The Effect of Gender in the Publication Patterns in Mathematics

Despite the increasing number of women graduating in mathematics, a systemic gender imbalance persists and is signified by a pronounced gender gap in the distribution of active researchers and professors. Especially at the level of university faculty, women mathematicians continue being drastically underrepresented, decades after the first affirmative action measures have been put into place. A solid publication record is of paramount importance for securing permanent positions. Thus, the question arises whether the publication patterns of men and women mathematicians differ in a significant way. Making use of the zbMATH database, one of the most comprehensive metadata sources on mathematical publications, we analyze the scholarly output of ~150,000 mathematicians from the past four decades whose gender we algorithmically inferred. We focus on development over time, collaboration through coautorships, presumed journal quality and distribution of research topics -- factors known to have a strong impact on job perspectives. We report significant differences between genders which may put women at a disadvantage when pursuing an academic career in mathematics.Comment: 24 pages, 12 figure

Postural patterns in the first year of life: contributions of maternal physical activity in the pregnancy period

BACKGROUND: The study objective was intended to verify whether the practice of maternal physical activity during the pregnancy period could be assumed as a contribution to the acquisition of postural patterns in the child during its first year of life. METHODS: A transversal and descriptive study was carried where we recorded the developments observed in a sample of 80 Portuguese children, according to the habits and type of physical activity of the mothers. Statistical descriptive and inferential test were performed. RESULTS: The results were clearly positive in terms of temporal gains of neck tonic control, and also in the acquisition of an erect position. CONCLUSIONS: Despite the average values are not statistical significant we have observed indicators that the maternal physical activity during pregnancy apparently is a factor that can favor the child’s motor development during their first year of life, particularly in the acquisition of postural patterns.info:eu-repo/semantics/publishedVersio

Gossip information increases reward-related oscillatory activity

Previous research has described the process by which the interaction between the firing in midbrain dopamine neurons and the hippocampus results in promoting memory for high-value motivational and rewarding events, both extrinsically and intrinsically driven (i.e. curiosity). Studies on social cognition and gossip have also revealed the activation of similar areas from the reward network. In this study we wanted to assess the electrophysiological correlates of the anticipation and processing of novel information (as an intrinsic cognitive reward) depending on the degree of elicited curiosity and the content of the information. 24 healthy volunteers participated in this EEG experiment. The task consisted of 150 questions and answers divided into three different conditions: trivia-like questions, personal-gossip information about celebrities and personal-neutral information about the same celebrities. Our main results from the ERPs and time-frequency analysis pinpointed main differences for gossip in comparison with personal-neutral and trivia-like conditions. Specifically, we found an increase in beta oscillatory activity in the outcome phase and a decrease of the same frequency band in the expectation phase. Larger amplitudes in P300 component were also found for gossip condition. Finally, gossip answers were the most remembered in a one-week memory test. The arousing value and saliency of gossip information, its rewarding effect evidenced by the increase of beta oscillatory power and the recruitment of areas from the brain reward network in previous fMRI studies, as well as its potential social value have been argued in order to explain its differential processing, encoding and recall

Sexual dysfunctions among people living with AIDS in Brazil

INTRODUCTION: Sexual dysfunction symptoms in patients with HIV have not been fully investigated in Brazil. OBJECTIVES: To investigate the association between sexual dysfunction symptoms and AIDS among participants in the Brazilian Sex Life Study. METHODS: The Brazilian Sex Life Study is a cross-sectional population study. The participants answered an anonymous self-responsive inquiry. It was applied to a population sample in 18 large Brazilian cities. Answers given by those who reported having AIDS (75) were compared with those who reported not having AIDS (control; 150). This was a case-control study nested in a cross-sectional population study. RESULTS: In females, AIDS was associated with "sexual inactivity over the last 12 months" and "does not maintain sexual arousal until the end of the sex act" (P < 0.05) after adjusting for race and thyroid disease. Compared to the control group, men with AIDS had more difficulty becoming sexually aroused (they required more help from their partner to begin the sex act, they required longer foreplay than they wished, they reported losing sexual desire before the end of the sex act, and they required longer to ejaculate than they desired) (P < 0.05). After adjusting for sexual orientation, sex hormone deficiency, depression, and alcoholism, only "does not have sexual desire," "have longer foreplay," and dyspareunia were associated with AIDS. DISCUSSION AND CONCLUSIONS: The results support the hypothesis that sexual dysfunctions are associated with AIDS. Men with AIDS need more time and stimulation to develop a sexual response, and a significant portion (37%) of women with AIDS reported sexual inactivity over the last 12 months

Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization

Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce

Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization

Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-domain overfitting, resulting in models generating acceptable narratives only for hatred similar to training data, with little portability to other targets or to real-world toxic language. This paper introduces novel attention regularization methodologies to improve the generalization capabilities of PLMs for counter narratives generation. Overfitting to training-specific terms is then discouraged, resulting in more diverse and richer narratives. We experiment with two attention-based regularization techniques on a benchmark English dataset. Regularized models produce better counter narratives than state-of-the-art approaches in most cases, both in terms of automatic metrics and human evaluation, especially when hateful targets are not present in the training data. This work paves the way for better and more flexible counter-speech generation models, a task for which datasets are highly challenging to produce.Comment: To appear at CS4OA workshop (INLG-SIGDial

Delineation of estuarine management areas using multivariate geostatistics: the case of Sado Estuary

The Sado Estuary is a coastal zone located in the south of Portugal where conflicts between conservation and development exist because of its location near industrialized urban zones and its designation as a natural reserve. The aim of this paper is to evaluate a set of multivariate geostatistical approaches to delineate spatially contiguous regions of sediment structure for Sado Estuary. These areas will be the supporting infrastructure of an environmental management system for this estuary. The boundaries of each homogeneous area were derived from three sediment characterization attributes through three different approaches: (1) cluster analysis of dissimilarity matrix function of geographical separation followed by indicator kriging of the cluster data, (2) discriminant analysis of kriged values of the three sediment attributes, and (3) a combination of methods 1 and 2. Final maximum likelihood classification was integrated into a geographical information system. All methods generated fairly spatially contiguous management areas that reproduce well the environment of the estuary. Map comparison techniques based on κ statistics showed that the resultant three maps are similar, supporting the choice of any of the methods as appropriate for management of the Sado Estuary. However, the results of method 1 seem to be in better agreement with estuary behavior, assessment of contamination sources, and previous work conducted at this site.info:eu-repo/semantics/publishedVersio

Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering

Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.Comment: To appear in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (long paper

Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study

In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English. We first present a comparative study to determine whether there is a particular Language Model (or class of LMs) and a particular decoding mechanism that are the most appropriate to generate CNs. Findings show that autoregressive models combined with stochastic decodings are the most promising. We then investigate how an LM performs in generating a CN with regard to an unseen target of hate. We find out that a key element for successful `out of target' experiments is not an overall similarity with the training data but the presence of a specific subset of training data, i.e. a target that shares some commonalities with the test target that can be defined a-priori. We finally introduce the idea of a pipeline based on the addition of an automatic post-editing step to refine generated CNs.Comment: To appear in "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL): Findings