University of San Andrés

We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on SE(3). The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities, as is standard in many aerial vehicles and omnidirectional mobile robots. Gaussian Process (GP) regression is integrated into a geometric feedback law to learn and compensate online for unknown, state-dependent disturbances and modeling imperfections affecting both attitude and position, while preserving the algebraic structure and coupling properties inherent to rigid-body motion. The proposed approach does not rely on explicit parametric models of the unknown effects, making it well-suited for robotic systems subject to sensor-induced disturbances, unmodeled actuation couplings, and environmental uncertainties. A Lyapunov-based analysis establishes probabilistic ultimate boundedness of the pose tracking error under bounded GP uncertainty, providing formal stability guarantees for the learning-based controller. Simulation results demonstrate accurate and smooth trajectory tracking in the presence of realistic, localized disturbances, including correlated rotational and translational effects arising from magnetometer perturbations. These results illustrate the potential of combining geometric modeling and probabilistic learning to achieve robust, data-efficient pose control for autonomous robotic systems.

The proliferation of sexual discourses in the digital age, accompanied by ongoing media panics over the perceived adverse effects of online sexual content, reflects persistent societal tensions about technology’s role in shaping sexual norms. While previous research has examined the spread of sexual advice on social media, there is limited understanding of how users engage with and reshape this advice. This study addresses this gap by analyzing how TikTok users interact with sex experts through a qualitative content analysis of top videos and their most-liked comments (n = 500). Our analysis reveals 4 mechanisms by which users engage with sexual advice: (1) challenging and validating expert authority, (2) collaboratively co-producing knowledge, (3) debating norms of intimacy and pleasure, and (4) finding and sharing emotional support and solidarity. Through these interactions, expertise on TikTok becomes a collective and evolving form of knowledge production.

INTRODUCTION:Digital speech biomarkers (DSBs) support the detection and monitoring of Alzheimer's disease (AD) in Latinos. However, they have not been benchmarked against standard cognitive and neuroimaging measures, missing a critical validation milestone. METHODS:Thirty-three AD patients and 33 healthy controls completed verbal fluency tasks, episodic memory and executive tests, and magnetic resonance imaging (MRI) (volume) and functional MRI (fMRI) (connectivity) scans. Between-group machine learning classification was compared among fluency-derived DSBs, episodic and executive test scores, MRI, and fMRI measures. RESULTS:The fluency classifier's performance (area under the curve [AUC] = 0.84) was comparable (p > 0.14) to the episodic (AUC = 0.90), executive (AUC = 0.79), and structural (AUC = 0.90) classifiers and superior to the functional classifier (AUC = 0.65, p = 0.002). Top discriminating features were word length and frequency, both associated with right (pre)frontal volume upon adjusting for sociodemographic factors. DISCUSSION:DSBs appear non-inferior to standard cognitive and imaging measures, supporting scalable AD assessments in Latinos. HIGHLIGHTS:We examined digital speech biomarkers (DSBs) for detecting AD in Latinos. DSBs were benchmarked against cognitive and neuroimaging features. DSB-based classifiers matched or outperformed cognitive and brain classifiers. Top DSBs included word length, phonological neighborhood, and frequency. Word length and frequency correlated with right (pre)frontal brain volume.

Retrieving evidence for language model queries from knowledge graphs requires balancing broad search across the graph with multi-hop traversal to follow relational links. Similarity-based retrievers provide coverage but remain shallow, whereas traversal-based methods rely on selecting seed nodes to start exploration, which can fail when queries span multiple entities and relations. We introduce ARK: Adaptive Retriever of Knowledge, a tool-using KG retriever that gives a language model control over this breadth-depth tradeoff using a two-operation toolset: global lexical search over node descriptors and one-hop neighborhood exploration that composes into multi-hop traversal. ARK alternates between breadth-oriented discovery and depth-oriented expansion without depending on a fragile seed selection, a pre-set hop depth, or requiring retrieval training. ARK adapts tool use to queries, using global search for language-heavy queries and neighborhood exploration for relation-heavy queries.On STaRK, ARK reaches 59.1% average Hit@1 and 67.4 average MRR, improving average Hit@1 by up to 31.4% and average MRR by up to 28.0% over retrieval-based and agent-based training-free methods.Finally, we distill ARK’s tool-use trajectories from a large teacher into an 8B model via label-free imitation, improving Hit@1 by +7.0, +26.6, and +13.5 absolute points over the base 8B model on AMAZON, MAG, and PRIME datasets, respectively, while retaining up to 98.5% of the teacher’s Hit@1 rate.

In this paper, we introduce a two-stage partitioning clustering procedure based on local depths. In the first stage, we find clusters of the local depth inner region of level α . In the second stage, the remaining points are assigned to one of these clusters according to a proximity criterion. In this way, the clusters found in the first stage play the role of flexible centers, that aim to mimic the shape of the groups. We analyze the performance of the procedure on multivariate and multivariate functional data, on real and synthetic datasets showing remarkable results.