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It is shown that a large class of events in a product probability space are highly sensitive to noise, in the sense that with high probability, the configuration with an arbitrary small percent of random errors gives almost no prediction…

Probability · Mathematics 2008-11-26 Itai Benjamini , Gil Kalai , Oded Schramm

In this paper, the optimal spectral efficiency (data rate divided by the message bandwidth) that minimizes the probability of causing disruptive interference for ad hoc wireless networks or cognitive radios is investigated. Two basic…

Information Theory · Computer Science 2015-03-18 Daniel W. Bliss , Siddhartan Govindasamy

Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. This can become particularly challenging when only partial and noisy observations of the signal are…

Machine Learning · Statistics 2019-01-15 Felipe Tobar

An upper bound on the capacity of a cascade of nonlinear and noisy channels is presented. The cascade mimics the split-step Fourier method for computing waveform propagation governed by the stochastic generalized nonlinear Schroedinger…

Information Theory · Computer Science 2015-04-24 Gerhard Kramer , Mansoor I. Yousefi , Frank R. Kschischang

The stochastic leverage effect, defined as the standardized covariation between the returns and their related volatility, is analyzed in a stochastic volatility model set-up. A novel estimator of the effect is defined using a pre-estimation…

Statistical Finance · Quantitative Finance 2021-03-09 Imma Valentina Curato , Simona Sanfelici

Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because -- among others -- signal and backgrounds are hard…

High Energy Physics - Phenomenology · Physics 2025-11-26 Ezequiel Alvarez , Manuel Szewc , Alejandro Szynkman , Santiago Tanco , Tatiana Tarutina

Being able to efficiently obtain an accurate estimate of the failure probability of SRAM components has become a central issue as model circuits shrink their scale to submicrometer with advanced technology nodes. In this work, we revisit…

Machine Learning · Computer Science 2023-08-01 Yanfang Liu , Guohao Dai , Wei W. Xing

I propose a superoscillation measurement method for subdiffraction incoherent optical sources, with potential applications in astronomy, remote sensing, fluorescence microscopy, and spectroscopy. The proposal, based on coherent optical…

Quantum Physics · Physics 2022-10-10 Mankei Tsang

The main result of this thesis is an efficient protocol to determine the frequencies of a signal $C(t)= \sum_k |a_k|^2 e^{i \omega_k t}$, which is given for a finite time, to a high degree of precision. Specifically, we develop a theorem…

Mathematical Physics · Physics 2024-12-12 Timothy Stroschein

Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality and energy conservation imply strict…

Optics · Physics 2022-11-23 Seunghwi Kim , Sergey Lepeshov , Alex Krasnok , Andrea Alù

The inherent non-linearity of intensity correlation functions can be used to spatially distinguish identical emitters beyond the diffraction limit, as achieved, for example, in Super-Resolution Optical Fluctuation Imaging (SOFI). Here, we…

Optics · Physics 2024-12-18 Yifan Chen , Chieh Tsao , Hendrik Utzat

A system obeying the harmonic oscillator equation of motion can be used as a force or proper acceleration sensor. In this short review we derive analytical expressions for the sensitivity of such sensors in a range of different situations,…

Classical Physics · Physics 2019-05-10 Gerard P. Conangla

Oversampled adaptive sensing (OAS) is a Bayesian framework recently proposed for effective sensing of structured signals in a time-limited setting. In contrast to the conventional blind oversampling, OAS uses the prior information on the…

Information Theory · Computer Science 2021-03-01 Ali Bereyhi , Saba Asaad , Ralf R. Müller

A wireless communication network is considered where any two nodes are connected if the signal-to-interference ratio (SIR) between them is greater than a threshold. We consider the the path-loss plus fading model of wireless signal…

Information Theory · Computer Science 2012-05-23 Rahul Vaze

We present a method of mode analysis to search for signals with frequency evolution and limited duration in a given data stream. Our method is a natural expansion of the Fourier analysis, and we can obtain the information about frequency…

General Relativity and Quantum Cosmology · Physics 2024-03-26 Fumihiko Ishiyama , Ryutaro Takahashi

We consider pulse propagation in a linear anomalously dispersive medium where the group velocity exceeds the speed of light in vacuum (c) or even becomes negative. A signal velocity is defined operationally based on the optical…

Optics · Physics 2009-11-07 A. Kuzmich , A. Dogariu , L. J. Wang , P. W. Milonni , R. Y. Chiao

The fast Fourier transform, FFT, is a useful and prevalent algorithm in signal processing. It characterizes the spectral components of a signal, or is used in combination with other operations to perform more complex computations such as…

Signal Processing · Electrical Eng. & Systems 2017-11-08 Hani Nejadriahi , David HillerKuss , Jonathan K. George , Volker J. Sorger

We utilize a method using frequency combs to construct waves that feature superoscillations - local regions of the wave that exhibit a change in phase that the bandlimits of the wave should not otherwise allow. This method has been shown to…

Fluctuations are important near phase transitions, where they can be difficult to describe quantitatively. Superconductivity in mesoscopic rings is particularly intriguing because the critical temperature is an oscillatory function of…

Superconductivity · Physics 2009-01-06 Nicholas C. Koshnick , Hendrik Bluhm , Martin E. Huber , Kathryn A. Moler

Understanding when and why interpolating methods generalize well has recently been a topic of interest in statistical learning theory. However, systematically connecting interpolating methods to achievable notions of optimality has only…

Machine Learning · Statistics 2021-10-22 Eduard Oravkin , Patrick Rebeschini
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