Identity Labels Research Articles

Which best “identifies” you? Deconstructing researchers’ decisions on sexual identity categories and labels in bi+ college student scholarship.

Which best “identifies” you? Deconstructing researchers’ decisions on sexual identity categories and labels in bi+ college student scholarship.

Similarity Regulation and Calibration Alignment for Weakly Supervised Text-Based Person Re-Identification

Traditional text-based person re-identification relies on identity labels. However, it is impossible to annotate large datasets, since identity annotation is expensive and time-consuming. Weakly supervised text-based person re-identification, where only text-image pairs are available without annotation of identities, is very practical in real life. While dealing with the weakly supervised person re-identification, two issues should be strengthed, i.e., alignment caused by different modal, and cross-modal matching ambiguity caused by the lack of identity labels. In this paper, we propose a Similarity Regulation and Calibration Alignment (SRCA) framework, which consists of two unimodal encoders for images and text respectively and a multi-modal encoder for the masked language modelling task. Firstly, a Similarity Regulation (SR) strategy is proposed to relax the strict one-to-one constraints for the local similarities between different pairs by introducing a novel soft objective. The soft objective can adjust hard objectives to achieve soft cross-modal alignment by establishing a many-to-many relationship between two modalities. Secondly, the Calibration Alignment (CA) module is proposed to improve intra-class compactness by modelling pseudo-label assignment as optimal transport. The ambiguity of cross-modal matching can be reduced by aligning features and pseudo-labels of different modalities and gradually calibrating the distribution of pseudo-labels. Experimental results show that our method has achieved obvious advantages compared with existing methods, and also demonstrated competitive performance compared with fully supervised methods.

Supplemental Material for Which Best “Identifies” You? Deconstructing Researchers’ Decisions on Sexual Identity Categories and Labels in Bi+ College Student Scholarship

Supplemental Material for Which Best “Identifies” You? Deconstructing Researchers’ Decisions on Sexual Identity Categories and Labels in Bi+ College Student Scholarship

Situational Fluidity and the Use of Identity Labels in Interactions

Situational Fluidity and the Use of Identity Labels in Interactions

Food for the soul: religious identity and ethical halal labelling in sharing economy apps

ABSTRACT Religion functions as a form of social identity characterized by perpetual group affiliation and deeply respected beliefs and values. Halal, for Muslim consumers, reflects the ethical dimension of food consumption. This study explores how religious self-identity and the value of Halal labelling impact Muslim consumers’ trust and purchase intentions on Indonesia’s leading online food delivery platforms, GrabFood and GoFood. Using a quantitative approach, data were collected through online surveys with purposive sampling from 209 Muslim users. The study found that religious self-identity and perceived usefulness of the halal label significantly influence purchase intentions, while trust did not mediate these relationships.

‘Hoping for a life that isn’t just surviving:’ a qualitative study on sexual minority college students’ experiences with identity formation and coming out at a religious institution in the USA

This study explores the identity formation and coming out experiences of 14 sexual minority students at a religiously affiliated university in the USA. Participants described their experiences of cultural, religious, and societal pressure that extended the process of self-acceptance and identity disclosure. We used consensual qualitative research method to analyse each interview. Our analysis led to the identification of eight domains: identity formation; barriers to understanding identity; evolution of label; adoption of label, avoidance of label; coming out process; barriers to coming out; and reactions to coming out. Self-defining moments such as adopting an affirming identity label, disclosing that identity to others, and beginning to initiate non-heterosexual relationships, formed the basis of these domains. Overall, we found that participants described some experiences identified in previous literature. However, we also analysed new information which indicates that the coming out process is lengthy, heavily dependent on context, and problematic issues may arise throughout a lifetime. Findings point to under-researched factors influencing the complexities sexual minority students face within conservative religious institutions.

The event attachment formation process of mega-event volunteers

ABSTRACT Although volunteers are important participants at mega-events, the changing relationship between volunteers and mega-events from the perspective of attachment remains under-explored. Taking the Beijing 2022 Winter Olympics as a case study, this study conducted a coding analysis of data collected through in-depth interviews and online materials. The results showed that the volunteers developed a sense of closeness to the Winter Olympics through the atmosphere, Olympic heritage, and place attachment in the pre-event stage. In the mid-event stage, on the basis of the new norms and order in space and time, the volunteers developed a strong sense of connection to the Winter Olympics through their experiences and perceptions of the event. In the post-event stage, they developed a sense of identification with the Winter Olympics through their internalisation of event memories, identity labels, and new habits. The volunteers’ attitudes showed a dramatic evolutionary conditional–unconditional–unreserved pattern together with self-transformation due to the Olympics. This study sheds light on future volunteer management and event development.

I know you are but who Am I? Presentation and labelling in young queer communities

Recent research shows a sharp increase in the number of queer-identified young people in the United States, along with an expansion of terms people use to describe their queer identities. Using semi-structured interview data collected from 30 queer-identified non-male 18-23-year-olds, we examine how young people today use terminology, identity, and appearance to communicate their sexual and/or gender identity. We find that the two most salient factors in how participants describe their sexual and gender identities are presentation and labelling. We then propose a distinction between one’s autonym and exonym, where an autonym is the identity label one uses to describe oneself and an exonym is the identity label others use to describe a person.

A dual-weight mechanism-based neural network for partial domain adaptation fault diagnosis of bearings under different working conditions

Abstract The intelligent fault diagnosis of bearings often entails a distribution discrepancy between training data and real data, which significantly limits the generalization ability of fault diagnosis models. Traditional domain adaptation methods aim to achieve knowledge transfer from the source domain to the target domain. However, in reality, the incompleteness of the target domain dataset leads to the issue of missing classes in the target domain, rendering the assumption of identical label categories in traditional partial domain adaptation (PDA) methods difficult to implement. This study proposes a dual-weighted mechanism for fault diagnosis models in the context of PDA (PDA method-based dual-weight mechanism) to address the issue of weight bias in target domain labels due to category absence. This approach employs a sample-based weighting mechanism and a similarity-based weighting mechanism to assign weights to different label categories, referred to as the dual-weight mechanism based on similarity and sample number (WSS). This method can promote positive transfer while suppressing negative transfer, thereby resolving the issue of weight bias in the target domain label space. Experiments that use the WSS for fault transfer tasks under identical conditions, with multiple categories and multiple conditions employed for the same category, demonstrate the superior performance of the WSS.

Conscious Delirium of a Traveling Body

The question of political economy in the arts offers a way into a creative-critical reflection on the challenges associated with navigating identity labels and living a freelance artist’s life pressured to conform to institutional expectations particularly within the UK's art scene. This piece is a personal account of a body resisting becoming a symbol, of staying conscious of our socio-political landscape and the ongoing Israeli settler-colonialism. I draw on examples from three of my artworks: the collaborative project From the Lips to the Moon, ongoing nights of longform improvised music and poetry in London; In Observance, a performative research and intervention at the United Nations in Geneva, involving archival documents related to Palestine; and Mishandled Archive, which involves dispersing family photographs and documents in public places, accompanied by daily dances. Through these works, this essay asks: How can historical evidence of imperialism and settler-colonialism be absorbed or resisted within the body? How can we transform loss into movement and togetherness? How late is too late as mounds of rubble and bodies multiply?

Pseudo-label assisted contrastive learning model for unsupervised open-set domain adaptation in fault diagnosis

The operation of mechanical equipment is frequently characterized by complexity and variability, leading to signal domain shifts. This phenomenon underscores the significance of cross-domain fault diagnosis for maintaining the reliability and safety of mechanical systems. Due to the absence of labeled data in many operational contexts, there's a clear need for an unsupervised domain adaptation technique that does not rely on labeled information. Moreover, traditional domain adaptation methods presuppose identical label distributions across source and target domains. Nevertheless, real-world engineering scenarios often present novel fault categories out of distribution, thereby challenging the efficacy of established domain adaption methods. To address these challenges, we proposed a pseudo-label assisted contrastive learning model (PLA-CLM) for Unsupervised Open-set Domain Adaptation. Based on contrastive learning, the proposed model effectively minimizes the discrepancy between samples of identical pseudo-label across domains, while simultaneously integrating distance, density, and entropy to isolate out-of-distribution samples. After training, the model adaptively identifies known faults and detects OOD faults using thresholds calculated based on sample distribution. Experimental results on two datasets demonstrate that our method surpasses existing approaches, ensuring enhanced reliability of mechanical systems’ operation and maintenance.

Ultra-inert lanthanide chelates as mass tags for multiplexed bioanalysis

Coordination compounds of lanthanides are indispensable in biomedical applications as MRI contrast agents and radiotherapeutics. However, since the introduction of the chelator DOTA four decades ago, there has been only limited progress on improving their thermodynamic stability and kinetic inertness, which are essential for safe in vivo use. Here, we present ClickZip, an innovative synthetic strategy employing a coordination-templated formation of a 1,5-triazole bridge that improves kinetic inertness up to a million-fold relative to DOTA, expanding utility of lanthanide chelates beyond traditional uses. Acting as unique mass tags, the ClickZip chelates can be released from (biological) samples by acidic hydrolysis, chromatographically distinguished from interfering lanthanide species, and sensitively detected by mass spectrometry. Lanthanides enclosed in ClickZip chelates are chemically almost indistinguishable, providing a more versatile alternative to chemically identical isotopic labels for multiplexed analysis. The bioanalytical potential is demonstrated on tagged cell-penetrating peptides in vitro, and anti-obesity prolactin-releasing peptides in vivo.

A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space.

Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic. We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed. Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%. This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.

Weakly Supervised Contrastive Learning for Unsupervised Vehicle Reidentification.

Reidentification (Re-id) of vehicles in a multicamera system is an essential process for traffic control automation. Previously, there have been efforts to reidentify vehicles based on shots of images with identity (id) labels, where the model training relies on the quality and quantity of the labels. However, labeling vehicle ids is a labor-intensive procedure. Instead of relying on expensive labels, we propose to exploit camera and tracklet ids that are automatically obtainable during a Re-id dataset construction. In this article, we present weakly supervised contrastive learning (WSCL) and domain adaptation (DA) techniques using camera and tracklet ids for unsupervised vehicle Re-id. We define each camera id as a subdomain and tracklet id as a label of a vehicle within each subdomain, i.e., weak label in the Re-id scenario. Within each subdomain, contrastive learning using tracklet ids is applied to learn a representation of vehicles. Then, DA is performed to match vehicle ids across the subdomains. We demonstrate the effectiveness of our method for unsupervised vehicle Re-id using various benchmarks. Experimental results show that the proposed method outperforms the recent state-of-the-art unsupervised Re-id methods. The source code is publicly available on https://github.com/andreYoo/WSCL_VeReid.

Are masculine faces “natural” for all men?: an experimental study about predictors of Chinese young adults’ attitudes towards trans men

ABSTRACT Researchers have increasingly paid attention to prejudice against trans people and its predictors. However, few studies have explored if potential predictors may interact to affect people’s attitudes towards trans people. In this experimental study, we examined the impacts of participant-related factors (i.e. gender and prior contact experience with gender or sexual minorities), target-related variables (i.e. gender identity and facial appearance), and their interactions on Chinese young adults’ attitudes towards trans men. We recruited 172 Chinese young adults to rate images of men with different gender identity labels (i.e. cisgender or trans men) and facial appearances (i.e. feminine or masculine). Results showed that a three-way interaction existed among targets’ gender identity labels, targets’ facial appearances, and participants’ genders. Overall, female participants were less likely than men to show more negative attitudes towards trans men than cisgender men. Participants showed more positive attitudes towards masculine-appearing trans men than feminine trans men. The current findings implied that people favoured trans men’s masculine faces, which match the roles of their gender identities.

A fly in the ointment: the re-discovered holotype of 'Trypeta' cylindrica Walker, 1853 belongs to the genus Coelopacidia Enderlein (Diptera: Tephritidae), not Chyliza Fallén (Psilidae).

The 'type' of the fly species 'Trypeta' cylindrica Walker, 1853, from the William Saunders Collection, was misidentified and subsequently placed in the genus Chyliza Fallén (Psilidae). A specimen from the same collection with identical label data but belonging to Coelopacidia Enderlein (Tephritidae) is hereby identified as the correct holotype and the species should therefore be referred to as Coelopacidia cylindrica (Walker, 1853). Chyliza pallidipes Lamb, 1917 stat. rev. is taken out of synonymy and regarded as the valid name for the species previously treated as 'Chyliza cylindrica'.

Learning differentiable categorical regions with Gumbel-Softmax for person re-identification

Locating diverse body parts and perceiving part visibility are essential to person re-identification (re-ID). Most existing methods employ an extra model, e.g., pose estimation or human parsing, to locate parts, or generate pseudo labels to train the part locator incorporated with the re-ID model. In this paper, we aim at learning diverse horizontal stripes with foreground refinement to pursue pixel-level part alignment via only using person identity labels. Specifically, we proposed a Gumbel-Softmax based Differential Categorical Region (DCR) learning method and make two contributions. (1) A stripe-wise regularization. Given an image, the part locator produce part probability maps. The continuous values in the probability maps are discretized into zero or argmax value in the horizontal stripes by the Gumbel-Softmax. Gumbel-Softmax allows us to use the argmax discrete value for part diversity regularization in the forward pass, but can still estimate gradients in the backward pass. (2) A self-refinement method to suppress the background noise in the stripes. We employ a lightweight foreground perception head to produce foreground probability map with only person identity labels supervision. Benefits from discretization of the categorical stripes, we can conveniently obtain the part pseudo label by element-wise multiplying the categorical stripes with foreground probability map. Finally, DCR can locate the body parts at pixel-level and extract part-aligned representation. Experimental results on both holistic and occluded re-ID datasets confirm that our approach significantly improves the learned representation and the achieved performance is on par with the state-of-the-art methods. The code is available at https://github.com/deepalchemist/differentiable-categorical-region

Dimensions of experienced gender and prospective self-injurious thoughts and behaviors in preadolescent children: A national study

ObjectivesGender diverse youth face higher risk of engaging in self-injurious thoughts and behaviors (SITBs) compared to cisgender youth. Limitations in past research include a focus on older adolescents, an emphasis on specific gender identity labels that may not be inclusive of the range of youth gender experiences, and reliance on cross-sectional data. Thus, the current study prospectively evaluated dimensions of experienced gender in relation to first-onset SITBs among preadolescents. MethodsData were drawn from the Adolescent Brain Cognitive Development Study, a longitudinal study of youth across the United States. Youth (n = 7909) were aged 10–11 during initial assessment, and follow-up was conducted one year later. Two dimensions of experienced gender, felt-gender incongruence (not feeling aligned with the gender associated with one's sex assigned at birth) and gender non-contentedness (feeling dissatisfaction with the gender associated with one's sex assigned at birth), were assessed. Primary outcomes included non-suicidal self-injury (NSSI), suicidal ideation (SI), and suicide attempts (SA). ResultsLogistic regressions were conducted stratified by sex assigned at birth. For youth assigned female at birth, felt-gender incongruence was prospectively associated with first-onset NSSI and SI and gender non-contentedness was prospectively associated with first-onset of NSSI. For youth assigned male at birth, gender non-contentedness was prospectively associated with first-onset SI. Diverse experienced gender did not prospectively predict SA. ConclusionsDimensions of experienced gender may be associated with subsequent first-onset SITBs among preadolescents. These findings support the need for future research on risk and protective factors that may mediate or moderate this relationship.

Child sexual abuse and social identity loss: A qualitative analysis of survivors' public accounts.

Emerging evidence suggests that social identities are an important determinant of adaptation following traumatic life experiences. In this paper, we analyse accounts of people who experienced child sexual abuse. Using publicly available talk of people who waived their right to anonymity following successful conviction of perpetrators, we conducted a thematic analysis focusing on trauma-related changes in their social identities. Analysis of these accounts highlighted two themes. The first highlights the acquisition in these accounts of unwanted and damaging identity labels. The second presents child sexual abuse as a key destructive force in terms of important identity work during childhood. Discussion of this analysis centres on the pathological consequences of social identity change. Both the loss of valued identities and the acquisition of aberrant and isolating identities are experienced and constructed as devastating by those affected by child sexual abuse. This has important implications, not only for those impacted by child sexual abuse but for how abuse is discussed in society, and how it is approached by policy makers, educators and individuals working with survivors and their families.

Portrayal or Betrayal: Heteronormativity and the Realism Burden in the 2022 Disney+ Pride Collection

ABSTRACT The presence and quality of LGBTQ+ representation in film and television has important implications for viewers, especially LGBTQ+ youth. This study conducted a content analysis of the 2022 Disney+ Pride Collection, a list of movies and episodes designated by Disney as including LGBTQ+ characters and stories. This analysis examined the depiction of LGBTQ+ fictional characters, finding that Black LGBTQ+ characters were more likely to be presented as adults and in romantic relationships compared to characters of other races, suggesting that their intersecting identities influence their portrayal toward more heteronormative scripts. In what we argue is a heteronormative evolution of the butch/femme binary, lesbian characters were frequently presented on a light/dark binary. Further, in what we describe as a “realism burden,” portrayals of discrimination and LGBTQ+ identity labeling were limited to realistic fiction and adult media, and were absent from the post-gay worlds of sci-fi and fantasy.