Miracle-dz/UniFAR-FTUs huggingface dataset:
State-of-the-Art Review on Probabilistic Seismic Demand Models of Bridges: Machine-Learning Application

Optimizing the serviceability of highway bridges is a fundamental prerequisite to provide proper infrastructure safety and emergency responses after natural hazards such as an earthquake. In this regard, fragility and resilience assessment have emerged as important means of describing the potential seismic risk and recovery process under uncertain inputs. Generating such assessments requires estimating the seismic demand of bridge components consisting of piers, deck, abutment, bearing, etc. The conventional probabilistic model to estimate the seismic demands was introduced more than two decades ago. Despite an extensive body of research ever attempting to improve demand models, the univariate demand model is the most common method used in practice. This work presents a comprehensive review of the evolution of demand models capturing machine-learning-based methodologies and their advantage in comparison to the conventional model. This study sheds light on understanding the existing demand models and their associated attributes along with their limitations. This study also provides an appraisal of the application of probabilistic demand models to generate fragility curves and subsequent application in the resilience assessment of bridges. Moreover, as a sound reference, this study highlights opportunities for future development leading to enhancement of the performance and applicability of the demand models.
Scheduling Web Crawl for Better Performance and Quality

Web crawler is an essential component of search engines, data mining and other Internet applications. Scheduling Web pages to be downloaded is an important aspect of crawling. Previous research on Web crawl focused on optimizing either crawl speed or quality of the Web pages downloaded. While both metrics are important, scheduling using one of them alone is insufficient and can bias or hurt overall crawl process. This paper explores the design space of crawl scheduling to balance performance and quality factors and optimize the global crawl efficiency. We design a network-efficient scheduling framework and use it to evaluate various scheduling strategies. We also define a new scheduling algorithm that factor both network performance and Web page quality into scheduling decision-making. Real world experiments clearly demonstrate the effectiveness of the two-level scheduling scheme and the new algorithm in improving overall crawl efficiency. Experiments also show that crawl-scheduling design can always be optimized based on full understanding of application properties.
Towards Runtime Classification of Ransomware by Madhumitha Balaji A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

The availability of computer systems is constantly being challenged by cybercriminals who seek to disrupt access to this indispensable technology and the data they contain as a means for making profit. This trend has given rise to a new form of malware that is known as ransomware, an invasive type of malware that is designed to appropriate compute resources in return for a ransom. According to the U.S. Department of Homeland Security, ransomware represents the fastest growing malware threat to individuals and organizations. With the cost of ransomware attacks projected to exceed $20 billion in the year 2021, it is imperative to explore solutions that can defend against such malware. In this study, we evaluate the effectiveness of machine learning algorithms and their suitability for detecting ransomware on x86 platforms. We show that dynamically extracting instruction op-codes from execution traces can be harnessed for training machine learning models that can used to perform runtime detection of ransomware. We evaluate different machine learning models and demonstrate that tracking a limited number of instruction opcodes commonly used cryptographic are sufficient for reliably detecting ransomware with high accuracy. We show that our method can achieve high detection rates above 99% while evaluating our solution against real ransomware available in a state-of-the-art dataset from VirusTotal.
PROM_A_256724 131..143

Oral health problems are associated with poor quality of life, with the potential to cause functional, aesthetic, nutritional, and psychological difficulties, in addition to pain and suffering. Traditionally, dental treatment outcomes are measured using purely clinical parameters; however, this may be ineffective as these parameters cannot adequately capture the full impact of poor oral health on the patient, or their respective coping strategies. From this perspective, there are significant benefits when the patient’s perception of their care is considered, and included in treatment planning and delivery. The impacts perceived by the patient on their treatment outcomes can be measured using patient-reported outcomes (PROS), or more specifically with dPROS, focused on dental patientreported outcomes. Although there are some instruments available for measuring these outcomes in clinical trials, very little information is available for explaining the context in which these outcomes are considered, and also how to capture this information using appropriate instruments, specially in evidence-based dental practice. This article aims to review the literature, seeking to describe what has been considered about assessing patient’s outcomes, as well as how to measure them, and explore the potential benefits of using dPROS in evidence-based prosthodontics and clinical care of partially and fully edentulous patients.
D ETECTING M ALWARE IN P ORTABLE E XECUTABLE F ILES USING M ACHINE L EARNING A PPROACH

There have been many solutions proposed to increase the ability to detection of malware in executable files in general and in Portable Executable files in particular. In this paper, we rely on the PE header structure of Portable Executablefiles to propose another approach in using Machine learning to classify these files, as malware files or benign files. Experimental results show that the proposed approach still uses the Random Forest algorithm for the classification problem but the accuracy and execution time are improved compared to some recent publications (accuracy reaches 99.71%)
Landscape Characteristics Affecting Small Mammal Occurrence in Heterogeneous Olive Grove Agro-Ecosystems

: Understanding how small mammals (SM) are associated with environmental characteristics in olive groves is important to identify potential threats to agriculture and assess the overall conservation value and functioning of agro-ecosystems. Here, we provide first insights on this topic applied to traditional olive groves in northeast (NE) Portugal by assessing the landscape attributes that determine SM occurrence, focusing on one species of conservation concern ( Microtus cabrerae Thomas 1906) and one species often perceived as a potential pest of olives ( Microtus lusitanicus Gerbe 1879). Based on SM genetic non-invasive sampling in 51 olive groves and surrounding habitats, we identified seven rodent species and one insectivore. Occupancy modelling indicated that SM were generally less detected within olive groves than in surrounding habitats. The vulnerable M. cabrerae reached a mean occupancy (95% CI) of 0.77 (0.61–0.87), while M. lusitanicus stood at 0.37 (0.24–0.52). M. cabrerae was more likely to occur in land mosaics with high density of agricultural field edges, while M. lusitanicus was more associated with high density of pastureland patches. Overall, our study suggests that the complex structure and spatial heterogeneity of traditionally managed olive grove agro-ecosystems may favor the occurrence of species-rich SM communities, possibly including well-established populations of species of conservation importance, while keeping potential pest species at relatively low occupancy rates.
Privacy II: Diﬀerential Privacy and Machine Learning

A major application of differential privacy that we will next talk about is machine learning – how to design machine learning classifiers trained on sensitive data that still preserve differential privacy of the training data. We will look at two classes of solutions – one-shot and iterative, and will present algorithms for each class. Observe that if we do not care about accuracy, or if we have enough data, then it is not too hard to build a private classifier. The main challenge in differentially privacy machine learning is to build one which has a good privacy-accuracy-sample-size tradeoff. A large body of work in this area has thus been devoted to finding algorithms which have increasingly better tradeoffs.
Integrating morphisms of Lie 2-algebras

Abstract Given two Lie 2-groups, we study the problem of integrating a weak morphism between the corresponding Lie 2-algebras to a weak morphism between the Lie 2-groups. To do so, we develop a theory of butterflies for 2-term L∞-algebras. In particular, we obtain a new description of the bicategory of 2-term L∞-algebras. An interesting observation here is that the role played by 1-connected Lie groups in Lie theory is now played by 2-connected Lie 2-groups. Using butterflies, we also give a functorial construction of 2-connected covers of Lie 2 -groups. Based on our results, we expect that a similar pattern generalizes to Lie n-groups and Lie n-algebras.
SPECT of the Brainand Heartâ€”FutureDirections

We Must Improve Spatial Resolution We must maximize spatial resolution not only for technetium-99m but for other radio nucides as well. Iodine (1231)and gallium-67 will continue to play important roles in the future of radiopharmaceutical development. Optimization of the rotating gamma camera must include improvements in software (reconstruction algorithms and attenuation correc tions), system electronics, and detector and collimator design. For example, the quality of I23Iimaging has improved dramatically with the introduction of cast collimators, slant hole collimators (2), long bore collimators (3), and fan-beam collimators (4). Gamma camera technology can be pushed only so far, however. While smaller community hospitals will continue to use the rotating gamma camera, patient volume will be great enough in most medical centers to justify special purpose instruments dedicated, for example, to brain imaging. Imaging systems with substantially greater crystal areas surrounding the target will result in high spatial and contrast resolution as well as greater sensitivity (5). While rotating gamma camera systems will be limited largely to radiotracers that are locked within the organ ofinterest, special purpose instruments will permit kinetic studies using radiotracers that have relatively short residence times within the brain. Rapid serial studies will also be possible, so that patients can be studied under a variety of pharmacologic and physiologic interventions.
Upper bound limit analysis of lateral pile capacity

The upper bound method of plasticity limit analysis is used to estimate the ultimate capacity of laterally loaded long piles. Based on a generic limiting force (or the ultimate soil resistance per unit length) profile, both freehead and fixed-head piles are considered. The current solutions can be reduced to available solutions for some special cases. Design charts may be constructed to estimate the lateral capacity of piles in terms of plastic moment and the parameters describing the limiting force profile.
Effects of Metoprolol on Experimental Spinal Cord Ischemia-Reperfusion Injury in Rats

Aim: The aim of this study was to investigate the neuroprotective effect of metoprolol and its efficacy in reducing lipid peroxidation levels in the spinal cord ischemia-reperfusion model in rats. Material and Methods: Twenty (20) Sprague-Dawley female rats weighing between 220 gr and 280 gr were randomly divided into 3 groups. Only laparotomy was performed in the control group, and the aorta abdominalis was revealed. In the groups other than the control group, clip compression was applied to the aorta abdominalis for 45 minutes. The ischemia group was not given any medication. Metoprolol was administered intraperitoneally at 0.5 mg/kg to the metoprolol group. Motor examination was made according to Tarlov scale at the 1 and 24 hours and then, spinal cords of all rat models were removed. Spinal cord tissue samples were collected for histopathological examination and for determining malondialdehyde (MDA) level. All rats were sacrificed by draining blood after their motor examinations. Results: According to motor examination findings at the 1 and 24 hours, metoprolol resulted in a statistically significant improvement in recovery (p=0.045). Histopathological examinations revealed that metoprolol contributed to neurological recovery by reducing neuronal necrosis. MDA levels, which is an indicator of lipid peroxidation, were significantly lower in the metoprolol group when compared to the ischemia group (p=0.001). Conclusion: Metoprolol was found to be significantly effective in reducing and/or preventing spinal cord ischemiareperfusion injury.
Ferroelectric and photocatalytic properties of Aurivillius phase Ca<sub>2</sub>Bi<sub>4</sub>Ti<sub>5</sub>O<sub>18</sub>

Aurivillius phase materials (bismuth layer structure ferroelectrics, BLSFs) with the general formula of (Bi2O2)(Am-1 BmO3m+1) have the crystalline structure that is an intergrowths of fluorite-like (Bi2O2) 2+ layers alternating with perovskite (Am-1BmO3m+1) 2− slabs along the crystallographic c-axis, where A is a mono-, di-, or/and trivalent ion and B is usually a transition metal ion.1-3 The integer m is the number of octahedral layers in the perovskite slab. The properties of BLSFs could be flexibly adjusted using versatile dopants as well as by changing the layer number, m. Aurivillius phase materials are promising candidates for nonvolatile ferroelectric random access memories and high-temperature piezoelectric transducers, due to their excellent fatigue-resistant properties, high Curie point (Tc), as well as their high-temperature stability. 4,5 Moreover, a number of Aurivillius phase compounds, such as Bi5Ti3FeO15, Bi6Ti3Fe2O18, etc, exhibit multiferroic properties. 6
hedvIg lAndenIuS enegRen loom weights in Archaic South Italy and Sicily: Five case studies

Textiles are perishables in the archaeological record unless specific environmental conditions are met. Fortunately, the textile tools used in their manufacture can provide a wealth of information and via experimental archaeology make visible to an extent what has been lost. The article presents and discusses the results obtained in a research project focused on textile tool technologies and identities in the context of settler and indigenous peoples, at select archaeological sites in South Italy and Sicily in the Archaic and Early Classical periods, with an emphasis on loom weights. Despite a common functional tool technology, the examined loom weights reveal an intriguing inter-site specificity, which, it is argued, is the result of hybrid expressions embedded in local traditions. Experimental archaeology testing is applied in the interpretation of the functional qualities of this common artefact.
Mom technology and hyperbolic 3-manifolds

This is an expository paper on Mom-technology, describing the recent work of the authors in this area (found in arXiv:math/0606072, arXiv:0705.4325, and arXiv:0809.0346) concerning the use of Mom-technology to find the minimum-volume compact hyperbolic 3-manifold and the 10 smallest cusped hyperbolic 3-manifolds. In addition we provide a survey of a selection of results on volumes of hyperbolic 3-manifolds, and a discussion of outstanding open problems in this area.
$W$-Sobolev spaces: Theory, Homogenization and Applications

Fix strictly increasing right continuous functions with left limits $W_i:\bb R \to \bb R$, $i=1,. ,d$, and let $W(x) = \sum_{i=1}^d W_i(x_i)$ for $x\in\bb R^d$. We construct the $W$-Sobolev spaces, which consist of functions $f$ having weak generalized gradients $\nabla_W f = (\partial_{W_1} f,.,\partial_{W_d} f)$. Several properties, that are analogous to classical results on Sobolev spaces, are obtained. $W$-generalized elliptic and parabolic equations are also established, along with results on existence and uniqueness of weak solutions of such equations. Homogenization results of suitable random operators are investigated. Finally, as an application of all the theory developed, we prove a hydrodynamic limit for gradient processes with conductances (induced by $W$) in random environments.
Kobe University Repository : Kernel タイトル Tit le Nonlinear and nonnormal filters using Monte Carlo methods

In this paper, a non-linear and non-normal filter using Monte-Carlo simulation techniques is proposed, where the density function derived from the measurement equation and the random draws of the state-vector generated from the transition equation are utilized. The proposed filter has less computational burden and easier programming than the other non-linear and non-normal filters such as the numerical integration procedure and the Monte-Carlo integration approach. Furthermore, the proposed filter is extended to prediction and smoothing algorithms. Finally, by Monte-Carlo experiments, we compare the non-linear and non-normal procedures.
Bergman metrics and geodesics in the space of Kähler metrics on principally polarized abelian varieties

Abstract It is well known in Kähler geometry that the infinite-dimensional symmetric space $\mathcal{H}$ of smooth Kähler metrics in a fixed Kähler class on a polarized Kähler manifold is well approximated by finite-dimensional submanifolds $\mathcal{B}_k\subset\mathcal{H}$ of Bergman metrics of height k. Then it is natural to ask whether geodesics in $\mathcal{H}$ can be approximated by Bergman geodesics in $\mathcal{B}_k$. For any polarized Kähler manifold, the approximation is in the C0 topology. For some special varieties, one expects better convergence: Song and Zelditch proved the C2 convergence for the torus-invariant metrics over toric varieties. In this article, we show that some C∞ approximation exists as well as a complete asymptotic expansion for principally polarized abelian varieties.
Collaborative learning within critical mathematics education

This article investigates ways in which collaborative learning in critical mathematics education can promote critical citizenship and democracy to counter neoliberal hegemony in education. Drawing on critical participatory action research in a U.S. high school classroom, the article argues that collaborative learning in critical mathematics education is a coherent alternative to neoliberal approaches to collaborative learning to promote justice based participatory
SMAD4 and TGFβ are architects of inverse genetic programs during fate-determination of antiviral memory CD8 T cells

Transforming growth factor β (TGFβ) is a morphogenic protein that augments antiviral immunity by altering the functional properties of pathogen-specific memory CD8 T cells. During infection TGFβ inhibits formation of effector (TEFF) and central memory CD8 T cells (TCM), while encouraging tissue-resident memory CD8 T cells (TRM) to settle in peripheral tissues. SMAD proteins are signaling intermediates that are used by members of the TGF cytokine family to modify gene expression. For this study, RNA-sequencing was used to explore how regulation via SMAD4 alters the transcriptional profile of antiviral CTLs during infection. We show that SMAD4 and TGFβ cooperatively regulate a collection of genes that determine whether specialized populations of pathogen-specific CTLs circulate around the body, or settle in peripheral tissue. The target genes include multiple homing receptors (CD103, KLRG1 and CD62L) and transcription factors (Hobit and EOMES) that support memory formation. While TGFβ uses a canonical SMAD-dependent signaling pathway to induce CD103 expression on TRM cells, an alternative SMAD4-dependent mechanism is required for formation of TEFF and TCM cells in the circulation. Graphical abstract TGFβ and SMAD4 modulate gene expression in reciprocal directions during differentiation of antiviral CTLs.
A Theoretical Study on the Structure of the Ekman Boundary Layer (I)

A form of mixing length was deduced so that in neutral conditions it would give von Karman's mixing length and, near the ground, match KEYPS law. The system of equations with the assumption of horizontal uniformity was solved both in stationary and in diurnal conditions. The considerations of energy equations indicated that we should solve the problem with the time derivative terms, and the results of time integrations explained some features of the diurnal changes of the planetary boundary layer.
Parallelization of a finite volumes discretization for anisotropic diffusion problems using an improved Schur complement technique

We present in this paper a new algorithm combining a finite volume method with an improved Schur complement technique to solve \begin{document}$ 2D $\end{document} anisotropic diffusion problems on general meshes. After having proved the convergence of the finite volume method, we have given a description of the proposed algorithm in the case of two nonoverlapping subdomains. Several numerical tests are achieved which illustrate the theoretical results of convergence of the finite volume method and show the advantages of the proposed algorithm.
CALL FOR PAPERS Cell Signaling: Proteins, Pathways and Mechanisms Positive cross talk between protein kinase D and -catenin in intestinal epithelial cells: impact on -catenin nuclear localization and phosphorylation at Ser

Jia Wang, Liang Han, James Sinnett-Smith, Li-Li Han, Jan V. Stevens, Nora Rozengurt, Steven H. Young, and Enrique Rozengurt Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, Los Angeles, California; CURE, Digestive Diseases Research Center, Los Angeles, California; Molecular Biology Institute, University of California at Los Angeles, Los Angeles, California; and Veterans Affairs Greater Los Angeles Health Care System, Los Angeles, California
On the possible exceptions for the transcendence of the log-gamma function at rational entries

In a recent work [JNT \textbf{129}, 2154 (2009)], Gun and co-workers have claimed that the number $\,\log{\Gamma(x)} + \log{\Gamma(1-x)}\,$, $x$ being a rational number between $0$ and $1$, is transcendental with at most \emph{one} possible exception, but the proof presented there in that work is \emph{incorrect}. Here in this paper, I point out the mistake they committed and I present a theorem that establishes the transcendence of those numbers with at most \emph{two} possible exceptions. As a consequence, I make use of the reflection property of this function to establish a criteria for the transcendence of $\,\log{\pi}$, a number whose irrationality is not proved yet. This has an interesting consequence for the transcendence of the product $\,\pi \cdot e$, another number whose irrationality remains unproven.