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This whitepaper focuses on the astrophysical systematics which are encountered in dark matter searches. Oftentimes in indirect and also in direct dark matter searches, astrophysical systematics are a major limiting factor to sensitivity to dark matter. Just as there are many forms of dark matter searches, there are many forms of backgrounds. We attempt to cover the major systematics arising in dark matter searches using photons  radio and gamma rays  to cosmic rays, neutrinos and gravitational waves. Examples include astrophysical sources of cosmic messengers and their interactions which can mimic dark matter signatures. In turn, these depend on commensurate studies in understanding the cosmic environment  gas distributions, magnetic field configurations  as well as relevant nuclear astrophysics. We also cover the astrophysics governing celestial bodies and galaxies used to probe dark matter, from black holes to dwarf galaxies. Finally, we cover astrophysical backgrounds related to probing the dark matter distribution and kinematics, which impact a wide range of dark matter studies. In the future, the rise of multimessenger astronomy, and novel analysis methods to exploit it for dark matter, will offer various strategic ways to continue to enhance our understanding of astrophysical backgrounds to deliver improved sensitivitymore »Free, publiclyaccessible full text available March 13, 2023

Kainz, W. ; Manley, E. ; Delmelle, E. ; Birkin, M. ; Gahegan, M. ; Kwan, MP. (Ed.)As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID19 (SARSCoV2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID19 case numbers were very high for three Florida counties, MiamiDade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tricounty region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID19 positive case data. Mobility patterns declined in each county when new COVID19 infections began to rise, beginning in midJune 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county),as well as social distancing or travelrelated factors (e.g., staying at home behaviors) over the two time periods priormore »

This paper establishes Hoeffding’s lemma and inequality for bounded functions of generalstatespace and not necessarily reversible Markov chains. The sharpness of these results is characterized by the optimality of the ratio between variance prox ies in the Markovdependent and independent settings. The boundedness of functions is shown necessary for such results to hold in general. To showcase the usefulness of the new results, we apply them for nonasymptotic analyses of MCMC estima tion, respondentdriven sampling and highdimensional covariance matrix estimation on time series data with a Markovian nature. In addition to statistical problems, we also apply them to study the timediscounted rewards in econometric models and the multiarmed bandit problem with Markovian rewards arising from the field of machine learning.

Propensity score methods account for selection bias in observational studies. However, the consistency of the propensity score estimators strongly depends on a correct specification of the propensity score model. Logistic regression and, with increasing popularity, machine learning tools are used to estimate propensity scores. We introduce a stacked generalization ensemble learning approach to improve propensity score estimation by fitting a meta learner on the predictions of a suitable set of diverse base learners. We perform a comprehensive Monte Carlo simulation study, implementing a broad range of scenarios that mimic characteristics of typical data sets in educational studies. The population average treatment effect is estimated using the propensity score in Inverse Probability of Treatment Weighting. Our proposed stacked ensembles, especially using gradient boosting machines as a meta learner trained on a set of 12 base learner predictions, led to superior reduction of bias compared to the current stateoftheart in propensity score estimation. Further, our simulations imply that commonly used balance measures (averaged standardized absolute mean differences) might be misleading as propensity score model selection criteria. We apply our proposed model  which we call GBMStack  to assess the population average treatment effect of a Supplemental Instruction (SI) program in anmore »

Personal moisture management fabrics that facilitate sweat transport away from the skin are highly desirable for wearer’s comfort and performance. Here, we demonstrate a “skinlike” directional liquid transport fabric, which enables continuous oneway liquid flow through spatially distributed channels acting like “sweating glands” yet repels external liquid contaminants. The water transmission rate can be 15 times greater than that of best commercial breathable fabrics. This exceptional property is achieved by creating gradient wettability channels across a predominantly superhydrophobic substrate. The flow directionality is explained by the Gibbs pinning criterion. The permeability, mechanical property, and abrasion resistance (up to 10,000 cycles) of the fabric were not affected by the treatment. In addition to functional clothing, this concept can be extended for developing materials for oilwater separation, wound dressing, geotechnical engineering, flexible microfluidics, and fuel cell membranes.

How do children’s visual concepts change across childhood, and how might these changes be reflected in their drawings? Here we investigate developmental changes in children’s ability to emphasize the relevant visual distinctions between object categories in their drawings. We collected over 13K drawings from children aged 210 years via a freestanding drawing station in a children’s museum. We hypothesized that older children would produce more recognizable drawings, and that this gain in recognizability would not be entirely explained by concurrent development in visuomotor control. To measure recognizability, we applied a pretrained deep convolutional neural network model to extract a highlevel feature representation of all drawings, and then trained a multiway linear classifier on these features. To measure visuomotor control, we developed an automated procedure to measure their ability to accurately trace complex shapes. We found consistent gains in the recognizability of drawings across ages that were not fully explained by children’s ability to accurately trace complex shapes. Furthermore, these gains were accompanied by an increase in how distinct different object categories were in feature space. Overall, these results demonstrate that children’s drawings include more distinctive visual features as they grow older.

In statistics and machine learning, we are interested in the eigenvectors (or singular vectors) of certain matrices (e.g.\ covariance matrices, data matrices, etc). However, those matrices are usually perturbed by noises or statistical errors, either from random sampling or structural patterns. The DavisKahan $\sin \theta$ theorem is often used to bound the difference between the eigenvectors of a matrix $A$ and those of a perturbed matrix $\widetilde{A} = A + E$, in terms of $\ell_2$ norm. In this paper, we prove that when $A$ is a lowrank and incoherent matrix, the $\ell_{\infty}$ norm perturbation bound of singular vectors (or eigenvectors in the symmetric case) is smaller by a factor of $\sqrt{d_1}$ or $\sqrt{d_2}$ for left and right vectors, where $d_1$ and $d_2$ are the matrix dimensions. The power of this new perturbation result is shown in robust covariance estimation, particularly when random variables have heavy tails. There, we propose new robust covariance estimators and establish their asymptotic properties using the newly developed perturbation bound. Our theoretical results are verified through extensive numerical experiments.

Study of the doubly charmed tetraquark $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$Abstract Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quarkantiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D 0 D 0 π + mass spectrum just below the D *+ D 0 mass threshold produced in protonproton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalar $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + tetraquark with a quark content of $${{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}\overline{{{{{{\rm{u}}}}}}}\overline{{{{{{\rm{d}}}}}}}$$ c c u ¯ d ¯ and spinparity quantum numbers J P = 1 + . Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate offshell D *+ mesons is consistent with the observed D 0 π + mass distribution. To analyse the mass of the resonance and its coupling to the D * D system, a dedicated model is developed under the assumption of an isoscalar axialvector $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state decaying to the Dmore »Free, publiclyaccessible full text available December 1, 2023