Two Mode Quantum Systems: Invariant Classification of Squeezing Transformations and Squeezed States

A general analysis of squeezing transformations for two mode systems is given based on the four dimensional real symplectic group Sp(4,\Re)\/. Within the framework of the unitary metaplectic representation of this group, a distinction between compact photon number conserving and noncompact photon number nonconserving squeezing transformations is made. We exploit the Sp(4,\Re)-SO(3,2)\/ local isomorphism and the U(2)\/ invariant squeezing criterion to divide the set of all squeezing transformations into a two parameter family of distinct equivalence classes with representative elements chosen for each class. Familiar two mode squeezing transformations in the literature are recognized in our framework and seen to form a set of measure zero. Examples of squeezed coherent and thermal states are worked out. The need to extend the heterodyne detection scheme to encompass all of U(2)\/ is emphasized, and known experimental situations where all U(2)\/ elements can be reproduced are briefly described.Comment: Revtex 37 pages, Latex figures include

Metamaterials Application in Sensing

Metamaterials are artificial media structured on a size scale smaller than wavelength of external stimuli, and they can exhibit a strong localization and enhancement of fields, which may provide novel tools to significantly enhance the sensitivity and resolution of sensors, and open new degrees of freedom in sensing design aspect. This paper mainly presents the recent progress concerning metamaterials-based sensing, and detailedly reviews the principle, detecting process and sensitivity of three distinct types of sensors based on metamaterials, as well as their challenges and prospects. Moreover, the design guidelines for each sensor and its performance are compared and summarized

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Experiences with PC-Based Real-Time Multimedia Collaboration Over IP

Intel recently completed a trial of standards-based realtime multimedia collaboration tools running on laptop computers over an Internet Protocol (IP) network. Key goals for the trial included validation of usage models and user benefits while utilizing the multimedia collaboration tools in a production work environment. Trial participants were equipped with a multimedia "softphone" application, a headset, and a Webcam that enabled them to establish high-quality small-group (multiparty) voice and video calls. Call setup was performed with the standard Session Initiation Protocol (SIP) and open-source products. These components provided a cost-effective, easy-to-setup and use collaboration environment, where all communications and collaboration were integrated into one device, the PC. This capability provided increased productivity because all information that people needed to do their jobs was literally at their finger tips. The tria

Helvetica

A documentary about a typeface? When said typeface is a ubiquitous piece of graphic design, yes. Helvetica--a sans-serif typeface developed in 1957 at the Haas Foundry in Munchenstein, Switzerland--has partisans and detractors, a great number of them graphic designers and theorists, who express their opinions on the famous font. It is seen as neutral and efficient, concise yet inexpressive, purposeful yet not caustic, utilitarian and unembellished, or as frustratingly familiar, perfectly subliminal, or as the typeface of socialism. From storefronts, street signs, product packaging, government forms, and advertisements, it is almost guaranteed that after viewing, you will be scanning the world examining Helvetica's continuing impact