Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Network Function Placement for Serving Weighted Services in NFV-Enabled Networks

Network function virtualization (NFV) is a novel network architecture yielding many optimization choices to Internet service providers due to its flexibility. Based on this technique, a network service is constructed from a chain of virtual network functions (VNFs). Although many studies deeply investigate the NFV and propose approaches to address the problem of deploying services in physical networks under different perspectives, they do not consider the importance of services. This issue becomes more critical once the number of network services ever-increasing while the network resources are limited. Here, each service is assigned a weight representing its importance. From this aspect, maximizing the total weight of services accepted to deploy over the physical network is a critical problem and poses many challenges, particularly for networks with limited resources. Therefore, we call this issue the VNF placement for weighted services (VPWS) problem. We then propose three algorithms termed first-fit server algorithm (FSA), best-fit server algorithm (BSA), and service segment-based algorithm (SSBA), respectively, to address the VPWS problem. Eventually, we perform simulations to evaluate the proposed algorithms and factors that affect the VNF placement. The experiment indicates that the total weight yielded by the SSBA outperforms those of the FSA and BSA.
Inference in contingency tables with ordered categories using Plackett's coefficient of association for bivariate distributions

SUMMARY By using a one-parameter class of bivariate distributions proposed by Plackett (1965), methods are derived for analysing the pattern of association in contingency tables with ordered categories. In an r x c table (r - 1) (c - 1) estimates of this parameter can be con- sidered and all of them, or subsets of them, can be tested for equality. This can answer the question whether the pattern of association is consistently the same over the whole table or not. Two well-known examples are considered using this method. The problems arising in the analysis of contingency tables with ordered categories usually go further than mere tests for independence. Generally, a specific analysis of the pattern of association between the two categories is required. For this purpose, contingency tables with ordered categories can be considered as samples from bivariate distributions. Models for symmetry, quasisymmetry, marginal homogeneity or palindromic symmetry can be applied (Bishop, Fienberg & Holland, 1975, p. 281 ff.; McCullagh, 1978) in order to analyse such tables. It is the major aim of these analyses to evaluate differences between the marginal variables by taking into account the association between them. In the present paper the central emphasis is put on the association itself. We use a class of bivariate distributions proposed by Plackett (1965) which seems to be very useful in this context. This class of bivariate distributions, introduced in detail in ? 2, is characterized by one real-valued parameter representing a measure of association. This parameter is closely related to the cross-product ratio in 2 x 2 tables. In an r x c table this parameter can be estimated in (r - 1) (c - 1) different ways. We derive the asymptotic distribution for this set of estimates. This enables us to construct tests for the equality of all parameters estimated or certain subsets of them. By this means we can determine whether the structure of association between the two marginal random variables is consistently the same over the whole table. This would mean that the bivariate distribu- tion, incorporating the interaction of the two marginal variables, can be explained by a simple one-parameter model. The interpretation of the model depends on the numerical value of the parameter and ranges from negative association over independence to positive association. Regardless of the numerical values of the estimates, a rejection of the hypothesis of equality of the para- meters would indicate that there is an interaction between the marginal variables which
“You Hide Pieces of Yourself”: Navigating White Spaces as U.S. Missionaries of Color

While extensive research has been conducted on the experiences of racial and ethnic minorities in predominately white institutions (PWIs) in the United States, there has been little research that examines the experiences of U.S. racial and ethnic minorities that serve for religious reasons with PWIs in international settings. Therefore, the purpose of this study was to understand how race shapes the relationship of long-term U.S. missionaries of color with their missions organization and teammates. Semi-structured, in-depth interviews were conducted with sixteen long-term U.S. missionaries of color serving internationally. The findings point to a number of ways that race shapes the experiences of these missionaries and have important implications for cross-cultural workers of diverse backgrounds as well as cross-cultural work teams and sending organizations. The results also point to the need for more research on diverse work teams in international settings.
Cropping and noise resilient steganography algorithm using secret image sharing

This paper proposes an image steganography scheme, in which a secret image is hidden into a cover image using a secret image sharing (SIS) scheme. Taking advantage of the fault tolerant property of the (k,n)-threshold SIS, where using any k of n shares (k≤n), the secret data can be recovered without any ambiguity, the proposed steganography algorithm becomes resilient to cropping and impulsive noise contamination. Among many SIS schemes proposed until now, Lin and Chan’s scheme is selected as SIS, due to its lossless recovery capability of a large amount of secret data. The proposed scheme is evaluated from several points of view, such as imperceptibility of the stegoimage respect to its original cover image, robustness of hidden data to cropping operation and impulsive noise contamination. The evaluation results show a high quality of the extracted secret image from the stegoimage when it suffered more than 20% cropping or high density noise contamination.
Temperature measurement of a horizontal cylinder in natural convection using a lateral shearing interferometer with a large shear amount

The temperature field of a horizontal cylinder in natural convection is studied experimentally using a lateral shearing interferometer (LSI) with a large shear amount. Real-time interferograms are obtained with and without the cylinder. The surface temperature of the cylinder dropped from 693.15 K to the ambient temperature 289.03 K. The temperature distribution around the cylinder was reconstructed and fitted using exponential and six-degree-polynomial functions. For high surface temperature, the results indicate that the six-degree-polynomial function has a considerable advantage over the exponential one. Experimental data were compared with that measured by thermocouples within a 0.19% derivation. LSI with a large shear amount can provide accurate measurement of the temperature field around a cylinder with a high surface temperature.
A novel algorithm of FCM clustering for interval valued data

A new algorithm of FCM clustering is proposed for interval valued data. it is proved that our algorithm is identical to Algorithm Ⅰ developed by Fan Jiu lun in the case of λ=1, while it is identical to Algorithm Ⅱ of the same reference in the case of λ→0, where λ is a weighting factor. Therefore, the proposed algorithm is a general form. Moreover, the new algorithm is of more definite physical significance, and can be implemented by the existing tools for the FCM algorithm.
Multilabel Classification over Category Taxonomies

Multilabel classification is the task of assigning a pattern to one or more classes or categories from a pre-defined set of classes. It is a crucial tool in knowledge and content management. Standard machine learning techniques such as Support Vector Machines (SVMs) and Perceptron have been successfully applied to this task. However, many real-world classification problems involve large numbers of overlapping categories that are arranged in a hierarchy or taxonomy. This poses a challenge to learning algorithms as they ignore the class hierarchies thereby losing valuable information. In this thesis, we propose to systematically incorporate prior knowledge on category taxonomy directly into the learning architecture. We present two methods, hierarchical SVM learning and hierarchical Perceptron learning. Both methods take a ranking view of the multilabel problem by focusing on ranking category relevances. In the hierarchical SVM, the hierarchical learning problem is expressed as a joint large margin formulation that simultaneously learns the discriminant functions of each class. As the resulting optimization problem can be prohibitively large, we also present a variable selection algorithm to efficiently solve it. In the hierarchical Perceptron method, the construction of weight vectors and the update rule are made to capture the category taxonomy. Both methods can leverage kernel techniques, work with arbitrary directed acyclic graph taxonomy, and be applied to general settings where categories can be characterized by attributes. We also present an automatic approach to learn a taxonomy if one isn't available. Our approach is adapted from the hierarchical agglomerative clustering algorithm. The learned hierarchy can then be used in existing hierarchical classification approaches. Extensive experiments demonstrate the performance advantage of our approaches.
Phylogeny of Shrew- and Mole-Borne Hantaviruses in Poland and Ukraine

Earlier, we demonstrated the co-circulation of genetically distinct non-rodent-borne hantaviruses, including Boginia virus (BOGV) in the Eurasian water shrew (Neomys fodiens), Seewis virus (SWSV) in the Eurasian common shrew (Sorex araneus) and Nova virus (NVAV) in the European mole (Talpa europaea), in central Poland. To further investigate the phylogeny of hantaviruses harbored by soricid and talpid reservoir hosts, we analyzed RNAlater®-preserved lung tissues from 320 shrews and 26 moles, both captured during 1990–2017 across Poland, and 10 European moles from Ukraine for hantavirus RNA through RT-PCR and DNA sequencing. SWSV and Altai virus (ALTV) were detected in Sorex araneus and Sorex minutus in Boginia and the Białowieża Forest, respectively, and NVAV was detected in Talpa europaea in Huta Dłutowska, Poland, and in Lviv, Ukraine. Phylogenetic analyses using maximum-likelihood and Bayesian methods showed geography-specific lineages of SWSV in Poland and elsewhere in Eurasia and of NVAV in Poland and Ukraine. The ATLV strain in Sorex minutus from the Białowieża Forest on the Polish–Belarusian border was distantly related to the ATLV strain previously reported in Sorex minutus from Chmiel in southeastern Poland. Overall, the gene phylogenies found support long-standing host-specific adaptation.
Can Virtual Reality Cognitive Rehabilitation Improve Executive Functioning and Coping Strategies in Traumatic Brain Injury? A Pilot Study

Executive dysfunction is among the most common and disabling facets of cognitive impairment following traumatic brain injury (TBI), and may include deficits in reasoning, planning, mental flexibility, some aspects of attention and orientation, awareness and behavior. Rehabilitation programs based on cognitive-behavioral approaches to retrain planning and problem-solving and other executive deficits may improve such cognitive dysfunction. The purpose of this study is to investigate the effects of non-immersive virtual reality-based training to improve executive abilities and to reduce anxiety and depression symptoms in patients with TBI. Twenty patients with moderate to severe TBI were enrolled at our Neurorehabilitation Unit and divided to receive either the standard cognitive training or the virtual reality (VR) based cognitive training using the virtual reality rehabilitation system (VRRS-Evo). Each group received the same amount of rehabilitative training, including ROT (Reality Orientation Therapy) and Executive Training (ET), but using a different approach, i.e., a paper and pencil and an advanced approach. All patients were evaluated with a specific psychometric battery before (T0) and after the end (T1) of each program. Comparing pre- and post- treatment scores, in the VR-CT group, we found statistically significant differences in all administered outcome measures for cognitive and executive functioning, i.e., MoCA (p < 0.005), FAB (p < 0.005), TMT-A (p < 0.005), TMT-B (p < 0.005), TMT-BA (p < 0.001), and mood, i.e., HRS-D (p < 0.008). In the Conventional cognitive training (C-CT) group, we found a significant improvement only in MoCA (p < 0.03), FAB (p < 0.02) and in TMT-BA (p < 0.01). Coping strategies also improved, with better results in the VR-CT group. Our results suggest that VR rehabilitation, using the VRRS system, may be a valuable and motivational approach to improve visuo-executive abilities and coping strategies as well as mood in chronic TBI patients.
A numerical Approach for solving classes of Linear and Nonlinear Volterra Integral Equations by Chebyshev polynomial

In this paper we propose a numerical method for solving classes of of linear and nonlinear Volterraintegral equations having regular as well as weakly singular kernels. The method is based upon replacingthe unknown function by a truncated shifted Chebyshev series. This yeilds either a linear system ofalgebraic equations that can be solved using matrix algebra or a nonlinear system that can be solved byNewton’s iterative method. This method is effective and so easy to apply with low cost of computingoperations. The accuracy and efficiency of the method can be shown through the illustrated numericalexamples.keywords: Volterra integral equations, Newton’s method, Simpson’s method, Chebyshev polynomials,Shifted Chebyshev polynomials.Mathematics Subject Classification: 45B05 , 45Bxx , 65R10
Analysis of Serially Dependent Multivariate Longitudinal Non-Gaussian Continuous Data

Serially dependent multivariate longitudinal non-Gaussian outcome data are commonly encountered in many fields of study, especially in biomedical sciences, finance, and so on. However, flexible methodologies for joint analysis of these outcomes are not well developed. Recently, Wu and de Leon (2014) and Withanage and de Leon (2015) introduced the class of Gaussian copula mixed models (GCMMs) for joint analysis of non-Gaussian outcomes. We adapt and extend the GCMM to settings that involve conditional as well as serial dependencies among longitudinal observations on the same or on different outcomes. We investigate the impact of failing to account for these dependencies via simulations. We illustrate our methodology on two datasets: one on data obtained from primary biliary cirrhosis patients, and the other on data from the Iowa Youth and Families Project.
The enhancement of speech intelligibility in high noise levels by high-pass filtering followed by rapid amplitude compression

This paper presents the results of an examination of rapid amplitude compression following high-pass filtering as a method for processing speech, prior to reception by the listener, as a means of enhancing the intelligibility of speech in high noise levels. Arguments supporting this particular signal processing method are based on the results of previous perceptual studies of speech in noise. In these previous studies, it has been shown that high-pass filtered/clipped speech offers a significant gain in the intelligibility of speech in white noise over that for unprocessed speech at the same signal-to-noise ratios. Similar results have also been obtained for speech processed by high-pass filtering alone. The present paper explores these effects and it proposes the use of high-pass filtering followed by rapid amplitude compression as a signal processing method for enhancing the intelligibility of speech in noise. It is shown that this new method resuits in a substantial improvement in the intelligibility of speech in white noise over normal speech and over previously implemented methods.
Pollution-Free and Highly Sensitive Lactate Detection in Cell Culture Based on a Microfluidic Chip

Cell metabolite detection is important for cell analysis. As a cellular metabolite, lactate and its detection play an important role in disease diagnosis, drug screening and clinical therapeutics. This paper reports a microfluidic chip integrated with a backflow prevention channel for cell culture and lactate detection. It can effectively realize the upstream and downstream separation of the culture chamber and the detection zone, and prevent the pollution of cells caused by the potential backflow of reagent and buffer solutions. Due to such a separation, it is possible to analyze the lactate concentration in the flow process without contamination of cells. With the information of residence time distribution of the microchannel networks and the detected time signal in the detection chamber, it is possible to calculate the lactate concentration as a function of time using the de-convolution method. We have further demonstrated the suitability of this detection method by measuring lactate production in human umbilical vein endothelial cells (HUVEC). The microfluidic chip presented here shows good stability in metabolite quick detection and can work continuously for more than a few days. It sheds new insights into pollution-free and high-sensitivity cell metabolism detection, showing broad application prospects in cell analysis, drug screening and disease diagnosis.
Plasmonic trapping of sub-micro objects with metallic antennae

Since optical trapping was first reported, its methods and targets have been broadened. In this paper, we propose ‘plasmonic clipping’, which traps objects on the plasmonic dot array. Localized surface plasmon polaritons (LSPPs), which localize optical energy in the nanometer-scale size and enhances the optical field, are excited in gaps between the dots. The objects are trapped by electric-field-gradient forces of LSPPs along the dot array. The dot arrays are arranged radially so that LSPPs are selectively excited in dot array corresponding to polarization direction of excitation light. The selective excitation results in directionally-selective ‘plasmonic clipping’. The radial dot arrays made of silver are numerically designed and fabricated by means of a focused ion beam (FIB). The arrays are illuminated with laser beam through the half wavelength plate to rotate polarization direction. As a result, the plasmonic clipping is observed along the array corresponding to polarization of the excitation light. It is expected to be utilized to align functional components for manufacturing, measurement, and material technologies.
Study on comparison for GCNNs and BPNN

This paper compared the difference and similarity for three types of GCNN and BPNN in neural networks' structure,activation function,weight adjustment algorithm,etc.The comparisons for approximation performance to sine function show that the 3rd improved GCNN(GCNN3) inherits fast convergent speed in the previous two GCNNs,and is as good as BPNN in stability.GCNN3 overcomes the instability shortage of the previous two GCNNs,and overcomes the slow convergence shortage of BPNN.Piecewise linear activation function in GCNN will in favor of its expanding applications.
Seasonal Variations in the Sources and Influential Factors of Aerosol Dissolved Black Carbon at a Southeast Coastal Site in China

Aerosols are a significant source of dissolved black carbon (DBC) in the ocean. However, how anthropogenic activities, including fossil fuel (FF) burning and human‐initiated biomass burning (BB), would affect the seasonal variations in the concentration and condensation degree of aerosol DBC was not well constrained. To fill this knowledge gap, we collected 33 aerosol samples from December 2018 to September 2019 at a southeast coastal site in China. Concentrations of DBC, water‐soluble organic carbon (WSOC), and elemental carbon (EC) were determined, and air quality data were collected. DBC concentration was 0.02–0.23 μgC m−3, with significantly higher values in spring and winter, and was affected by sources, rainfall frequency, and secondary processes. The source of DBC, as indicated by DBC/WSOC (range: 0.009–0.045) and DBC/EC (range: 0.007–0.065), was dominated by FF burning. Furthermore, the significantly higher DBC/WSOC value in late April to July indicated enhanced contribution from BB that was mainly associated with airmass transported from Southeast Asia (SEA). We further estimated that ∼30% of aerosol DBC was from BB from May to July. The condensation degree of DBC was variable, with significantly higher values in winter and spring, which might be associated with prolonged photo‐dissolution. Significant correlations were also observed between DBC and PM10 and PM2.5, which might be utilized to estimate DBC deposition flux, albeit requiring more studies. Our study emphasized the influence of human activities (including BB activities) on atmospheric DBC deposition, which might affect the regional and even global DBC pools.
Clear speech effects for vowels produced by monolingual and bilingual talkers

The present study investigates the hypothesis that bilinguals may produce a smaller intelligibility benefit than monolinguals when asked to speak clearly. Three groups of talkers were recorded: 13 monolingual native English speakers, 22 early Spanish‐English bilinguals, with an age of onset of learning English (AOL) of 12 or earlier, and 14 later Spanish‐English bilinguals, with an AOL of 15 or later. Talkers produced the target words ‘‘bead, bid, bayed, bed, bad’’ and ‘‘bod’’ in both clear and conversational speech styles. Two repetitions of each target word were mixed with noise and presented to monolingual English‐speaking listeners in a six‐alternative forced‐choice task across two days of testing. Stimuli were also presented in quiet on two subsequent days of testing. In preliminary data from 13 listeners, the early bilinguals were slightly more intelligible in noise than the monolingual talkers, with both groups showing a similar degree of clear speech benefit. Later bilinguals were less intelligibl.
Tree-Length Distribution Skewness: An Indicator of Phylogenetic Information

Computer simulations in which phylogenies were generated under various conditions were used to examine the relationship between the phylogenetic signal of a character data set, the skewness of the tree-length distribution, and the position of the real tree relative to the most parsimonious tree for a four-character-state system. Character data that are consistent with one phylogenetic hypothesis produce tree-length distributions that are highly skewed to the left, whereas character data consistent with many phylogenetic hypotheses produce more symmetrical tree-length distributions that cannot be distinguished from tree-length distribution, as measured by the g 1 statistics, and the amount of phylogenetic signal varies depending on tree topology, the proportion of the sequence that is informative and the number of sequence data
Advertising decisions for a segmented market

We consider the problem of choosing the levels of a set of advertising media in order to maximize the firm profit when the market is heterogeneous. Advertising efforts affect the demand of the different segments variably and we assume that the advertising effects on demand over time are mediated by a vector goodwill variable. A first general advertising decision problem is stated and solved in the non-linear programming framework. A preference index is then obtained for the medium selection problem when each segment demand function is linear in goodwill and each medium advertising cost function is quadratic in its level. Finally the theoretical case of disjoint advertising media is discussed.