Assessment of the Quality of Public Data Reporting by Nonprofit Social Service Agencies Receiving Funding from the Lexington-Fayette Urban County Government in FY 2013

In 2012 and 2013, Lexington Fayette Urban County Government (LFUCG) Department of Social Service requested a “Social Service Needs Assessment Project” in Fayette County, Kentucky. In Aug, 31, 2012, Social Service Department established the “Needs Assessment Budget”. During Mar-Oct, 2013, two teams from The University of Kentucky College of Social Work (COSW) and the Martin School of Public Policy and Public Administration worked together to accomplish this project. One of the tasks of this project is to assess the quality of data reported by the social service partner agencies of Lexington. “Partner organization” is the name government officials give to nonprofits that receive funding assistance from the Urban County Council. This particular review concerned the partner agencies that received LFUCG funding in FY 2013. The author was asked to make recommendations regarding data consistency and accountability to help LFUCG officials decide on whether they should impose additional data reporting requirements on agencies seeking public funding allocations in future years. This project was part of a larger effort by LFUCG officials to improve their ability to allocate public resources to local nonprofit social service organizations in a more rigorous manner, rather than simply continuing appropriations that had been made in the past. There were two goals with this project. First, was to identify the best practices of data reporting for nonprofit organizations according to various relevant literatures. Second, was to analyze data made available by the twenty-one Lexington partner organizations in FY 2013. The research questions are: do the partner organizations follow best-practices in making data available to the public, and what is the quality of the data they do make available. In general, the partner agencies did well in reporting financial information and basic information about the organization. This is likely due to the fact that IRS requires such information to be reported annually on Form 990 in order for organizations to maintain their tax exempt status. However, these organizations are generally weak in reporting data on their effectiveness, impacts, and in providing helpful information on their websites

Giving What a User Needs: Constructing Reference Groups in Fitness Technologies

Mobile fitness technologies are designed to support exercise behavior. A distinguishing aspect of these technologies is their social component. Though research has examined the social support effects of this social component, less attention has been given to its social comparison effects. A fundamental aspect of social comparison is the referent groups on which the comparison is based. The paper examines the relative effects on exercise behavior of social comparisons based on referent groups constructed using demographic similarity, goal similarity, and social closeness. We will test our proposed design through a randomized field experiment on a mobile fitness application

Post-Racial African-American Writing in Percival Everett’s Erasure

As the voice that America now entered the postracial era came out, race was more frequently discussed in literature field. The word post-race symbolizes a shift of African-American writers’ consciousness of race and their own racial identity in their novels in the post-racial era. This paper applies Ramon Saldivar’s definition of postrace to analyze the novel Erasure by Percival Everett, the African-American writer of new generation, revealing the meaning of race for the contemporary African-American writers.

Deformations and cohomology theory of Ω\Omega-family Rota-Baxter algebras of arbitrary weight

In this paper, we firstly construct an $L_\infty[1]$-algebra via the method of higher derived brackets, whose Maurer-Cartan elements correspond to relative $\Omega$-family Rota-Baxter algebras structures of weight $\lambda$. For a relative $\Omega$-family Rota-Baxter algebra of weight $\lambda$, the corresponding twisted $L_{\infty}[1]$-algebra controls its deformations, which leads to the cohomology theory of relative $\Omega$-family Rota-Baxter algebras of weight $\lambda$. Moreover, we also obtain the corresponding results for absolute $\Omega$-family Rota-Baxter algebras of weight $\lambda$ from the relative version. At last, we study formal deformations of relative (resp. absolute) $\Omega$-family Rota-Baxter algebras of weight $\lambda$, which can be explained by the lower degree cohomology groups.Comment: 25 pages, all comments welcom

Efficient SGD Neural Network Training via Sublinear Activated Neuron Identification

Deep learning has been widely used in many fields, but the model training process usually consumes massive computational resources and time. Therefore, designing an efficient neural network training method with a provable convergence guarantee is a fundamental and important research question. In this paper, we present a static half-space report data structure that consists of a fully connected two-layer neural network for shifted ReLU activation to enable activated neuron identification in sublinear time via geometric search. We also prove that our algorithm can converge in $O(M^2/\epsilon^2)$ time with network size quadratic in the coefficient norm upper bound $M$ and error term $\epsilon$

Accelerating Frank-Wolfe Algorithm using Low-Dimensional and Adaptive Data Structures

In this paper, we study the problem of speeding up a type of optimization algorithms called Frank-Wolfe, a conditional gradient method. We develop and employ two novel inner product search data structures, improving the prior fastest algorithm in [Shrivastava, Song and Xu, NeurIPS 2021]. * The first data structure uses low-dimensional random projection to reduce the problem to a lower dimension, then uses efficient inner product data structure. It has preprocessing time $\tilde O(nd^{\omega-1}+dn^{1+o(1)})$ and per iteration cost $\tilde O(d+n^\rho)$ for small constant $\rho$. * The second data structure leverages the recent development in adaptive inner product search data structure that can output estimations to all inner products. It has preprocessing time $\tilde O(nd)$ and per iteration cost $\tilde O(d+n)$. The first algorithm improves the state-of-the-art (with preprocessing time $\tilde O(d^2n^{1+o(1)})$ and per iteration cost $\tilde O(dn^\rho)$) in all cases, while the second one provides an even faster preprocessing time and is suitable when the number of iterations is small

Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training

In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem~(\cite{prr89}): Given two sets $A \subset \{-1,+1\}^d$ and $B \subset \{-1,+1\}^d$ with $|A|=|B| = n$, if there are exact $k$ pairs whose inner product passes a certain threshold, i.e., $\{(a_1, b_1), \cdots, (a_k, b_k)\} \subset A \times B$ such that $\forall i \in [k], \langle a_i,b_i \rangle \geq \rho \cdot d$, for a threshold $\rho \in (0,1)$, the goal is to identify those $k$ heavy inner products. We provide an algorithm that runs in $O(n^{2 \omega / 3+ o(1)})$ time to find the $k$ inner product pairs that surpass $\rho \cdot d$ threshold with high probability, where $\omega$ is the current matrix multiplication exponent. By solving this problem, our method speed up the training of neural networks with ReLU activation function.Comment: IEEE BigData 202