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Arxiv
2d
226
Image Credit: Arxiv
Truth in Text: A Meta-Analysis of ML-Based Cyber Information Influence Detection Approaches
- Cyber information influence, or disinformation, is a significant threat to social progress and government stability.
- ML techniques, including traditional ML algorithms and deep learning models, are being used to detect disinformation in online media.
- A two-stage meta-analysis was conducted to assess the effectiveness of ML models in detecting disinformation.
- The majority of the ML detection techniques sampled achieved over 80% accuracy, with a mean sample effectiveness of 79.18% accuracy.
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Arxiv
2d
333
Image Credit: Arxiv
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA
- Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA
- The paper discusses the use of a Parameter-Efficient Fine-tuning method called Low-Rank Adaptation (LoRA) to fine-tune a more computationally efficient version of the automatic speech recognition model, Whisper, for aviation communication transcription.
- The authors used the Air Traffic Control Corpus dataset and performed a grid search to optimize the hyperparameters of distil-Whisper using a 5-fold cross-validation.
- The fine-tuned model achieved an average word error rate of 3.86% across five folds, indicating its potential for accurate transcription of aviation communication.
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Arxiv
2d
380
Image Credit: Arxiv
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
- Spontaneous speech emotion data often have uncertainty in labels due to grader opinion variation.
- Using the probability density function of emotion grades as targets instead of consensus grades improves performance on benchmark evaluation sets.
- Saliency-driven foundation model representation selection helps train a state-of-the-art speech emotion model for both dimensional and categorical emotion recognition.
- Performance evaluation across multiple test-sets, along with analysis across gender and speakers, is necessary to assess the usefulness of emotion models.
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Arxiv
2d
384
Image Credit: Arxiv
Risk-Calibrated Affective Speech Recognition via Conformal Coverage Guarantees: A Stochastic Calibrative Framework for Emergent Uncertainty Quantification
- Traffic safety challenges arising from extreme driver emotions highlight the urgent need for reliable emotion recognition systems.
- Traditional deep learning approaches in speech emotion recognition suffer from overfitting and poorly calibrated confidence estimates.
- A framework integrating Conformal Prediction (CP) and Risk Control is proposed, using Mel-spectrogram features processed through a pre-trained convolutional neural network.
- The Risk Control framework enables task-specific adaptation through customizable loss functions, dynamically adjusting prediction set sizes while maintaining coverage guarantees.
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Arxiv
2d
388
Image Credit: Arxiv
Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study
- Researchers have developed a fine-tuned Transformer model to detect and localize chirp-like patterns in EEG spectrograms, which are important biomarkers for seizure dynamics.
- The study utilized synthetic spectrograms with chirp parameters to create a benchmark for chirp localization.
- The Vision Transformer (ViT) model was adapted for regression to predict chirp parameters, and attention layers were fine-tuned using Low-Rank Adaptation (LoRA).
- The model achieved a strong alignment between predicted and actual labels, with a correlation of 0.9841 for chirp start time.
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Arxiv
2d
130
Image Credit: Arxiv
A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI
- A large-scale vision-language dataset derived from open scientific literature, Biomedica, has been introduced to advance biomedical generalist AI.
- The dataset contains over 6 million scientific articles, 24 million image-text pairs, and 27 metadata fields, including expert human annotations.
- Scalable streaming and search APIs are provided for easy access to the dataset, facilitating seamless integration with AI systems.
- The utility of the Biomedica dataset has been demonstrated through the development of embedding models, chat-style models, and retrieval-augmented chat agents, outperforming previous open systems.
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Arxiv
2d
253
Image Credit: Arxiv
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
- Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task.
- SymGNN is a graph neural network architecture that leverages material symmetries to improve adsorption property prediction.
- The model successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO2 adsorption.
- The study suggests promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials.
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Arxiv
2d
198
Image Credit: Arxiv
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation
- This paper explores the risks of unintentional and malicious disclosure in large language models trained for code generation.
- Unintentional disclosure refers to the language model presenting secrets to users without user intent, while malicious disclosure refers to presenting secrets to an attacker.
- The study assesses the risks of unintentional and malicious disclosure in the Open Language Model (OLMo) family of models and the Dolma training datasets.
- The results show that changes in data source and processing greatly affect the risk of unintended memorization, and the risk of disclosing sensitive information varies based on prompt strategies and types of sensitive information.
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Arxiv
2d
214
Image Credit: Arxiv
Boosting Large Language Models with Mask Fine-Tuning
- A new language model fine-tuning paradigm called Mask Fine-Tuning (MFT) has been introduced.
- MFT breaks the integrity of the model to improve its performance.
- Extensive experiments show consistent performance boosts across various domains and backbones.
- MFT extends the functionality of mask learning for model compression to a more general scope.
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Arxiv
2d
333
Image Credit: Arxiv
Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments
- Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood.
- A deep learning model designed to detect CHD using phonocardiogram (PCG) signals achieved high accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%.
- The model demonstrated robust performance on diverse datasets from Bangladesh, as well as public datasets, showing its generalizability to different populations and data sources.
- The research suggests that an AI-driven digital stethoscope could be a cost-effective screening tool for CHD in resource-limited settings, improving patient outcomes.
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Arxiv
2d
356
Image Credit: Arxiv
Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games
- Researchers study a long-run mean-variance team stochastic game (MV-TSG) and propose a Mean-Variance Multi-Agent Policy Iteration (MV-MAPI) algorithm.
- The MV-TSG faces challenges with the non-additive and non-Markovian variance metric, as well as non-stationary environment due to simultaneous policy updates.
- The MV-MAPI algorithm converges to a first-order stationary point, with specific conditions for local Nash equilibria and local optima.
- To solve large-scale MV-TSGs with unknown environmental parameters, a multi-agent reinforcement learning algorithm named MV-MATRPO is proposed.
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Arxiv
2d
55
Image Credit: Arxiv
Patronus: Bringing Transparency to Diffusion Models with Prototypes
- Diffusion-based generative models, such as Denoising Diffusion Probabilistic Models (DDPMs), have achieved remarkable success in image generation.
- A new interpretable diffusion model called Patronus is introduced, which integrates a prototypical network into DDPMs.
- Patronus enhances interpretability by showing the learned prototypes and how they influence the generation process.
- The model supports downstream tasks like image manipulation and can reveal shortcut learning in the generation process.
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Arxiv
2d
356
Image Credit: Arxiv
Quantum Doeblin Coefficients: Interpretations and Applications
- This study focuses on investigating quantum Doeblin coefficients as a generalization of classical concepts in information theory.
- The researchers define new quantum Doeblin coefficients with desirable properties and efficient computability.
- Various interpretations of the quantum Doeblin coefficients are presented, including their representations as minimal singlet fractions and exclusion values.
- The study also explores multiple applications of quantum Doeblin coefficients in various areas, providing improvements over prior literature in terms of generality and efficiency.
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Arxiv
2d
380
Image Credit: Arxiv
Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach
- Accurate brain tissue segmentation in non-human primates (NHPs) is critical for understanding neurological disorders.
- A novel approach utilizing STU-Net with transfer learning enhances segmentation accuracy in NHP brain MRI.
- The method effectively delineates complex tissue boundaries and captures fine anatomical details specific to NHP brains.
- This study introduces a robust method for multi-class brain tissue segmentation in NHPs, benefiting research in evolutionary neuroscience and preclinical studies of neurological disorders.
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Arxiv
2d
202
Image Credit: Arxiv
Nested Stochastic Gradient Descent for (Generalized) Sinkhorn Distance-Regularized Distributionally Robust Optimization
- The paper proposes a nested stochastic gradient descent algorithm for solving regularized nonconvex Distributionally Robust Optimization (DRO) problems.
- The algorithm is designed to handle DRO problems with generalized Sinkhorn distance and nonconvex, unbounded loss functions.
- The proposed algorithm has polynomial iteration and sample complexities that are independent of data size and parameter dimension.
- Numerical experiments demonstrate the efficiency and robustness of the algorithm.
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