Deep learning enhances jamming and defense in wireless communications.
problem Improving wireless communication security against jamming attacks.
method Adversarial machine learning for jamming and a GAN for training data augmentation.
result Deep learning significantly reduces transmitter performance compared to random jamming.
A strategy for spectrum sharing in CRNs with multiple PT power levels.
problem Efficient spectrum usage for secondary users in CRNs with multiple PT power levels.
method Data-driven/machine learning based multi-level spectrum sensing and prediction-transmission structures.
result The proposed strategy effectively aligns the ST with the PT power levels, improving spectrum usage.
With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data cont…
GAN-based spoofing attacks improve wireless signal authentication.
problem Improving wireless signal authentication against sophisticated spoofing attacks.
method Generative Adversarial Network (GAN) for generating synthetic signals.
result GAN-based spoofing attacks significantly increase the success probability of wireless signal spoofing.
Adversarial attacks are ineffective when the surrogate models are trained with different channel effects.
problem Adversarial attacks against wireless signal classifiers are ineffective when the adversary's surrogate model differs from the transmitter's classifier.
method Investigated different topologies to analyze how channel effects influence the performance of adversarial attacks.
result Surrogate models trained with different channel-induced inputs severely limit the attack performance.
Adversaries can fool deep learning modulator classifiers over wireless channels.
problem Vulnerability of deep learning modulator classifiers to adversarial attacks over wireless channels.
method Presented various adversarial attacks considering channel effects, including targeted and non-targeted attacks, and a universal adversarial perturbation attack.
result Modulation classification is vulnerable to adversarial attacks over wireless channels with realistic channel effects.
New model estimates unknown number of users in asynchronous multiuser communication.
problem Estimating unknown number of users in asynchronous multiuser communication.
method Infinite factorial finite state machine model with Bayesian nonparametric approach.
result Effective recovery of data-generating process for various scenarios.
The paper develops algorithms to minimize queue length regret in a communication system.
problem Minimizing the difference between actual and optimal queue lengths over time slots.
method Introduces queue length regret and applies algorithms from stochastic multi-armed bandit problem to analyze system performance.
result Order optimal O(1) queue length regret can be achieved with queue-length based policies. Paper proposes a new method for quantizing channel state information to optimize resource allocation.
problem Optimizing resource allocation for a receiver sending quantized channel state information to a transmitter.
method Introducing a task-oriented approach where the receiver sends the right amount of information to the transmitter.
result Analytical solution for optimal task-oriented CSI quantizer found for a specific energy-efficient power control problem.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
problem Predicting currency returns using volatility shocks.
method Constructed a dynamic, directed network of volatility connections using option-implied volatilities.
result Currencies that transmit more volatility shocks earn lower excess returns.
A deep RL framework optimizes resource allocation in wireless networks.
problem Optimizing resource allocation and interference in wireless networks.
method Multi-agent deep reinforcement learning for distributed decision-making.
result Our approach outperforms decentralized and centralized baselines in terms of user rates.
RadioUNet uses deep learning to estimate pathloss in urban environments.
problem Accurate pathloss estimation for urban environments.
method Deep neural networks trained on physical simulation data.
result RadioUNet generates pathloss estimations faster and more accurately than existing methods.
In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applyin…
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.
problem Model collapse due to statistical optimization of synthetic data.
method Introduces Prompt-driven Cognitive Computing Framework (PMCSF) with Cognitive State Decoder (CSD) and Cognitive Text Encoder (CTE).
result The framework generates text with cognitive imperfections, reducing maximum drawdown and delivering defensive alpha.
Neural network improves K-factor estimation for OFDM systems.
problem Estimating Ricean K factor for link quality in OFDM systems.
method Classified as a classification problem, neural network estimates K factor at transmitter side.
result High accuracy in K factor estimation with reduced feedback bandwidth.
Cognitive model discovery framework for ill-structured domains.
problem Discover accurate cognitive models without student data or human knowledge.
method Cognitive Representation Learner (CogRL) framework for ill-structured domains.
result CogRL representations can accurately discover cognitive models in ill-structured domains.
The paper brings order to the diverse cognitive fallacies.
problem The proliferation of cognitive fallacies in human reasoning.
method Formally establishing implication relationships (IRs) between fallacies.
result A systematic approach to categorize and understand cognitive fallacies.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
problem Cognitive load's effect on financial market information processing.
method Developed a theoretical framework and tested it with exogenous disclosure complexity variation.
result Cognitive load significantly impairs price discovery, particularly for less sophisticated investors.
Pension benefits in rural China lead to cognitive decline among the elderly.
problem Cognitive decline in late adulthood among rural Chinese elderly.
method Examined the effects of a new pension scheme on cognitive performance.
result Pension benefits negatively impact cognitive functioning, particularly delayed recall.
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain func…
CC charts radio geometry for user localization.
problem Locating users in radio environments.
method Unsupervised learning from passive CSI.
result Extracts channel features for spatial comparison.
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
problem Ambiguous data and cognitive biases impact the interpretability of machine learning models in humanitarian decision making.
method The study will explore the effects of data ambiguity and cognitive biases on machine learning algorithms in humanitarian contexts.
result The research aims to uncover the specific ways in which ambiguous data and cognitive biases influence the interpretability of machine learning models in humanitarian decision making.
New method identifies latent variables in cognitive models using neural networks.
problem Inference of latent variables in complex cognitive models is limited.
method Recurrent neural networks and simulation-based inference for latent variable sequences.
result Extends neural Bayes estimation to broader classes of cognitive models.
Deep learning predicts semitransparent pigment mixtures for novice painters.
problem Support novice painters in learning color mixing.
method Built a watercolor dataset with transmittance and reflectance data. Used a deep neural network to train a model for predicting pigment mixtures.
result Trained model accurately predicts semitransparent pigment mixtures.
End-to-end learning of codes for secure BPSK communication in Gaussian wiretap channel.
problem Secure communication in the presence of an adversary for BPSK modulation.
method Learning finite-length codes using deep neural networks (DNNs) with mutual information estimation.
result Learned codes achieve near-optimal security performance, as demonstrated by numerical results.
This paper tackles the interpretability gap between deep learning models and human cognition.
problem The gap between deep learning models and human cognitive competence.
method A universal learning framework is proposed to evaluate and improve model interpretability.
result The uniqueness of solution and conditions for unique solution are proved.
Cognitive KR model learns new facts from few examples.
problem Inferring new facts from small KG datasets.
method CogKR combines summary and reasoning modules with cognitive science principles.
result Significantly outperforms previous models on one-shot KG reasoning.
A distributed RL framework optimizes radio resource management for wireless networks.
problem Interference in wireless networks limits performance; maximizing average and worst-case throughput is challenging.
method Multi-agent deep reinforcement learning (RL) for distributed link scheduling.
result The framework achieves superior average and 5th percentile user throughput compared to decentralized methods.
While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what exten…
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
problem Understanding the energy cost of decision-making in living systems.
method Applied Landauer's principle to analyze belief dynamics in a two-armed bandit game.
result Cognitive energy cost correlates with optimal decision-making outcomes.
End-to-end framework classifies cognitive workload in real-time driving scenarios.
problem Challenging task of classifying human cognitive states from behavioral and physiological signals.
method End-to-end framework using mixture Hyper Long Short Term Memory Networks (HyperNetworks).
result Framework outperforms previous methods with 83.9% precision and 87.8% recall.
Machine learning detects and classifies cognitive distortions in mental health texts.
problem Detect and classify cognitive distortions in mental health texts to improve treatment.
method Machine learning framework using crowdsourced and therapy dataset.
result Model achieved high F1 scores for detecting and classifying cognitive distortions.
New filters improve radar target inference in complex scenarios.
problem Improving radar target inference in highly non-linear system models.
method Developed inverse cubature Kalman filter (I-CKF), inverse quadrature Kalman filter (I-QKF), and inverse cubature-quadrature Kalman filter (I-CQKF) for non-linear systems.
result Numerical experiments show improved estimation accuracy compared to existing methods.
New framework aligns latent representations over-the-air using intelligent metasurfaces.
problem Heterogeneous transmitter-receiver models produce misaligned latent representations in semantic communication.
method Intelligent metasurfaces (SIM) emulate supervised and zero-shot semantic aligners directly in the wave domain.
result SIMs achieve up to 90% task accuracy in high SNR regimes, robust to low SNR.
New method combines brain imaging data from multiple studies to improve cognitive decoding.
problem Low statistical power in individual neuroimaging studies.
method A new methodology to analyze brain responses across tasks without a unified theoretical framework.
result Improves decoding performance for 80% of 35 functional-imaging studies.
Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memor…
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
Study improves robustness of Bayesian inference for cognitive models.
problem Outliers and contaminants affect parameter estimation in cognitive models.
method Robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks.
result Introducing contaminants from a Cauchy distribution increases robustness.
Develops an inverse particle filter for cognitive systems.
problem Tracking cognitive adversaries in counter-adversarial applications.
method Global filtering approach using Monte Carlo methods and differentiable I-PF.
result Demonstrates convergence to optimal inverse filter and improved estimation performance.
New methods make Bayesian inference feasible for complex cognitive models.
problem Bayesian inference is hard for complex, unknown models.
method Amortized neural network estimators for simulation-based inference.
result Effort in training networks amortizes over multiple uses.
New cognitive model priors improve human decision prediction.
problem Predicting human decisions with high precision remains challenging.
method Pretrained neural networks on synthetic data generated by cognitive models, fine-tuning on real human data.
result Fine-tuned neural networks achieve unprecedented state-of-the-art improvements on benchmark datasets.
QCML uses quantum geometry to represent data.
problem Data representation and the curse of dimensionality.
method QCML encodes data as Hermitian matrices in Hilbert space.
result Data geometry reveals intrinsic dimension and topological properties.
Study uses neural networks to filter financial spillovers from noise.
problem Accurately measuring spillovers in financial markets from noise.
method Neural network-based denoising of covariance matrices.
result Developed markets are net transmitters of volatility spillovers, but can become receivers during stress.
The paper explores new learning principles based on least cognitive action.
problem Formulating learning problems in a way that incorporates time and perception.
method Introduces the principle of least cognitive action and proves its well-posedness.
result Proves the existence of a minimum for a special form of cognitive action, leading to fourth-order differential equations of learning.
Deep learning models can learn confounding factors instead of device fingerprints in wireless signals.
problem Learning device fingerprints from complex-valued deep neural networks in the presence of confounding factors.
method Investigating complex-valued deep neural networks (DNNs) to distinguish between wireless transmitters, focusing on clock drift and channel variations.
result DNNs learn confounding features rather than device-specific characteristics, requiring strategies to promote generalization.
We propose a neural superstatistics method to estimate dynamic cognitive models from time series data.
problem Memoryless cognitive models ignore parameter fluctuations, leading to inaccurate predictions.
method Developed a simulation-based deep learning method for Bayesian inference of superstatistical models.
result Deep learning method efficiently recovers time-varying and time-invariant parameters.
CogFormer trains a transformer to estimate parameters across various cognitive models.
problem Difficulty in fitting complex cognitive models and iterating over varying assumptions.
method Meta-amortized framework using a transformer to estimate parameters across multiple models.
result CogFormer accurately estimates parameters across different model families with minimal retraining.