Hasil untuk "Women. Feminism"

Rawan AlSaad, Farah El Rayess, Rajat Thomas

BackgroundMood swings in reproductive-age women arise from interacting hormonal, metabolic, and lifestyle factors, yet scalable screening tools remain limited. Artificial intelligence (AI) and machine learning (ML) approaches offer the potential to integrate diverse predictors and enable early, data-driven risk stratification.ObjectiveTo evaluate the performance of ML algorithms in predicting mood swings among reproductive-age women using menstrual, metabolic, and lifestyle survey data and to identify the most influential predictors.MethodsThe study cohort included 465 reproductive-age women, with fifteen survey-derived features categorized into metabolic (e.g., BMI, recent weight gain, polycystic ovary syndrome), menstrual (regular periods, period length), lifestyle (fast-food consumption, daily exercise), symptom burden score, and demographic (age) categories. We compared five ML models: Random Forest, SVM, Gradient Boosting, LightGBM, and CatBoost, using precision, recall, F1, accuracy, and AUCPR metrics. Feature importance was assessed with permutation feature importance (PFI) and shapley additive explanations (SHAP).ResultsAcross models, the highest values achieved were precision 0.83, recall 0.91, accuracy 0.74, and AUCPR 0.87. PFI and SHAP converged on symptom burden as the dominant predictor, with additional signal from lifestyle indicators (higher fast-food consumption, lower daily exercise) and metabolic/dermatologic markers. Menstrual regularity/length contributed minimally; age showed a modest inverse association.ConclusionsLow-cost, self-reported features can support ML prediction of mood swings in reproductive-age women with good performance. Findings motivate prospective validation, dynamic prediction with wearables, and evaluation of AI-based approaches for early detection of women's mental health concerns in community and primary care settings.

Yeming Cai, Zhenglin Li, Yang Wang

Breast cancer is a leading cause of death among women globally, and early detection is critical for improving survival rates. This paper introduces an innovative framework that integrates Vision Transformers (ViT) and Graph Neural Networks (GNN) to enhance breast cancer detection using the CBIS-DDSM dataset. Our framework leverages ViT's ability to capture global image features and GNN's strength in modeling structural relationships, achieving an accuracy of 84.2%, outperforming traditional methods. Additionally, interpretable attention heatmaps provide insights into the model's decision-making process, aiding radiologists in clinical settings.

Siva Teja Kakileti, Bharath Govindaraju, Sudhakar Sampangi et al.

Mammography, the current standard for breast cancer screening, has reduced sensitivity in women with dense breast tissue, contributing to missed or delayed diagnoses. Thermalytix, an AI-based thermal imaging modality, captures functional vascular and metabolic cues that may complement mammographic structural data. This study investigates whether a breast density-informed multi-modal AI framework can improve cancer detection by dynamically selecting the appropriate imaging modality based on breast tissue composition. A total of 324 women underwent both mammography and thermal imaging. Mammography images were analyzed using a multi-view deep learning model, while Thermalytix assessed thermal images through vascular and thermal radiomics. The proposed framework utilized Mammography AI for fatty breasts and Thermalytix AI for dense breasts, optimizing predictions based on tissue type. This multi-modal AI framework achieved a sensitivity of 94.55% (95% CI: 88.54-100) and specificity of 79.93% (95% CI: 75.14-84.71), outperforming standalone mammography AI (sensitivity 81.82%, specificity 86.25%) and Thermalytix AI (sensitivity 92.73%, specificity 75.46%). Importantly, the sensitivity of Mammography dropped significantly in dense breasts (67.86%) versus fatty breasts (96.30%), whereas Thermalytix AI maintained high and consistent sensitivity in both (92.59% and 92.86%, respectively). This demonstrates that a density-informed multi-modal AI framework can overcome key limitations of unimodal screening and deliver high performance across diverse breast compositions. The proposed framework is interpretable, low-cost, and easily deployable, offering a practical path to improving breast cancer screening outcomes in both high-resource and resource-limited settings.

Miriam Doh, Aditya Gulati, Matei Mancas et al.

This paper examines how synthetically generated faces and machine learning-based gender classification algorithms are affected by algorithmic lookism, the preferential treatment based on appearance. In experiments with 13,200 synthetically generated faces, we find that: (1) text-to-image (T2I) systems tend to associate facial attractiveness to unrelated positive traits like intelligence and trustworthiness; and (2) gender classification models exhibit higher error rates on "less-attractive" faces, especially among non-White women. These result raise fairness concerns regarding digital identity systems.

Chiara Lodi, Agnese Sacchi, Francesco Vidoli

We investigated the impact of female politicians on waste collection in Italian municipalities in different territories observed over the years 2010-2019. We used a staggered difference-in-differences design to obtain a causal interpretation of the estimated effects. We find that the majority of women in the municipal council positively influence pro-environmental individual behaviour. The impact of a female-majority council is heterogeneous by region and more pronounced in areas with lower social capital. Female politicians as catalysts for positive change fade after 5-6 years, likely due to persistent social norms locally, thus stressing the need for additional cultural actions with long-lasting effects.

Kacy Zhou, Jiawen Wen, Nan Yang et al.

While deep learning has become a core functional module of most software systems, concerns regarding the fairness of ML predictions have emerged as a significant issue that affects prediction results due to discrimination. Intersectional bias, which disproportionately affects members of subgroups, is a prime example of this. For instance, a machine learning model might exhibit bias against darker-skinned women, while not showing bias against individuals with darker skin or women. This problem calls for effective fairness testing before the deployment of such deep learning models in real-world scenarios. However, research into detecting such bias is currently limited compared to research on individual and group fairness. Existing tools to investigate intersectional bias lack important features such as support for multiple fairness metrics, fast and efficient computation, and user-friendly interpretation. This paper introduces Fairpriori, a novel biased subgroup discovery method, which aims to address these limitations. Fairpriori incorporates the frequent itemset generation algorithm to facilitate effective and efficient investigation of intersectional bias by producing fast fairness metric calculations on subgroups of a dataset. Through comparison with the state-of-the-art methods (e.g., Themis, FairFictPlay, and TestSGD) under similar conditions, Fairpriori demonstrates superior effectiveness and efficiency when identifying intersectional bias. Specifically, Fairpriori is easier to use and interpret, supports a wider range of use cases by accommodating multiple fairness metrics, and exhibits higher efficiency in computing fairness metrics. These findings showcase Fairpriori's potential for effectively uncovering subgroups affected by intersectional bias, supported by its open-source tooling at https://anonymous.4open.science/r/Fairpriori-0320.

Ari Rappoport

Migraine (MGR) ranks first among diseases in terms of years of lost healthy life in young adult and adult women. Currently, there is no theory of MGR. This paper presents a complete theory of migraine that explains its etiology, symptoms, pathology, and treatment. Migraine involves partially saturated (usually chronically high) sympathetic nervous system (SNS) activity, mainly due to higher sensitivity of the metabolic sensors that recruit it. MGR headache occurs when SNS activity is desensitized or excessive, resulting in hyperexcitability of baroreceptors, oxidative stress, and activation of pain pathways via TRPV1 channels and CGRP. The theory is supported by overwhelming evidence, and explains the properties of current MGR treatments.

Tatsuhiro Baba, Abu Saleh Musa Miah, Jungpil Shin et al.

Cervical cancer is a crucial global health concern for women, and the persistent infection of High-risk HPV mainly triggers this remains a global health challenge, with young women diagnosis rates soaring from 10\% to 40\% over three decades. While Pap smear screening is a prevalent diagnostic method, visual image analysis can be lengthy and often leads to mistakes. Early detection of the disease can contribute significantly to improving patient outcomes. In recent decades, many researchers have employed machine learning techniques that achieved promise in cervical cancer detection processes based on medical images. In recent years, many researchers have employed various deep-learning techniques to achieve high-performance accuracy in detecting cervical cancer but are still facing various challenges. This research proposes an innovative and novel approach to automate cervical cancer image classification using Multi-Head Self-Attention (MHSA) and convolutional neural networks (CNNs). The proposed method leverages the strengths of both MHSA mechanisms and CNN to effectively capture both local and global features within cervical images in two streams. MHSA facilitates the model's ability to focus on relevant regions of interest, while CNN extracts hierarchical features that contribute to accurate classification. Finally, we combined the two stream features and fed them into the classification module to refine the feature and the classification. To evaluate the performance of the proposed approach, we used the SIPaKMeD dataset, which classifies cervical cells into five categories. Our model achieved a remarkable accuracy of 98.522\%. This performance has high recognition accuracy of medical image classification and holds promise for its applicability in other medical image recognition tasks.

Emma Peltier

This paper looks into the city access of roma women in precarious living conditions. Its aim is to understand how anti-roma racism and gender shape access to public space. Two spatial practices are studied: begging and daily mobility. The methodology is consisting of long term observations and interviews. I show that living in a slum exposes to gender and racist based violences. In response women use tactics like clothing, avoiding night, being accompanied. Finally begging and walking through the forest are more likely to expose to violences. It leads some of them to fall back on domestic space.

Irena Chen, Zhenke Wu, Sioban D. Harlow et al.

Women are at increased risk of bone loss during the menopausal transition; in fact, nearly 50\% of women's lifetime bone loss occurs during this time. The longitudinal relationships between estradiol (E2) and follicle-stimulating hormone (FSH), two hormones that change have characteristic changes during the menopausal transition, and bone health outcomes are complex. However, in addition to level and rate of change in E2 and FSH, variability in these hormones across the menopausal transition may be an important predictor of bone health, but this question has yet to be well explored. We introduce a joint model that characterizes individual mean estradiol (E2) trajectories and the individual residual variances and links these variances to bone health trajectories. In our application, we found that higher FSH variability was associated with declines in bone mineral density (BMD) before menopause, but this association was moderated over time after the menopausal transition. Additionally, higher mean E2, but not E2 variability, was associated with slower decreases in during the menopausal transition. We also include a simulation study that shows that naive two-stage methods often fail to propagate uncertainty in the individual-level variance estimates, resulting in estimation bias and invalid interval coverage.

Barbora Buhnova

With all the opportunities and risks that technology holds in connection to our safe and sustainable future, it is becoming increasingly important to involve a larger portion of our society in becoming active co-creators of our digitalized future -- moving from the passenger seat to the driver seat. Yet, despite extensive efforts around the world, little progress has been made in growing the representation of certain communities and groups in software engineering. This chapter shares one successful project, called Czechitas, triggering a major social change in Czechia, involving 1 000+ volunteers to support 50 000+ women on their way towards software engineering education and career.

Ahmedin Aliyi Usso, Hassen Abdi Adem, Addisu Alemu et al.

BackgroundCompassionate and respectful maternity care during childbirth has been identified as a potential strategy to prevent and reduce maternal mortality and morbidity. Despite its importance, there is a paucity of information on the level of disrespect and abuse meted out to mothers in eastern Ethiopia. This study assesses the level of disrespect and abuse suffered by women during childbirth, and the associated factors, in public health facilities in the rural East Hararghe Zone in eastern Ethiopia.MethodsA cross-sectional study was conducted among 530 women who gave birth in 20 public health facilities in the East Hararghe Zone during the period between 1 April and 30 April 2020. Data were collected using a validated questionnaire. Bivariable and multivariable binary logistic regression analyses were employed to identify the factors associated with disrespect and abuse during childbirth. Adjusted odds ratio (AOR) (95% CI) was used to report this association, and statistical significance was set at P < 0.05.ResultsOverall, 77% (95% CI: 73%–81%) of women reported at least one type of disrespect and abuse during childbirth in the East Hararghe Zone public health facilities. In this study, factors such as households having an average monthly income of below 57.22 USD (AOR = 2.29, 95% CI: 1.41–3.71), mothers residing at more than 30 min away from a nearby health facility (AOR = 2.10, 95% CI: 1.30–3.39), those not receiving antenatal care (AOR = 4.29, 95% CI: 2.17–8.52), and those giving birth during nighttime (AOR = 2.16, 95% CI: 1.37–3.41) were associated with at least one type of disrespect and abuse during childbirth.ConclusionMore than three in every four women who gave birth in the East Hararghe Zone public health facilities were disrespected and abused during childbirth. Encouraging all pregnant women to pay attention to antenatal care visits and improving the quality of healthcare service during nighttime in all health facilities will be essential for preventing and reducing disrespect and abuse and its negative consequences.

Gerald A. Goldin

Prediction of ``anyons'', often attributed exclusively to Wilczek, came first from Leinaas & Myrheim in 1977, and independently from Goldin, Menikoff, & Sharp in 1980-81. In 2020, experimentalists successfully created anyonic excitations. This paper discusses why the possibility of quantum particles in two-dimensional space with intermediate exchange statistics eluded physicists for so long after bosons and fermions were understood. The history suggests ideas for the preparation of future researchers. I conclude by addressing failures to attribute scientific achievements accurately. Such practices disproportionately hurt women and minorities in physics, and are harmful to science.

Nick James, Max Menzies, Howard Bondell

This paper applies existing and new approaches to study trends in the performance of elite athletes over time. We study both track and field scores of men and women athletes on a yearly basis from 2001 to 2019, revealing several trends and findings. First, we perform a detailed regression study to reveal the existence of an "Olympic effect", where average performance improves during Olympic years. Next, we study the rate of change in athlete performance and fail to reject the notion that athlete scores are leveling off, at least among the top 100 annual scores. Third, we examine the relationship in performance trends among men and women's categories of the same event, revealing striking similarity, together with some anomalous events. Finally, we analyze the geographic composition of the world's top athletes, attempting to understand how the diversity by country and continent varies over time across events. We challenge a widely held conception of athletics, that certain events are more geographically dominated than others. Our methods and findings could be applied more generally to identify evolutionary dynamics in group performance and highlight spatio-temporal trends in group composition.

Amador Durán, Pablo Fernández, Beatriz Bernárdez et al.

Context. Pair programming (PP) has been found to increase student interest in Computer Science, particularly so for women, and would therefore appear to be a way to help remedy their under-representation, which could be partially motivated by gender stereotypes applied to software engineers, assuming that men perform better than their women peers. If this same bias is present in pair programming, it could work against the goal of improving gender balance. Objective. In a remote setting in which students cannot directly observe their peers, we aim to explore whether they behave differently when the perceived gender of their remote PP partners changes, searching for differences in (i) the perceived productivity compared to solo programming; (ii) the partner's perceived technical competency compared to their own; (iii) the partner's perceived skill level; (iv) the interaction behavior, such as the frequency of source code additions, deletions, etc.; and (v) the type and relative frequencies of dialog messages in a chat window. Method. Using the twincode platform, several behaviors are automatically measured during the remote PP process, together with two questionnaires and a semantic tagging of the pairs' chats. A series of experiments to identify the effect, if any, of possible gender bias shall be performed. The control group will have no information about their partner's gender, whereas the treatment group will receive such information but will be selectively deceived about their partner's gender. For each response variable we will (i) compare control and experimental groups for the score distance between two in-pair tasks; then, using the data from the experimental group only, we will (ii) compare scores using the partner's perceived gender as a within-subjects variable; and (iii) analyze the interaction between the partner's perceived gender and the subject's gender.

Vanessa Caroline Massuchetto

Nivedita Tripathi, Senthil Yogamani

Automated Parking is becoming a standard feature in modern vehicles. Existing parking systems build a local map to be able to plan for maneuvering towards a detected slot. Next generation parking systems have an use case where they build a persistent map of the environment where the car is frequently parked, say for example, home parking or office parking. The pre-built map helps in re-localizing the vehicle better when its trying to park the next time. This is achieved by augmenting the parking system with a Visual SLAM pipeline and the feature is called trained trajectory parking in the automotive industry. In this paper, we discuss the use cases, design and implementation of a trained trajectory automated parking system. The proposed system is deployed on commercial vehicles and the consumer application is illustrated in \url{https://youtu.be/nRWF5KhyJZU}. The focus of this paper is on the application and the details of vision algorithms are kept at high level.

Andreas Müller, Madeleine Rousset Grenon

Athena (named after the ancient Greek goddess of wisdom) is a pre-university study programme for mathematics and physics organised by the Faculty of Science at the University of Geneva. It targets pupils enrolled in the final or penultimate year of Secondary II (high school), giving them an opportunity to explore and discover university-level studies in mathematics and physics. The programme aims to enhance pupils interest for the physical and mathematical sciences by introducing them to new topics, all while giving them a taste for student life. It also seeks to promote scientific careers to young pupils, especially to young women, as well as improving the transition between Secondary II and university

Helga Eggebø, Henrik Karlstrøm, Elisabeth Stubberud

Denne artikkelen presenterer funn fra en undersøkelse om levekår blant skeive med innvandrerbakgrunn i Norge. Med utgangspunkt i et interseksjonelt perspektiv analyserer vi 251 respondenters erfaringer med diskriminering og sosial ekskludering. Resultatene viser at over halvparten rapporterte om negative kommentarer eller handlinger fordi de bryter med normer for kjønn eller seksualitet, og noen flere på grunn av innvandrerbakgrunn. Om lag 1 av 3 rapporterte om opplevelser av ekskludering fra minoritetsmiljø fordi de er skeive, og 1 av 5 om eksklusjon fra skeive miljøer på grunn av innvandrerbakgrunn. Disse sammenhengene mellom sosial ekskludering og den doble minoritetsstatusen til skeive med innvandrerbakgrunn har i liten grad blitt undersøkt systematisk med kvantitative data tidligere. Kvantitative undersøkelser basert på selvrekrutterte utvalg – slik denne studien er et eksempel på – utgjør et viktig supplement til kvalitativ forskning på den ene siden og representative undersøkelser på den andre. Basert på de empiriske analysene i artikkelen drøfter vi hvordan kontekstsensitive interseksjonelle perspektiver kan kombineres med kvantitative metoder som tradisjonelt har vært rettet mot generaliserbare funn.

Salete Maria da Silva, Fernanda Victória Meneses da Silva

Transcrição da palestra ministrada pela Profa. Salete Maria da Silva para o Grupo de Pesquisa Direito e Sexualidade.