Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study

Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to overtly influence. As such, they are a perfect measure for a better understanding of unintentional behavior cues about social-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogue based on facial expressions only. We exploit computer vision capabilities along with causal inference theory for quantitative verification of hypotheses on the direction of emotional influence, i.e., causal effect relationships, in dyadic dialogues. We address two main issues. First, in a dyadic dialogue, emotional influence occurs over transient time intervals and with intensity and direction that are variant over time. To this end, we propose a relevant interval selection approach that we use prior to causal inference to identify those transient intervals where causal inference should be applied. Second, we propose to use fine-grained facial expressions that are present when strong distinct facial emotions are not visible. To specify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions over selected relevant intervals. We tested our approach on newly, experimentally obtained data. Based on the quantitative verification of hypotheses on the direction of emotional influence, we were able to show that the proposed approach is most promising to reveal the causal effect pattern in various instructed interaction conditions.Comment: arXiv admin note: text overlap with arXiv:1810.1217

Inductive biases in deep learning models for weather prediction

Deep learning has recently gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction (NWP) models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and how they contribute to model performance remains unclear. Here, we review and analyse the inductive biases of six state-of-the-art DLWP models, involving a deeper look at five key design elements: input data, forecasting objective, loss components, layered design of the deep learning architectures, and optimisation methods. We show how the design choices made in each of the five design elements relate to structural assumptions. Given recent developments in the broader DL community, we anticipate that the future of DLWP will likely see a wider use of foundation models -- large models pre-trained on big databases with self-supervised learning -- combined with explicit physics-informed inductive biases that allow the models to provide competitive forecasts even at the more challenging subseasonal-to-seasonal scales

Education and National Development

The New England Regional Meeting of the Comparative and International Education Society was held on the campus of Springfield College on April 29, 1977. The Conference was co-sponsored by the Division of Community Education, Springfield College, and the Center for International Education, University of Massachusetts. The theme of the conference was Education and National Development. The papers delivered ranged in topics from a global perspective to the use of ethnic and multicultural education to assist in national development. Case studies of specific cultural areas highlight the conference. The papers provided a format for discussing and recording the experiences and research endeavors of the participants. A total of fourteen papers were delivered. This publication contains the papers delivered at the Conference

Construction and performance of a micro-pattern stereo detector with two gas electron multipliers [online]

The construction of a micro-pattern gas detector of dimensions 40 x 10 cm² is described. Two gas electron multiplier foils (GEM) provide the internal amplification stages. A two-layer readout structure was used, manufactured in the same technology as the GEM foils. The strips of each layer cross at an effective crossing angle of 6.7 degrees and have a 406 micro-m pitch. The performance of the detector has been evaluated in a muon beam at CERN using a silicon telescope as reference system. The position resolutions of two orthogonal coordinates are measured to be 50 micro-m and 1 mm, respectively. The muon detection efficiency for two-dimensional space points reaches 96%. Key words: detector, position sensitive, GEM, two-layer readou

Performance test of a micro-pattern stereo detector with two gas electron multipliers [online]

We report on the performance of a large micro-pattern detector with two gas electron multiplier foils (GEM) and a two-layer readout structure at ground potential. The two readout layers each have a 406 micro-m pitch and cross at an effective angle of 6.7 degrees. This structure allows for two orthogonal coordinates to be determined. Using a muon beam at CERN together with a silicon tracking system, the position resolutions of the two coordinates are measured to be 50 micro-m and 1 mm respectively (1 stand.dev.). The muon detection efficiency for the two-dimensional space points reaches 96%. The detector was found to be well operational over a wide range in the settings of the different electrical fields

Efficacy of progesterone supplementation during early pregnancy in cows: a meta-analysis

Progesterone is a critical hormone during early pregnancy in the cow. As a result, a number of studies have investigated the effects of progesterone supplementation on pregnancy rates. In this study, a meta-analysis using a univariate binary random effects model was carried out on 84 specific treatments reported in 53 publications involving control (n = 9905) and progesterone-treated (n = 9135) cows. Although the results of individual studies showed wide variations (−40% to +50% point changes), progesterone treatment resulted in an overall increase in pregnancy rate odds ratio (OR = 1.12; P < 0.01). Improvements in pregnancy rate were only observed in cows treated at natural estrus (OR = 1.41, P < 0.01) and not following synchronization of estrus or ovulation. Although treatment between Days 3 to 7 postinsemination was beneficial (OR = 1.15; P < 0.01), treatment earlier or later than this was not. Progesterone supplementation was beneficial in cows of lower fertility (<45% control pregnancy rate) but not in cows with higher fertility. These results indicated that the benefit of progesterone supplementation on fertility of cows required exogenous progesterone supplementation to start between Day 3 to 7 and the appropriate reproductive status (i.e., lower fertility, natural estrus) of the treated cows

Die Vorschriften zur Anlegung von Muendelgeld Eine wirtschaftliche und rechtliche Betrachtung unter besonderer Beruecksichtigung von Muendelvermoegensanlagen in festverzinslichen Wertpapieren, Spareinlagen und Aktien

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