University of Ottawa

Decentralized federated learning has no central server, and link noise accumulates as models are relayed hop by hop, becoming more pronounced as the learning rate decays; Byzantine nodes can also mimic honest model averages to win loss-based selection. FedSharp keeps each predecessor's model copies at every node, screens out single-parameter deviations using coordinate-wise median deviations, averages the lowest-loss candidates, and adds noise-adaptive gradient-norm regularization with proximal training. On a 100-node ring with 33 Byzantine nodes, MNIST accuracy is 93.5%–94.9%, versus 29.4%–38.1% for a single-model-selection baseline.

Decentralized federated learning has no central server, and link noise accumulates as models are relayed hop by hop, becoming more pronounced as the learning rate decays; Byzantine nodes can also mimic honest model averages to win loss-based selection. FedSharp keeps each predecessor's model copies at every node, screens out single-parameter deviations using coordinate-wise median deviations, averages the lowest-loss candidates, and adds noise-adaptive gradient-norm regularization with proximal training. On a 100-node ring with 33 Byzantine nodes, MNIST accuracy is 93.5%–94.9%, versus 29.4%–38.1% for a single-model-selection baseline.

The limits of token-by-token generation are now fully characterized: a sampler with little private state cannot sample a pseudorandom generator's output, while an insertional sampler that may write at any position can.

The limits of token-by-token generation are now fully characterized: a sampler with little private state cannot sample a pseudorandom generator's output, while an insertional sampler that may write at any position can.

Cloud gaming transmission moves bitrate decisions from whole chunks down to individual game objects: an off-the-shelf semantic segmentation model splits each frame into objects, and a deep reinforcement learning agent uses the player's in-game movement speed, object semantics and motion, and current network conditions to judge which objects the player actually attends to, giving those higher resolution while lowering the rest to save bandwidth. Across simulated 5G, 4G and WiFi scenarios the scheme beats existing ABR algorithms on the proposed QoE metrics, with normalized average QoE at least 20% higher in 5G, and a 34-participant subjective study gives it a mean opinion score of 4.16 versus 2.49 for Pensieve, 3.74 for T-Gaming and 2.86 for MetaABR.

Cloud gaming transmission moves bitrate decisions from whole chunks down to individual game objects: an off-the-shelf semantic segmentation model splits each frame into objects, and a deep reinforcement learning agent uses the player's in-game movement speed, object semantics and motion, and current network conditions to judge which objects the player actually attends to, giving those higher resolution while lowering the rest to save bandwidth. Across simulated 5G, 4G and WiFi scenarios the scheme beats existing ABR algorithms on the proposed QoE metrics, with normalized average QoE at least 20% higher in 5G, and a 34-participant subjective study gives it a mean opinion score of 4.16 versus 2.49 for Pensieve, 3.74 for T-Gaming and 2.86 for MetaABR.

The authors propose BRACE, the first differentially private retrieval mechanism for log-sum-ReLU dense associative memory (LSR-DAM): it identifies memory points whose membership changes under privacy noise and applies an adaptive boundary correction before injecting calibrated noise, controlling error accumulation from boundary switching in compact-support retrieval. They prove minimax-optimal terminal and full-trajectory retrieval error rates and establish trajectory central limit theorems characterizing privacy-induced variability; synthetic experiments show a tiny perturbation changes 11 gate memberships, and MNIST retrieval reduces prediction MSE from 0.061 for the corrupted input to 0.011, nearly matching noiseless retrieval.

The authors propose BRACE, the first differentially private retrieval mechanism for log-sum-ReLU dense associative memory (LSR-DAM): it identifies memory points whose membership changes under privacy noise and applies an adaptive boundary correction before injecting calibrated noise, controlling error accumulation from boundary switching in compact-support retrieval. They prove minimax-optimal terminal and full-trajectory retrieval error rates and establish trajectory central limit theorems characterizing privacy-induced variability; synthetic experiments show a tiny perturbation changes 11 gate memberships, and MNIST retrieval reduces prediction MSE from 0.061 for the corrupted input to 0.011, nearly matching noiseless retrieval.

DreamTest adapts a recurrent state-space model into a testing world model that learns agent behaviour and environment dynamics from a DRL agent's training log, then scores a candidate configuration by generating imagined rollouts in parallel and combining termination-weighted failure predictions, guiding search without executing the simulator; on Parking, Humanoid and DonkeyCar its mean AUPRC exceeds the strongest baseline by about 97%, 12% and 39%, gains on five out-of-distribution sets reach 145%, 29% and 44%, and under the same simulator-validation budget the best DreamTest-plus-search combinations find 29%, 22% and 79% more novel failures on average.

DreamTest adapts a recurrent state-space model into a testing world model that learns agent behaviour and environment dynamics from a DRL agent's training log, then scores a candidate configuration by generating imagined rollouts in parallel and combining termination-weighted failure predictions, guiding search without executing the simulator; on Parking, Humanoid and DonkeyCar its mean AUPRC exceeds the strongest baseline by about 97%, 12% and 39%, gains on five out-of-distribution sets reach 145%, 29% and 44%, and under the same simulator-validation budget the best DreamTest-plus-search combinations find 29%, 22% and 79% more novel failures on average.

Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic

Addressing AI tools entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic