A new framework leverages deep learning and Bayesian methods to improve the accuracy and reliability of wireless channel predictions, crucial for next-generation communication systems.
Researchers have developed a new technique to subtly manipulate graph neural networks, creating backdoor vulnerabilities that are difficult to detect.
![A building’s structure functions as a reservoir computer, localizing footsteps by converting the mechanical impulses of walking into dispersive vibrational fields sampled by implanted accelerometers, then projecting these signals-normalized and reduced via Principal Component Analysis-into reservoir state vectors used with trained weights to accurately estimate footstep location [latex] \hat{\mathbf{z}}\_{k}=(\hat{x}\_{k},\hat{y}\_{k}) [/latex].](https://arxiv.org/html/2603.04610v1/2603.04610v1/x1.png)
New research demonstrates that a building’s own structure can be harnessed as a sensor network to pinpoint footstep locations within its walls.
A new study investigates whether artificial intelligence can help legal professionals build robust statistical evidence to support claims of racial disparities in sentencing.
![At an initial time, Large Eddy Simulation (LES) solutions align with filtered Direct Numerical Simulation (DNS) data; however, for the Clark model, this alignment breaks down around [latex]t = 2.1[/latex], leading to computational instability and preventing solution completion.](https://arxiv.org/html/2603.05325v1/2603.05325v1/figures/snellius/velocities-z.png)
New research explores how data-driven approaches, particularly those respecting fundamental physical symmetries, can improve the accuracy of large-eddy simulations.
![The research details the implementation of convolutional neural networks across varying dimensionalities-one-dimensional for spectral analysis [latex]CNN1D[/latex], two-dimensional for spatial analysis [latex]CNN2D[/latex], and three-dimensional to integrate both spectral and spatial information [latex]CNN3D[/latex]-demonstrating a progression in model complexity for comprehensive data interpretation.](https://arxiv.org/html/2603.04720v1/2603.04720v1/cnn.png)
A new study systematically evaluates techniques to compress deep learning models, making them more practical for land cover classification using complex hyperspectral data.
![The BNN-I6 model accurately predicts (n,p) reaction cross sections across a wide range of nuclei, with root-mean-square deviations [latex]\sigma_{rms}[/latex] generally remaining small-indicating successful capture of the systematic dependence on neutron and proton number-though slightly larger deviations emerge for mid-mass and heavy nuclei, potentially due to increased structural complexity and limited experimental data in those regions.](https://arxiv.org/html/2603.04789v1/2603.04789v1/x6.png)
A new machine learning approach leverages Bayesian Neural Networks to accurately forecast neutron-induced reaction probabilities and, crucially, quantify the uncertainty in those predictions.
A new deep learning model leverages multimodal weather data and physical principles to significantly improve short-term precipitation forecasting.
A new framework uses radar sensing to intelligently disrupt eavesdropping attempts, enhancing the security of integrated sensing and communication systems.
Researchers have developed an agentic AI framework that moves beyond simple detection to offer users self-discovery prompts when manipulative communication patterns are identified in ongoing conversations.