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3,367 papers
This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis, and shows that English medical fine-tuning produces the dominant encoder shift, whereas multilingual continuation largely preserves the adapted representation space.
The results suggest that the empirical success of memory replay goes beyond the mitigation of forgetting, actively reintroducing the benefits of data co-observation into the learning process.
The Explanation Consistency Score (ECS) is introduced, a fairness-aware metric based on Jensen-Shannon divergence that quantifies the similarity of attribution maps across subgroups that suggests predictive fairness and explanation consistency capture complementary dimensions of model behavior, motivating fairness evaluations that extend beyond predictive performance.
Flama is presented, an open-source Python framework for developing and deploying production-ready web APIs, machine learning services, and large-language-model (LLM) applications that unifies REST API development, predictive model serving, and generative AI inference in one architecture.
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Improvements show that CutMix has only a minor impact on segmentation accuracy but consistently improves the reliability, particularly under distribution shifts, indicating that CutMix primarily enhances the trustworthiness of the model’s calibration and uncertainty rather than the raw segmentation prediction itself.
This is the first systematic evaluation of UQ methods applied to a foundation model for semantic segmentation and highlights both the promise and the current limitations of uncertainty-aware foundation models, pointing to the need for future work that jointly optimizes accuracy, robustness, and efficiency for real-world deployment.
The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compatible with this ambition remains uncertain. We apply deep learning to high-resolution socioeconomic and sectoral data across EU27 member states till 2023 to project sectoral CO$_2$ trajectories under current trends, extrapolating observed sectoral momentum without assuming changes in the pace or effectiveness of the policy environment beyond what is already reflected in historical data. We project that EU27 emissions will exceed the 2030 target by 35% (620 Mt CO$_2$ shortfall), with only a small minority of countries on trajectories consistent with the bloc's commitments. While the Power sector achieves target-consistent reductions driven by the renewable transition, Mobility shows minimal progress and accounts for over a third of total emissions by 2030, reflecting a structural inertia across member states rather than geographically concentrated lag. Our findings indicate that substantial additional intervention is required to close Europe's ambition-implementation gap, and call for establishing up-to-date energy information in Europe.
This work proposes a fluctuation-aware evaluation framework for PPG-based BP estimation based on time-series change point detection and introduces a targeted re-calibration framework triggered by detected BP change points, improving robustness without modifying model architectures.
DAEI is proposed, a denoising-aware embedding inversion pipeline that combines a residual denoising autoencoder with generative text inversion where the denoiser is trained in an unsupervised manner using Stein's unbiased risk estimate to enable denoising from noisy observations alone.
VAKE (Verifiable Activation of Parametric KnowledgE), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning, is proposed.
The prediction of equilibrium beach profiles (EBPs) under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and managing coastal ecosystems under changing environmental conditions. However, it remains challenging due to the highly nonlinear interactions among wave, tide, and sedimentary processes. Traditional empirical and numerical models often exhibit limited adaptability across diverse coastal environments, with especially pronounced limitations in beach systems where tidal processes are important. To improve data-driven prediction under these conditions, this study proposes MorphoGP, a unified category-specific Gaussian process (GP) framework for predicting EBPs under tidal influence. The framework first introduces a ContourCluster model based on contrastive learning to classify tide-influenced beach morphologies automatically. Within each morphological category, a specialized GP expert learns statistical associations between environmental descriptors including waves, tides, and sediments and the beach profile’s shape. A gating net then integrates the outputs of all experts through a probabilistic weighting mechanism to produce the final prediction. It should be noted that MorphoGP is a data-driven predictive framework and does not explicitly resolve the full wave–tide–sediment transport dynamics. Instead, it incorporates physically relevant descriptors to support prediction and model interpretation. Evaluated on data from over 180 beach profiles from tide-influenced coasts along the Chinese coast, MorphoGP achieves improved predictive performance compared with conventional and deep learning (DL) models, reducing the test root mean square error (RMSE) by about 59.3% compared with the best baseline and achieving a final RMSE of 0.297 m. Additionally, feature relevance analysis suggests that tidal parameters are strongly associated with equilibrium morphology, alongside wave and sediment characteristics. The proposed framework provides a physically informed, data-driven tool for equilibrium beach-profile prediction under tidal influence and coastal management, while stronger process-level physical coupling remains an important direction for future development. The source code is available at https://github.com/Ch1hyaAnon/MorphoGP.git
This work proposes a novel MLaaS Performance Drift Detection framework for IoT environments that employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features, and designs an Adaptive-Temporal Performance Drift Detection Mechanism that dynamically adjusts monitoring frequency based on behavioral and data variations.
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
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