Do we need N3^3LO Parton Distributions?

We discuss the uncertainty on processes computed using next-to-next-to leading (NNLO) parton distributions (PDFs) due to the neglect of higher order perturbative corrections in the PDF determination, in the specific case of Higgs production in gluon fusion. By studying the behaviour of the perturbative series for this process, we show that this uncertainty is negligible in comparison to the theoretical uncertainty on the matrix element. We then take this as a case study for the use of the Cacciari-Houdeau method for the estimate of theoretical uncertainties, and show that the method provides an effective way of treating theoretical uncertainties on the matrtix element and the PDF on the same footing.Comment: 10 pages 5 figures. Final version, to be published in Phys. Lett. B. Comparison with top production (figs 4-5) added. Several typos corrected and references updated. Grant info adde

The Animate Object Of Kinetic Art, 1955-1968

This dissertation examines the development of kinetic art—a genre comprising motorized, manipulable, and otherwise transformable objects—in Europe and the United States from 1955 to 1968. Despite kinetic art’s popularity in its moment, existing scholarly narratives often treat the movement as a positivist affirmation of postwar technology or an art of mere entertainment. This dissertation is the first comprehensive scholarly project to resituate the movement within the history of performance and “live” art forms, by looking closely at how artists created objects that behaved in complex, often unpredictable ways in real time. It argues that the critical debates concerning agency and intention that surrounded moving artworks should be understood within broader aesthetic and social concerns in the postwar period—from artists’ attempts to grapple with the legacy of modernist abstraction, to popular attitudes toward the rise of automated labor and cybernetics. It further draws from contemporaneous phenomenological discourses to consider the ways kinetic artworks modulated viewers’ experiences of artistic duration. Structured around case studies of four artists, the chapters draw from archival material and close examinations of artworks to elucidate diverse approaches to the kinetic. Chapter One examines Jean Tinguely’s early motorized reliefs, modeled on the paintings of the historical avant-garde, and argues that their shifting compositions enact an intensifying doubt about the principles of abstract composition. Chapter Two addresses Pol Bury’s exploration of perception in his slow-moving objects, linking the intense experiences of anticipation and suspense they generate to their Cold War context. Chapter Three treats Gianni Colombo’s flexible rubber and Styrofoam artworks, connecting them to the burgeoning field of Italian design and Umberto Eco’s nascent concept of the “open work.” Finally, Chapter Four investigates Robert Breer’s Float sculptures, and demonstrates how these works parody Minimalist principles while also intervening into cybernetic debates about behavior and intentionality in self-driven objects. While grounded in the postwar period, this project intersects with contemporary scholarly interests in performance, animation, and materiality

Envisioning a Different Journey: Negotiating Academe with a Chronically Ill Child

To combine the roles of mother, scholar, and professor means walking a precarious tight rope between our own hopes and expectations and larger constraints and options. These roles carry heavy social weight—with iconic images and high expectations of what is deemed suitable and satisfactory. The personal narrative that follows chronicles the author’s ten year journey of navigating these roles amid the daily reality of caring for a child with a degenerative life-threatening illness. The essay discusses examples of best practices that higher education faculty and administrators may execute to create a culture of care in academe

A Study to Determine How Much Correlation Exists between I.Q. and Academic Success in Mathematics, Science, English, and Social Studies

This study explores the usefulness of I.Q. designations by examining correlations between I.Q. and academic success. The researcher examines the academic grades/scores of 100 seventh grade students, grouped by I.Q. (as determined by the SRA Verbal Form) to determine correlation. Correlation between I.Q. and grades was both positive and low, indicating that while I.Q. may predict success, it does not determine it. The researcher found a stronger correlation between humanities grades and I.Q. than mathematics grades and I.Q., though there was insufficient data to determine a trend. However, the researcher notes that a person of average I.Q. has an almost equal chance of achieving a high grade as a person of high I.Q. The researcher notes that teacher attitudes towards student conduct had a significant impact on their grading practices, which may affect grade/I.Q. correlations. The author suggests future research on the correlation between student engagement and academic success, and its effect on promoting academic success among lower I.Q. students

The relationship between parental alcoholism and the vocational identity, occupational self-efficacy, and career decision-making status of university students

The main purpose of this study was to compare the vocational identity, occupational self-efficacy, and career decision-making status of Adult Children of Alcoholics (ACOAs) and Adult Children of Nonalcoholics. Approximately one thousand undergraduate students were administered measures of personal, social, and vocational functioning. Data were analyzed for a sample of 91 ACOAs and 180 non-ACOAs. No between-group differences in vocational functioning were found, although ACOAs reported a greater level of family dysfunction than their counterparts from nonalcoholic homes. Personal and demographic variables were modest predictors of vocational functioning for the sample as a whole. The results of this study raised questions regarding the validity of the ACOA concept. Furthermore, despite its high internal reliability, it appears that for a large number of respondents, the Children of Alcoholics Screening Test (CAST) may be tapping constructs or dimensions other than those originally intended. Contemporary research needs and directions are discussed

XAI approach for addressing the dataset shift problem: BCI as a case study

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions leading ML systems toward poor generalisation performances. Therefore, such systems can be unreliable and risky, particularly when used in safety-critical domains. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are used. In fact, EEG signals are highly non-stationary signals both over time and between different subjects. Despite several efforts in developing BCI systems to deal with different acquisition times or subjects, performance in many BCI applications remains low. Exploiting the knowledge from eXplainable Artificial Intelligence (XAI) methods can help develop EEG-based AI approaches, overcoming the performance returned by the current ones. The proposed framework will give greater robustness and reliability to BCI systems with respect to the current state of the art, alleviating the dataset shift problem and allowing a BCI system to be used by different subjects at different times without the need for further calibration/training stages

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself

Monitoring Water Turbidity Using Remote Sensing Techniques

In the present work, the use of optical cameras for turbidity measurements is tested on the Bode River in Germany, which is one of the best-instrumented catchments in Central Germany with a long-term time series on water quantity and quality. Four trap cameras have been installed on monitored cross-sections with the aim to explore the potential of RGB indices for the description of water turbidity. A description of the experimental setup and some preliminary results are introduced