1,394 research outputs found
Minimax Estimation of Nonregular Parameters and Discontinuity in Minimax Risk
When a parameter of interest is nondifferentiable in the probability, the
existing theory of semiparametric efficient estimation is not applicable, as it
does not have an influence function. Song (2014) recently developed a local
asymptotic minimax estimation theory for a parameter that is a
nondifferentiable transform of a regular parameter, where the nondifferentiable
transform is a composite map of a continuous piecewise linear map with a single
kink point and a translation-scale equivariant map. The contribution of this
paper is two fold. First, this paper extends the local asymptotic minimax
theory to nondifferentiable transforms that are a composite map of a Lipschitz
continuous map having a finite set of nondifferentiability points and a
translation-scale equivariant map. Second, this paper investigates the
discontinuity of the local asymptotic minimax risk in the true probability and
shows that the proposed estimator remains to be optimal even when the risk is
locally robustified not only over the scores at the true probability, but also
over the true probability itself. However, the local robustification does not
resolve the issue of discontinuity in the local asymptotic minimax risk
Adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using Reinforcement Learning
Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be re-allocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that maintain a balanced LOE among the CSOC locations. Furthermore, the scalability experiments highlight the practicality of adapting the method to a large number of CSOC locations
Derived categories of Burniat surfaces and exceptional collections
We construct an exceptional collection of maximal possible length
6 on any of the Burniat surfaces with , a 4-dimensional family of
surfaces of general type with . We also calculate the DG algebra of
endomorphisms of this collection and show that the subcategory generated by
this collection is the same for all Burniat surfaces.
The semiorthogonal complement of is an "almost
phantom" category: it has trivial Hochschild homology, and K_0(\mathcal
A)=\bZ_2^6.Comment: 15 pages, 1 figure; further remarks expande
Topological Modes in One Dimensional Solids and Photonic Crystals
This is the final version of the article. Available from American Physical Society via the DOI in this record.It is shown theoretically that a one-dimensional crystal with time reversal symmetry is characterized by a Z_{2} topological invariant that predicts the existence or otherwise of edge states. This is confirmed experimentally through the construction and simulation of a photonic crystal analogue in the microwave regime.We thank an anonymous referee for helpful comments. The authors acknowledge financial support from the EPSRC through the QUEST program grant (Grant No. EP/I034548/1), C.A.M.B. was supported by QinetiQ and the EPSRC through the Industrial CASE scheme (Grant No. 08000346). H.M. is supported by a grant from the U. S. Department of Energy to the Particle Astrophysics Theory group at CWRU. T.J.A. is funded by the Research Corporation for Science Advancement through a Cottrell Award
Inconsistency of the MLE for the joint distribution of interval censored survival times and continuous marks
This paper considers the nonparametric maximum likelihood estimator (MLE) for
the joint distribution function of an interval censored survival time and a
continuous mark variable. We provide a new explicit formula for the MLE in this
problem. We use this formula and the mark specific cumulative hazard function
of Huang and Louis (1998) to obtain the almost sure limit of the MLE. This
result leads to necessary and sufficient conditions for consistency of the MLE
which imply that the MLE is inconsistent in general. We show that the
inconsistency can be repaired by discretizing the marks. Our theoretical
results are supported by simulations.Comment: 27 pages, 4 figure
Accelerating growth of HFC-227ea (1,1,1,2,3,3,3-heptafluoropropane) in the atmosphere
We report the first measurements of 1,1,1,2,3,3,3-heptafluoropropane (HFC-227ea), a substitute for ozone depleting compounds, in air samples originating from remote regions of the atmosphere and present evidence for its accelerating growth. Observed mixing ratios ranged from below 0.01 ppt in deep firn air to 0.59 ppt in the current northern mid-latitudinal upper troposphere. Firn air samples collected in Greenland were used to reconstruct a history of atmospheric abundance. Year-on-year increases were deduced, with acceleration in the growth rate from 0.029 ppt per year in 2000 to 0.056 ppt per year in 2007. Upper tropospheric air samples provide evidence for a continuing growth until late 2009. Furthermore we calculated a stratospheric lifetime of 370 years from measurements of air samples collected on board high altitude aircraft and balloons. Emission estimates were determined from the reconstructed atmospheric trend and suggest that current "bottom-up" estimates of global emissions for 2005 are too high by a factor of three
Bayesian estimation of one-parameter qubit gates
We address estimation of one-parameter unitary gates for qubit systems and
seek for optimal probes and measurements. Single- and two-qubit probes are
analyzed in details focusing on precision and stability of the estimation
procedure. Bayesian inference is employed and compared with the ultimate
quantum limits to precision, taking into account the biased nature of Bayes
estimator in the non asymptotic regime. Besides, through the evaluation of the
asymptotic a posteriori distribution for the gate parameter and the comparison
with the results of Monte Carlo simulated experiments, we show that asymptotic
optimality of Bayes estimator is actually achieved after a limited number of
runs. The robustness of the estimation procedure against fluctuations of the
measurement settings is investigated and the use of entanglement to improve the
overall stability of the estimation scheme is also analyzed in some details.Comment: 10 pages, 5 figure
Investigating child participation in the everyday talk of a teacher and children in a preparatory year
In early years research, policy and education, a democratic perspective that positions children as participants and citizens is increasingly emphasized. These ideas take seriously listening to childrenâs opinions and respecting childrenâs influence over their everyday affairs. While much political and social investment has been paid to the inclusion of participatory approaches little has been reported on the practical achievement of such an approach in the day to day of early childhood education within school settings. This paper investigates talk and interaction in the everyday activities of a teacher and children in an Australian preparatory class (for children age 4-6 years) to see how ideas of child participation are experienced. We use an interactional analytic approach to demonstrate how participatory methods are employed in practical ways to manage routine interactions. Analysis shows that whilst the teacher seeks the childrenâs opinion and involves them in decision-making, child participation is at times constrained by the context and institutional categories of âteacherâ and âstudentâ that are jointly produced in their talk. The paper highlights tensions that arise for teachers as they balance a pedagogical intent of âteachingâ and the associated institutional expectations, with efforts to engage children in decision-making. Recommendations include adopting a variety of conversational styles when engaging with children; consideration of temporal concerns and the need to acknowledge the culture of the school
Towards Machine Wald
The past century has seen a steady increase in the need of estimating and
predicting complex systems and making (possibly critical) decisions with
limited information. Although computers have made possible the numerical
evaluation of sophisticated statistical models, these models are still designed
\emph{by humans} because there is currently no known recipe or algorithm for
dividing the design of a statistical model into a sequence of arithmetic
operations. Indeed enabling computers to \emph{think} as \emph{humans} have the
ability to do when faced with uncertainty is challenging in several major ways:
(1) Finding optimal statistical models remains to be formulated as a well posed
problem when information on the system of interest is incomplete and comes in
the form of a complex combination of sample data, partial knowledge of
constitutive relations and a limited description of the distribution of input
random variables. (2) The space of admissible scenarios along with the space of
relevant information, assumptions, and/or beliefs, tend to be infinite
dimensional, whereas calculus on a computer is necessarily discrete and finite.
With this purpose, this paper explores the foundations of a rigorous framework
for the scientific computation of optimal statistical estimators/models and
reviews their connections with Decision Theory, Machine Learning, Bayesian
Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty
Quantification and Information Based Complexity.Comment: 37 page
Who knows best? A Q methodology study to explore perspectives of professional stakeholders and community participants on health in low-income communities
Abstract Background Health inequalities in the UK have proved to be stubborn, and health gaps between best and worst-off are widening. While there is growing understanding of how the main causes of poor health are perceived among different stakeholders, similar insight is lacking regarding what solutions should be prioritised. Furthermore, we do not know the relationship between perceived causes and solutions to health inequalities, whether there is agreement between professional stakeholders and people living in low-income communities or agreement within these groups. Methods Q methodology was used to identify and describe the shared perspectives (âsubjectivitiesâ) that exist on i) why health is worse in low-income communities (âCausesâ) and ii) the ways that health could be improved in these same communities (âSolutionsâ). Purposively selected individuals (n =â53) from low-income communities (n =â25) and professional stakeholder groups (n =â28) ranked ordered sets of statements â 34 âCausesâ and 39 âSolutionsâ â onto quasi-normal shaped grids according to their point of view. Factor analysis was used to identify shared points of view. âCausesâ and âSolutionsâ were analysed independently, before examining correlations between perspectives on causes and perspectives on solutions. Results Analysis produced three factor solutions for both the âCausesâ and âSolutionsâ. Broadly summarised these accounts for âCausesâ are: i) âUnfair Societyâ, ii) âDependent, workless and lazyâ, iii) âIntergenerational hardshipsâ and for âSolutionsâ: i) âEmpower communitiesâ, ii) âPaternalismâ, iii) âRedistributionâ. No professionals defined (i.e. had a significant association with one factor only) the âCausesâ factor âDependent, workless and lazyâ and the âSolutionsâ factor âPaternalismâ. No community participants defined the âSolutionsâ factor âRedistributionâ. The direction of correlations between the two sets of factor solutions â âCausesâ and âSolutionsâ â appear to be intuitive, given the accounts identified. Conclusions Despite the plurality of views there was broad agreement across accounts about issues relating to money. This is important as it points a way forward for tackling health inequalities, highlighting areas for policy and future research to focus on
- âŠ