern (d values (in Å), Irel, hkl) are 8.44, 60, 110; 3.392, 25, 131; 3.281, 39, 240; 3.140, 100, 310; 2.816, 45, 330; 2.7104, 38, 151; 1.3654, 26, 461; and 1.4451, 33, 6‾61. Refined unit-cell parameters from single-crystal diffraction data are a=9.89596(13), b=18.015(2), c=5.32164(7) Å, β=105.003(13)∘ and V=916.38(2) Å3 for Z=2. Refinement of the crystal structure yielded R=2.26 % for 2722 reflections with Io>2σ(I). The Mn2+ and Fe2+ ions show preference for the M1 and M3 octahedrally coordinated sites, whereas Fe3+ is strongly ordered at M2. The A-group cations, K and Na, are split over two subsites, A(m) and A(2), respectively.
A Repository of Conversational Datasets

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using 1-of-100 accuracy. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.