Miracle-dz/UniFAR-FTUs huggingface dataset:
Ion Torrent PGM as Tool for Fungal Community Analysis: A Case Study of Endophytes in Eucalyptus grandis Reveals High Taxonomic Diversity

The Kingdom Fungi adds substantially to the diversity of life, but due to their cryptic morphology and lifestyle, tremendous diversity, paucity of formally described specimens, and the difficulty in isolating environmental strains into culture, fungal communities are difficult to characterize. This is especially true for endophytic communities of fungi living in healthy plant tissue. The developments in next generation sequencing technologies are, however, starting to reveal the true extent of fungal diversity. One of the promising new technologies, namely semiconductor sequencing, has thus far not been used in fungal diversity assessments. In this study we sequenced the internal transcribed spacer 1 (ITS1) nuclear encoded ribosomal RNA of the endophytic community of the economically important tree, Eucalyptus grandis, from South Africa using the Ion Torrent Personal Genome Machine (PGM). We determined the impact of various analysis parameters on the interpretation of the results, namely different sequence quality parameter settings, different sequence similarity cutoffs for clustering and filtering of databases for removal of sequences with incomplete taxonomy. Sequence similarity cutoff values only had a marginal effect on the identified family numbers, whereas different sequence quality filters had a large effect (89 vs. 48 families between least and most stringent filters). Database filtering had a small, but statistically significant, effect on the assignment of sequences to reference sequences. The community was dominated by Ascomycota, and particularly by families in the Dothidiomycetes that harbor well-known plant pathogens. The study demonstrates that semiconductor sequencing is an ideal strategy for environmental sequencing of fungal communities. It also highlights some potential pitfalls in subsequent data analyses when using a technology with relatively short read lengths.
The Mental Representation of Brand Names : Are Brand Names a Class by Themselves ?

What are brand names? This question has fascinated marketers and researchers for decades and has been addressed from various perspectives. There is the marketing view that brand names are part of the actual product and services (e.g., Javed, 1993); there is the branding view that brand names are linguistic symbols associated with a set of new meanings (Aaker, 1991; Keller, 1998); there is the social psychological view that brand names can be emotion-laden identities (e.g., Mehrabian, 1992), and so on. This chapter investigates brand names from a new perspective. Attention is focused on brand names’ linguistic attributes, such as differences in name types (e.g., existing items vs. newly coined items), and their rich array of inherent perceptual features, such as case, color, size, fonts, and so forth. One unique aspect of our method resides in the use of the laterality framework as our experimental paradigm, which enables us to investigate the hemispheric processing and neuropsychological status of brand names by measuring participants’ accuracy and reaction times in lexical decision tasks, as well as participants’memory of the brand names. Interestingly, brand names as a class of words have been relatively unexplored by cognitive neuroscientists, which might be due to the implicit assumption that they are a subset of proper names. We suggest that brand names have a special neuropsychological status, but one that is different from proper names. This chapter is structured in the following way: First, it offers a brief background on the neuropsychological status of proper names and brand names. Next, it reports some research results. Finally, there is a discussion of the implications of the results for marketing and psycholinguistics.
Mismatch negativity, social cognition, and functional outcomes in patients after traumatic brain injury

Mismatch negativity is generated automatically, and is an early monitoring indicator of neuronal integrity impairment and functional abnormality in patients with brain injury, leading to decline of cognitive function. Antipsychotic medication cannot affect mismatch negativity. The present study aimed to explore the relationships of mismatch negativity with neurocognition, daily life and social functional outcomes in patients after brain injury. Twelve patients with traumatic brain injury and 12 healthy controls were recruited in this study. We examined neurocognition with the Wechsler Adult Intelligence Scale-Revised China, and daily and social functional outcomes with the Activity of Daily Living Scale and Social Disability Screening Schedule, respectively. Mismatch negativity was analyzed from electroencephalogram recording. The results showed that mismatch negativity amplitudes decreased in patients with traumatic brain injury compared with healthy controls. Mismatch negativity amplitude was negatively correlated with measurements of neurocognition and positively correlated with functional outcomes in patients after traumatic brain injury. Further, the most significant positive correlations were found between mismatch negativity in the fronto-central region and measures of functional outcomes. The most significant positive correlations were also found between mismatch negativity at the FCz electrode and daily living function. Mismatch negativity amplitudes were extremely positively associated with Social Disability Screening Schedule scores at the Fz electrode in brain injury patients. These experimental findings suggest that mismatch negativity might efficiently reflect functional outcomes in patients after traumatic brain injury.
Validation of 3D kinetics code 'TRIKIN' using OECD PWR core transient benchmark

A space-time kinetics code, ‘TRIKIN’, was developed and validated to analyse a vast array of transient problems involving reactivity and power distribution anomalies in VVER reactors. Neutronics model of TRIKIN is based on centre mesh finite difference approach for solving space- and time-dependent neutron flux in a 3D core whereas thermal hydraulics model is based on fuel pin simulation and employs a semi-implicit scheme for numerical solution. The code was validated against a series of rod ejection accidents with different levels of reactivity feedback. With likely induction of square lattice Pressurised Water Reactors (PWRs) in India, capabilities of TRIKIN code have been extended to analyse space-time kinetics related problems in this geometry. Newly added features of TRIKIN have been validated against an NEA rod withdrawal accident benchmark problem at Hot Zero Power (HZP) state. It has been demonstrated that TRIKIN results are in agreement with benchmark results.
On the possible correlation between the orbital periods of extrasolar planets and the metallicity of the host stars

We investigate a possible correlation between the orbital periods P of the extrasolar planet sample and the metallicity [Fe/H] of their parent stars. Close-in planets, on orbits of a few days, are more likely to be found around metal-rich stars. Simulations show that a weak correlation is present. This correlation becomes stronger when only single stars with one detected planet are considered. We discuss several potential sources of bias that might mimic the correlation, and find that they can be ruled out, but not with high significance. If real, the absence of very short-period planets around the stellar sample with [Fe/H] < 0.0 can be interpreted as evidence of a metallicity dependence of the migration rates of giant planets during formation in the protoplanetary disc. The observed P‐[Fe/H] correlation can be falsified or confirmed by conducting spectroscopic or astrometric surveys of metal-poor stars ([Fe/H] < −0.5) in the field. Ke yw ords: methods: statistical ‐ stars: abundances ‐ planetary systems: formation.
Complexification is an essential concept in evolutionary robotics that develops dynamic neural networks over generations with the aim of increasing their capabilities. Artificial networks networks are complexified by altering, adding and removing nodes and connections from the neural network structu

Complexification is an essential concept in evolutionary robotics that develops dynamic neural networks over generations with the aim of increasing their capabilities. Artificial networks networks are complexified by altering, adding and removing nodes and connections from the neural network structure. The dimensionality of the input layer in a neural network also plays a significant role in the development of intelligent behavior in robots. Reducing dimensionality is proven to increase the speed of evolution by categorizing the robots environment. Our aim is to evolve a robot that can perform basic soccer skills by reducing the dimensionality of its input vector through categorization of its environment, and gradually complexifying the neural network using a fitness function. In phase one of our experiment, we use a low-dimensional Growing Neural Gas (GNG) to categorize the robots environment into nodes that represent highdimensional sensory inputs. In phase two, NeuroEvolution of Augmenting Topologies (NEAT) is used to evolve an intelligent brain by complexifying the neural network over evolutionary generations based on a specified fitness function. Our results showed significant increases in average and best fitness over multiple generations of evolution and supported the viability of a combination GNG and NEAT for evolving intelligent robots.
China's Growth: The Making of an Economic Superpower

China's economic growth has transformed the country from one of the poorest in the world to its second largest economy. Understanding the drivers of growth remains elusive as the country is affected by both its transition from central planning and the challenges of a developing country. This book examines the main themes of growth, offering micro level evidence to shed light on the macro drivers of the economy. It also focuses on law and informal institutions of the economy to highlight the importance of entrepreneurship and the development of the private sector.
Efficient Mechanisms for Mergers and Acquisitions

We characterize incentive-efficient merger outcomes when payments can be made both in cash and stock. Each firm has private information about both its stand-alone value and a component of the (possibly negative) potential synergies. We study two cases: when transfers can, and cannot, be made contingent on the value of any new firm. When they can, we show that redistributing shares of any nonmerging firm generates information rents and provides necessary and sufficient conditions for the implementability of efficient merger rules. When they cannot, private information undermines efficiency more when it concerns stand-alone values than synergies. Here, acquisitions emerge as optimal mechanisms. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
A Psychometric Analysis of the Three Gunas

The Vedic Personality Inventory was devised to assess the validity of the Vedic concept of the three gunas or modes of nature as a psychological categorization system. The sample of 619 subjects included persons of varying ages and occupations from a middle-size city in southeastern United States, and also of subscribers to a magazine focusing on Eastern-style spirituality. The original 90-item inventory was shortened to 56 items on the basis of reliability and validity analyses. Cronbach alpha for the three subscales ranged from . 93 to . 94, and the corrected item-total correlation of every item score with its subscale score was greater than . 50. Three measures of convergent validity and four measures of discriminant validity provide evidence for construct validity. The loading of every item on the scale is stronger for the intended subscale than for any other subscale. Although each subscale contains congeneric items, the factors are not independent. The nonorthogonality is consistent with Vedic theory. This inventory requires psychometric development and testing cross-culturally as well as to be experimentally implemented in group research and individual assessment.
Hierarchical agent monitoring design approach towards self-aware parallel systems-on-chip

Hierarchical agent framework is proposed to construct a monitoring layer towards self-aware parallel systems-on-chip (SoCs). With monitoring services as a new design dimension, systems are capable of observing and reconfiguring themselves dynamically at all levels of granularity, based on application requirements and platform conditions. Agents with hierarchical priorities work adaptively and cooperatively to maintain and improve system performance in the presence of variations and faults. Function partitioning of agents and hierarchical monitoring operations on parallel SoCs are analyzed. Applying the design approach on the Network-on-Chip (NoC) platform demonstrates the design process and benefits using the novel approach.
The Political Legacy of Commercial Television : Evidence from the Rise ( and Fall ) of Berlusconi ∗

We investigate the long-term political effects of early exposure to Berlusconi’s TV network, Mediaset, exploiting its staggered introduction over the national territory and variation in signal reception due to idiosyncratic geomorphological factors. We find that municipalities exposed to Mediaset prior to 1985 exhibit greater electoral support for Berlusconi’s party between 1 and 2 percentage points when he first ran for office in 1994, relative to municipalities that were exposed only later on. The difference is extremely persistent, disappearing only with the elections of 2013 about 20 years after the entry of Berlusconi into politics. Any effect of differential exposure before 1985 can hardly be explained by partisan bias in the news, as the latter were introduced on Mediaset channels only starting in 1991, at which time the network was available to the entire population. Instead, we present evidence that earlier exposure to commercial TV is associated with a substantial decline in civic participation between 1981 and 1991, which later favored the political success of Berlusconi.
Enhancing the educational value of video games

Lowering the barrier between education and real entertainment is an important challenge in order to better exploit the potential of computers and reach a demographic that is traditionally averse to learning. To this end, it is important to investigate how to exploit the appeal of video games (VGs) to also favor and induce learning via playing video games. Achieving this goal is not only a matter of content, since simply “superimposed” educational content risks being perceived as boring. Hence we believe that the game should feature mechanisms for acquiring knowledge and skill that are smoothly embedded in a meaningful, homogeneous, and compelling whole. Thus, there is a need to compartmentalize components of a game engine so that it becomes easy and efficient to integrate the graphics/interface—which has already been done very well by state-of-the-art successful video games and the educational aspect which is typically poor in those same games. Hence we have defined a general set of mechanisms and modules that can be inserted in state-of-the-art VG environments and are aimed at promoting various kinds of knowledge and procedural skill acquisition. In order to investigate and validate this concept, we have built an educational game, SeaGame, using a state-of-the-art commercial game development approach, and enriched the environment with instances of developed educational modules. Analyzing user test results, we conclude that SeaGame is perceived quite similarly to commercial VGs, which suggests that the proposed mechanisms do not compromise the overall enjoyability of the game, which is key to attracting a wide demographic that is not currently involved in educational activities during their leisure time. The results of this research can be generalized, since the standards of commercial games and the proposed educational enhancements can be instantiated in a variety of educational contexts and applied to different types of content.
Locally Born Olfactory Bulb Stem Cells Proliferate in Response to Insulin-Related Factors and Require Endogenous Insulin-Like Growth Factor-I for Differentiation into Neurons and Glia

After late embryogenesis, new neurons are continuously added to the olfactory bulb (OB) from stem cells located in the forebrain subventricular zone. Nonetheless, stem cells have not been described within the embryonic olfactory bulb. Here we report the isolation of local olfactory bulb stem cells from the embryonic day 12.5–14.5 mouse embryo. These cells were 99.2% nestin positive and proliferated extensively in culture to at least 150 cell doublings. Clonal analysis demonstrated that neurons (TuJ1+), astrocytes (GFAP+), and oligodendrocytes (O4+) could be generated from single-plated cells, indicating that they are multipotent. At least 90% of proliferating cells expressed insulin-like growth factor-I (IGF-I), (pro)insulin, and their cognate receptors; these growth factors collaborated with fibroblast growth factor-2 plus epidermal growth factor (EGF) to promote stem cell proliferation and sphere formation. Cells fromIgf-I−/−mice, however, proliferated as extensively as didIgf-I+/+ cells. Differentiation and survival of stem cell-generated neurons and glia showed strong dependence on exogenous IGF-I, but oligodendrocyte differentiation also required insulin at low concentration. Furthermore, the percentages of stem cell-generated neurons, astrocytes, and oligodendrocytes were markedly lower in the cultures prepared from theIgf-I−/−mice compared with those ofIgf-I+/+. Concordantly, lack of IGF-I resulted in abnormal formation of the olfactory bulb mitral cell layer and altered radial glia morphology. These results support the presence within the embryonic mouse olfactory bulb of stem cells with specific requirements for insulin-related growth factors for proliferation or differentiation. They demonstrate that IGF-I is an endogenous factor regulating the differentiation of stem and other precursor cells within the olfactory bulb.
Individual Differences in the Expression of a “General” Learning Ability in Mice

Human performance on diverse tests of intellect are impacted by a “general” regulatory factor that accounts for up to 50% of the variance between individuals on intelligence tests. Neurobiological determinants of general cognitive abilities are essentially unknown, owing in part to the paucity of animal research wherein neurobiological analyses are possible. We report a methodology with which we have assessed individual differences in the general learning abilities of laboratory mice. Abilities of mice on tests of associative fear conditioning, operant avoidance, path integration, discrimination, and spatial navigation were assessed. Tasks were designed so that each made unique sensory, motor, motivational, and information processing demands on the animals. A sample of 56 genetically diverse outbred mice (CD-1) was used to assess individuals' acquisition on each task. Indicative of a common source of variance, positive correlations were found between individuals' performance on all tasks. When tested on multiple test batteries, the overall performance ranks of individuals were found to be highly reliable and were “normally” distributed. Factor analysis of learning performance variables determined that a single factor accounted for 38% of the total variance across animals. Animals' levels of native activity and body weights accounted for little of the variability in learning, although animals' propensity for exploration loaded strongly (and was positively correlated) with learning abilities. These results indicate that diverse learning abilities of laboratory mice are influenced by a common source of variance and, moreover, that the general learning abilities of individual mice can be specified relative to a sample of peers.
NOVEL SELECTION METHODS FOR MONTE-CARLO TREE SEARCH

Preface In this thesis I present the result of my investigation into regret minimization for Monte-Carlo Tree Search. The thesis presents the motivation, background, and formal definition of a novel search technique based on minimizing both simple and cumulative regret in a game tree: Hybrid MCTS (H-MCTS). The technique minimizes the two types of regret in a single search-tree. This ensures that recommendations made by the algorithm have a low simple regret, and at the same time internal nodes are sampled efficiently. It was developed for, and tested in six two-Special thanks goes to both Dr. Mark Winands and Dr. Marc Lanctot for providing the inspiration and guidance required to develop this novel algorithm. Their combined experience was crucial to obtain the results presented in this work. Thanks goes to Prof. Dr. Tristan Cazenave for his time and assistance with the implementation of SHOT, and for the experiments he performed in his award-winning engine. Thanks also to Dr. Steve Kroon for his insightful input and assistance in proofreading the work. Moreover , I would like to thank my wife Priscilla for her support, and for her patience and understanding. Without both her emotional and financial assistance you would not be reading this thesis. Summary Monte-Carlo Tree Search (MCTS) is a best-first search technique, which bases decisions on sampling the state-space of a domain. In different domains, MCTS has proven to be an effective approach when complex decision-making based on future rewards and outcomes is required. The technique was initially inspired by algorithms used to solve multi-armed bandit (MAB) problems. Such a problem can be described as a single-ply MCTS search, in which an agent is given a choice of options (arms), each with their own probability distribution. Sampling an arm returns a random result from its underlying distribution, and the goal of the agent is to maximize its reward and/or provide a recommendation of which arm has the most rewarding distribution. Based on the context of the MAB problem, the agent's goal is to either minimize simple regret, i.e., the regret of not recommending the best action, or cumulative regret, i.e., regret accumulated over time. Applying this theory to MCTS however, may require more consideration. In a recursive MAB (such as MCTS), where the distribution of each arm is based on an underlying growing search-tree, minimizing a single type of regret throughout the tree implies that at each ply of …
Controller design for quadrotor UAVs using reinforcement learning

Quadrotor UAVs are one of the most preferred type of small unmanned aerial vehicles because of the very simple mechanical construction and propulsion principle. However, the nonlinear dynamic behavior requires a rather advanced stabilizing control of these vehicles. One possible approach that relaxes the difficult task of nonlinear control design is the application of a learning algorithm that allows the training of suitable control actions. Here we apply reinforcement learning as one form of unsupervised learning. In this paper, we first propose a nonlinear autopilot for quadrotor UAVs based on feedback linearization. This controller is then compared to an autopilot which has been learned by reinforcement learning using fitted value iteration with regard to design effort and performance. First simulation and experimental results underline the outcome of this comparison.