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Arxiv
3d
232
Image Credit: Arxiv
Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization
- An operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed.
- The algorithm dynamically adjusts task priority and resource allocation strategy, improving task completion efficiency, system throughput, and response speed.
- Experimental results show that the Double DQN algorithm performs well under different system loads and is particularly effective for I/O intensive tasks.
- The algorithm also demonstrates high optimization ability in resource utilization and can intelligently adjust resource allocation based on the system state.
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Arxiv
3d
192
Image Credit: Arxiv
Data-Driven Forecasting of High-Dimensional Transient and Stationary Processes via Space-Time Projection
- Space-Time Projection (STP) is introduced as a data-driven forecasting approach for high-dimensional and time-resolved data.
- STP computes extended space-time proper orthogonal modes from training data to generate forecasts by projecting these modes onto new data.
- The method relies on the orthogonality and optimal correlation of the modes, and no additional hyperparameters are required.
- Comparative studies with LSTM neural networks showed that STP consistently provided more accurate forecasts.
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Arxiv
3d
320
Image Credit: Arxiv
A Low-complexity Structured Neural Network to Realize States of Dynamical Systems
- This paper introduces a structured neural network (StNN) for realizing states of dynamical systems by leveraging data-driven learning.
- The StNN utilizes a low-complexity operator called the Hankel operator, derived from time-delay measurements, to solve dynamical systems.
- Numerical simulations comparing the StNN with other techniques show that it reduces the number of parameters and computational complexity.
- The proposed StNN enables the prediction and understanding of future states in state-space dynamical systems.
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Arxiv
3d
240
Image Credit: Arxiv
Steering Large Agent Populations using Mean-Field Schrodinger Bridges with Gaussian Mixture Models
- The Mean-Field Schrodinger Bridge (MFSB) problem aims to find the minimum effort control policy for a swarm of cooperative agents.
- New efficient parameterization is proposed to approximate MFSB solutions for Gaussian Mixture Model boundary distributions.
- The proposed approach uses a mixture of elementary policies to solve a Gaussian-to-Gaussian Covariance Steering problem.
- The method can handle probabilistic hard constraints and is applied to various numerical examples.
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Arxiv
3d
72
Image Credit: Arxiv
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion
- Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion
- SUMMER is a novel heterogeneous multimodal integration framework for emotion recognition
- It leverages Mixture of Experts with Hierarchical Cross-modal Fusion and Interactive Knowledge Distillation
- Experiments show that SUMMER outperforms state-of-the-art methods in recognizing minority and semantically similar emotions
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4 Likes
Arxiv
3d
92
Image Credit: Arxiv
PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution
- In the paradigm of decentralized learning, a group of agents collaborates to learn a global model using distributed datasets without a central server.
- The heterogeneity of the local data across agents makes learning a robust global model challenging.
- PDSL is a privacy-preserved decentralized stochastic learning algorithm that addresses these challenges using Shapley values to measure neighbor contributions and differential privacy to prevent privacy leakage.
- The PDSL algorithm demonstrates efficacy in privacy preservation and convergence, supported by theoretical analysis and extensive experiments.
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Arxiv
3d
68
Image Credit: Arxiv
Time-Series Forecasting via Topological Information Supervised Framework with Efficient Topological Feature Learning
- Topological Data Analysis (TDA) is used for extracting features from complex data structures.
- Integration of TDA with time-series prediction faces challenges related to temporal dependencies and computational bottlenecks.
- The Topological Information Supervised (TIS) Prediction framework proposes using neural networks and CGANs to generate synthetic topological features.
- TIS models, TIS-BiGRU and TIS-Informer, outperform conventional predictors in capturing short-term and long-term temporal dependencies.
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Arxiv
3d
188
Image Credit: Arxiv
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
- A new framework combining graph neural networks and reinforcement learning has been proposed to design high-efficiency organic photovoltaic (OPV) molecules.
- The integrated approach includes large-scale pretraining of graph neural networks and a Generative Pretrained Transformer 2 (GPT-2) based reinforcement learning strategy.
- The proposed approach has predicted efficiencies approaching 21%, and provides design guidelines for enhancing power conversion efficiency (PCE).
- To support further discovery, the largest open-source OPV dataset is being built, and collaboration with experimental teams is planned for synthesizing and characterizing AI-designed molecules.
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11 Likes
Arxiv
3d
204
Image Credit: Arxiv
An extension of linear self-attention for in-context learning
- In-context learning is a key characteristic of transformers.
- Self-attention mechanism in transformers lacks flexibility in certain tasks.
- Linear self-attention is extended by introducing a bias matrix.
- The extended linear self-attention enables flexible matrix manipulations.
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12 Likes
Arxiv
3d
224
Image Credit: Arxiv
Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics
- Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts.
- Lack of transparency in complex AI systems hinders their adoption in high-risk applications like healthcare.
- Conformal analysis is being deployed to address the problem of lack of transparency in foundation models for skin lesion classification.
- The method of conformal analysis provides coverage guarantee at population level and uncertainty score for each individual, making it a helpful fairness metric for evaluating AI models.
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Arxiv
3d
312
Image Credit: Arxiv
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
- Counterfactual reasoning is often seen as the 'holy grail' of causal learning, but its reliability in real-world complex settings is largely unexplored.
- This work investigates the limitations of counterfactual reasoning within the framework of Structural Causal Models.
- Realistic assumptions, such as model uncertainty and chaotic dynamics, can lead to counterintuitive outcomes.
- This study encourages caution when applying counterfactual reasoning in chaotic and uncertain situations.
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18 Likes
Arxiv
3d
44
Image Credit: Arxiv
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
- Nonlinear matrix decomposition with the ReLU function finds application in various fields.
- The standard ReLU-NMD model minimizes the least squares error while the Latent-ReLU-NMD model introduces a latent variable to achieve a different low-rank solution.
- The 3B-ReLU-NMD model allows elimination of the rank constraint in Latent-ReLU-NMD.
- A novel extrapolated variant, eBCD-NMD, of block coordinate descent (BCD) for 3B-ReLU-NMD is proven to be convergent and offers significant acceleration.
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2 Likes
Arxiv
3d
44
Image Credit: Arxiv
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation
- A new method called CE-LoRA (communication-efficient federated LoRA adaptation) has been introduced to address challenges in fine-tuning pre-trained foundation models in federated learning.
- CE-LoRA utilizes a tri-factorization low-rank adaptation approach with personalized model parameter aggregation.
- By introducing a small-size dense matrix and considering client similarity, CE-LoRA reduces communication cost and achieves comparable empirical performance.
- Experiments show that CE-LoRA significantly reduces communication overhead, improves performance under non-iid data conditions, and enhances data privacy protection.
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2 Likes
Arxiv
3d
284
Image Credit: Arxiv
An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy
- An integrated intelligent method of fault diagnosis for gears using acceleration signals is proposed.
- The method is based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms.
- The proposed method incorporates a dilated residual structure and a feature fusion layer for multi-scale analysis of fault features.
- Comparative experiments demonstrate the effectiveness of the proposed method for end-to-end fault diagnosis of gears.
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17 Likes
Arxiv
3d
392
Image Credit: Arxiv
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
- Renewable resources can benefit from skillful subseasonal to seasonal (S2S) wind speed forecasts.
- DiffScale is a diffusion model that enhances S2S wind speed predictions by downscaling and correcting forecast errors.
- DiffScale can super-resolve spatial information for continuous downscaling factors and lead times, without auto-regression or sequence prediction.
- Synthetic experiments showed that DiffScale significantly improves wind speed prediction quality, outperforming baselines up to week 3.
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