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Let $\mathrm{Mat}_{n \times n}(\mathbb{C})$ be the affine space of $n \times n$ complex matrices with coordinate ring $\mathbb{C}[\mathbf{x}_{n \times n}]$. We define graded quotients of $\mathbb{C}[\mathbf{x}_{n \times n}]$ which carry an action of the symmetric group $\mathfrak{S}_n$ by simultaneous permutation of rows and columns. These quotient rings are obtained by applying the orbit harmonics method to matrix loci corresponding to all involutions in $\mathfrak{S}_n$ and the conjugacy classes of involutions in $\mathfrak{S}_n$ with a given number of fixed points. In the case of perfect matchings on $\{1, \dots, n\}$ with $n$ even, the Hilbert series of our quotient ring is related to Tracy-Widom distributions and its graded Frobenius image gives a refinement of the plethysm $s_{n/2}[s_2]$., Comment: 33 pages

The COVID-19 pandemic has significantly exacerbated existing educational disparities in Georgia's K-12 system, particularly in terms of racial and ethnic achievement gaps. Utilizing machine learning methods, the study conducts a comprehensive analysis of student achievement rates across different demographics, regions, and subjects. The findings highlight a significant decline in proficiency in English and Math during the pandemic, with a noticeable contraction in score distribution and a greater impact on economically disadvantaged and Black students. Socio-economic status, as represented by the Directly Certified Percentage -- the percentage of students eligible for free lunch, emerges as the most crucial factor, with additional insights drawn from faculty resources such as teacher salaries and expenditure on instruction. The study also identifies disparities in achievement rates between urban and rural settings, as well as variations across counties, underscoring the influence of geographical and socio-economic factors. The data suggests that targeted interventions and resource allocation, particularly in schools with higher percentages of economically disadvantaged students, are essential for mitigating educational disparities.

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.

Single-walled carbon nanotubes (SWCNTs) are promising absorbers and emitters to enable novel photonic and optoelectronic applications but are also known to severely suffer from low optical quantum yields. Here we demonstrate SWCNTs excitons coupled to plasmonic nanocavities reaching deeply into the Purcell regime with FP=234 (average FP=76), near unity quantum yields of 70% (average 41%), and a photon emission rate of 1.7 MHz into the first lens. The measured ultra-narrow exciton linewidth (18 micro eV) implies furthermore generation of indistinguishable single photons from a SWCNT. To demonstrate utility beyond quantum light sources we show that nanocavity-coupled SWCNTs perform as single-molecule thermometers detecting plasmonically induced heat (Delta T=150K) in a unique interplay of excitons, phonons, and plasmons at the nanoscale., Comment: 17 pages, 5 figures

This paper is concerned with a numerical simulation of shape optimization in a two-dimensional viscous incompressible flow governed by Navier--Stokes equations with mixed boundary conditions containing the pressure. The minimization problem of total dissipated energy was established in the fluid domain. We derive the structures of shape gradient of the cost functional by using the differentiability of a minimax formulation involving a Lagrange functional with a function space parametrization technique. Finally a gradient type algorithm is effectively used for our problem., Comment: 20 pages, 22 figures

This paper is concerned with the problem of shape optimization of two-dimensional flows governed by the time-dependent Navier-Stokes equations. We derive the structures of shape gradients with respect to the shape of the variable domain for time-dependent cost functionals by using the state derivative with respect to the shape of the fluid domain and its associated adjoint state. Finally we apply a gradient type algorithm to our problem and numerical examples show that our theory is useful for practical purpose and the proposed algorithm is feasible in low Reynolds number flow., Comment: 22 pages, 5 figures

This paper is concerned with the optimal shape design of the newtonian viscous incompressible fluids driven by the stationary nonhomogeneous Navier-Stokes equations. We use three approaches to derive the structures of shape gradients for some given cost functionals. The first one is to use the Piola transformation and derive the state derivative and its associated adjoint state; the second one is to use the differentiability of a minimax formulation involving a Lagrangian functional with a function space parametrization technique; the last one is to employ the differentiability of a minimax formulation with a function space embedding technique. Finally we apply a gradient type algorithm to our problem and numerical examples show that our theory is useful for practical purpose and the proposed algorithm is feasible., Comment: 29 pages,10 figures