Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Budget Management Framework for Real-Time Bidding in Online Advertising

Real-time bidding (RTB) has achieved great success and significantly improved the efficiency and transparency of online advertising. It allows advertisers to purchase ad impressions via auctions. Advertisers who adopt RTB always seek an optimal strategy of budget spending to reach as a wider range of audiences with a more sustainable impact as possible. Traditional bidding strategies, such as fixed bid and performance-based bid, easily lead to being either too aggressive (budgets wiped out too fast) or conservative (budget surplus exists at the end with low clicks), due to lack of optimal budget management. In this paper, we study the optimization of budget efficiency under the smooth delivery constraint for display advertising. We model the problem as a multi-constrained budget allocation optimization and use a heuristic algorithm to solve an approximate optimal budget allocation. The key to the solution is to determine the bidding function for each time slot. Here, we propose a piecewise bidding strategy to filter out the low-quality impressions, where each time slot has its own predicted click-through rate (pCTR) threshold – only when the pCTR of an impression is not lower than the threshold, will the campaign participate in the bidding. However, determining the pCTR threshold is challenging due to market uncertainty, we tackle this problem by modeling the distributions of pCTRs and market prices in each time slot. On this basis, we derive an optimal bidding function for each time slot to make the bid price more adaptable to its available budget. We conduct our experiments on a public real-world dataset and the results show that our proposed method performs the best in terms of various standard metrics (e.g., the number of clicks, cost-per-click) under a given budget comparing to the state-of-the-art baselines.
Design, synthesis, anti-proliferative activity, and molecular docking studies of novel benzo[f]chromene, chromeno [2,3-d]pyrimidines and chromenotriazolo[1,5-c]pyrimidines

Abstract In seeking to establish new anticancer agents, a group of novel substituted chromeno[2,3-d]pyrimidine and chromenotriazolo[1,5-c]pyrimidine derivatives were designed and synthesized as potential anti-proliferative agents. Chromeno[2,3-d]pyrimidine derivatives were prepared via reaction of ethyl formimidate derivative 2 with different nitrogen nucleophiles and chromenotriazolo[1,5-c]pyrimidine derivatives were obtained from treatment of cyanomethyl derivative 14 with various electrophilic reagents. The structures of the synthesized compounds were substantiated on the basis of spectral data and elemental analysis. All the synthesized products were evaluated for their antiproliferative activity against two human tumor cell lines; breast adenocarcinoma (MCF-7) and hepatocellular carcinoma (HepG-2) in addition to normal fibroblasts (WI-38). Derivatives 8 and 21 had significant and selective anti-proliferative activity against liver and breast cell lines without harming the normal fibroblasts. The molecular docking studies of most active compounds 8, 10, and 21 were performed to examine their binding pattern with protein receptors (PDB: 1SA0). Graphical Abstract
Iron and sulfur control during pressure leaching of sulfide concentrates in the presence of chloride ions at 150°C

iii Lay summary . v Preface . vi Table of contents . ix List of tables. xvi List of figures . xx List of symbols . xxviii List of abbreviations . xxix Acknowledgments . xxxii Dedication . xxxiv Chapter 1: Introduction . 1 1.1 Background . 1 1.2 Thesis outline . 4 Chapter 2: Literature review . 6
CAD Model Segmentation Via Deep Learning

Computer aided design (CAD) models are widely employed in the current computer aided engineering or finite element analysis (FEA) systems that necessitate an optimal meshing as a function of their geometry. To this effect, the sub-mapping method is advantageous, as it segments the CAD model into different sub-parts, with the aim mesh them independently. Many of the existing 3D shape segmentation methods in literature are not suited to CAD models. Therefore, we propose a novel approach for the segmentation of CAD models by harnessing deep learning technologies. First, we refined the model and extracted local geometric features from its shape. Subsequently, we devised a convolutional neural network (CNN)-inspired neural network trained with a custom dataset. Experimental results demonstrate the robustness of our approach and its potential to adapt to augmented datasets in future.
Genetic characterization of a small closed island population of Norwegian coastal goat

ABSTRACT The Norwegian coastal goat is a national and endangered breed. Coastal goat populations are mainly divided with a large mainland and two small island populations. The objective of this study is to describe genetic diversity in the feral Skorpa island population and its relationship to the mainland coastal goat population (Selje) using the Norwegian milk goat population as a reference. Analyses were based on 96 samples genotyped by the CaprineSNP50 Beadchip from three populations; 7 Skorpa (SK), 37 Selje (SE) and 52 Norwegian milk goats (MG). The SK population had significantly less genetic variation and higher levels of inbreeding than the two other populations. It was more distant from the two mainland populations than they were from each other. The marginal contribution of the SK population to genetic diversity was small. Means of introducing genetic diversity into the SK population should be considered if the population is prioritized for conservation.
ANALISIS SENTIMEN PROGRAM ACARA DI SCTV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE

Kepuasan penonton merupakan suatu faktor yang menjadi bahan pertimbangan dalam menentukan keberhasilan program acara televisi. Dengan kepuasan penonton perusahaan tahu harus membuat jadwal tayang yang baik dan menentukan berapa durasi acara maupun berapa episode yang harus dibuat. Selain itu perusahaan dapat meminimalisir kerugian karena jumlah penonton yang tidak sesuai harapan, maka dari itu dapat dilakukan suatu Analisis Sentimen pada Tweet Twitter SCTV yang berupaya untuk mengetahui sentimen dari setiap Tweet. Metode klasifikasi yang digunakan adalah Naive Bayes dan Support Vector Machine. Setelah dilakukan penelitian akurasi terbaik dari Metode Naive Bayes dan Metode Support Vector Machine adalah Support Vector Machine dengan Seluruh Program Acara didapatkan hasil akurasi 88,57%. Kata kunci : Analisis Sentimen, Naive Bayes, Support Vector Machine
Bacteria maintain Slightly Beneficial Genes and Selfish Genetic Elements through the evolution of Horizontal Gene Transfer

Horizontal gene transfer (HGT) is a key component of bacterial evolution, which in concert with gene loss can result in rapid changes in gene content. While HGT can evidently aid bacteria to adapt to new environments, it also carries risks since bacteria may pick up selfish genetic elements (SGEs). Here, we use modeling to study how bacterial growth rates are affected by HGT of slightly beneficial genes, if bacteria can evolve HGT to improve their growth rates, and when HGT is evolutionarily maintained in light of harmful SGEs. We find that we can distinguish between four classes of slightly beneficial genes: indispensable, enrichable, rescuable, and unrescuable genes. Rescuable genes — genes that confer small fitness benefits and are lost in the absence of HGT — can be collectively retained by a bacterial community that engages in HGT. Although this 9gene-sharing9 cannot evolve in well-mixed cultures, it does evolve in a spatially structured population such as a biofilm. Although HGT does indeed enable infection by harmful SGEs, HGT is nevertheless evolutionarily maintained by the hosts, explaining the stable coexistence and co-evolution of bacteria and SGEs.
Revisiting the Determination of Percent Aspirin Lab: Using a Limiting Reactant Approach for Students To Also Determine the Amount of Iron(III) Chloride

The spectrophotometric determination of the percent composition of an aspirin tablet is a common first-year, general chemistry undergraduate laboratory procedure. The experiment requires that a known amount of pure acetylsalicylic acid be quantitatively converted into the soluble disodium salicylic acid that is further reacted with excess iron(III) chloride solution to form a deep blue tetraaqua-octahedral complex. This complex, through serial dilutions, is used to construct a Beer’s law plot that may be used to determine the concentration of aspirin. In a novel twist, this paper describes how the same Beer’s law plot can be used to determine the concentration of iron in the iron(III) complex, which is then used to determine the concentration of the original iron(III) chloride solution used to make the aspirin–iron(III) complex. The method has been adapted by high school students in determining iron concentrations in stream waters.
Active Management in Defined Contribution Plans

We analyze the problem that fiduciaries face when monitoring and selecting from a universe of active mutual funds within a defined contribution (DC) plan. In a DC plan, a fiduciary must recognize that there are two levels of decision makers, namely, the fiduciary, who decides which funds will compose the DC plan, and the individual plan participants, who must decide which funds to invest in and the timing of their investment. Moreover, plan participants, and to some degree the fiduciary, need to be able to make investment decisions without being investment professionals. We find that due to the general lack of consistency in performance of mutual funds, fiduciaries and plan participants would be better served by selecting passive rather than active funds across the US equity mutual fund space. Moreover, the most consistently outperforming funds tend to have meaningfully higher tracking errors relative to their stated benchmarks, which makes effective asset allocation in a DC plan more difficult. TOPICS: Wealth management, retirement, mutual fund performance, passive strategies Key Findings • There are two levels of decision makers in defined contribution (DC) plans—fiduciaries and plan participants—and this complicates active fund selection. • Most DC fiduciaries would be better served by replacing active US equity mutual funds with low-cost passive alternatives for both wealth aggregation and performance consistency reasons. • Consistently outperforming active funds are rare and tend to have higher tracking error, which makes asset allocation more complicated.
Learning APIs through Mining Code Snippet Examples

Developers extensively use and reuse the Application Programming Interfaces (APIs) to faster the development time and effort. In order to do this, developers need to learn and remember APIs for effectively using them in their codebase. However, APIs are difficult to learn as they are large in numbers and are not properly documented and the documentation contains a lot of text to remember. To support developers learning and using those APIs, this thesis focuses three different studies that (1) enhances the code completion features of the modern integrated development environments (IDEs), (2) make the online forum code snippets compilable and (3) annotates the code elements of the dynamically typed programming language (e.g, JavaScript) by their types. Towards this direction, we first explore the method name, argument and code completion techniques in the literature and find that none of them is suitable for completing a full method call sequence which consists of a name and a list of arguments. Thus we propose a Bi-LSTM based encoder-decoder model with attention mechanism and beam search, DAMCA that takes all three lexical, syntactic and semantic contexts of a method call and returns a list of method call sequences as the completion suggestions. Evaluation results show that the proposed technique outperforms the state-of-the-art method name, argument, code completion and program synthesis techniques for method call sequence completion. Next, we explore the techniques that are proposed for resolving the Fully Qualified Name (FQN) of the API element of the online forums code snippets. We find that the techniques restrict themselves by the locally specific code tokens only. We incorporate globally related tokens with the local tokens and use likelihood, context similarity, and name similarity to resolve the API element. Experimental results show that the proposed technique outperforms the stateof-the-art techniques with faster training. Finally, in our third study, we explore the techniques developed for statically typed programming languages (i.e, Java) for dynamically typed programming languages (i.e, JavaScript). The evaluation results show that the techniques performed very poorly for JavaScript. Next, we investigate the causes and built a technique that leverages Word2Vec, context similarity as the global models and previous outputs on the same project as a local model. The combination of models outperforms the technique developed for Java. We then compare the proposed technique with state-of-the-art deep learning based techniques developed for JavaScript. The experimental results suggest that the proposed technique has faster training time than the deep learning based technique without sacrificing accuracy. We believe that findings from this research and proposed techniques have the potential to help developers learning different aspects of APIs, thus ease software development and improve the productivity of developers.
The intersection between the representation of the stimuli and the choice by neural ensembles in the primary visual cortex of the macaque.

Representation of stimuli by neural ensembles and the correlation of neural activity with the behavioral choice are, in principle, two different computational problems, however, it is only the intersection between the two that is relevant for animals behavior. Here, we propose a decoding model that learns its decoding weights in the presence of the information on both the stimulus class and the future behavioral choice. We then test the model on trials that only differ in the choice and show that the choice can be read-out from the activity of populations in V1. The read-out model is a linear weighted sum of spikes, decoding the behavioral choice in single trials and without temporal averaging. The generalization of learning suggests that the representation of the stimulus class and of the behavioral choice have a non-zero intersection. In addition, we show how the spike timing is required for discrimination, that bursty neurons carry more information than non-bursty neurons and that neurons in the superficial layer are the most important for discrimination. HighlightsO_LILearning from both stimuli and behavioral choice generalizes to the representation of the choice alone. C_LIO_LIWith generalization of learning, the choice signal can be read-out from parallel spike trains in V1. C_LIO_LICorrect attribution to binary coding pools and correct spike timing are necessary for the read-out. C_LIO_LIBursty neurons convey more information than non-bursty neurons. Discrimination is the strongest in the superficial layer of the cortex. C_LI
Physiological plasticity of high-temperature intertidal cyanobacterial microbial mats to temperature and salinity: daily and seasonal in situ photosynthetic performance

ABSTRACT The physiological plasticity of five cyanobacteria microbial mats from an extreme high temperature intertidal environment (SW Iceland) was analysed both daily and seasonally. Daily cycles under in situ natural conditions were monitored from June to October using pulse-amplitude modulated (PAM) fluorometry and pigment composition in order to study the photosynthetic performance of these microbial mats in relation to environmental fluctuations linked to irradiance and temperature. In vitro temperature and salinity experiments of photosynthetic responses were also conducted. A total of 10 taxa were identified, the most abundant species being Cyanobacterium sp. and Geitlerinema sp. The microbial mats showed a remarkable photosynthetic adaptation to daily and seasonal changes in temperature and solar radiation. For all microbial mats, rETRmax values decreased from June to October, while Fv/Fm remained constant in terms of absolute values. Although high irradiances during June and July affected photosynthesis through photoinhibition, recovery was observed under high temperatures (between 40–50°C), which supports the hypothesis that temperature is a determining factor in the photosynthetic performance of these cyanobacterial mats. Our results showed a significant increase in the Chla-b and phycobiliproteins content from June to October, as well as a significant decrease in total carotenoids content.
DESIGNING FRAMEWORK FOR ACADEMIC TRAINING OF CAREER PRACTITIONERS IN EUROPE

In the article framework for academic training of career practitioners in Europe from the perspective of 6 years joint work of 45 universities of EU and associated countries as well stakeholders in the projects of NICE and NICE-2 is presented. Role of NICE professional roles (NPRs), professional functions, competences related to those professional functions as well to European qualifi cations framework (2008) in designing of framework is revealed. Linking of diff erent career practitioners’ roles to higher education degree programmes (BA, MA and PhD) are substantiated, as well as strengthens and weakness of framework discussed. DEFINITIONS OF KEY TERMS • Career specialist career practitioner, who works in all of the NICE professional roles regularly, depending on what clients need most. • NICE – Network for Innovation in Career Guidance and Counselling in Europe (www. nice-network.eu). • NICE Competences six main NICE competences of career practitioners: career counselling, career education, career assessment and information, career service management, ISSN 2335-2191 http://dx.doi. org/10.7220/2335-2205.3. 1
Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis—and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation–maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions—in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.
Northeast Cyberteam Program – A Workforce Development Strategy for Research Computing

Cyberinfrastructure is as important for research in the 21st century as test tubes and microscopes were in the 20th century. Familiarity with and effective use of cyberinfrastructure at small and mid-sized institutions is essential if their faculty and students are to remain competitive. The Northeast Cyberteam Program is a 3-year NSF-funded regional initiative to increase effective use of cyberinfrastructure by researchers and educators at small and mid-sized institutions in northern New England by making it easier to obtain support from Research Computing Facilitators. Research Computing Facilitators combine technical knowledge and strong interpersonal skills with a service mindset, and use their connections with cyberinfrastructure providers to ensure that researchers and educators have access to the best available resources. It is widely recognized that Research Computing Facilitators are critical to successful utilization of cyberinfrastructure, but in very short supply. The Northeast Cyberteam aims to build a pool of Research Computing Facilitators in the region and a process to share them across institutional boundaries. Concurrently, we are providing
Is peer-led discovery based learning effective in promoting leadership skills among middle school children? Evidence from India

ABSTRACT The objective of this study was to compare the effectiveness of discovery based learning and direct instruction strategies in peer-led leadership workshops in enhancing the leadership skills of middle school children in India. Three hundred and seventy-five, 10-13-year-old girls participated in the study. The children completed a paper and pencil Likert-type survey about their perception about leadership skills. The girls were randomly grouped into discovery based learning and direct instruction groups for the peer-led workshops. The researcher analysed the data using analysis of covariance (ANCOVA) to determine if there were significant differences in the participants’ perception of leadership skills. The results indicated that there were significant differences between the discovery based and direct instruction groups in the perception of their own leadership skills (F = 9.2, p < .05) and their perceptions of appropriate skills a good leader needs to have (F = 22.29, p < .001).