Pattern Discrimination Research Articles

EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training.

The superior performance of modern computer vision backbones (e.g., vision Transformers learned on ImageNet-1 K/22 K) usually comes with a costly training procedure. This study contributes to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these two aspects and design curriculum learning schedules by proposing tailored searching algorithms. Moreover, we present useful techniques for deploying our approach efficiently in challenging practical scenarios, such as large-scale parallel training, and limited input/output or data pre-processing speed. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. As an off-the-shelf approach, it reduces the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer) by [Formula: see text] on ImageNet-1 K/22 K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE).

Similar freezing spectra of particles in plant canopies and in the air at a high-altitude site

Abstract. Plant canopies are an important source of biological particles aerosolized into the atmosphere. Certain aerosolized microorganisms are able to freeze slightly supercooled cloud droplets and therefore affect mixed-phase cloud development. Still, spatiotemporal variability of such biological ice-nucleating particles (INPs) is currently poorly understood. Here, we study this variability between late summer and leaf shedding on the scale of individual leaves collected about fortnightly from four temperate broadleaf tree species (Fagus sylvatica, Juglans regia, Prunus avium and Tilia platyphyllos) on a hillside (Gempen, 650 m a.s.l. (metres above sea level)) and in a vertical canopy profile of one Fagus sylvatica (Hölstein, 550 m a.s.l.) in north-western Switzerland. The cumulative concentration of INPs active at ≥-10 °C (INPs−10) did not vary significantly between the investigated tree species but, as inferred from leaf mass per area and leaf carbon isotopic ratios, seemed to be lower on sun leaves as compared with shade leaves. Between August and mid-November, the median INP concentration increased from 4 to 38 INP−10 cm−2 of leaf area and was positively correlated with mean relative humidity throughout 24 h prior to sampling (Spearman's r=0.52, p<0.0001, n=64). In 53 of the total 64 samples collected at the Gempen site, differential INP spectra between −3 and −10 °C exhibited clearly discriminable patterns: in 53 % of the spectra, the number of additionally activated INPs increased persistently with each 1 °C decrease in temperature; the remaining spectra displayed significant peaks in differential INP concentration above −9 °C, most frequently in the temperature interval between −8 and −9 °C (21 %) and between −7 and −8 °C (17 %). Interestingly, the three most frequent patterns in differential INP spectra on leaves in Gempen were also prevalent in similar fractions in air samples with clearly discriminable patterns at the high-altitude Jungfraujoch site (3580 m a.s.l., Switzerland) collected during summer in the previous year. These findings corroborate the idea that a large fraction of the airborne biological INP population above the Alps during summer originates from plant surfaces. Which parameter or set of parameters could affect biological INP populations on both scales – upwind airsheds of high-altitude sites as well as individual leaves – is an intriguing question for further exploration. A first guess is that leaf wetness duration plays a role.

Characterisation of IBD heterogeneity using serum proteomics: A multicentre study.

Recent genetic and transcriptomic data highlight the need for improved molecular characterisation of inflammatory bowel disease (IBD). Proteomics may advance the delineation of IBD phenotypes since it accounts for post-transcriptional modifications. We aimed to assess the IBD spectrum based on inflammatory serum proteins and identify discriminative patterns of underlying biological subtypes across multiple European cohorts. Using proximity extension methodology, we measured 86 inflammation-related serum proteins in 1551 IBD patients and 312 healthy controls (HC). We screened for proteins exhibiting significantly different levels among IBD subtypes and between IBD and HC. Classification models for differentiating between Crohn's disease (CD) and ulcerative colitis (UC) were employed to explore the IBD spectrum based on estimated probability scores. Levels of multiple proteins, such as IL-17A, MMP-10, and FGF-19, differed (fold-change>1.2; FDR<0.05) between ileal vs colonic IBD. Using multivariable models, a protein signature reflecting the IBD spectrum was identified, positioning colonic CD between UC and ileal CD, which were at opposite ends of the spectrum. Based on area under the curve (AUC) estimates, classification models more accurately differentiated UC from ileal CD (median AUCs>0.73) than colonic CD (median AUCs<0.62). Models differentiating colonic CD from ileal CD demonstrated intermediate performance (median AUCs 0.67-0.69). Our findings in serum proteins support the presence of a continuous IBD spectrum rather than a clear separation of CD and UC. Within the spectrum, disease location may reflect a more similar disease than CD vs UC, as colonic CD resembled UC more closely than ileal CD.

Pattern recognition-based aptasensor array for discrimination of matrix effect

Nucleic acid aptamers have attracted considerable attention in recent years, particularly in the fields of disease diagnosis and treatment and biosensing. However, complex matrix effects have severely hindered their progress towards practical application. In this study, the novel strategy of a pattern recognition-based aptasensor array was introduced for target recognition based on the predictable selectivity capability of aptamers by circumventing the challenge of the interference of the matrix effect. As one proof of concept, we chose lactoferrin as the target protein, three selected aptamers with differential affinity as its recognition probes, eleven matrix samples of milk powder as the pattern discrimination model, and AuNPs color as the response signals. In the AuNPs colorimetric arrays, the lactoferrin-added milk powder matrix induced different degrees of protection on the surface of AuNPs particles due to different degrees of binding of lactoferrin to the three aptamer sequences and therefore exhibited differentiated red-to-blue color change along with salt-induced aggregation behaviors. The difference in color responses exhibited with and without lactoferrin addition was considered as the fingerprint pattern of the lactoferrin in each milk powder matrix, followed by the supervised machine learning analysis of linear discriminant analysis (LDA) for better orthogonality to complete the identification and prediction of the unknown samples. By this strategy, eleven kinds of milk powder at the added concentration of lactoferrin (50 nmol/L) presented a distinct response pattern and have been identified and classified with accuracies of 100 % via LDA patterns (three aptamers × eleven milk powder matrices × five replicates) with three canonical factors (91.2 %, 8.58 %, and 0.22 %). Furthermore, the accuracy of 22 unknown samples was 100 %, indicating the achievement in differentiating between different milk powder matrices and the identification of each matrix. The proposed aptasensor array complements the traditional “lock-and-key” single aptasensor and opens a new direction for developing sensitive sensing array systems circumventing complex matrix effects.

LSTM-CRP: Algorithm-Hardware Co-Design and Implementation of Cache Replacement Policy Using Long Short-Term Memory

As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discrimination of complex access patterns. Therefore, this paper presents the LSTM-CRP algorithm as well as its efficient hardware implementation, which employs the long short-term memory (LSTM) for access pattern identification at run-time to guide cache replacement algorithm. LSTM-CRP first converts the address into a novel key according to the frequency of the access address and a virtual capacity of the cache, which has the advantages of low information redundancy and high timeliness. Using the key as the inputs of four offline-trained LSTM network-based predictors, LSTM-CRP can accurately classify different access patterns and identify current cache characteristics in a timely manner via an online set dueling mechanism on sampling caches. For efficient implementation, heterogeneous lightweight LSTM networks are dedicatedly constructed in LSTM-CRP to lower hardware overhead and inference delay. The experimental results show that LSTM-CRP was able to averagely improve the cache hit rate by 20.10%, 15.35%, 12.11% and 8.49% compared with LRU, RRIP, Hawkeye and Glider, respectively. Implemented on Xilinx XCVU9P FPGA at the cost of 15,973 LUTs and 1610 FF registers, LSTM-CRP was running at a 200 MHz frequency with 2.74 W power consumption.

Discrimination of Left Atrial Strain Patterns in Hypertensive Heart Disease and Hypertrophic Cardiomyopathy: a Cardiac Magnetic Resonance Feature.

To assess left atrial (LA) strain parameters using cardiovascular magnetic resonance imaging feature tracking (cardiac MRI-FT) for differentiating hypertensive heart disease (HHD) from hypertrophic cardiomyopathy (HCM), which are two left ventricular hypertrophic diseases that could present with similar morphologies in early stage but differ in clinical symptoms and treatment strategies. 45 patients with HHD, 85 patients with HCM (non-obstructive hypertrophiccardiomyopathy [HNCM, n = 45] and obstructive hypertrophic cardiomyopathy [HOCM, n = 40]) and 30 healthy controls (HC) were retrospectively included. LA volumes, strain, and strain rate were determined by manually contouring on the two- and four-chamber views of the CMR-FT module using CVI 42 software. LA volume parameters including LA maximum, precontraction, and minimum volume index, and total, passive, and active emptying fractions were obtained using the biplane methods. The LA strain parameters, including total strain (εs), passive strain (εe), active strain (εa), peak positive strain rate (SRs), early peak negative strain rate (SRe), and late peak negative strain rate (SRa), were obtained from the LA strain curve. The LA strain and LA strain rate were impaired in both HHD group and HCM group, and they were the most severely impaired in the HOCM group. εs (AUC = 0.691, P = 0.006; the best cutoff value, 25.1%), εa (AUC = 0.654, P = 0.027; the best cutoff value, 10.5%), SRs (AUC = 0.710, P = 0.003; the best cutoff value, 0.81 1/s) and SRa (AUC = 0.667, P = 0.016; the best cutoff value, -1.30 1/s) showed significant differences in the identification between HHD and HNCM. All LA strain parameters were different in the identification between HHD and HOCM (all P < 0.05).LA strain parameters can be helpful for differentiating HHD from HCM, providing valuable insights for diagnosis.

Miscarriage: cases of unfair incrimination reveal weaknesses in the criminalization of abortion

The article examines the historical, religious and legal context of abortion, highlighting its criminalization in Ecuador and its impact on women's health and rights. Subsequently, it addresses the injustice of criminalization derived from a situation of spontaneous abortion and proposes to evaluate the theoretical elements that should be considered to avoid the issuance of sentences to women who experience this condition. To this end, the methodology employed focused on the analysis of representative cases. The cases analyzed include Sara, Marta, Paola and Maribel, who suffered unjust detentions and cruel treatment in the judicial system due to medical complications or situations of gender violence. The findings reveal a pattern of discrimination and stigmatization against low-income women and marginalized communities and conclude that the criminalization of abortion increases risks to women's health and lives, perpetuating gender-based violence and inequality. This ultimately highlights the urgent need to reform abortion laws and policies in Ecuador to protect human rights and promote gender equality.

CRAFTING A FOUCAULDIAN GENEALOGY METHOD FOR ANALYZING CURRICULUM-AS-DISCOURSE IN SOUTH AFRICA

Introduction/Frame: (Contextualization/Justification) Viewing curriculum as discourse can enable educators to identify how historical markers of discrimination are repeated. Historical markers are archaeologically embedded in the various dimensions of a curriculum and find expression in preferred ways of thinking, speaking, doing, and being. Such historical hypernorms are often maintained by practical rationalities, or technologies of power that reify their taken-for-granted legitimacy as ‘truth regimes’. Research Question/Objectives: This phase of the study aimed to critically explore and disclose how the (socio/politico-historically constructed) rules of knowledge-formation in a contemporary occupational therapy curriculum-as-discourse, are reproduced and maintained as truths at a university that historically supported apartheid in South Africa. Methodology: A Foucauldian Discourse Analysis (FDA) approach was used to systematically craft a genealogy analysis method from foundational Foucauldian theory. Adjacent, a lens of the ‘dimensions of the curriculum’ was employed: i.e., the formal, the informal, the hidden, and the negated curriculum. Various data sources were categorized accordingly, e.g., overarching policy documents; social events; timetables and prescribed texts; physical teaching spaces; espoused values, and underlying assumptions modelled during assessment in teaching and learning spaces. Results: Following the four rules of knowledge-formation that ensued from an archaeology analysis, the contemporary reification of the historical markers was problematised through various technologies of power and self, as well modes of reasoning. Examples are pervasive white demographics of both students and faculty reifying cognitive monopoly and mono-cultural epistemologies; the rationalization of highly ritualized (over)assessment of students suppressing adult-to-adult transactionalism in andragogy practices; the reproduction of mono-cultural epistemologies in clinical training operating in the hidden curriculum. Final considerations: Consciously dismantling the rationalising strategies for the continuation of discriminatory patterns of inclusion and exclusion in a curriculum, can open a space for critical dialogue, disruption, and reconfiguration of a curriculum-as-discourse towards social justice, and epistemic freedom in (higher) education.

Geometric Morphometric and Pattern Discrimination of Handwritten Numeral Characters Based on Local Ethnicities and Native Linguistic Disparities in Malaysia for Forensic Applications

Handwriting can be influenced by factors like culture and native language disparities; however, the specific research to discriminate handwritten numeral characters for forensic questioned document investigation involving the diverse ethnicities (Malay, Chinese and Indian) in Malaysian population remains unreported. Despite its application in forensic anthropology, utilization of the landmark based analysis using the Geometric Morphometric (GMM) technique for examining handwritten numeral character specimens appears sparse. Therefore, this present research that attempted to discriminate ethnicities background of 390 consenting adult participants in Malaysia based on the handwritten numeral characters (0-9) using GMM technique, merits forensic and scientific considerations. Results revealed that while there were universal ones, ethnic-specific handwritten numeral characters that may possibly be influenced by culture and native language (Malay, Chinese and Tamil languages) also prevailed. Significant differences (p &lt; 0.0001) in ethnic-specific characters for the Malay, Chinese and Indian participants who attended the different national primary school settings in Malaysia were also observed. In conclusion, the discrimination of handwritten numeral characters for forensic examination of questioned documents among Malay, Chinese and Indian authors for the development of forensic intelligence is empirically supported.

The Crisis of the 1930s and American Antisemitism

Abstract This article explores the economic and psychological impact of the Great Depression on the American population, emphasizing the rise of antisemitism during the 1930s. We begin with a historical overview of the economic downturn following the Wall Street Crash of 1929, with an emphasis on the ensuing unemployment and social despair. We argue that the representation of the Great Depression diverged from reality, with public discourse constructing an ‘imagined crisis’ that amplified fears and uncertainties. This crisis rhetoric led to the scapegoating of the Jewish community, which was unjustly blamed for the nation’s hardships. The article provides an in-depth analysis of how Henry Ford’s dissemination of “The Protocols of the Elders of Zion” and Father Charles E. Coughlin’s radio broadcasts contributed to spreading antisemitic sentiments by exploiting economic anxieties and promoting a narrative of Jewish conspiracy. Furthermore, the article discusses the historical context of American antisemitism, identifying it as a complex phenomenon influenced by various factors, including religious, economic, and political antisemitism. Despite efforts to combat antisemitic rhetoric, figures like Ford and Coughlin played a significant role in shaping public perceptions and directing blame towards the Jewish community during times of crisis. We conclude that the crisis of the 1930s, marked by unprecedented economic hardship and social unrest, was exacerbated by ideological discourses that targeted Jews as scapegoats, reflecting a disturbing pattern of prejudice and discrimination that has echoed into contemporary times.

Discriminatory discourses against Morocco and Moroccan immigrants in the Spanish context. A corpus-assisted critical discourse analysis of ‘Jovenlandia/jovenlandés’ (Youthland/Youthlandian) on X

Media discourse has provided a breeding ground for the advent of new patterns of discrimination with the power of influencing the public narrative. This goal of this study is to analyze how the recently-created terms ‘Jovenlandia’ (Youthland) and ‘jovenlandés’ (Youthlandian) are employed mockingly on X in response to the use of generic ‘joven’ (youth, youngster) in news articles. With the use of these terms, X users imply a supposed cancel culture in mass media that withholds data of Muslim immigrant criminals. The theoretical approach adopted combines corpus tools with Critical Discourse Analysis to examine 3856 posts on X during the 2023 Spanish general election campaign (from May 29th to July 23rd 2023). The analysis is based on Wodak’s discourse-historical approach and examines referential and predication strategies to unveil the linguistic expressions to represent immigrants. The results show that the words serve to construct a racist discursive representation of Moroccan migrant identities on the platform X in Spain. Immigrants are portrayed as violent, criminals, and rapists, whereas Morocco is depicted as a land of criminals with homophobic and sexist traditions. The discursive strategies against migrants were commonly grounded in the Muslims’ perceived failure to assimilate into the Spanish culture. In this sense, pork meat is used as a symbolic cultural criterion for evaluating authentic Spanish adherence. Spaniards are self-portrayed as victims of the left-wing government decisions and X users appeal to the extreme-right wing party Vox to solve the problem.

Novel Framework for Exploring Human–Water Symbiosis Relationship: Analysis, Quantification, Discrimination, and Attribution

There is an interdependent symbiotic relationship between humans and water; scientific and effective assessment of the human–water symbiosis relationship is of great significance for the promotion of sustainable development. This study developed a novel framework of the human–water symbiosis relationship under an integrated perspective, which included theoretical interpretation, quantitative assessment, pattern discrimination, and an attribution analysis. Based on the symbiosis theory, the theoretical analysis of the human–water relationship was carried out to analyze the three basic elements of the human–water system, and then the evaluation index system of the human–water symbiosis system was constructed to quantitatively assess the development level of the human system and the water system. The Lotka–Volterra model was used to identify the symbiotic pattern, and the human–water symbiosis index was calculated to characterize the health state of the human–water symbiosis system. The main influencing factors of the human–water symbiosis system were further identified through an attribution analysis. Finally, a case study was carried out with 18 cities in Henan Province. Results reveal that (a) the proposed method can effectively realize the quantitative characterization of the human–water symbiosis relationship, with good applicability and obvious advantages; (b) the human–water symbiosis pattern of cities in Henan Province is dominated by the “human system parasitizes water system (H+W−)” pattern, and more attention should be paid to the water system in the subsequent development of it; and (c) the main factors influencing the human system, the water system, and the human–water symbiosis system are the research and development (R&amp;D) personnel equivalent full-time (H7), per capita water resources (W1), and proportion of water conservancy and ecological water conservancy construction investment (W6), respectively. The findings can provide theoretical and methodological support for the study of the human–water symbiosis relationship and sustainable development in other regions.

The Workplace Discrimination Experiences of Americans With Disabilities: An Examination of Pacific and Northwest Regions

Background:The purpose of this study was to increase the body of knowledge regarding the workplace discrimination experiences of individuals with disabilities residing in the Pacific and Northwest (PNW) regions by exploring their allegations filed under Title I of the Americans with Disabilities Act (ADA).Objective:The goals of this article are to describe region- and state-level patterns in workplace discrimination and to provide valuable information to stakeholders to better understand and address job discrimination within their respective areas of practice.Methods:The Integrated Mission System database documents every available closed allegation of job discrimination under the ADA. Data for each state include overall frequencies and proportions. Data are presented as respective national and regional rankings for comparison purposes. Descriptive and nonparametric statistics were obtained for each of the five specific research questions.Findings:Variables selected for study include the frequency of allegations, the impairment of the charging party, alternative prongs of the ADA definition of disability, the nature of the unlawful discrimination alleged (known as issues), and the outcome of the EEOC investigation (known as merit or nonmerit). These are presented in tables for ease of comparison. Finally, the next steps in research and other possibilities for knowledge translation are discussed.Conclusion:The findings suggest that workplace discrimination against individuals with disabilities varies significantly by state in the PNW, highlighting the need for tailored interventions at the state level. Differences in support services, advocacy, and public awareness about ADA protections contribute to these disparities. Future efforts should focus on enhancing ADA literacy and targeted interventions to address state-specific issues in reducing discrimination in the workplace.

Interpretable sequence clustering

Categorical sequence clustering is vital across various domains; however, the interpretability of cluster assignments presents considerable challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, which complicates the explanation of their outcomes. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages k−1 patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.

“I was told that people might hurt me”: Experiences of religious discrimination among Jewish, Christian, and Alevi youth in Turkey

AbstractThis study investigates the experiences of young members of Alevi, Christian, and Jewish religious minorities in Turkey, a secular country with a Sunni Muslim majority. Data was collected through semi structured interviews with 29 participants aged from 17 to 24. A thematic analysis was employed to explore patterns of discrimination and exclusion in their everyday lives. The experiences of Jewish and Christian participants are characterized by ostracism, increased vulnerability to injury, and passing as a member of the majority. Alevi participants report being subjected to intense pressure to become assimilated into majority Sunni practices, as well as frequent exposure to harshly unfavourable stereotypes, and ongoing rejection of Alevi theology as a ‘true’ belief system. The discussion centres on the issue of misrecognition and, particularly, denial of identity, contextualizes the findings into broader dynamics of nation‐state politics, and focuses attention on the agency of minorities and majorities. Limitations of the study and recommendations for future research have also been presented. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

MCGnet with multi-level gated unit transformations: A time series prediction-based model for unknown pattern abnormality detection

The task of anomaly detection in data patterns remains challenging due to the diverse of fault patterns, which often render existing models ineffective when encountered with unknown or unseen data patterns in the training data. Furthermore, insufficient samples, the presence of chaotic behavior, unbalanced anomaly patterns are potential limiting factors in the practical application. While prediction error-based approaches attempt to address these challenges, the discriminability of abnormal patterns in existing scenarios is not yet sufficient, thus limiting the ability to identify abnormal phenomena. To overcome these limitations, we proposed a novel model called MCGnet. The backbone architecture of MCGnet employed a novel design of continuous multi-layer circular convolution transformations. This design enhances the expressive capability of the model by expanding the differences among data patterns. Additionally, we introduced in parallel the newly proposed temporal-frequency gated Gaussian unit, which generates richer feature representations. Subsequently, the obtained feature representations were fed into an explicit causal network to learn complex patterns of data along temporal dependencies. The MCGnet not only solves high-precision time series prediction problems but also significantly improves the discriminability between normal and abnormal patterns. Since the proposed model is accurately modeled using only a small amount of normal data, it bypasses the limiting factors such as insufficient fault samples and sample imbalance, and is able to effectively deal with various unknown data patterns. The accuracy of the proposed method was verified through simulation data (Duffing system) and its potential for engineering applications was demonstrated through real-world data (rotor system).

Biomarker patterns and mechanistic insights into hypothermia from a postmortem metabolomics investigation

Postmortem metabolomics holds promise for identifying crucial biological markers relevant to death investigations and clinical scenarios. We aimed to assess its applicability in diagnosing hypothermia, a condition lacking definitive biomarkers. Our retrospective analysis involved 1095 postmortem femoral blood samples, including 150 hypothermia cases, 278 matched controls, and 667 randomly selected test cases, analyzed using UHPLC-QTOF mass spectrometry. The model demonstrated robustness with an R2 and Q2 value of 0.73 and 0.68, achieving 94% classification accuracy, 92% sensitivity, and 96% specificity. Discriminative metabolite patterns, including acylcarnitines, stress hormones, and NAD metabolites, along with identified pathways, suggest that metabolomics analysis can be helpful to diagnose fatal hypothermia. Exposure to cold seems to trigger a stress response in the body, increasing cortisol production to maintain core temperature, possibly explaining the observed upregulation of cortisol levels and alterations in metabolic markers related to renal function. In addition, thermogenesis seems to increase metabolism in brown adipose tissue, contributing to changes in nicotinamide metabolism and elevated levels of ketone bodies and acylcarnitines, these findings highlight the effectiveness of UHPLC-QTOF mass spectrometry, multivariate analysis, and pathway identification of postmortem samples in identifying metabolite markers with forensic and clinical significance. The discovered patterns may offer valuable clinical insights and diagnostic markers, emphasizing the broader potential of postmortem metabolomics in understanding critical states or diseases.

South Sudanese Refugee Higher Education Access in Uganda: An Intersectionality Enquiry

This study was guided by the intersectionality theory, originally developed by Kimberlé Crenshaw. According to Crenshaw, the theory assumes that people have multiple biological, social and cultural identity markers that intersect, resulting in experiences of privilege or disadvantage, inclusion or discrimination. However, the theory maintains that marginalized individuals are not homogeneous and they experience discrimination and inclusion in different ways. The intersectionality theory also recognizes power dynamics and how structural, political and representational domains, intersect and produce complex patterns of discrimination, inequality and disadvantage. Ultimately, the intersectionality theory asserts that effective activism and social change require recognizing and addressing the intersections of various forms of oppression, and advocating for justice across multiple fronts. This exposes discrimination and exclusion that would have otherwise gone unnoticed, also giving voice to the disadvantaged and excluded. The study adopted a qualitative approach, an exploratory case study design, and an advocacy/ participatory philosophical lens. Twenty-seven purposively sampled participants took part in the study. They included 12 undergraduates from two private Ugandan universities, 13 government and non-governmental organization (NGO) officials, two officials from public and private universities, all involved in refugee higher education. Data was collected through a literature review, in-depth interviews with key informants and students, and a students’ focus group discussion. The study established that higher education access for South Sudanese refugees in Uganda involves multiple intersectional prohibitive and also supportive factors. The study therefore recommends that through situational analysis and needs assessment, Uganda develops clear objectives, activities and outcomes in the Education Response Plan, to establish the divergent supportive and prohibitive factors and cater for the various refugee student needs. The study also designed the refugee access and resilience (RARE) model to assist in the aforementioned regard

Diversifying Gender Categories and the Sex/Gender System

The growing visibility of transgender and nonbinary people raises important sociological questions about how the structure of sex and gender is shifting and underscores necessary changes to research practice. We review what is known about emerging gender identities and their implications for sociological understandings of the relationship between sex and gender and the maintenance of the sex/gender system of inequality. Transgender and nonbinary identities are increasingly common among younger cohorts and improved survey measurements of sex and gender are expanding information about these changes. In the United States, an additional gender category seems to be solidifying in public usage even as the higher status of masculinity over femininity persists. The continuing power of the normative binary contributes to both violent backlash and characteristic patterns of discrimination against gender diverse people; yet, underlying support for nondiscrimination in the workplace is stronger than commonly recognized. New, more consistent efforts to account for gender diversity in social science research are needed to fully understand these changes.

PaFESD: Patterns augmented by Features Epileptic Seizure Detection.

The detection of epileptic seizures can have a significant impact on the patients' quality of life and on their caregivers. In this paper we propose a method for detecting such seizures from electroencephalogram (EEG) data named Patterns augmented by Features Epileptic Seizure Detection (PaFESD). The main novelty of our proposal consists in a detection model that combines EEG signal features with pattern matching. After cleaning the signal and removing artifacts (as eye blinking or muscle movement noise), time-domain and frequency-domain features are extracted to filter out non-seizure regions of the EEG. Jointly, pattern matching based on Dynamic Time Warping (DTW) distance is also leveraged to identify the most discriminative patterns of the seizures, even under scarce training data. The proposed model is evaluated on all patients in the CHB-MIT database, and the results show that it is able to detect seizures with an average F1 score of 98.9%. Furthermore, our approach achieves a F1 score of 100% (no false alarms or missed true seizures) for 20 of the patients (out of 24). Additionally, we automatically detect the most seizure/non-seizure discriminative EEG channel so that a wearable with only two electrodes would suffice to warn patients of seizures.