43,810 research outputs found
A Q zheng zhuan.
No full text'阿Q正傳'電影文學劇本 /許炎 /徐遲 ---p.1'阿Q正傳'原著小說 /魯迅 ---p.40'阿Q正傳'從小說到電影 /袁仰安 ---p.72寫在'阿Q正傳'後面 /許炎 ---p.78槍斃阿Q精神 /雲彩 ---p.80名著的改編 /林歡 ---p.83我演阿Q /關山 ---p.85[魯迅原著] 許炎, 徐遲改編.Play.[Lu Xun yuan zhu] Xu Yan, Xu Chi gai bian
q-Differential equations for q-classical polynomials and q-Jacobi-Stirling numbers
No full textWe introduce, characterise and provide a combinatorial interpretation for the so-called q-Jacobi–Stirling numbers.
This study is motivated by their key role in the (reciprocal) expansion of any power of a second order
q-differential operator having the q-classical polynomials as eigenfunctions in terms of other even order operators,
which we explicitly construct in this work. The results here obtained can be viewed as the q-version of
those given by Everitt et al. and by the first author, whilst the combinatorics of this new set of numbers is a
q-version of the Jacobi–Stirling numbers given by Gelineau and the second author
Bayesian methods for non-standard missing data problems
No full textMissing data presents challenges to statistical analysis in many applications such as clinical trials, cluster detection, etc. This thesis analyzes and develops methodologies in some non-standard missing data problems. We first consider non-ignorable drop-out in longitudinal clinical trials. Common simple approaches such as complete case analysis or last observation carried forward can lead to biased estimates and underestimation of uncertainty. We pursue a model-based approach in the context of Bayesian framework to provide more useful inferences. Second, non-compliance is another way to deviate from pre-designed protocols. Traditional methods circumvent the issue with simplifying assumptions such as intention to treat. Consequently they might produce misleading results. We adopt a counter-factual approach, known as the Rubin Causal Model, essentially reducing the analysis to a missing data problem. We address the issue in particular when drop-out is also involved. In relation to the first two research topics to provide better and more accurate assessment of a treatment or procedure, we develop a Bayesian sequential meta-analysis framework to aggregate results from all available studies. We conduct a case study and build a risk profile of a treatment to provide early alert of emerging problems. Last, the question whether a spatial pattern is randomly distributed has been of interest in many applications. We extend and generalize a latent model approach to overlapping cluster detection. We employ this methodology to design an urban mobile sensor network for the surveillance of nuclear materials. With simulation studies, we demonstrate that the method is efficient and powerful in detection of overlapping clusters.Ph.D.Includes abstractVitaIncludes bibliographical referencesby Jerry Q. Chen
On Semialgebraic Range Reporting
No full textSemialgebraic range searching, arguably the most general version of range searching, is a fundamental problem in computational geometry. In the problem, we are to preprocess a set of points in ℝ^D such that the subset of points inside a semialgebraic region described by a constant number of polynomial inequalities of degree Δ can be found efficiently.
Relatively recently, several major advances were made on this problem. Using algebraic techniques, "near-linear space" data structures [Agarwal et al., 2013; Matoušek and Patáková, 2015] with almost optimal query time of Q(n) = O(n^{1-1/D+o(1)}) were obtained. For "fast query" data structures (i.e., when Q(n) = n^{o(1)}), it was conjectured that a similar improvement is possible, i.e., it is possible to achieve space S(n) = O(n^{D+o(1)}). The conjecture was refuted very recently by Afshani and Cheng [Afshani and Cheng, 2021]. In the plane, i.e., D = 2, they proved that S(n) = Ω(n^{Δ+1 - o(1)}/Q(n)^{(Δ+3)Δ/2}) which shows Ω(n^{Δ+1-o(1)}) space is needed for Q(n) = n^{o(1)}. While this refutes the conjecture, it still leaves a number of unresolved issues: the lower bound only works in 2D and for fast queries, and neither the exponent of n or Q(n) seem to be tight even for D = 2, as the best known upper bounds have S(n) = O(n^{m+o(1)}/Q(n)^{(m-1)D/(D-1)}) where m = binom(D+Δ,D)-1 = Ω(Δ^D) is the maximum number of parameters to define a monic degree-Δ D-variate polynomial, for any constant dimension D and degree Δ.
In this paper, we resolve two of the issues: we prove a lower bound in D-dimensions, for constant D, and show that when the query time is n^{o(1)}+O(k), the space usage is Ω(n^{m-o(1)}), which almost matches the Õ(n^{m}) upper bound and essentially closes the problem for the fast-query case, as far as the exponent of n is considered in the pointer machine model. When considering the exponent of Q(n), we show that the analysis in [Afshani and Cheng, 2021] is tight for D = 2, by presenting matching upper bounds for uniform random point sets. This shows either the existing upper bounds can be improved or to obtain better lower bounds a new fundamentally different input set needs to be constructed
Recommended from our members
Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Tobin's Q and Financial Policy
No full textRecent research in macroeconomics has emphasized the importance of linking the financial and real sectors and the need for working with optimizing models. Tobin’s Q model of investment would appear to provide a framework that can satisfy these two criteria. In contrast to the original presentation of the Q model, the formal development has not recognized that the firm actively participates in a number of financial markets; in this broader context, we show that Q is likely to be an uninformative and possibly misleading signal for investment expenditures . We then endeavor to turn this negative theoretical result to positive advantage in resolving a number of empirical problems with Q models, but the modifications dictated by the theory receive little support from the data.
Lower Bounds for Intersection Reporting Among Flat Objects
No full textRecently, Ezra and Sharir [Esther Ezra and Micha Sharir, 2022] showed an O(n^{3/2+σ}) space and O(n^{1/2+σ}) query time data structure for ray shooting among triangles in ℝ³. This improves the upper bound given by the classical S(n)Q(n)⁴ = O(n^{4+σ}) space-time tradeoff for the first time in almost 25 years and in fact lies on the tradeoff curve of S(n)Q(n)³ = O(n^{3+σ}). However, it seems difficult to apply their techniques beyond this specific space and time combination. This pheonomenon appears persistently in almost all recent advances of flat object intersection searching, e.g., line-tetrahedron intersection in ℝ⁴ [Esther Ezra and Micha Sharir, 2022], triangle-triangle intersection in ℝ⁴ [Esther Ezra and Micha Sharir, 2022], or even among flat semialgebraic objects [Agarwal et al., 2022].
We give a timely explanation to this phenomenon from a lower bound perspective. We prove that given a set of (d-1)-dimensional simplicies in ℝ^d, any data structure that can report all intersections with a query line in small (n^o(1)) query time must use Ω(n^{2(d-1)-o(1)}) space. This dashes the hope of any significant improvement to the tradeoff curves for small query time and almost matches the classical upper bound. We also obtain an almost matching space lower bound of Ω(n^{6-o(1)}) for triangle-triangle intersection reporting in ℝ⁴ when the query time is small. Along the way, we further develop the previous lower bound techniques by Afshani and Cheng [Afshani and Cheng, 2021; Afshani and Cheng, 2022]
A novel model for porous scaffolds to match the mechanical anisotropy and the hierarchical structure of bone
No full textRelative Factor Price Changes and Equity Prices
No full textThis paper suggests that the decline in equity prices, and thus in Tobin's average q, during the 1970s may be attributable to changes in expected relative factor prices. More specifically, q is shown to be a negative function of the extent to which current relative factor price expectations differ from those when capital was put in place. Because relative factor prices became more volatile after 1967, the observed decline in average q, and thus in stock prices, can be explained by the "relative price" hypothesis.
- …
