Untethered Desense Testing of Radio-Frequency Devices

An electronic device, e.g., laptop, etc., in radio-frequency communication with another electronic device can lose receiver sensitivity when certain components turn on. This is because electrical switching activity in the components can result in electromagnetic interference. Traditional methods of measuring receiver desensitization tether the device to a computer that carries out test sequences and logs measurements. However, the presence of the tethering cable itself causes additional electromagnetic interference. This disclosure describes techniques that enable untethered measurement of receiver desensitization. Per the techniques, an RF tester measures the packet error rate (PER) based on over-the-air acknowledgements received from the device under test. Receiver sensitivity, with and without active components, is measured by reducing transmit power until the PER just crosses a threshold. The device is characterized for receive-desensitization accurately and in a nearly real-use situation

Explicit upper bounds for the number of primes simultaneously representable by any set of irreducible polynomials

Using an explicit version of Selberg's upper sieve, we obtain explicit upper bounds for the number of $n\leq x$ such that a non-empty set of irreducible polynomials $F_i(n)$ with integer coefficients are simultaneously prime; this set can contain as many polynomials as desired. To demonstrate, we present computations for some irreducible polynomials and obtain an explicit upper bound for the number of Sophie Germain primes up to $x$, which have practical applications in cryptography.Comment: 18 pages, one table, comments welcome

Explicit Interval Estimates for Prime Numbers

Using a smoothing function and recent knowledge on the zeros of the Riemann zeta-function, we compute pairs of $(\Delta, x_0)$ such that for all $x \geq x_0$ there exists at least one prime in the interval $(x(1 - \Delta^{-1}), x]$.Comment: 15 pages, 3 tables, 1 figur

Feature Selection in the Contrastive Analysis Setting

Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of variation that are irrelevant to a given task. For example, a biomedical data analyst may wish to find a small set of genes to use as a proxy for variations in genomic data only present among patients with a given disease (target) as opposed to healthy control subjects (background). However, as of yet the problem of feature selection in the CA setting has received little attention from the machine learning community. In this work we present contrastive feature selection (CFS), a method for performing feature selection in the CA setting. We motivate our approach with a novel information-theoretic analysis of representation learning in the CA setting, and we empirically validate CFS on a semi-synthetic dataset and four real-world biomedical datasets. We find that our method consistently outperforms previously proposed state-of-the-art supervised and fully unsupervised feature selection methods not designed for the CA setting. An open-source implementation of our method is available at https://github.com/suinleelab/CFS.Comment: NeurIPS 202