The university's undergraduate schools—Yeshiva College, Stern College for Women, Katz School of Science and Health, and Syms School of Business—offer a dual curriculum inspired by Modern–Centrist–Orthodox Judaism's hashkafa (philosophy) of Torah Umadda ("Torah and secular knowledge"), combining academic education with the study of the Torah. While the majority of students at the university are of the Jewish faith, many students, especially at the Cardozo School of Law, the School of Business, and the Graduate School of Psychology, are not Jewish.Yeshiva University is an independent institution chartered by New York State. It is accredited by the Middle States Commission on Higher Education and by several professional agencies. It is classified among "R-2: Doctoral Universities – High Research Activity".
High rates of race/ethnicity-based discrimination have been documented among Black and Hispanic/Latine individuals and are linked to health consequences including cigarette use and dependence. Despite the high prevalence of cigarette smoking and race/ethnicity-based discrimination specifically among Black and Hispanic/Latine people with HIV (PWH), limited research has examined the relationship between race/ethnicity-based discrimination and cigarette smoking among PWH. This study examined race/ethnicity as a potential moderator of the relationship between race/ethnicity-based discrimination and both cigarette abstinence and cigarette dependence among Black and Hispanic/Latine PWH. This was a secondary analysis of data from a prospective, randomized controlled smoking cessation trial for PWH motivated to quit smoking combustible cigarettes where participants (N = 326, 50.9
An "Awareness Toolkit" for Alzheimer's caregivers was developed, and this intervention provided Alzheimer's-related education, community resources, and self-care strategies. The research aimed to determine the toolkit's impact on caregivers' perceived preparedness, burden, and posttraumatic growth. A total of 48 caregivers participated, randomly assigned to either an intervention group (i.e. received the Awareness Toolkit and case management services) or a control group. Measures of caregiver burden, preparedness for caregiving, and posttraumatic growth were obtained via the Zarit Caregiver Burden Assessment, the Preparedness for Caregiving Scale, and the Posttraumatic Growth Inventory-Short Form, respectively. The results indicated a significant improvement in preparedness for caregivers in the intervention group compared to the control group. Additionally, female caregivers demonstrated a significantly higher level of posttraumatic growth than their male counterparts. The findings suggest that interventions like the Awareness Toolkit can be valuable in preparing caregivers for their role.
Noisy labels are often present in large, accessible datasets. Learning with noisy labels can degrade the generalization performance of DNNs. While Semi-Supervised Learning (SSL) approaches have shown promise by predicting pseudo-labels to correct noisy labels, we find and demonstrate a fundamental limitation of SSL-based label correction methods: hard samples near decision boundaries significantly weaken the memorization effect that these methods rely on. This leads to erroneous pseudo-labels and creates a negative feedback loop where models gradually memorize these errors, further degrading performance. To overcome the issue, inspired by AdaBoost and building on these insights, we propose HEALON (Hard samplE Adaptive Labeling with Optimal reweighting for Noisy labels), a novel framework for learning with noisy labels that effectively addresses the hard sample challenge through an optimal weighting strategy that balances their influence during training. The framework primarily consists of two steps: Weighted Implicit Ensemble (WIE) and Weight Optimization for HArd Sample (WOHAS). WIE combines the Adaboost strategy with multiple sets of weight distributions obtained from WOHAS to train a single model, allowing the model to converge to multiple local optima along its optimization path and predict pseudo-labels. A sample difficulty minimization method is then designed to aggregate the predicted labels, generating near-global optimal pseudo-labels for each noisy sample, followed by a multiple "snapshot" weights strategy to minimize computational cost. WOHAS quantifies sample difficulty using information entropy and derives optimal weights to WIE via a cumulative sample difficulty strategy, balancing the impact of hard samples while preserving the memorization effect. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods in both pseudo-label correction accuracy and overall classification performance.
BACKGROUND:As nicotine pouches are increasingly adopted as a harm reduction method for tobacco cessation, it is important to understand who incorporates nicotine pouches into cessation attempts. This preliminary study examined correlates of nicotine pouch use for tobacco cessation in a United States (US) nationally representative sample. METHODS:Data were from Wave 7 (2022-2023) of the US Population Assessment of Tobacco and Health Study. Outcomes were past-12-month use of nicotine pouches to quit cigarettes or other non-electronic nicotine delivery system (ENDS) and other nicotine/tobacco products (N = 3622) and to quit ENDS (N = 1934). Predictor variables included sociodemographic, psychological, and behavioral factors. RESULTS:Ninety individuals reported using nicotine pouches during their most recent quit attempt for non-ENDS products, while 65 individuals reported use during the quit attempt for ENDS. Multivariable regression analyses showed that males were significantly more likely to use nicotine pouches to quit non-ENDS and ENDS (Adjusted Prevalence Ratio [APR] = 2.00, 95% Confidence Interval [CI] = 1.19, 3.37; APR = 6.28, 95% CI = 2.77, 14.25). Having used other methods to quit other nicotine/tobacco products was associated with higher likelihoods of having used nicotine pouches to quit non-ENDS (APR = 2.43, 95% CI = 1.48, 4.01) and ENDS (APR = 1.95, 95% CI = 1.05, 3.61). Having used nicotine pouches to quit non-ENDS was less likely to be reported by Black individuals (APR = 0.31, 95% CI = 0.11, 0.88). CONCLUSIONS:This preliminary study observed positive associations of male gender and use of other cessation methods and a negative association of Black race with having used nicotine pouches to quit other nicotine/tobacco product use. IMPLICATIONS:In a national United States sample, having used nicotine pouches to quit other nicotine/tobacco product use was more likely to be associated with being male and using other nicotine/tobacco product use cessation methods, but less likely to be associated with Black race. Ongoing surveillance and research examining the health effects of nicotine pouch use is warranted, particularly regarding sex and racial/ethnic differences in nicotine pouch use as a harm reduction approach for other nicotine/tobacco product use cessation.
As large language models become a default source of guidance on personal, moral, and existential questions, it matters whether they draw on the religious frameworks that have historically shaped such reasoning, or systematically omit them. In this paper, we ask a deliberately narrow question: when posed an everyday ethical question for which religious perspectives may be valuable, do LLMs invoke religion at all? In contrast to benchmarks that look for the presence of political leanings or social bias, we look for the absence of religious representation as a dimension of value alignment and bias in LLMs. We term this “omissive bias.” To measure omissive bias, we contribute the AllFaith Religious Representation Benchmark: 150 ethically and personally salient questions, sourced from in-the-wild chat transcripts and faith-community contributors, paired with an LLM-as-judge rubric that gives full credit for any mention of a religion, a religious practice, or a religious leader. The questions are not themselves about religion–they are open-ended questions about grief, forgiveness, relationships, purpose, and honesty, where religion is one valuable perspective among several. We also run a human-subjects survey to compare LLM behavior against human expectations. Evaluating 27 models, we find that LLMs consistently underrepresent religion relative to human expectations. The omission is asymmetric: models invoke religion more readily for abstract existential questions (meaning, death, truth) than for the practical personal situations–grief, marriage, family conflict, addiction–where many people most rely on it. It is not our purpose to adjudicate which values LLMs should hold. We argue, more modestly, that current LLM responses overlook critical opportunities to reflect religious frameworks that many people draw on when navigating personal and ethical challenges.
