The 2-loop partition function of large N gauge theories with adjoint matter on S^3

We compute the 2-loop thermal partition function of Yang-Mills theory on a small 3-sphere, in the large N limit with weak 't Hooft coupling. We include N_s scalars and N_f chiral fermions in the adjoint representation of the gauge group (S)U(N), with arbitrary Yukawa and quartic scalar couplings, assuming only commutator interactions. From this computation one can extract information on the perturbative corrections to the spectrum of the theory, and the correction to its Hagedorn temperature. Furthermore, the computation of the 2-loop partition function is a necessary step towards determining the order of the deconfinement phase transition at weak coupling, for which a 3-loop computation is needed

A neural perspective on when and why trait greed comes at the expense of others

Depending on the point of view, conceptions of greed range from being a desirable and inevitable feature of a well-regulated, well-balanced economy to the root of all evil - radix omnium malorum avaritia (Tim 6.10). Regarding the latter, it has been proposed that greedy individuals strive for obtaining desired goods at all costs. Here, we show that trait greed predicts selfish economic decisions that come at the expense of others in a resource dilemma. This effect was amplified when individuals strived for obtaining real money, as compared to points, and when their revenue was at the expense of another person, as compared to a computer. On the neural level, we show that individuals high, compared to low in trait greed showed a characteristic signature in the EEG, a reduced P3 effect to positive, compared to negative feedback, indicating that they may have a lack of sensitivity to adjust behavior according to positive and negative stimuli from the environment. Brain-behavior relations further confirmed this lack of sensitivity to behavior adjustment as a potential underlying neuro-cognitive mechanism which explains selfish and reckless behavior that may come at the expense of others

Similarities between action potentials and acoustic pulses in a van der Waals fluid

An action potential is typically described as a purely electrical change that propagates along the membrane of excitable cells. However, recent experiments have demonstrated that non-linear acoustic pulses that propagate along lipid interfaces and traverse the melting transition, share many similar properties with action potentials. Despite the striking experimental similarities, a comprehensive theoretical study of acoustic pulses in lipid systems is still lacking. Here we demonstrate that an idealized description of an interface near phase transition captures many properties of acoustic pulses in lipid monolayers, as well as action potentials in living cells. The possibility that action potentials may better be described as acoustic pulses in soft interfaces near phase transition is illustrated by the following similar properties: correspondence of time and velocity scales, qualitative pulse shape, sigmoidal response to stimulation amplitude (an `all-or-none' behavior), appearance in multiple observables (particularly, an adiabatic change of temperature), excitation by many types of stimulations, as well as annihilation upon collision. An implication of this work is that crucial functional information of the cell may be overlooked by focusing only on electrical measurements.Comment: 8 pages, 5 figure

Compositional and structural variabilities of MG-rich iron oxide spinels from tuffite.

A maghemita (yFe2O3) de tufito e, excepcionalmente, rica em magnesio, se comparada as comumente encontradas em outros litossistemas maficos. Na tentativa de investigar em detalhes a variabilidade composicional e estrutural desses oxidos naturais de ferro, alguns conjuntos de cristais foram separados de amostras coletadas a diferentes posicoes de um manto de intemperismo de tufito. Esses conjuntos de cristais foram, individualmente, estudados por difracao de raios-X, espectroscopia Mossbauer, medidas de magnetizacao e analise quimica. Da difratometria de raios-X, observou-se que o parametro da celula cubica (ao) varia de 0,834(1) a 0,8412(1) nm. Os valores mais baixos de ao sao caracteristicos de maghemita; os mais altos sao atribuidos a magnetita, mineral magnetico precursor. Os teores de Fe0 alcancam 17 mass % e os valaores de magnetizacao espontanea variam de 8 a 32 J T1 kg1. Os espectros Mossbauer, obtidos com a amostra mantida a temperatura do ambiente, na ausencia de campo magnetico aplicado, sao bastante complexos, om indicacoes de ocorrencia de superposicao de distribuicoes de campo hiperfino, devidas ao Fe3+ e ao ion de valencia mista Fe3+12+. A variabilidade estrutural dos oxidos de ferro, isoestruturais ao espinelio e ricos em Mg e Ti, e, essencialmente, relacionada com os graus variaveis de oxidacao do mineral precursor, a magnetita rica em Mg e Ti

The Development of Intellect in Emerging Adults: Predictors of Longitudinal Trajectories

Intellect is an important personality trait, especially with regard to the prediction and explanation of intellectual performance, such as occupational or academic success. However, much less is known about the development of Intellect. I present results from a longitudinal study spanning eight years to investigate changes in Intellect during a critical period: the transition from school to vocation. The study is based on a large and heterogeneous sample with up to 1964 participants. Using a facet approach, I investigate predictors of longitudinal trajectories theoretically derived from construct definition, including subjective and objective attributes of education and profession; attitudes regarding the malleability of personality traits; as well as personality traits beyond Intellect, especially intelligence. Results reveal some support for the social investment principle according to neo-socioanalytic theory, as epistemic job demands and epistemic leisure activities predicted the increase in Intellect over time. The study contributes to our understanding of the development of personality traits related to intellectual achievement, including important internal and external predictors of longitudinal trajectories

Curious enough to start up? How epistemic curiosity and entrepreneurial alertness influence entrepreneurship orientation and intention

Epistemic curiosity as the desire to acquire new knowledge and ideas is considered as an important attribute for successful entrepreneurs among practitioners, yet there is lacking empirical evidence of epistemic curiosity having an effect on entrepreneurial outcomes. This study aims to put a spotlight on epistemic curiosity as a predictor for entrepreneurial intentions and orientation. We found that epistemic curiosity has a stronger influence on entrepreneurial outcomes in comparison to the Big Five personality trait openness to experience, which is a widely used and conceptually related predictor for entrepreneurship. Furthermore, we found evidence for a mediating role of entrepreneurial alertness which gives further insights about how personality influences the ability to recognize business opportunities and leads to the formation of entrepreneurship orientation and intentions. Our findings contribute to the field of entrepreneurship research by emphasizing that epistemic curiosity may be one of the most important personality indicators for the emergence of entrepreneurial intentions and behavior

The development of intellect in adolescents and emerging adults: A longitudinal study of environmental influences and underlying processes

A large number of studies have been conducted on the structure of Intellect, which is one of the facets of Openness/Intellect. However, far less is known about the development of Intellect and the impact of external influences such as critical life events. In the present study, we investigated socialization and selection effects of Intellect in relation to the subjective perception of life events and self-efficacy. In a large German longitudinal sample of adolescents and emerging adults ( N = 1477), we used mixed linear models to assess mean-level changes and moderating effects across three measurement occasions. We found significant change in Intellect but no evidence of the influence of experiencing a critical life event. Self-efficacy predicted mean levels and change over time in Intellect yet did not interact with perceptions of life events. Further research ideas are discussed

The value of a smile: Facial expression affects ultimatum-game responses

In social interaction, the facial expression of an opponent contains information that may influence the interaction. We asked whether facial expression affects decision-making in the ultimatum game. In this two-person game, the proposer divides a sum of money into two parts, one for each player, and then the responder decides whether to accept the offer or reject it. Rejection means that neither player gets any money. Results of a large-sample study support our hypothesis that offers from proposers with a smiling facial expression are more often accepted, compared to a neutral facial expression. Moreover, we found lower acceptance rates for offers from proposers with an angry facial expression

On cell surface deformation during an action potential

The excitation of many cells and tissues is associated with cell mechanical changes. The evidence presented herein corroborates that single cells deform during an action potential (AP). It is demonstrated that excitation of plant cells (Chara braunii internodes) is accompanied by out-of-plane displacements of the cell surface in the micrometer range (1-10 micron). The onset of cellular deformation coincides with the depolarization phase of the AP. The mechanical pulse (i) propagates with the same velocity as the electrical pulse (within experimental accuracy; 10 mm/s), (ii) is reversible, (iii) in most cases of biphasic nature (109 out of 152 experiments) and (iv) presumably independent of actin-myosin-motility. The existence of transient mechanical changes in the cell cortex is confirmed by micropipette aspiration experiments. A theoretical analysis demonstrates that this observation can be explained by a reversible change in the mechanical properties of the cell surface (transmembrane pressure, surface tension and bending rigidity). Taken together, these findings contribute to the ongoing debate about the physical nature of cellular excitability

Detecção de exceções em bases de dados massivas usando GPUs

Outlier detection is an important data mining task for nding unusual data records in datasets. These anomalies often carry useful information that can be employed in a wide range of practical applications, such as network intrusion detection, fraud discovery in credit card or insurance databases, among several others. There are several challenges associated with the outlier detection problem and its computational cost is a major one. Signi cant research has been done to improve these methods' runtime complexity through the use of data partitioning, ordering and pruning rules. Though these advancements allow the outlier detection to be performed in near-linear time, they are not enough to enable processing large-scale datasets in a reasonable time. Even state-of-the-art methods are limited to processing small scale datasets and/or limited to nd just a tiny fraction of the true top-n outliers. Recently, GPU-based implementations have emerged as an alternative to address the computational bottleneck. They have shown promising results but, to the best of our knowledge, all distance-based GPU algorithms currently available are designed for in-memory detection: they require the dataset to t and be loaded into the GPU's memory. Consequently, their applicability is limited because they can not be used in scenarios where the GPU's computational power would the most useful: to process large scale datasets. The goal of this work is to use GPUs to accelerate the outlier detection process in terabyte-scale, disk-resident datasets. To achieve it, we have to develop algorithms and strategies to overcome the massive reductions in the GPU's computation throughput caused by disk accesses. We made two main contributions in this work. First, we developed set of tools and abstractions for out-of-core distance-based outlier detection in GPUs, such as an e ective parallelization strategy; algorithms and high-performance GPU kernels of essential operations for distance-based outlier detection; and an I/O subsystem that reduces data transfer overhead while allowing I/O and computation overlapping. The second main contributions is the development of a novel distancebased outlier detection algorithm for GPUs, DROIDg, capable of processing large scale and disk-resident datasets in reasonable time. It leverages a new ranking heuristic, proposed by ourselves, to improve the e ciency of its pruning rule, thereby massively reducing the amount of computation required by the detection. Our experimental analysis focused on assessing the performance bene ts of using GPUs for outlier detection in large-scale datasets. Thus, we compared DROIDg against some of the best out-of-core outlier detection algorithms available for CPUs: Orca, Diskaware and Dolphin. DROIDg achieved speedups between 10X and 137X over the best sequential algorithm. Moreover, it displayed far superior scalability with regards to the dataset size and number of outliers being detected. These results showed that GPUs enable the outlier detection to be performed at scales far beyond what even state-of-the-art CPU algorithms are capable of.Detecção de exceções é um importante método de mineração de dados, utilizado para encontrar registros inesperados em bases de dados. Essas anomalias comumente carregam informações úteis e podem ser utilizadas em diversas aplicações, tais como detecção de intrusões em rede, detecção de fraudes em bases de cartões de crédito e seguro, dentre outras. Existem diversos desafios associados com detecção de exceções e o principal é o custo computacional. Muita pesquisa foi feita para melhorar a complexidade temporal de tais métodos, por meio de particionamento de dados, ordenação e regras de poda. Mesmo assim, o estado-da-arte só é capaz de detectar, em tempo hábil, uma pequena quantidade das top-n exceções. Recentemente, implementações para GPU foram propostas a m de contornar o custo computacional do problema. Os resultados obtidos foram promissores, porém, no melhor do nosso conhecimento, os algoritmos para GPU propostos até o momento estão restritos a processar bases de dados carregados na memória da GPU. Consequentemente, estes métodos têm aplicabilidade limitada pois não podem ser utilizados nos casos onde a GPU seria mais útil: bases de dados de larga escala. O objetivo deste trabalho é utilizar GPUs para acelerar o processo de detecção de exceções em bases de dados de larga escala, residentes em disco. Dessa forma, desenvolvemos algoritmos e estratégias para minimizar a redução do throughput de computação causado por acessos ao disco. Este trabalho possui duas contribuições principais. Primeiro, nós desenvolvemos um conjunto de ferramentas e abstrações que facilitam a implementação algoritmos, para GPUs, de detecção de exceções em bases de dados armazenadas em disco. Entre tais abstrações temos uma nova estratégia de paralelização; algoritmos e kernels para operações essenciais à detecção de exceções; e um novo subsistema de I/O, capaz de reduzir o overhead de transferência de dados e permitir a execução concorrente de computação e I/O. Nossa segunda contribuição é um novo algoritmo, DROIDg, para a detecção de exceções, baseadas em distância, usando GPUs. Ele utiliza uma nova heurística de ordenação, a qual propusemos, que melhora a e ciência de sua regra de poda, dessa forma reduzindo enormemente a quantidade de computação necessária para realizar a detecção. Nossa análise experimental focou em determinar a aceleração que GPUs podem fornecer à detecção de exceções em bases de dados larga escala. Portanto, comparamos DROIDg contra alguns dos melhores algoritmos sequenciais out-of-core disponíveis na literatura: Orca, Diskaware e Dolphin. DROIDg alcançou speedups de 10X até 137X sob o melhor algoritmo para CPUs. Além disso, ele demonstrou escalabilidade consideravelmente maior com relação ao tamanho da base de dados e, também, do número de exceções sendo detectadas. Estes resultados demonstram que GPUs permitem realizar a detecção de exceções em escalas muito além do que, até mesmo, os algoritmos estado-da-arte para CPU são capazes