The National Education Policy (NEP) 2020 marked a paradigm shift from examination-centric schooling toward holistic development by integrating Indian Knowledge Systems (IKS), yoga, mindfulness, value education, and experiential pedagogies. This systematic review synthesizes empirical and policy literature published bet...
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23071859. Authors of the review Name: Stephanie R. U, ORCID: https://orcid.org/0000-0002-6220-4661 Bio: Stephanie U is a PhD candidate whose work investigates the role of circadian...
Stephanie U, 2 other authors· Zenodo (CERN European Organi...· 0 citations
Abstract Introduction Prostate cancer and its treatments can affect multiple dimensions of sexual health, extending beyond erectile function to sexual desire, orgasm, sexual satisfaction, body image, intimacy and relational well-being. Nurses are well positioned to assess these concerns and provide continuing education...
Xiaoting Wang, Yuan Chen, Lei Li· Research Square· 0 citations
TSPO-PET effectively distinguishes PwMS from HC and differentiates MS subtypes, but radiotracer, quantification method, image analysis method, reference tissue, and region of interest significantly impact TSPO-PET outcomes and must be considered when designing and interpreting studies.
Arian Madani Neishaboori, M. Agarwal, Ally Kalinsky et al.· Neurology(R) neuroimmunology...· 0 citations
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Recovering dynamical systems from noisy observations is a recurring challenge across scientific domains, including neuroscience and physics. Latent stochastic differential equations (SDEs) address this by modeling the system as an unobserved state that evolves according to a learnable SDE and generates the observations...
Henry D. Smith, Brian L. Trippe, Scott W. Linderman· 0 citations
We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parameters, through the empirical Jacobian Gra...
Ayub Kharel, Ilja Kuzborskij, Patrick Rebeschini et al.· 0 citations
This paper proposes a new formulation of functional Gaussian Process regression on manifolds, based on an Empirical Bayes approach, in the spatiotemporal random field context. We apply the machinery of tight Gaussian measures in separable Hilbert spaces, exploiting the invariance property of covariance kernels under th...
MD Ruiz-Medina, AE Madrid, A Torres-Signes et al.· 0 citations
Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straightforward only when they are scalar-valued, limiting CP to real-valued scores or ad-hoc one-dimensional reductions. Vector-valued scores arise naturally in multi-output regression...
It is increasingly important to evaluate the characteristics of systems based on large language models (LLMs). Evaluations in this context often rely on a curated benchmark set of input prompts provided to the LLM, where the output for each prompt may be assigned a binary or ordinal score and the aggregation of scores...
Saatvik Kher, Shang Wu, Rachel Longjohn et al.· 0 citations
Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available ("few-shot" learning). Increased task diversity is often believed to enhance meta-learning by providing richer information across tasks. Ho...
Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova et al.· 0 citations
Regression problems with continuous bounded outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new covariate value. Most of the existing literature has focused either on point pred...
Zhanli Wu, Fabrizio Leisen, F. Javier Rubio· 0 citations
We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-linearizable functions. Our approach leverages decentralized optimization techniques with the flexibility of upper-linearizable function frameworks, effectively generalizing tradi...
Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
