DeepXS: Fast approximation of MSSM electroweak cross sections at NLO

We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for $pp\to\tilde{\chi}^+_1\tilde{\chi}^-_1,$ $\tilde{\chi}^0_2\tilde{\chi}^0_2$ and $\tilde{\chi}^0_2\tilde{\chi}^\pm_1$ as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below $0.5\,\%$ allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of $\mathcal{O}(10^7)$ from $\mathcal{O}(\rm{min})$ to $\mathcal{O}(\mu\rm{s})$ per evaluation.Comment: 7 pages, 3 figure

Identifying WIMP dark matter from particle and astroparticle data

One of the most promising strategies to identify the nature of dark matter consists in the search for new particles at accelerators and with so-called direct detection experiments. Working within the framework of simplified models, and making use of machine learning tools to speed up statistical inference, we address the question of what we can learn about dark matter from a detection at the LHC and a forthcoming direct detection experiment. We show that with a combination of accelerator and direct detection data, it is possible to identify newly discovered particles as dark matter, by reconstructing their relic density assuming they are weakly interacting massive particles (WIMPs) thermally produced in the early Universe, and demonstrating that it is consistent with the measured dark matter abundance. An inconsistency between these two quantities would instead point either towards additional physics in the dark sector, or towards a non-standard cosmology, with a thermal history substantially different from that of the standard cosmological model.Comment: 24 pages (+21 pages of appendices and references) and 14 figures. v2: Updated to match JCAP version; includes minor clarifications in text and updated reference

Malware Infections in the U.S. during the COVID-19 Pandemic: An Empirical Study

The COVID-19 pandemic has changed the world in many ways, especially in the landscape of cyber threats. The pandemic has pro-vided cybercriminals with more opportunities to commit crimes due to more people engaging in online activities, along with the increased use of computers for school, work, and social events. The current study seeks to explore cybercrime trends, in particular malware infections, during the COVID-19 pandemic. Thus, this study examines the relationship between the number of malware in-fections, COVID-19 positive cases, closed non-essential businesses, and closed K-12 public schools in the United States. Data utilized in this study derives from (1) Kaspersky Cyberthreat Real-Time Map, (2) Centers for Disease Control and Prevention (CDC), and (3) COVID-19 US State Policy Database over the course of six months from January of 2020 to June of 2020. The findings of this study reveal that there are associations between the number of malware infections, COVID-19 positive cases, and closed non-essential busi-nesses. However, interestingly, there is no link between the number of malware infections and closed K-12 public schools. Policy impli-cations and the limitations of this study are also discussed

Implementing Behavior Analysis and Intervention for Individuals with Cognitive Impairments in Skilled Nursing Facilities: Summary of Results

Summary of Purpose The purpose of the project was to provide behavioral consultation and services to aging persons with cognitive impairment at skilled nursing facilities in Michigan. The goal was to use empirically supported non-pharmacological approaches to reduce behavioral and psychological symptoms of dementia (BPSD; wandering, agitation, disruptive vocalizations, etc.) and help slow down or remediate lost skills, reduce the use of medication to manage BPSD, to improve staff knowledge and abilities, and to develop modules that can be adopted and used by other skilled nursing facilities.The project was led by Dr. Janet Hahn, a social gerontologist with extensive experience studying nursing home culture change and the quality of long-term care services. The intervention project team consisted of doctoral, masters and undergraduate level behavior analysts with advanced training in working with aging populations, under the direction of Dr. Jonathan Baker (doctoral level board certified behavior analyst and behavioral gerontologist). The project was funded by the Civil Money Penalties fund of the Michigan Department of Health and Human Services from May 2016 and to April 2019. The project was conducted with oversight by the Western Michigan University Human Subjects Institutional Review Board, under approved protocol HSIRB Project Number 16-09-07, titled Implementing Behavior Analysis and Intervention for Individuals with Cognitive Impairment in Skilled Nursing Facilities

Grounding the Unreal

The scientific successes of the last 400 years strongly suggest a picture on which our scientific theories exhibit a layered structure of dependence and determination. Economics is dependent on and determined by psychology; psychology in its turn is, plausibly, dependent on and determined by biology; and so it goes. It is tempting to explain this layered structure of dependence and determination among our theories by appeal to a corresponding layered structure of dependence and determination among the entities putatively treated by those theories. In this paper, I argue that we can resist this temptation: we can explain the sense in which, e.g., the biological truths are dependent on and determined by chemical truths without appealing to properly biological or chemical entities. This opens the door to a view on which, though there are more truths than just the purely physical truths, there are no entities, states, or properties other than the purely physical entities, states, and properties. I argue that some familiar strategies to explicate the idea of a layered structure of theories by appeal to reduction, ground, and truthmaking encounter difficulties. I then show how these difficulties point the way to a more satisfactory treatment which appeals to something very close to the notion of ground. Finally, I show how this treatment provides a theoretical setting in which we might fruitfully frame debates about which entities there really are

Identification of a Novel Plasmid-Borne Gentamicin Resistance Gene in Nontyphoidal Salmonella Isolated from Retail Turkey

The spread of antibiotic-resistant bacteria presents a global health challenge. Efficient surveillance of bacteria harboring antibiotic resistance genes (ARGs) is a critical aspect to controlling the spread. Increased access to microbial genomic data from many diverse populations informs this surveillance but only when functional ARGs are identifiable within the data set. Current, homology-based approaches are effective at identifying the majority of ARGs within given clinical and nonclinical data sets for several pathogens, yet there are still some whose identities remain elusive. By coupling phenotypic profiling with genotypic data, these unknown ARGs can be identified to strengthen homology-based searches. To prove the efficacy and feasibility of this approach, a published data set from the U.S. National Antimicrobial Resistance Monitoring System (NARMS), for which the phenotypic and genotypic data of 640 Salmonella isolates are available, was subjected to this analysis. Six isolates recovered from the NARMS retail meat program between 2011 and 2013 were identified previously as phenotypically resistant to gentamicin but contained no known gentamicin resistance gene. Using the phenotypic and genotypic data, a comparative genomics approach was employed to identify the gene responsible for the observed resistance in all six of the isolates. This gene, grdA, is harbored on a 9,016-bp plasmid that is transferrable to Escherichia coli, confers gentamicin resistance to E. coli, and has never before been reported to confer gentamicin resistance. Bioinformatic analysis of the encoded protein suggests an ATP binding motif. This work demonstrates the advantages associated with coupling genomics technologies with phenotypic data for novel ARG identification

Angular Momentum Loss from Cool Stars: An Empirical Expression and Connection to Stellar Activity

We show here that the rotation period data in open clusters allow the empirical determination of an expression for the rate of loss of angular momentum from cool stars on the main sequence. One significant component of the expression, the dependence on rotation rate, persists from prior work; others do not. The expression has a bifurcation, as before, that corresponds to an observed bifurcation in the rotation periods of coeval open cluster stars. The dual dependencies of this loss rate on stellar mass are captured by two functions, $f(B-V)$ and $T(B-V)$, that can be determined from the rotation period observations. Equivalent masses and other [UBVRIJHK] colors are provided in Table 1. Dimensional considerations, and a comparison with appropriate calculated quantities suggest interpretations for $f$ and $T$, both of which appear to be related closely (but differently) to the calculated convective turnover timescale, $\tau_c$, in cool stars. This identification enables us to write down symmetrical expressions for the angular momentum loss rate and the deceleration of cool stars, and also to revive the convective turnover timescale as a vital connection between stellar rotation and stellar activity physics.Comment: 20 pages, 9 color figures; this version includes corrections listed in the associated journal erratu

Biodegradable electroactive polymers for electrochemically-triggered drug delivery

We report biodegradable electroactive polymer (EAP)-based materials and their application as drug delivery devices. Copolymers composed of oligoaniline-based electroactive blocks linked to either polyethylene glycol or polycaprolactone blocks via ester bonds were synthesized in three steps from commercially available starting materials and isolated without the need for column chromatography. The physicochemical and electrochemical properties of the polymers were characterized with a variety of techniques. The ability of the polymers to deliver the anti-inflammatory drug dexamethasone phosphate on the application of electrochemical stimuli was studied spectroscopically. Films of the polymers were shown to be degradable and cell adhesive in vitro. Such EAP-based materials have prospects for integration in implantable fully biodegradable/bioerodible EAP-based drug delivery devices that are capable of controlling the chronopharmacology of drugs for future clinical application