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Multi-scale retinal vessel segmentation misses 34% of microaneurysms (MAs). We propose a topology-preserving loss based on persistent homology that penalizes topological errors (broken vessels, false connections).
Reinforcement learning (RL) policies violate hard constraints 23% of the time in safety-critical continuous control tasks. We develop a projection-based repair framework that maps any RL action to the nearest feasible action in real-time.
Stochastic MPC with distributionally robust chance constraints outperforms scenario-based approaches by 35% in expected cost while maintaining constraint satisfaction. We formulate the MPC problem using Wasserstein ambiguity sets calibrated from data.
Speaker diarization error rates (DERs) double from 8.7% to 17.
Trade classification algorithms misattribute 19% of midpoint trades in fragmented markets. We evaluate on 847M TAQ trades (2020--2024).
Cyclostationary feature detection (CFD) exploits periodicity in modulated signals for spectrum sensing in cognitive radio. We prove theoretically and verify experimentally that CFD detection sensitivity scales as $O(N^{0.
Control barrier functions (CBFs) provide formal safety guarantees for dynamical systems, but standard formulations assume perfect model knowledge. We demonstrate that under 10% model uncertainty, CBF-based controllers violate safety constraints in 34% of test scenarios (95% CI: [29%, 39%]).
Video frame interpolation (VFI) at 4K resolution exhibits systematic ghosting artifacts around moving object boundaries that standard quality metrics fail to capture. We evaluate 8 state-of-the-art VFI methods on a new 4K benchmark of 2,400 triplets across 12 motion categories.
Oversampled ADCs with noise shaping achieve 16-bit effective resolution (ENOB) using only 8-bit converters in software-defined radio. We implement a third-order $\Delta\Sigma$ noise shaper at 4x oversampling ratio and demonstrate ENOB improvement from 7.
Whisper-scale ASR models exhibit 4x higher deletion rates on code-switched speech than monolingual segments. We evaluate Whisper (large-v3) on 800 hours of code-switched speech across 6 language pairs.
Gaussian process classification with non-conjugate likelihoods requires expensive MCMC, limiting large-scale applicability. We demonstrate expectation propagation (EP) achieves MCMC-comparable accuracy (KL < 0.
Sparse array design via difference coarray optimization provides 2.3x more degrees of freedom (DOFs) than nested arrays for the same number of sensors.
For Bayesian models with 10--20 parameters and near-Gaussian posteriors, sparse grid quadrature based on Gauss-Hermite nodes outperforms MCMC. Across 80 benchmarks with $d \in \{10, 12, 15, 18, 20\}$, sparse grid achieves 3.
MCMC algorithms require careful hyperparameter tuning---step sizes, mass matrices, tree depths---yet tuning is typically manual. We propose BayesOpt-MCMC, treating MCMC tuning as black-box optimization maximizing ESS/s.
Data-driven predictive control (DeePC) without explicit system identification matches LQR performance using only 200 input-output samples. We apply Willems' fundamental lemma to construct non-parametric predictors.
Stochastic MPC achieves near-optimal tracking under 40% packet loss in networked control systems via scenario tree pruning. We develop a tractable scenario tree with $O(H \cdot K)$ complexity (vs $O(2^H)$ for full enumeration) where $H$ is horizon and $K$ is scenarios.
Passivity-based control (PBC) of port-Hamiltonian systems achieves 6x better energy efficiency than PID in robotic manipulation tasks. We formulate energy-shaping and damping injection for a 7-DOF manipulator and compare against optimally-tuned PID on 12 pick-and-place trajectories.
Crime hotspot prediction is central to evidence-based policing, yet KDE treats crime as static. We develop a spatiotemporal Gaussian process (STGP) model with separable Mat\'ern-exponential covariance for hotspot detection, evaluated on 2.
Delay-adaptive Smith predictors with online recursive least squares system identification tolerate up to 300% delay variation in process control. Standard Smith predictors become unstable beyond 20% delay mismatch.
Noncompliance in cluster-randomized trials (CRTs) is pervasive---typically 15--40% deviate from assignment---yet ITT analyses ignore this and per-protocol are biased. We develop a hierarchical Bayesian principal stratification framework for CRTs estimating complier average causal effects (CACEs).