Quantum neural networks exhibit membership privacy leakage that a proposed quantum machine unlearning framework with three mechanisms can mitigate in simulations and cloud device tests.
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LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.
EADP filters textual noise via statistical entropy then casts token selection as submodular maximization with spatial prior to preserve fine-grained cues in VLMs under strict budgets.
Wafer-scale 2-inch beta-Ga2O3 MOSFETs fabricated with >3 kV breakdown and reported uniformity in threshold voltage, current density, and breakdown across the wafer.
citing papers explorer
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From Membership-Privacy Leakage to Quantum Machine Unlearning
Quantum neural networks exhibit membership privacy leakage that a proposed quantum machine unlearning framework with three mechanisms can mitigate in simulations and cloud device tests.
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LatencyScope: A System-Level Mathematical Framework for 5G RAN Latency
LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.
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Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
EADP filters textual noise via statistical entropy then casts token selection as submodular maximization with spatial prior to preserve fine-grained cues in VLMs under strict budgets.
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Wafer-scale Demonstration of High-voltage beta-Ga2O3 MOSFETs with Excellent Uniformity and over 3kV Breakdown Voltages
Wafer-scale 2-inch beta-Ga2O3 MOSFETs fabricated with >3 kV breakdown and reported uniformity in threshold voltage, current density, and breakdown across the wafer.