Generalized squeezing operators, bipartite Wigner functions and entanglement via Wehrl's entropy functionals

We introduce a new class of unitary transformations based on the su(1,1) Lie algebra that generalizes, for certain particular representations of its generators, well-known squeezing transformations in quantum optics. To illustrate our results, we focus on the two-mode bosonic representation and show how the parametric amplifier model can be modified in order to generate such a generalized squeezing operator. Furthermore, we obtain a general expression for the bipartite Wigner function which allows us to identify two distinct sources of entanglement, here labelled by dynamical and kinematical entanglement. We also establish a quantitative estimate of entanglement for bipartite systems through some basic definitions of entropy functionals in continuous phase-space representations.Comment: 16 page

Author Correction: Scalable and robust SARS-CoV-2 testing in an academic center.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Wi-Fi Sensing for Human Identification Through ESP32 Devices: An Experimental Study

Recent studies explore the possibility of detecting events in a room via Wi-Fi Sensing. This practice exploits the interaction between waves carrying Wi-Fi signals and the elements present in an environment. These interactions are called Channel State Information (CSI) and can be analyzed and exploited to infer information about the environment, such as 'device-free' Human Activity Recognition, Human Identification, and more. Considering identification, we recently saw an increasing trend in the usage of low-end devices such as ESP32. Being small and low-power, they are cheap and versatile, however, the quality of the collected data is inferior. In this work, we use state-of-the-art tools to perform Human Identification using the ESP32. Software is created to act as an interface between the collected data and the algorithms suitable for Wi-Fi Sensing. To evaluate the final design, we performed a data collection in a controlled environment. The experiments show an accuracy of 95% in distinguishing two users while 74% in distinzuishing three

Modeling T-cell activation using gene expression profiling and state-space models

Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a wellestablished model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics.These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Results: Bootstrap confidence intervals are developed for parameters representing ‘gene–gene ’ interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. Availability: Supplementary data and Matlab computer source code will be made available on the web at the URL given below

Brachiterapia coronarica per la ristenosi intrastent a carico di biforcazioni: follow up angiografico a 9 mesi.

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