Telecomunicaciones, información y comunicación

Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis

- This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following...

Size Should Not Matter: Evaluating Network Visualizations with Stress

- The normalized stress metric is widely used to assess graph drawing quality, measuring how closely distances between vertices in a layout match their graph-theoretic distances. This metric is a...

The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI

- This article addresses the intersection of various educational theories and their relationship with the education of computer science students, with a focus on the importance of understanding...

Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge

- The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation...

Unconditional Unclonable Encryption

- We give an unconditional construction of information-theoretically secure one-time private-key unclonable encryption scheme for one-bit messages, with efficient encryption and decryption and...

Morphing Graphs on Hyperbolic Surfaces

- We propose the first algorithm to morph geometric graphs on hyperbolic surfaces. It is based on a generalization of Tutte's spring embedding theorem on essentially 3-vertex-connected graphs. We...

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

- Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving patient outcomes. Speech-based CI detection has...

Cautious optimism for deep parameterized quantum circuits

- A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performance on unseen data...

Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas

- The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment. Predicting these quantities is essential for...

Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)

- Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency....

A six-neuron counterexample to the target-free clique conjecture

- The target-free clique conjecture asserts that the supports of stable fixed points of a nondegenerate combinatorial threshold-linear network (CTLN) are exactly its target-free cliques: bidirected...

Efficient classical simulation of large-scale unitary cluster Jastrow circuits

- Recent experiments on quantum computers have challenged the limits of classical computation in chemistry, simulating ground states of strongly correlated molecules. Many of these experiments have...

From Berg-Purcell precision bounds to clock-limited information capacity

- Physical limits to chemical sensing are traditionally expressed as Berg-Purcell bounds on estimation accuracy. Whether these bounds also limit the total amount of information a molecular receptor can...

Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting

- The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment....

Approximate Quantum State Preparation Through Proximal Policy Optimization

- In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the...

Automatic knot selection in smooth additive models

- B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the number and placement of changepoints, known as knots,...

An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware

- Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy...

Risk-Limiting Audits for Parliamentary Majorities

- Existing methods for risk-limiting audits typically focus on certifying individual contests. In parliamentary elections, however, the politically relevant outcome is often whether a party has won...

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

- Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical...

yancc: A GPU-accelerated, differentiable solver for neoclassical transport in tokamaks and stellarators

- We present yancc, a new GPU-accelerated solver for the drift kinetic equation that computes neoclassical transport fluxes, flows, and currents in tokamaks and stellarators. The drift kinetic equation...

Machine Learning for Charge State Characterization of Isolated Double Quantum Dots

- Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and...