Vassar College

Arterial pressure waveform shape conveys information regarding interactions between the left ventricle and aorta that could provide an estimate of biological heart age and cardiovascular disease (CVD) risk. Artificial intelligence heart age (AI-HA) was estimated by averaging results from 2 convolutional neural networks trained to predict mitral annulus tissue Doppler e' and s' peak velocities using an uncalibrated arterial tonometry or photoplethysmography waveform as input. Models were developed using FHS (Framingham Heart Study) participant pressure waveforms and echocardiographic measurements (N=6916 participants, 38 174 waveforms, 56% women, mean age 61±12). We validated AI-HA using Cox modeling in an FHS holdout set of baseline radial waveforms (N=7018, 54% women, age 50±16 years) and in UK Biobank participants (N=67 986, 53% women, age 57±8 years). In FHS (up to 10 years of follow-up, 148 heart failure [HF] and 331 CVD events), using models that adjusted for PREVENT (AHA Predicting Risk of CVD Events) risk factors, AI-HA was associated with incident HF (hazard ratio, 2.09 [CI, 1.64-2.68]; continuous net reclassification, 0.22 [CI, 0.13-0.30]) and CVD (hazard ratio, 1.52 [CI, 1.28-1.81]; continuous net reclassification, 0.13 [CI, 0.07-0.20]). In UK Biobank (up to 10 years of follow-up, 1408 HF and 2709 CVD events), AI-HA was associated with incident HF (hazard ratio, 1.23 [CI, 1.13-1.33]; continuous net reclassification, 0.09 [CI, 0.06-0.12]) and CVD (hazard ratio, 1.22 [CI, 1.15-1.30]; continuous net reclassification, 0.08 [CI, 0.06-0.09]). AI-HA is a novel and accessible measure of left ventricle function and HF risk in community-based samples.

Can secessionist movements in Africa be conceptualized as forms of anti-(neo)colonial resistance? This article studies this question in relation to the Indigenous Peoples of Biafra (IPOB) movement in Nigeria. It adds nuance to the dominant interpretation by African secessionism and conflict scholars that characterize IPOB as ethnonationalist and destabilizing. While these are components of the movement, this article theorizes that IPOB’s secessionist project also strategically deploys a critique of the (neo)colonial Nigerian state. This critique is emancipatory in orientation but simultaneously deployed alongside polarizing and exclusionary politics. Through a content analysis that uses structural topic modeling (STM) alongside qualitative readings, this study analyzes changes in the topical themes of over 250 speeches given by movement leader Nnamdi Kanu on his Radio Biafra broadcast from 2013 to 2019. It finds that Kanu’s discursive strategies include anti-(neo)colonial narratives and recurring themes of liberation from the Nigerian state and its international allies. The findings provide a nuanced view of IPOB as a movement that uses several and, at times, contradictory rhetorical instruments in pursuit of its broader secessionist objectives. Notwithstanding, its adoption of anti-(neo)colonial resistance should not be overlooked.

Artificial intelligence (AI) is increasingly being used in the diagnosis, treatment, and monitoring of neurodevelopmental disorders, enabling earlier detection, personalised interventions, and continuous support. Traditional machine-learning models such as logistic regression, random forests, and support vector machines remain valuable for their interpretability and their ability to integrate multimodal clinical data. Deep-learning (DL) approaches, including convolutional neural networks and transformer-based architectures, improve the analysis of neuroimaging and behavioural datasets and strengthen diagnostic and prognostic performance. Important challenges remain, including limited transparency in DL systems, ongoing concerns about data privacy and algorithmic bias, and a lack of large and diverse paediatric datasets that restricts generalisability. Interpretability tools such as SHAP and LIME offer partial solutions but still lack standardised evaluation. At the same time, AI-driven robotic platforms are enhancing therapeutic engagement and supporting skill acquisition in children with neurodevelopmental conditions. This review highlights that AI tools have strong potential to act as clinical adjuncts rather than replacements, providing earlier detection, personalised management, and scalable care models. Realising this potential will require rigorous validation, ethical safeguards, and thoughtful integration into human-led care pathways.

Recent authoritarian rhetoric in the United States comes as a surprise to many who, before this, felt that U.S. democracy was secure. However, scholars argue U.S. democracy has never been stable to the extent that many believe. In this paper we use a synthesis of perspectives informed by critical race theory, critical and cultural psychologies, and research on collective memory to call attention to how the United States’ colorblind ignorance to histories of racism informs perceptions of democracy as infallible. Through historical case studies we highlight how the U.S. has a long tradition of authoritarianism if viewed from the perspectives of people of color and ignorance to this past allows the U.S. to be vulnerable to authoritarian rhetoric in the present day. We suggest narratives of critical history that challenge majoritarian perspectives can disrupt this rhetoric. We conclude by discussing the importance of knowledge of racial history in making clear the ways in which the treatment of POC has never been democratic and how this knowledge can inform resistance and new conceptualizations of a color conscious democracy.

We study the inherent barriers to constructing non-adaptively sound succinct non-interactive arguments (SNARGs) for NP with a CRS whose length is sublinear in the witness length. Our results cover both the standard SNARGs and SNARGs with an additional updatable feature (i.e. incrementally verifiable computation for NP).•For updatable SNARGs, we show a black-box separation from falsifiable assumptions for uniform polynomial-time reductions, assuming sub-exponential hardness of learning with error.•For general SNARGs, we show a black-box separation from falsifiable assumptions for non-uniform polynomial-time reductions that only make one query to the adversary, assuming the existence of sub-exponentially secure super-bit generators. We observe that all known SNARG constructions from polynomial hardness of standard assumptions have 1-query soundness reductions. Thus, our result complements existing constructions.Previously, the seminal work [Gentry-Wichs, STOC’11] showed a black-box separation of SNARGs from falsifiable assumptions in the adaptive soundness setting. We explore whether any barriers exist in the non-adaptive setting. To obtain our result, we derive a simulation lemma for unbounded polynomial-length auxiliary inputs assuming super-bit generators.

We study the task of learning Multi-Index Models (MIMs) in the presence of label noise under the Gaussian distribution. A K-MIM on ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> is any function f that only depends on a K-dimensional subspace, i.e., f(x) = g(Wx) for a link function g on ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> and a K × d matrix W. We consider a class of well-behaved MIMs with finite ranges that satisfy certain regularity properties. Our main contribution is a general noise-tolerant learning algorithm for this class whose complexity is qualitatively optimal in the Statistical Query (SQ) model. At a high-level, our algorithm attempts to iteratively construct better approximations to the defining subspace by computing low-degree moments of our function conditional on its projection to the subspace computed thus far, and adding directions with relatively large empirical moments. For well-behaved MIMs, we show that this procedure efficiently finds a subspace V so that f(x) is close to a function of the projection of x onto V, which can then be found by brute-force. Conversely, for functions for which these conditional moments do not necessarily help in finding better subspaces, we prove an SQ lower bound providing evidence that no efficient algorithm exists.As concrete applications of our general algorithm, we provide significantly faster noise-tolerant learners for two well-studied concept classes:•Multiclass Linear Classifiers A multiclass linear classifier is any function f : ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → [K] of the form f(x) = argmax<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i∈[K]</inf>(w<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</sup>•x+t<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf>) , where w<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</sup> ∈ ℝ d and t<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> ∈ ℝ. We give a constant-factor approximate agnostic learner for this class, i.e., an algorithm that achieves 0-1 error O(OPT)+ϵ. Our algorithm has sample complexity N = O(d)2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poly(K/ϵ)</sup> and computational complexity poly(N). This is the first constant-factor agnostic learner for this class whose complexity is a fixed-degree polynomial in d. In the agnostic model, it was previously known that achieving error OPT+ϵ requires time d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poly(1/ϵ)</sup>, even for K = 2. Perhaps surprisingly, we prove an SQ lower bound showing that achieving error OPT+ϵ, for ϵ = 1/poly(K), incurs complexity d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(K)</sup> even for the simpler case of Random Classification Noise.•Intersections of Halfspaces An intersection of K halfspaces is any function f : ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → {±1} such that there exist K halfspaces h<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf>(x) with f(x) = 1 if and only if h<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf>(x) = 1 for all i ∈ [K]. We give an approximate agnostic learner for this class achieving 0-1 error $K\tilde O({\text{OPT}}) + \varepsilon $. Our algorithm has sample complexity N = O(d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>)2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poly(K/ϵ)</sup> and computational complexity poly(N). This is the first agnostic learner for this class with near-optimal dependence on OPT in its error, whose complexity is a fixed-degree polynomial in d. Previous algorithms either achieved significantly worse error guarantees, or incurred d<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poly(1/ϵ)</sup> time (even for K = 2).Furthermore, we show that in the presence of random classification noise, the complexity of our algorithm is significantly better, scaling polynomially with 1/ϵ.