arXiv:2408.10609v4 Announce Type: replace-cross Abstract: We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field. Our approach includes a modular and user-friendly model development and evaluation platform, a collection of diverse perturbational datasets, and a set of metrics designed to fairly compare models […]
Bridging Perception and Reasoning: Dual-Pipeline Neuro-Symbolic Landing for UAVs in Cluttered Environments
arXiv:2510.22204v1 Announce Type: cross Abstract: Autonomous landing in unstructured (cluttered, uneven, and map-poor) environments is a core requirement for Unmanned Aerial Vehicles (UAVs), yet purely vision-based or deep learning models often falter under covariate shift and provide limited interpretability. We propose NeuroSymLand, a neuro-symbolic framework that tightly couples two complementary pipelines: (i) an offline pipeline, […]
HW/SW Co-design of a PCM/PWM converter: a System Level Approach based in the SpecC Methodology
arXiv:2510.22046v1 Announce Type: new Abstract: We present a case study applying the SpecC methodology within a system-level hardware/software co-design flow to a PCM-to-PWM converter, the core of a Class-D audio amplifier. The converter was modeled and explored with SpecC methodology to derive an HW/SW partition. Using system-level estimates and fast functional simulation, we evaluated mappings […]
Rational Adversaries and the Maintenance of Fragility: A Game-Theoretic Theory of Rational Stagnation
arXiv:2510.22232v1 Announce Type: cross Abstract: Cooperative systems often remain in persistently suboptimal yet stable states. This paper explains such “rational stagnation” as an equilibrium sustained by a rational adversary whose utility follows the principle of potential loss, $u_D = U_ideal – U_actual$. Starting from the Prisoner’s Dilemma, we show that the transformation $u_i’ = a,u_i […]
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
arXiv:2502.19158v2 Announce Type: replace-cross Abstract: While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences across users, overlooking diverse human values and minority viewpoints. Although personalized preference learning addresses this by tailoring separate preferences for individual users, the field lacks standardized […]
A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
arXiv:2510.22266v1 Announce Type: cross Abstract: Identifying the factors that influence student performance in basic education is a central challenge for formulating effective public policies in Brazil. This study introduces a multi-level machine learning approach to classify the proficiency of 9th-grade and high school students using microdata from the System of Assessment of Basic Education (SAEB). […]
Towards Error-Centric Intelligence II: Energy-Structured Causal Models
arXiv:2510.22050v1 Announce Type: new Abstract: Contemporary machine learning optimizes for predictive accuracy, yet systems that achieve state of the art performance remain causally opaque: their internal representations provide no principled handle for intervention. We can retrain such models, but we cannot surgically edit specific mechanisms while holding others fixed, because learned latent variables lack causal […]
Harnessing the Power of Large Language Models for Software Testing Education: A Focus on ISTQB Syllabus
arXiv:2510.22318v1 Announce Type: cross Abstract: Software testing is a critical component in the software engineering field and is important for software engineering education. Thus, it is vital for academia to continuously improve and update educational methods to reflect the current state of the field. The International Software Testing Qualifications Board (ISTQB) certification framework is globally […]
Modeling Cell Dynamics and Interactions with Unbalanced Mean Field Schr”odinger Bridge
arXiv:2505.11197v3 Announce Type: replace-cross Abstract: Modeling the dynamics from sparsely time-resolved snapshot data is crucial for understanding complex cellular processes and behavior. Existing methods leverage optimal transport, Schr”odinger bridge theory, or their variants to simultaneously infer stochastic, unbalanced dynamics from snapshot data. However, these approaches remain limited in their ability to account for cell-cell interactions. […]
TraceTrans: Translation and Spatial Tracing for Surgical Prediction
arXiv:2510.22379v1 Announce Type: cross Abstract: Image-to-image translation models have achieved notable success in converting images across visual domains and are increasingly used for medical tasks such as predicting post-operative outcomes and modeling disease progression. However, most existing methods primarily aim to match the target distribution and often neglect spatial correspondences between the source and translated […]