The University of San Andrés (Spanish: Universidad de San Andrés) is a private university located in Victoria, Buenos Aires, Argentina on the shores of the Rio de la Plata, in the metropolitan area of Greater Buenos Aires. It is a small institution, with approximately 900 undergraduate students and 500 graduate students.It is served by one of the largest private libraries in the country, Max von Buch. Maintaining over 70,000 volumes, the library was recognized by the Andrew W. Mellon Foundation though their Program for Latin American Libraries and Archives. The university provides more than 70 study abroad programs with universities in Europe, North America, Latin America, and Australia. San Andrés is the first institution in Argentina to offer a double degree accredited by Grandes Ecoles ESCP-Europe.The Universidad de San Andrés is one of the only two liberal arts colleges in Argentina, along with Universidad Torcuato Di Tella. The main campus is located in the town of Victoria, San Fernando Partido (a northern suburb of Buenos Aires). It also has offices in downtown Buenos Aires.
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.
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.
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.
We study the dimensionality of two-dimensional Kolmogorov flows over a wide range of Reynolds numbers and forcing wave numbers K-f={2,4,8} using two complementary approaches: convolutional autoencoders and a Kaplan-Yorke estimation based on Lyapunov analysis. As the Reynolds number increases, two distinct transitions are observed: the first corresponds to the destabilization of a periodic orbit, while the second marks the saturation of the large-scale motions. When expressed in terms of the forcing Reynolds number, these transitions occur at nearly the same value for all forcing wave numbers, suggesting a universal scaling with respect to the forcing scale. By filtering the data to retain only the large-scale range (k
The growing use of artificial intelligence (AI) in environmental governance is transforming how climate risks are monitored, modeled, and managed. However, most AI-based systems remain grounded in Western epistemological frameworks, frequently overlooking Indigenous knowledge systems (IKS) that provide place-based, relational, and long-term understandings of ecological change. This article examines the opportunities and challenges of integrating Indigenous knowledge into AI-driven approaches to climate resilience. Using an interdisciplinary qualitative methodology that combines critical literature review, comparative case analysis, and environmental justice theory, the study analyzes documented initiatives from Indigenous territories in the Global South. These cases illustrate how automated tools—such as climate prediction models, resource monitoring platforms, and conservation algorithms—can either reinforce data extractivism and epistemic marginalization or contribute to more inclusive and context-sensitive forms of environmental governance. The article argues that meaningful integration of Indigenous knowledge into AI systems requires more than technical adaptation. It demands ethical governance frameworks that recognize epistemic plurality, community consent, and Indigenous data sovereignty. By critically assessing both risks and enabling conditions, this study contributes to emerging debates on decolonial AI and highlights pathways through which AI-based environmental governance can support climate resilience while respecting Indigenous rights and knowledge systems.
