Scalar Quarkonia at Finite Temperature

Masses and decay constants of the scalar quarkonia, $\chi_{Q0} (Q=b,c)$ with quantum numbers $I^G(J^{PC})=0^{+}(0^{++})$ are calculated in the framework of the QCD sum rules approach both in vacuum and finite temperature. The masses and decay constants remain unchanged up to $T\simeq100~MeV$ but they start to diminish with increasing the temperature after this point. At near the critic or deconfinement temperature, the decay constants reach approximately to 25% of their values in vacuum, while the masses are decreased about 6% and 23% for bottom and charm cases, respectively. The results at zero temperature are in a good consistency with the existing experimental values and predictions of the other nonperturbative approaches. Our predictions on the decay constants in vacuum as well as the behavior of the masses and decay constants with respect to the temperature can be checked in the future experiments.Comment: 12 Pages, 9 Figures and 2 Table

Appeal No. 0750: Paul A. Grim v. Division of Mineral Resources Management

Chief\u27s Order 2005-2

Analysis of Various Polarization Asymmetries In The Inclusive b→sℓ+ℓ−b\to s \ell^+ \ell^- Decay In The Fourth-Generation Standard Model

In this study a systematical analysis of various polarization asymmetries in inclusive b \rar s \ell^+ \ell^- decay in the standard model (SM) with four generation of quarks is carried out. We found that the various asymmetries are sensitive to the new mixing and quark masses for both of the $\mu$ and $\tau$ channels. Sizeable deviations from the SM values are obtained. Hence, b \rar s \ell^+ \ell^- decay is a valuable tool for searching physics beyond the SM, especially in the indirect searches for the fourth-generation of quarks ($t', b')$.Comment: 19 Pages, 10 Figures, 3 Table

Conditional Adversarial Camera Model Anonymization

The model of camera that was used to capture a particular photographic image (model attribution) is typically inferred from high-frequency model-specific artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as the process of transforming both high and low spatial frequency information. We augment the objective with the loss from a pre-trained dual-stream model attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.Comment: ECCV 2020 - Advances in Image Manipulation workshop (AIM 2020

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

Actionable Patient Safety Solutions (APSS) #6: Hand-off Communications

Hand-off communications, or hand-off processes, involve the transition of care as well as the transfer of patient-specific information by one healthcare professional to another with the purpose of providing a patient with safe, continuous care. A successful hand-off can only be achieved by effective communication

Protective Policy Index (PPI) global dataset of origins and stringency of COVID 19 mitigation policies

This the final version. Available on open access from Nature Research via the DOI in this recordData Records: We have created a Github repository (https://github.com/COVID-policy-response-lab/PPI-data) to store the datasets with the Public Health Protective Policy Index and its components. A copy of the included datafiles, as described below, was deposited with openICPSR15. It presently requires creating an account with the depository. Data access is free. Data location is at https://www.openicpsr.org/openicpsr/project/123401. The datasets are stored as csv files with five types of layouts. “PPI_country_m1.csv” is a file with country-level aggregates of region-level PPIs, computed using method 1, and their components. Each row corresponds to a country-date. The rows are identified using the country name (cname), numeric and 2-letter ISO 3166-1 codes (isocode and isoabbr respectively), as well as a date variable. The names of the policy variables contain four components: the name of the broader category, the name of the category, the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies), as well as suffix “ave”. For example, the average Total PPI is denoted as “ppi.all.tot.ave”, and the average stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat.ave”. See the codebook for the complete list of variables. “PPI_country_m2.csv” is a file with country-level aggregates of region-level PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_country_m1.csv”, with the addition of suffix “0.2” at the end of the policy variable names. “PPI_regions_XX_m1.csv” (replace XX with the 2-letter ISO 3166-1 country codes) are country-specific files with region-specific PPIs, computed using method 1, and their components. The identifying variables include the numeric and 2-letter ISO 3166-1 codes of the country (isocode and isoabbr respectively), the name of the region (state_province), its ISO 3166-2 code (iso_state), as well as a date variable. The names of the policy variables contain three components: the name of the broader category, the name of the category, and the level of issuing government (“nat” refers to the national policies, “reg” refers to the subnational policies, and “tot” refers to the combination of national and subnational policies). For example, the average Total PPI is denoted as “ppi.all.tot”, and the stringency of the closures of air borders by the national government is denoted as “borders.air_bord.nat”. “PPI_regions_XX_m2.csv” (replace XX with the 2-letter ISO 3166-1 country codes are country-specific files with region-specific PPIs, computed using method 2, and their components. The identifying variables and the naming convention for the policy variables is the same as in “PPI_regions_XX_m1.csv”, with the addition of the suffix “0.2” at the end of the policy variable names. “changes_regions_m1.csv” is an auxiliary file that describes the changes in the policy states, as recorded in the “PPI_regions_XX_m1.csv” files. Each row in this file corresponds to a change in a value of a policy state variable in a region and of a specific government level. The case identifying variables include the name of the country (cname), the numeric and 2-letter ISO 3166-1 code of the country (isocode and isoabbr, respectively), the name of the region (state_province) and its ISO 3166-2 code, date, policy dimension, and a marker of policies issued by a regional government (subnational). Among others, the attributes included in this file include the branch of the government (branch) and the date when the change was announced (report_date).Code availability; The code used to produce our calculations is available at https://github.com/COVID-policy-response-lab/PPI-dataWe have developed and made accessible for multidisciplinary audience a unique global dataset of the behavior of political actors during the COVID-19 pandemic as measured by their policy-making efforts to protect their publics. The dataset presents consistently coded cross-national data at subnational and national levels on the daily level of stringency of public health policies by level of government overall and within specific policy categories, and reports branches of government that adopted these policies. The data on these public mandates of protective behaviors is collected from media announcements and government publications. The dataset allows comparisons of governments’ policy efforts and timing across the world and can serve as a source of information on policy determinants of pandemic outcomes–both societal and possibly medical