Time-reversal symmetry underlies physical-layer security schemes exploiting channel reciprocity. We demonstrate that at 28 GHz mmWave frequencies, reciprocity breaks down due to asymmetric power amplifier nonlinearities between transmit and receive chains.
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%]).
Cryptocurrency portfolio risk cannot be captured by Gaussian models. Tempered stable distributions reduce VaR estimation error by 45.
Systemic risk indicators based on Shapley values from cooperative game theory predict bank failures 6 months earlier than CoVaR and SRISK. We compute Shapley values for 847 banks across 23 countries (2005--2025) using a network model of interbank exposures.
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.
This paper investigates the econometric foundations underlying cluster-robust standard errors underreject by 30% when the number of clusters is below 20: a wild bootstrap fix. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
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.
Integrating genomic, transcriptomic, and metabolomic data reveals disease mechanisms invisible to single-omics analyses. We apply sparse canonical correlation analysis (sCCA) to 2,847 T2D patients and 3,124 controls from 3 cohorts.
We provide causal evidence that conditional cash transfers increase vaccination rates by 19 percentage points when disbursed via mobile phones: evidence from pakistan. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
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.
Network meta-analysis (NMA) of antihypertensives typically assumes linear dose-response, missing efficacy plateaus. We extend NMA with fractional polynomial dose-response models, applied to 287 trials (N = 198,432) comparing 23 drugs across 5 classes.
Fractional Fourier transform (FrFT) order estimation from chirp signals achieves the Cram'er-Rao bound (CRB) at SNR = -8 dB using a maximum likelihood estimator with Newton refinement. Standard FrFT order search achieves CRB only at SNR $\geq$ 0 dB.
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.
We provide causal evidence that currency devaluations in sub-saharan africa increase food insecurity by 18% within 6 months: a synthetic control study. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
Switched system stability under arbitrary switching requires common Lyapunov functions (CLFs). We construct an explicit counterexample---a family of 3 stable linear subsystems in $\mathbb{R}^4$ with pairwise CLFs but no common CLF---that diverges under a specific switching signal.
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.
This paper investigates the econometric foundations underlying identification in triangular systems with discrete endogenous variables requires rank conditions that fail 73% of the time in practice. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
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.