Sommerfeld Enhancement of DM Annihilation: Resonance Structure, Freeze-Out and CMB Spectral Bound

In the last few years there has been some interest in WIMP Dark Matter models featuring a velocity dependent cross section through the Sommerfeld enhancement mechanism, which is a nonrelativistic effect due to massive bosons in the dark sector. In the first part of this article, we find analytic expressions for the boost factor for three different model potentials, the Coulomb potential, the spherical well and the spherical cone well and compare with the numerical solution of the Yukawa potential. We find that the resonance pattern of all the potentials can be cast into the same universal form. In the second part of the article we perform a detailed computation of the Dark Matter relic density for models having Sommerfeld enhancement by solving the Boltzmann equation numerically. We calculate the expected distortions of the CMB blackbody spectrum from WIMP annihilations and compare these to the bounds set by FIRAS. We conclude that only a small part of the parameter space can be ruled out by the FIRAS observations.Comment: 15 pages, 15 figures, version accepted by JCA

Fast and Efficient Malware Detection with Joint Static and Dynamic Features Through Transfer Learning

In malware detection, dynamic analysis extracts the runtime behavior of malware samples in a controlled environment and static analysis extracts features using reverse engineering tools. While the former faces the challenges of anti-virtualization and evasive behavior of malware samples, the latter faces the challenges of code obfuscation. To tackle these drawbacks, prior works proposed to develop detection models by aggregating dynamic and static features, thus leveraging the advantages of both approaches. However, simply concatenating dynamic and static features raises an issue of imbalanced contribution due to the heterogeneous dimensions of feature vectors to the performance of malware detection models. Yet, dynamic analysis is a time-consuming task and requires a secure environment, leading to detection delays and high costs for maintaining the analysis infrastructure. In this paper, we first introduce a method of constructing aggregated features via concatenating latent features learned through deep learning with equally-contributed dimensions. We then develop a knowledge distillation technique to transfer knowledge learned from aggregated features by a teacher model to a student model trained only on static features and use the trained student model for the detection of new malware samples. We carry out extensive experiments with a dataset of 86709 samples including both benign and malware samples. The experimental results show that the teacher model trained on aggregated features constructed by our method outperforms the state-of-the-art models with an improvement of up to 2.38% in detection accuracy. The distilled student model not only achieves high performance (97.81% in terms of accuracy) as that of the teacher model but also significantly reduces the detection time (from 70046.6 ms to 194.9 ms) without requiring dynamic analysis.Comment: Accepted for presentation and publication at the 21st International Conference on Applied Cryptography and Network Security (ACNS 2023

Structure-Function Relationships Affecting the Sensing Mechanism of Monolayer-Protected Cluster Doped Xerogel Amperometric Glucose Biosensors

A systematic study of the structure–function relationships critical to understanding the sensing mechanism of 1st generation amperometric glucose biosensors with an embedded nanoparticle (NP) network is presented. Xerogel-based films featuring embedded glucose oxidase enzyme and doped with alkanethiolate-protected gold NPs, known as monolayer protected clusters (MPCs), exhibit significantly enhanced performance compared to analogous systems without NPs including higher sensitivity, faster response time, and extended linear/dynamic ranges. The proposed mechanism involves diffusion of the glucose to glucose oxidase within the xerogel, enzymatic reaction production of H2O2 with subsequent diffusion to the embedded network of MPCs where it is oxidized, an event immediately reported via fast electron transfer (ET) through the MPC system to the working electrode. Various aspects of the film construct and strategy are systematically probed using amperometry, voltammetry, and solid-state electronic conductivity measurements, including the effects of MPC peripheral chain length, MPC functionalization via place-exchange reaction, MPC core size, and the MPC density or concentration within the xerogel composite films. The collective results of these experiments support the proposed mechanism and identify interparticle spacing and the electronic communication through the MPC network is the most significant factor in the sensing scheme with the diffusional aspects of the mechanism that may be affected by film/MPC hydrophobicity and functionality (i.e., glucose and H2O2 diffusion) shown to be less substantial contributors to the overall enhanced performance. Understanding the structure–function relationships of effective sensing schemes allows for the employment of the strategy for future biosensor design toward clinically relevant targets

Bayesian and frequentist investigation of prior effects in EFTofLSS analyses of full-shape BOSS and eBOSS data

Previous studies based on Bayesian methods have shown that the constraints on cosmological parameters from the Baryonic Oscillation Spectroscopic Survey (BOSS) full-shape data using the Effective Field Theory of Large Scale Structure (EFTofLSS) depend on the choice of prior on the EFT nuisance parameters. In this work, we explore this prior dependence by adopting a frequentist approach based on the profile likelihood method, which is inherently independent of priors, considering data from BOSS, eBOSS and Planck. We find that the priors on the EFT parameters in the Bayesian inference are informative and that prior volume effects are important. This is reflected in shifts of the posterior mean compared to the maximum likelihood estimate by up to 1.0 {\sigma} (1.6 {\sigma}) and in a widening of intervals informed from frequentist compared to Bayesian intervals by factors of up to 1.9 (1.6) for BOSS (eBOSS) in the baseline configuration, while the constraints from Planck are unchanged. Our frequentist confidence intervals give no indication of a tension between BOSS/eBOSS and Planck. However, we find that the profile likelihood prefers extreme values of the EFT parameters, highlighting the importance of combining Bayesian and frequentist approaches for a fully nuanced cosmological inference. We show that the improved statistical power of future data will reconcile the constraints from frequentist and Bayesian inference using the EFTofLSS.Comment: 20 pages, 8 figures, 6 table

Quantifying inequities in COVID-19 vaccine distribution over time by social vulnerability, race and ethnicity, and location: A population-level analysis in St. Louis and Kansas City, Missouri

BACKGROUND: Equity in vaccination coverage is a cornerstone for a successful public health response to COVID-19. To deepen understanding of the extent to which vaccination coverage compares with initial strategies for equitable vaccination, we explore primary vaccine series and booster rollout over time and by race/ethnicity, social vulnerability, and geography. METHODS AND FINDINGS: We analyzed data from the Missouri Department of Health and Senior Services on all COVID-19 vaccinations administered across 7 counties in the St. Louis region and 4 counties in the Kansas City region. We compared rates of receiving the primary COVID-19 vaccine series and boosters relative to time, race/ethnicity, zip-code-level Social Vulnerability Index (SVI), vaccine location type, and COVID-19 disease burden. We adapted a well-established tool for measuring inequity-the Lorenz curve-to quantify inequities in COVID-19 vaccination relative to these key metrics. Between 15 December 2020 and 15 February 2022, 1,763,036 individuals completed the primary series and 872,324 received a booster. During early phases of the primary series rollout, Black and Hispanic individuals from high SVI zip codes were vaccinated at less than half the rate of White individuals from low SVI zip codes, but rates increased over time until they were higher than rates in White individuals after June 2021; Asian individuals maintained high levels of vaccination throughout. Increasing vaccination rates in Black and Hispanic communities corresponded with periods when more vaccinations were offered at small community-based sites such as pharmacies rather than larger health systems and mass vaccination sites. Using Lorenz curves, zip codes in the quartile with the lowest rates of primary series completion accounted for 19.3%, 18.1%, 10.8%, and 8.8% of vaccinations while representing 25% of the total population, cases, deaths, or population-level SVI, respectively. When tracking Gini coefficients, these disparities were greatest earlier during rollout, but improvements were slow and modest and vaccine disparities remained across all metrics even after 1 year. Patterns of disparities for boosters were similar but often of much greater magnitude during rollout in fall 2021. Study limitations include inherent limitations in the vaccine registry dataset such as missing and misclassified race/ethnicity and zip code variables and potential changes in zip code population sizes since census enumeration. CONCLUSIONS: Inequities in the initial COVID-19 vaccination and booster rollout in 2 large US metropolitan areas were apparent across racial/ethnic communities, across levels of social vulnerability, over time, and across types of vaccination administration sites. Disparities in receipt of the primary vaccine series attenuated over time during a period in which sites of vaccination administration diversified, but were recapitulated during booster rollout. These findings highlight how public health strategies from the outset must directly target these deeply embedded structural and systemic determinants of disparities and track equity metrics over time to avoid perpetuating inequities in healthcare access

A systematic study of J/psi suppression in cold nuclear matter

Based on a Glauber model, a statistical analysis of all mid-rapidity J/psi hadroproduction and leptoproduction data on nuclear targets is carried out. This allows us to determine the J/psi-nucleon inelastic cross section, whose knowledge is crucial to interpret the J/psi suppression observed in heavy-ion collisions, at SPS and at RHIC. The values of sigma are extracted from each experiment. A clear tension between the different data sets is reported. The global fit of all data gives sigma=3.4+/-0.2 mb, which is significantly smaller than previous estimates. A similar value, sigma=3.5+/-0.2 mb, is obtained when the nDS nuclear parton densities are included in the analysis, although we emphasize that the present uncertainties on gluon (anti)shadowing do not allow for a precise determination of sigma. Finally, no significant energy dependence of the J/psi-N interaction is observed, unless strong nuclear modifications of the parton densities are assumed.Comment: 25 pages, 5 figure

Studies of di-jet survival and surface emission bias in Au+Au collisions via angular correlations with respect to back-to-back leading hadrons

We report first results from an analysis based on a new multi-hadron correlation technique, exploring jet-medium interactions and di-jet surface emission bias at RHIC. Pairs of back-to-back high transverse momentum hadrons are used for triggers to study associated hadron distributions. In contrast with two- and three-particle correlations with a single trigger with similar kinematic selections, the associated hadron distribution of both trigger sides reveals no modification in either relative pseudo-rapidity or relative azimuthal angle from d+Au to central Au+Au collisions. We determine associated hadron yields and spectra as well as production rates for such correlated back-to-back triggers to gain additional insights on medium properties.Comment: By the STAR Collaboration. 6 pages, 2 figure