Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
problem Reduce communication frequency in networked AP systems while maintaining control performance.
method Develops a DRL-based controller that avoids explicit update timing learning, using a semi-Markov decision process (SMDP).
result Improves communication efficiency without sacrificing control performance.
Paper uses imitation learning to create efficient insulin policies from MPC demonstrations.
problem Resource-constrained medical devices struggle with complex MPC optimizations and state estimation errors.
method Imitation learning of neural network policies from MPC-computed demonstrations, using Bayesian inference with Monte Carlo Dropout.
result Trained policies generalize well to different patient cohorts, outperforming traditional MPC with state estimation.
Paper uses rare-event simulation to analyze personalized AP controllers for T1D patients.
problem Rare adverse events in personalized AP controllers for T1D patients.
method Generative models of patient characteristics, FDA-approved T1D simulator, rare-event simulation.
result 72,000x speedup in simulation speed and 2-10x increase in adverse condition sampling.
Deep RL improves blood glucose control for T1D patients.
problem Managing blood glucose levels for people with type 1 diabetes.
method Developed deep reinforcement learning techniques for automated blood glucose control.
result Deep RL approach outperforms baseline control algorithms, reducing glycemic risk and hypoglycemia.
SML improves pancreatic mass diagnosis accuracy using CT images.
problem Improving accuracy in pancreatic mass screening using CT imaging.
method Spectral machine learning method trained on 30,000 images, choosing fundamental images based on eigenvectors and removing irrelevant pixels.
result Achieved 94.6% test accuracy in diagnosing 113 patients.
CBDL uses credal sets to improve uncertainty quantification in deep learning.
problem Uncertainty in predictions and robustness to distribution shifts in deep learning.
method Train an infinite ensemble of Bayesian Neural Networks using credal sets.
result CBDL distinguishes between aleatoric and epistemic uncertainties and quantifies them better than single BNNs.
Graph convolutional networks refine organ segmentation using uncertainty analysis.
problem Challenges in organ segmentation due to variability and tissue similarity.
method Uncertainty analysis of graph convolutional networks for semi-supervised learning.
result Improved segmentation accuracy (1% for pancreas, 2% for spleen) compared to state-of-the-art methods.
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
Artificial datasets can serve as a form of regularization for deep learning.
problem Real data shortage in deep learning.
method Injecting noise to high-level features in artificial data generation.
result Artificial data generation can be treated as a form of 'deep' regularization.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
PAMS is a Python-based platform for simulating artificial markets.
problem Simulating complex market behaviors for research and education.
method Developed as a Python-based simulator with deep learning integration.
result Demonstrated effectiveness through agent price prediction studies.
Paper reduces Hausdorff Distance in medical image segmentation.
problem Reduction of Hausdorff Distance in medical image segmentation.
method Three novel loss functions for training CNNs to estimate and reduce HD.
result Approximately 18-45% reduction in HD without degrading other performance metrics.
The paper solves European option pricing under Heston model using artificial boundary method.
problem Valuation of European call options under Heston stochastic volatility model.
method Asymptotic solution in powers of volatility, artificial boundary method for truncated domain, artificial boundary conditions.
result Artificial boundary conditions improve accuracy and outperform Heston's original boundary conditions.
In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of infor…
Study evaluates saliency maps on artificial data with different backgrounds.
problem Objective evaluation of saliency methods on artificial data with varying backgrounds.
method Developed a framework to generate artificial data with synthetic lesions and a known ground truth map, evaluated two data sets with different backgrounds (Perlin noise and 2D brain MRI slices).
result Heatmaps vary strongly between saliency methods and backgrounds.
The paper reports the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the …
Survey on AI math foundations, focusing on neural networks.
problem Lack of rigorous mathematical foundation for AI.
method Survey and discussion of theoretical directions in AI.
result Discussion of open problems in AI math.
We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works at the level of individual (artificial) molecules, subject of reversible, but otherwise determinist…
A test measures artificial agents' human-like behavior in video games.
problem Measuring the believability of artificial agents' human-like behavior.
method Developed a non-parametric two-sample hypothesis test.
result The p-value correlates with human judgment of human-like behavior. Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
problem Impact of lockdowns on US case and death counts during the pandemic.
method Artificial counterfactual approach comparing states with and without lockdowns.
result Average two times more cases would have occurred without lockdowns.
Proposes a new jackknife method for time series hyperparameter selection.
problem Hyperparameter selection for time series models.
method Artificial delete-d jackknife approach.
result Asymptotic and finite-sample advantages demonstrated.
4-bit quantization reduces U-Net memory by 8x with minimal accuracy loss.
problem Reducing memory and computation time in deep learning models.
method Fixed-point quantization of U-Net architecture.
result 8x reduction in memory usage with minimal accuracy loss.
Sequential processing biases asset allocation in artificial stock markets.
problem Systematic bias in asset allocation due to sequential processing of order books.
method Examined the impact of sequential versus parallel clearing mechanisms on multi-asset price dynamics.
result Sequential processing introduces a significant bias affecting the allocation of traders' capital.
AI improves credit rating predictions over traditional methods.
problem Improving credit rating predictions for global corporate entities.
method Applying deep learning techniques, specifically neural networks with categorical embeddings, to a large dataset of corporate obligations.
result Deep learning models achieve adequate accuracy in predicting different credit rating classes.
The paper monitors artificial neural networks using embeddings and multivariate control charts.
problem Ensuring the stability of artificial neural networks during deployment.
method Monitoring the latent feature representation (embedding) of ANN outputs using multivariate control charts based on data depth and ranks.
result The proposed method effectively detects nonstationary data streams in ANN outputs.
Survey compares methods for generating artificial outliers.
problem Difficulty in detecting genuine outliers.
method Generates artificial outliers to approximate genuine ones.
result Variability in quality of generation approaches.
New method improves neural network robustness with brain-like learning.
problem Traditional methods struggle with robustness in adversarial attacks.
method Generalized likelihood ratio method with brain-like learning mechanisms.
result Significant improvement in robustness of neural networks.
Study examines how taxes affect wealth inequality in economic models.
problem Reducing economic inequality in models of economic activity.
method Examined Artificial Chemistry models and various tax measures.
result Effective tax measures can reduce economic inequality.
Paper simplifies ANN structure into a functional form.
problem Current ANN structure is complex and difficult to analyze.
method Uses activation integral concept to represent ANN structure as a function.
result Simplified mathematical representation of ANN structure.
The paper shows how uncertainty quantification improves counterfactual explainability in AI.
problem Lack of foundational concepts in transparency research.
method Integrates uncertainty quantification into counterfactual explainability.
result Demonstrates competitive performance of an uncertainty-based explainer.
We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility. In order to achieve this goal they ought to use all the information available and consider all the choices available to choose an optimal choice. This paper investigates what happens when decisions are m…
This work introduces 'Artificial Entanglement' to understand LLMs' fine-tuning effectiveness.
problem Understanding the effectiveness of parameter-efficient fine-tuning methods for large language models.
method Adopting a quantum-information-inspired perspective, the study measures 'Artificial Entanglement' in neural networks.
result LoRA and FFT induce distinct internal entanglement signatures but not external ones, suggesting a 'no-hair' property.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.
problem Design and optimization of high burnup accident-tolerant fuels for SMRs.
method Artificial intelligence and multi-scale modeling (neutronics, thermal hydraulics, fuel performance).
result Demonstrated the effectiveness of AI in modeling and optimizing SMR fuels.
Generative models create artificial patient data for distributed analysis.
problem Privacy restrictions prevent pooling individual patient data for research.
method Deep Boltzmann machines (DBMs) and DataSHIELD software implementation.
result Patterns from real data can be recovered in artificial data sets.
Estimates input from output of nonlinear systems using ANN.
problem Estimating unknown compositional input from system output.
method Artificial Neural Networks (ANNs) for nonlinear system inversion.
result ANNs can compete with optimal bounds for linear systems and demonstrate promising results for nonlinear systems.
Study proposes a new approach for deep hedging using artificial market simulations.
problem Challenges in selecting the best model for underlying asset simulations in deep hedging.
method Proposes artificial market simulations to replicate financial market stylized facts.
result Achieves similar performance to traditional approaches without mathematical finance models.
The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in …
Introduces an artificial cyber lab to test and identify cyber resilience measures.
problem Systemic cyber risks and their control methods.
method Classical contagion models and artificial cyber lab simulations.
result Identified two classes of measures: security- and topology-based interventions.
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction …
Study shows bifurcating price dynamics in ASME with traders.
problem Understanding price dynamics in artificial stock markets.
method Agent-based model of endogenous traders interacting through a LOB.
result Bistability in price equilibria: zero-price and persistent positive-price states.
Financial services need explainable AI for fair lending.
problem Challenges in credit decisioning due to compliance and ethical considerations.
method Use of explainable AI models to address fairness and ethics in lending.
result Explainable AI models improve fairness and ethics in lending decisions.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.
In order to study the application of artificial intelligence (AI) to dental imaging, we applied AI technology to classify a set of panoramic radiographs using (a) a convolutional neural network (CNN) which is a form of an artificial neural network (ANN), (b) representative image cognition algorithms that implement scal…
Law derived for neural networks with sparse connections.
problem Understanding the behavior of neural networks with sparse connections.
method Law of large numbers for empirical distribution of parameters derived.
result Law for neural networks with sparse connections derived.
New artificial neuron doubles weight for improved deep learning accuracy.
problem Improving deep learning models' accuracy.
method Introducing a double-weight neuron, tested on MNIST and CIFAR-10 datasets.
result Significant improvement in classification accuracy for MNIST and CIFAR-10 datasets.
Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive lis…
Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effect…