Miracle-dz/UniFAR-FTUs huggingface dataset:
: a survey and some thoughts.

While computational aesthetic evaluation has been applied to images and visual output, it is not as widely employed for generative music systems. Computational aesthetic evaluation is not to be confounded with numerical evaluation of the system’s output; such a notion is in danger of oﬀering a reduced and impoverished interpretation of the aesthetic experience, which is innately dialogical, between the creator or the user, the sociological context, and the creative process or product. This paper reviews common computational aesthetic measures that have been used for musical applications, whilst arguing for a pragmatist perspective and a framework foregrounding the primacy of intentionality and agency in inducing aesthetic responses.
IN VITRO ANTIOXIDANT AND FREE RADICAL SCAVENGING ACTIVITY OF ISOLONGIFOLENE FROM MURRAYA KOENIGII LEAVES

Objective: The present research was undertaken to evaluate the invitro antioxidant activity of isolongifolene. Isolongifolene is a Sesquiterpene and it is present in Murraya koenigii leaves. Methods: The antioxidant activity of isolongifolene was determined by various methods like DPPH, ABTS, hydroxyl radical, nitric oxide, hydrogen peroxide, super oxide radical scavenging, FRAP,TRAP and Reducing Power Results: our results showed that isolongifolene exhibited the potent scavenging activity. The EC50 value for in vitro antioxidant assays namely DPPH, ABTS, hydroxyl radical, nitric oxide, hydrogen peroxide, super oxide radical scavenging, FRAP,TRAP and Reducing Power, were 77.34, 40.9, 16.27, 238.3, 25.01, 16.79, 1.311, 6.701, 0.418 µg/ml respectively. Isolongifolene showed EC50 with minimum concentration and more effective in scavenging activities. Conclusion: Hence based on the results we suggest that Isolongifolene compound can be recommended as a potent antioxidant. It indicates that isolongifolene is a better source of antioxidant which might be helpful to prevent the progress of oxidative stress.
The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models

We derive the class of affine arbitrage-free dynamic term structure models that approximate the widely-used Nelson-Siegel yield curve specification. These arbitrage-free NelsonSiegel (AFNS) models can be expressed as slightly restricted versions of the canonical representation of the three-factor affine arbitrage-free model. Imposing the Nelson-Siegel structure on the canonical model greatly facilitates estimation and can improve predictive performance. In the future, AFNS models appear likely to be a useful workhorse representation for term structure research. For helpful comments we thank the editors, referees and seminar/conference participants at the University of Chicago, Copenhagen Business School (especially Anders Bjerre Trolle and Peter Feldhütter), Stanford University (especially Ken Singleton), the Wharton School, Cambridge University, HEC Paris, Humboldt University, the NBER summer Institute, the Warwick Royal Economic Society annual meeting, the Coimbra Workshop on Financial Time Series, the New York inaugural meeting of SoFiE, the Montreal CIRANO/CIREQ Financial Econometrics meeting, and the Brazilian Finance Association annual meeting. For research assistance we thank Georg Strasser, Rong Hai, and Justin Weidner. The views expressed are those of the authors and do not necessarily reflect the views of others at the Federal Reserve Bank of San Francisco. This version: March 2010.
Bridging and bonding : social capital in the music festival experience

This paper uses the theoretical concept of social capital as its framework to examine festivals in the context of social and cultural policy. Government policies have cited the arts as a tool for combating social exclusion, overcoming barriers between people and fostering community cohesion. Social capital theorist Robert Putnam specifically suggests that cultural events can bring together diverse social groups. To investigate these claims in practice, this study collected empirical data at three festivals: a pop festival, an opera festival and a folk festival. The empirical data, comprising observations, screening questionnaires and in-depth interviews, was analysed using critical discourse analysis to bring out styles and discourses relating to social interactions. It was found that the reinforcement of existing relationships, termed bonding social capital by Putnam, was an important part of the festival experience. The formation of bridging social capital: that is, new and enduring social connections with previously
The unexplored potential of trade in services in Africa : from hair stylists and teachers to accountants and doctors

The book speaks volumes about the latent potential for trade in services in Africa and the regulatory hurdles that providers face in materializing such flows, and proposes concrete policy action for integrating fragmented services markets in Africa. The contributions to this volume seek to shed some light on uncharted opportunities for services trade in Africa, and invigorate and deepen the discussion about the role of services in trade diversification and economic upgrading on the continent. The focus is on less explored areas such as informal trade in services and trade in more sophisticated but equally neglected sectors such as professional services, education and health services, and services related to mining that are rarely associated with services trade in Africa. Novel data collection methods such as crowdsourcing and mystery shopping, pioneering knowledge transfer practices and experiences with innovative policy reforms applied to both modern and traditional services sectors are explored to draw attention to available assessment tools and policy instruments for possible refinement and broader application across sectors.
Historical Changes of Modes and their Substructure Modeled as Pitch Distributions in Plainchant from the 1100s to the 1500s

Large-scale quantitative investigations into the cultural evolution of music have mostly focused on only a limited range of time periods and genres. Here, we analyze more than 40000 pieces of plainchant to better understand the evolution of modes and pitch distributions in a period of ﬁve centuries that saw the development of the Western modal practice. Speciﬁcally, we employ a hierarchical Markov mixture model to analyze the eight medieval modes and their sub-structure represented as pitch distributions and observe their historical changes. We found that the individual modes exhibit internal clusters, that the relative frequencies of the eight modes remained remarkably stable over time, and that there were comparatively large changes in the pitch distributions of individual modes. We discuss our results on the background of musicological insights and point to the need for further interdisciplinary work.
Expression of transcription factors involved with dehydration in contrasting rice genotypes submitted to different levels of soil moisture

Plant tolerance to abiotic stresses, such as water deficit, is triggered by complex multicomponent signaling pathways. One of the plant responses to stress conditions is expression of a large number of genes whose products are involved in various adaptive functions. Transcription factors (TFs) are important regulators of gene expression, modulating the transcription initiation rate of target genes; they are critical components in signal transduction in response to abiotic stress. Our hypothesis is that genotypes with contrasting tolerance signal water deficit through TFs (10 genes of the DREB family and bZIP) with differing degrees of expression. We believe that these genes will have greater expression in the flooded genotype because drought is an atypical condition in these plants. We tested two rice (Oryza sativa) genotypes: BRS Querência (flooded) and AN Cambará (dryland). When they reached stage V5, the plants were submitted to water deficit, at different levels of soil moisture, 20%, 10% and recovery. In general, we observed that expression varies according to the soil moisture and the
Stakeholders' Perceptions of Modern Technology Usage in Secondary Schools in Mvomero District, Tanzania

Modern technology has been perceived as a tool to achieve educational development goals and lifelong learning. To integrate modern technology into education, the government of Tanzania established a curriculum for ordinary secondary education in 2010 that recognizes technology as an instrument for offering quality education, and as a resource for enlightening teaching and learning, providing learners with real-world experience. The study aimed to assess stakeholders' perceptions of modern technology usage in secondary schools in Mvomero District. Using diffusion of innovation theory, a mixed research approach was used with a cross-sectional design. A sample of 185 participants was included, and data were collected using questionnaires, interviews, and observation methods. The findings of the study reveal that modern technology is commonly used in secondary school teaching and learning, with a positive relationship between stakeholder perception and technology use. The results obtained through multiple regression analysis show a positive and significant relationship between stakeholder perception and the use of modern technology in teaching and learning; more specifically, it was shown that perceived ease of use (PEU) has (β=.127, P <.002), perceived usefulness (PU) has (β=.644, P<.000), and user satisfaction (ST) has (β =1.78, p<.000). However, challenges such as insufficient teachers, funding, and inadequate infrastructure hinder the adoption of modern technology. The study recommends government support for enhancing school ICT infrastructure, building teachers ICT skills, ensuring a constant power supply, integrating ICT skills into the curriculum, and subsidizing the cost of ICT devices and Internet packages.
Neurobiology of Aging

Similar to elderly humans, aged outbred Long-Evans rats exhibit individual differences in memory abilities, including a subset of aged rats that maintain memory function on par with young adults. Such individuals provide a basis for investigating mechanisms of resilience to age-related decline. The present study examined hippocampal gene expression in young adults and aged rats with preserved memory function under behavioral task conditions well established for assessing information processing central to the formation of episodic memory. Although behavioral measures and hippocampal gene induction associated with neural activity and synaptic plasticity were similar across age groups, a marker for inhibitory interneuron function in the hippocampal formation was distinctively increased only in aged rats but not in young adults. Because heightened hippocampal neural activity is associated with age-related memory impairment across species, including rats, monkeys, and humans, this ﬁ nding may represent an adaptive homeostatic adjustment necessary to maintain neural plasticity and memory function in aging.
Online Decision Tree Construction with Deep Reinforcement Learning

Decision Trees are widely used in machine learning and data mining tasks, mainly because they can be easily interpreted; due to their popularity, Decision Trees were adapted for settings with streams of data, commonly in the form of Hoeffding Trees. While these methods are fast and incremental, they are also greedy in the sense that they optimise multiple local criteria (generally based on Entropy or Gini impurity) which makes them prone to suboptimality with respect to a global objective metric. On the other hand, Reinforcement Learning (RL) aims at maximizing a long term objective, and as such, it is a good candidate for alleviating this suboptimality problem of the standard Decision Tree methods. In this work, we show that looking for the most accurate Decision Tree with the lowest depth is equivalent to solving an RL problem, then we implement Deep RL algorithms DQN, Double DQN and Advantage Actor-Critic to seek the optimal Decision Tree, this choice being motivated by the scalability of these methods to problems with large state spaces unlike Q-Learning. We compare these methods with Hoeffding Trees on real-world data sets and show that DQN and Double DQN perform best in general.
Machine-learning algorithm mines unreported ‘dark’ reactions to predict successful syntheses

Inorganic–organic hybrid materials 1–3 such as organically templated metal oxides 1 , metal–organic frameworks (MOFs) 2 and organohalide perovskites 4 have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table 5–9 . Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation-and data-driven approaches (promoted by efforts such as the Materials Genome Initiative 10 ) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility 11 , photovoltaic properties 12 , gas adsorption capacity 13 or lithium-ion intercalation 14 ) to identify promising target candidates for synthetic efforts 11,15 ; determination of the structure–property relationship from large bodies of experimental data 16,17 , enabled by integration with high-throughput synthesis and measurement tools 18 ; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification 19,20 or gas adsorption properties 21 ). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions— failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis
Combined effects of chemical and natural stressors on Daphnia magna in a context of global change: extrapolating from short-term experiments on individuals to long-term effects at the population level

The exponentially increasing human population and the associated rise in chemical and nutrient input through ongoing urban, industrial and agricultural activity have exerted substantial pressure on aquatic ecosystems. The research conducted in the context of this PhD dissertation is situated in the field of ecotoxicology, which is concerned with investigating how individuals, populations, communities, and ecosystems respond to chemical stress. As such, ecotoxicology underpins many important legal frameworks related to environmental protection, such as ecological risk assessment (ERA) and the setting of environmental quality standards (EQS) for chemicals. Typically, the effects of single substances have been tested by means of laboratory toxicity tests, on a few (more or less relevant) model test species. There is however a pressing need to evaluate the combined effects of stressors, as research suggests that mixtures at No-Observed-Effect-Concentration (NOEC) levels of individual substances may cause adverse effects when they are combined. With global climate change projections, co-occurrences of natural and chemical stressors are only predicted to increase. The aim of the PhD thesis was to investigate the combined effects of metals (copper and zinc) with additional stressors (harmful algal blooms and global warming) at different organization levels (individual vs. population), and time-scales (short term vs. long term) on the freshwater zooplankton Daphnia magna.
Visualizing LLM Text Style Transfer: visually dissecting how to talk like a pirate

Text Style Transfer (TST) retains semantic content while modifying stylistic features. Exploratory visualization of LLM-generated TST via semantically aligned text visualization reveals advanced stylistic techniques such as use of metaphors. LLM style inquiry can be used to articulate advanced stylistic devices such as interjections, idioms and rhetorical devices and visually depicted as multivariate style heatmaps.
Preselection via Classiﬁcation: A Case Study on Evolutionary Multiobjective Optimization

—In evolutionary algorithms, a preselection operator aims to select the promising offspring solutions from a candidate offspring set. It is usually based on the estimated or real objective values of the candidate offspring solutions. In a sense, the preselection can be treated as a classiﬁcation procedure, which classiﬁes the candidate offspring solutions into promising ones and unpromising ones. Following this idea, we propose a classiﬁcation based preselection (CPS) strategy for evolutionary multiobjective optimization. When applying classiﬁcation based preselection, an evolutionary algorithm maintains two external populations (training data set) that consist of some selected good and bad solutions found so far; then it trains a classiﬁer based on the training data set in each generation. Finally it uses the classiﬁer to ﬁlter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution. In such cases, it is not necessary to estimate or evaluate the objective values of the candidate offspring solutions. The classiﬁcation based preselection is applied to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is empirically studied on two sets of test instances. The experimental results suggest that classiﬁcation based preselection can successfully improve the performance of these MOEAs.
Non Linear Divisible Loads: There is No Free Lunch

Divisible Load Theory (DLT) has received a lot of attention in the past decade. A divisible load is a perfect parallel task, that can be split arbitrarily and executed in parallel on a set of possibly heterogeneous resources. The success of DLT is strongly related to the existence of many optimal resource allocation and scheduling algorithms, what strongly differs from general scheduling theory. Moreover, recently, close relationships have been underlined between DLT, that provides a fruitful theoretical framework for scheduling jobs on heterogeneous platforms, and MapReduce, that provides a simple and efficient programming framework to deploy applications on large scale distributed platforms. The success of both have suggested to extend their framework to non-linear complexity tasks. In this paper, we show that both DLT and MapReduce are better suited to workloads with linear complexity. In particular, we prove that divisible load theory cannot directly be applied to quadratic workloads, such as it has been proposed recently. We precisely state the limits for classical DLT studies and we review and propose solutions based on a careful preparation of the dataset and clever data partitioning algorithms. In particular, through simulations, we show the possible impact of this approach on the volume of communications generated by MapReduce, in the context of Matrix Multiplication and Outer Product algorithms.
Detrital zircons from the Nanaimo basin, Vancouver Island, British Columbia: An independent test of Late Cretaceous to Cenozoic northward translation

The development of the Cordilleran orogen of western North American is disputed despite a century of study. Paleomagnetic observations require large‐scale dextral displacements of crustal fragments along the western margin of North America, from low latitudes to moderate latitudes during the Cretaceous‐Paleogene. A lack of corroborating geological evidence for large‐scale (>1500 km) displacements has prevented the widespread integration of paleomagnetic data into most contemporary tectonic models for the margin. Here we use detrital zircons from the Nanaimo basin, southwestern British Columbia, Canada as an independent test of its Late Cretaceous paleogeographic position. We compare 4310 detrital zircon U/Pb dates from 16 samples to potential source areas in western North America to test hypothesized northern and southern Late Cretaceous paleogeographic positions. Our detrital zircon data suggest that sediment in the Nanaimo basin derives from either a geographically restricted portion of the Belt‐Purcell basin or the Mojave‐Sonoran region of southwestern North America. A paleogeographic position for the basin adjacent to the Mojave‐Sonoran region is preferred as it is consistent with the paleomagnetic results, but further geological, isotopic, or geophysical data are required to rule out a Belt‐Purcell source.
Goal-oriented asteroid mapping under uncertainties using Sequential Convex Programming

Operations in proximity of minor bodies demands high levels of autonomy to achieve cost-effective safe and reliable solutions. Autonomous path planning capability plays a pivotal role in this, allowing safe operations in very challenging environment. A goal-oriented proximity path planning methodology is presented here based on repeated trajectory refinement via sequential convex programming. The objective of the proposed algorithm is to refine an initial guess trajectory to best observe a set of features on the target surface with specific observation requirements while respecting physical and operational constraints. Maneuvering epochs are imposed on a given time horizon to satisfy ground-station requirements and their magnitude is limited by control capability. Navigation and control uncertainties are taken into account to obtain a robust solution within a modular and flexible implementation framework. In particular, navigation performances are assumed to be known at the maneuvering points enabling compatibility and integrability with on-board navigation filters, while control accuracy is modelled taking into account magnitude error and firing misalignments, identifying a conservative approach to guarantee the satisfaction of mission objectives under uncertainty. The methodology is applied to the case of proximity operations about the 433 Eros asteroid, due to the large amount of data available on this system thanks to the NASA NEAR mission flight data. A characterization of the operative environment is presented, highlighting the main perturbation contributions and including some of them inside the algorithm. Results of a Monte Carlo analysis performed on the surface feature distribution on a given initial trajectory show that the solution found always improves observations. Overall, this work presents an important step forward in enabling goal-oriented autonomous guidance capability for small bodies proximity operations.