Vahid Monfared, Mohammad Hadi Gharib, Ali Sabri
et al.
Prostate cancer is a leading cause of mortality in men, yet interpretation of T2-weighted prostate MRI remains challenging due to subtle and heterogeneous lesions. We developed an interpretable framework for automatic cancer detection using a small dataset of 162 T2-weighted images (102 cancer, 60 normal), addressing data scarcity through transfer learning and augmentation. We performed a comprehensive comparison of Vision Transformers (ViT, Swin), CNNs (ResNet18), and classical methods (Logistic Regression, SVM, HOG+SVM). Transfer-learned ResNet18 achieved the best performance (90.9% accuracy, 95.2% sensitivity, AUC 0.905) with only 11M parameters, while Vision Transformers showed lower performance despite substantially higher complexity. Notably, HOG+SVM achieved comparable accuracy (AUC 0.917), highlighting the effectiveness of handcrafted features in small datasets. Unlike state-of-the-art approaches relying on biparametric MRI (T2+DWI) and large cohorts, our method achieves competitive performance using only T2-weighted images, reducing acquisition complexity and computational cost. In a reader study of 22 cases, five radiologists achieved a mean sensitivity of 67.5% (Fleiss Kappa = 0.524), compared to 95.2% for the AI model, suggesting potential for AI-assisted screening to reduce missed cancers and improve consistency. Code and data are publicly available.
Simón Peña-Fernández, Urko Peña-Alonso, Ainara Larrondo-Ureta
et al.
Journalists have incorporated social networks into their work as a standard tool, enhancing their ability to produce and disseminate information and making it easier for them to connect more directly with their audiences. However, this greater presence in the digital public sphere has also increased their exposure to harassment and hate speech, particularly in the case of women journalists. This study analyzes the presence of harassment and hate speech in responses (n = 60,684) to messages that 200 journalists and media outlets posted on X (formerly Twitter) accounts during the days immediately preceding and following the July 23 (23-J) general elections held in Spain in 2023. The results indicate that the most common forms of harassment were insults and political hate, which were more frequently aimed at personal accounts than institutional ones, highlighting the significant role of political polarization-particularly during election periods-in shaping the hostility that journalists face. Moreover, although, generally speaking, the total number of harassing messages was similar for men and women, it was found that a greater number of sexist messages were aimed at women journalists, and an ideological dimension was identified in the hate speech that extremists or right-wing populists directed at them. This study corroborates that this is a minor but systemic issue, particularly from a political and gender perspective. To counteract this, the media must develop proactive policies and protective actions extending even to the individual level, where this issue usually applies.
Messi H. J. Lee, Soyeon Jeon, Jacob M. Montgomery
et al.
Current research on bias in Vision Language Models (VLMs) has important limitations: it is focused exclusively on trait associations while ignoring other forms of stereotyping, it examines specific contexts where biases are expected to appear, and it conceptualizes social categories like race and gender as binary, ignoring the multifaceted nature of these identities. Using standardized facial images that vary in prototypicality, we test four VLMs for both trait associations and homogeneity bias in open-ended contexts. We find that VLMs consistently generate more uniform stories for women compared to men, with people who are more gender prototypical in appearance being represented more uniformly. By contrast, VLMs represent White Americans more uniformly than Black Americans. Unlike with gender prototypicality, race prototypicality was not related to stronger uniformity. In terms of trait associations, we find limited evidence of stereotyping-Black Americans were consistently linked with basketball across all models, while other racial associations (i.e., art, healthcare, appearance) varied by specific VLM. These findings demonstrate that VLM stereotyping manifests in ways that go beyond simple group membership, suggesting that conventional bias mitigation strategies may be insufficient to address VLM stereotyping and that homogeneity bias persists even when trait associations are less apparent in model outputs.
The Bradley-Terry model is widely used for the analysis of pairwise comparison data and, in essence, produces a ranking of the items under comparison. We embed the Bradley-Terry model within a stochastic block model, allowing items to cluster. The resulting Bradley-Terry SBM (BT-SBM) ranks clusters so that items within a cluster share the same tied rank. We develop a fully Bayesian specification in which all quantities-the number of blocks, their strengths, and item assignments-are jointly learned via a fast Gibbs sampler derived through a Thurstonian data augmentation. Despite its efficiency, the sampler yields coherent and interpretable posterior summaries for all model components. Our motivating application analyzes men's tennis results from ATP tournaments over the seasons 2000-2022. We find that the top 100 players can be broadly partitioned into three or four tiers in most seasons. Moreover, the size of the strongest tier was small from the mid-2000s to 2018 and has increased since, providing evidence that men's tennis has become more competitive in recent years.
The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there's a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.
Prostate cancer is a highly prevalent cancer and ranks as the second leading cause of cancer-related deaths in men globally. Recently, the utilization of multi-modality transrectal ultrasound (TRUS) has gained significant traction as a valuable technique for guiding prostate biopsies. In this study, we propose a novel learning framework for clinically significant prostate cancer (csPCa) classification using multi-modality TRUS. The proposed framework employs two separate 3D ResNet-50 to extract distinctive features from B-mode and shear wave elastography (SWE). Additionally, an attention module is incorporated to effectively refine B-mode features and aggregate the extracted features from both modalities. Furthermore, we utilize few shot segmentation task to enhance the capacity of classification encoder. Due to the limited availability of csPCa masks, a prototype correction module is employed to extract representative prototypes of csPCa. The performance of the framework is assessed on a large-scale dataset consisting of 512 TRUS videos with biopsy-proved prostate cancer. The results demonstrate the strong capability in accurately identifying csPCa, achieving an area under the curve (AUC) of 0.86. Moreover, the framework generates visual class activation mapping (CAM), which can serve as valuable assistance for localizing csPCa. These CAM images may offer valuable guidance during TRUS-guided targeted biopsies, enhancing the efficacy of the biopsy procedure.The code is available at https://github.com/2313595986/SmileCode.
Thomas Heller, Patrick Diehl, Zachary Byerly
et al.
To achieve scalability with today's heterogeneous HPC resources, we need a dramatic shift in our thinking; MPI+X is not enough. Asynchronous Many Task (AMT) runtime systems break down the global barriers imposed by the Bulk Synchronous Programming model. HPX is an open-source, C++ Standards compliant AMT runtime system that is developed by a diverse international community of collaborators called The Ste||ar Group. HPX provides features which allow application developers to naturally use key design patterns, such as overlapping communication and computation, decentralizing of control flow, oversubscribing execution resources and sending work to data instead of data to work. The Ste||ar Group comprises physicists, engineers, and computer scientists; men and women from many different institutions and affiliations, and over a dozen different countries. We are committed to advancing the development of scalable parallel applications by providing a platform for collaborating and exchanging ideas. In this paper, we give a detailed description of the features HPX provides and how they help achieve scalability and programmability, a list of applications of HPX including two large NSF funded collaborations (STORM, for storm surge forecasting; and STAR (OctoTiger) an astro-physics project which runs at 96.8% parallel efficiency on 643,280 cores), and we end with a description of how HPX and the Ste||ar Group fit into the open source community.
Inequality prevails in science. Individual inequality means that most perish quickly and only a few are successful, while gender inequality implies that there are differences in achievements for women and men. Using large-scale bibliographic data and following a computational approach, we study the evolution of individual and gender inequality for cohorts from 1970 to 2000 in the whole field of computer science as it grows and becomes a team-based science. We find that individual inequality in productivity (publications) increases over a scholar's career but is historically invariant, while individual inequality in impact (citations), albeit larger, is stable across cohorts and careers. Gender inequality prevails regarding productivity, but there is no evidence for differences in impact. The Matthew Effect is shown to accumulate advantages to early achievements and to become stronger over the decades, indicating the rise of a "publish or perish" imperative. Only some authors manage to reap the benefits that publishing in teams promises. The Matthew Effect then amplifies initial differences and propagates the gender gap. Women continue to fall behind because they continue to be at a higher risk of dropping out for reasons that have nothing to do with early-career achievements or social support. Our findings suggest that mentoring programs for women to improve their social-networking skills can help to reduce gender inequality.
Amador Durán, Pablo Fernández, Beatriz Bernárdez
et al.
Context. Software Engineering (SE) has low female representation due to gender bias that men are better at programming. Pair programming (PP) is common in industry and can increase student interest in SE, especially women; but if gender bias affects PP, it may discourage women from joining the field. Objective. We explore gender bias in PP. In a remote setting where students cannot see their peers' gender, we study how perceived productivity, technical competency and collaboration/interaction behaviors of SE students vary by perceived gender of their remote partner. Method. We developed an online PP platform (twincode) with a collaborative editing window and a chat pane. Control group had no gender information about their partner, while treatment group saw a gendered avatar as a man or woman. Avatar gender was swapped between tasks to analyze 45 variables on collaborative coding behavior, chat utterances and questionnaire responses of 46 pairs in original study at the University of Seville and 23 pairs in the replication at the University of California, Berkeley. Results. No significant effect of gender bias treatment or interaction between perceived partner's gender and subject's gender in any variable in original study. In replication, significant effects with moderate to large sizes in four variables within experimental group comparing subjects' actions when partner was male vs female.
Equal pay is an essential component of gender equality, one of the Sustainable Development Goals of the United Nations. Using resume data of over ten million Chinese online job seekers in 2015, we study the current gender pay gap in China. The results show that on average women only earned 71.57\% of what men earned in China. The gender pay gap exists across all age groups and educational levels. Contrary to the commonly held view that developments in education, economy, and a more open culture would reduce the gender pay gap, the fusion analysis of resume data and socio-economic data presents that they have not helped reach the gender pay equality in China. China seems to be stuck in a place where traditional methods cannot make further progress. Our analysis further shows that 81.47\% of the variance in the gender pay gap can be potentially attributed to discrimination. In particular, compared with the unmarried, both the gender pay gap itself and proportion potentially attributed to discrimination of the married are larger, indicating that married women suffer greater inequality and more discrimination than unmarried ones. Taken together, we suggest that more research attention should be paid to the effect of discrimination in understanding gender pay gap based on the family constraint theory. We also suggest the Chinese government to increase investment in family-supportive policies and grants in addition to female education.
Md Ershadul Haque, Salah Uddin, Md Ariful Islam
et al.
The combination of big data and deep learning is a world-shattering technology that can greatly impact any objective if used properly. With the availability of a large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped to predict the future trend of any health problems. From the literature survey, we found the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing long short-term memory (LSTM) and predict the future trend of heart failure based on that health record. Hence the fundamental commitment of this work is to predict the failure of the heart using an LSTM based on the patient's electronic medicinal information. We have analyzed a dataset containing the medical records of 299 heart failure patients collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan). The patients consisted of 105 women and 194 men and their ages ranged from 40 and 95 years old. The dataset contains 13 features, which report clinical, body, and lifestyle information responsible for heart failure. We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction.
Nandan Kulkarni, Christopher Compton, Jooseppi Luna
et al.
Staying hydrated and drinking fluids is extremely crucial to stay healthy and maintaining even basic bodily functions. Studies have shown that dehydration leads to loss of productivity, cognitive impairment and mood in both men and women. However, there are no such an existing tool that can monitor dehydration continuously and provide alert to users before it affects on their health. In this paper, we propose to utilize wearable Electrodermal Activity (EDA) sensors in conjunction with signal processing and machine learning techniques to develop first time ever a dehydration self-monitoring tool, \emph{Monitoring My Dehydration} (MMD), that can instantly detect the hydration level of human skin. Moreover, we develop an Android application over Bluetooth to connect with wearable EDA sensor integrated wristband to track hydration levels of the users real-time and instantly alert to the users when the hydration level goes beyond the danger level. To validate our developed tool's performance, we recruit 5 users, carefully designed the water intake routines to annotate the dehydration ground truth and trained state-of-art machine learning models to predict instant hydration level i.e., well-hydrated, hydrated, dehydrated and very dehydrated. Our system provides an accuracy of 84.5% in estimating dehydration level with an sensitivity of 87.5% and a specificity of 90.3% which provides us confidence of moving forward with our method for larger longitudinal study.
Vítor Albiero, Krishnapriya K. S., Kushal Vangara
et al.
We present a comprehensive analysis of how and why face recognition accuracy differs between men and women. We show that accuracy is lower for women due to the combination of (1) the impostor distribution for women having a skew toward higher similarity scores, and (2) the genuine distribution for women having a skew toward lower similarity scores. We show that this phenomenon of the impostor and genuine distributions for women shifting closer towards each other is general across datasets of African-American, Caucasian, and Asian faces. We show that the distribution of facial expressions may differ between male/female, but that the accuracy difference persists for image subsets rated confidently as neutral expression. The accuracy difference also persists for image subsets rated as close to zero pitch angle. Even when removing images with forehead partially occluded by hair/hat, the same impostor/genuine accuracy difference persists. We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time. Lastly, we show that the accuracy difference persists even if a state-of-the-art deep learning method is trained from scratch using training data explicitly balanced between male and female images and subjects.
Indicator functions mentioned in the title are constructed on an arbitrary nondiscrete locally compact Abelian group of finite dimension. Moreover, they can be obtained by small perturbation from any indicator function fixed beforehand. In the case of a noncompact group, the term "Fourier sums" should be understood as "partial Fourier integrals". A certain weighted version of the result is also provided. This version leads to a new Men$'$shov-type correction theorem.
This paper discusses match result prediction ability of ATP ranking points, which is official ranking points for men's professional tennis players. The structure of overall ATP World Tour and the ranking point attribution system leads that the ranking point ratio between two players is an essential variable. The match result prediction model is a logistic model. The fact is verified using statistics of over 24000 matches from 2009.
Due to the aging population, spinal diseases get more and more common nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States. Thus the numbers of surgical spinal procedures are also increasing with the aging population and precise diagnosis plays a vital role in reducing complication and recurrence of symptoms. Spinal imaging of vertebral column is a tedious process subjected to interpretation errors. In this contribution, we aim to reduce time and error for vertebral interpretation by applying and studying the GrowCut-algorithm for boundary segmentation between vertebral body compacta and surrounding structures. GrowCut is a competitive region growing algorithm using cellular automata. For our study, vertebral T2-weighted Magnetic Resonance Imaging (MRI) scans were first manually outlined by neurosurgeons. Then, the vertebral bodies were segmented in the medical images by a GrowCut-trained physician using the semi-automated GrowCut-algorithm. Afterwards, results of both segmentation processes were compared using the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD) which yielded to a DSC of 82.99+/-5.03% and a HD of 18.91+/-7.2 voxel, respectively. In addition, the times have been measured during the manual and the GrowCut segmentations, showing that a GrowCut-segmentation - with an average time of less than six minutes (5.77+/-0.73) - is significantly shorter than a pure manual outlining.
Elizabeth M. Morgan, Matthew G. Steiner, Elisabeth Morgan Thompson
Male heterosexual identity development has received little empirical attention. The current study examines sexual orientation questioning processes of heterosexual-identified men and offers a comparison of these processes with those employed by their sexual-minority counterparts. Participants included 184 male college students (ages 18 to 23, M = 19.6), 149 primarily identified as “exclusively straight or heterosexual” and 35 as sexual minorities. Of exclusively straight respondents, 53 percent ( n = 79) and all of the sexual-minority respondents indicated having questioned their sexual orientation. Heterosexual men’s questioning processes included five categories: unelaborated questioning, other-sex exploration, the social context as informants or sites of knowledge, hypothetical thinking and perspective taking, and attraction comparisons between men and women. Several unifying and differentiating themes emerged between sexual orientation groups. Results suggest that conventional notions of a “standardized” heterosexual identity appear simplistic and reveal ways in which men’s identification with a majority heterosexual sexual identity can be purposeful.
We present an analysis of the FUSE spectra of eight high-declination dwarf novae obtained from a Cycle 7 FUSE survey. These DN systems have not been previously studied in the UV and little is known about their white dwarfs (WDs) or accretion disks. We carry out the spectral analysis of the FUSE data using synthetic spectra generated with the codes TLUSTY and SYNSPEC. For two faint objects (AQ Men, V433 Ara) we can only assess a lower limit for the WD temperature or mass accretion rate. NSV 10934 was caught in a quiescent state and its spectrum is consistent with a low mass accretion rate disk. For 5 objects (HP Nor, DT Aps, AM Cas, FO Per and ES Dra) we obtain WD temperatures between 34,000K and 40,000K and/or mass accretion rates consistent with intermediate to outburst states. These temperatures reflect the heating of the WD due to on-going accretion and are similar to the temperatures of other DNs observed on the rise to, and in decline from outburst. The WD Temperatures we obtain should therefore be considered as upper limits, and it is likely that during quiescence AM Cas, FO Per and ES Dra are near the average WD Teff for catalcysmic variables above the period gap (30,000K), similar to U Gem, SS Aur and RX And.