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In the paper, the peak solutions of a class equation is studied, the peak solutions are the maximal and minimal solutions. There is an iterative algorithm given for the solutions of the class equation. First, the existence of the peak solutions of the class equations is obtained. Second, when the peak solutions exist, an iterative algorithm is established to converge to the peak solutions of the class equation. By an upper bound and a lower bound of the solutions of the equation solution as the initial matrix, the iterative algorithm of the paper converges to the peak solutions of the class equation. The convergence problem of the algorithm is proved by the mathematical induction in the paper. The above results are verified by the examples.

The localisation of fluorophores is an important aspect of determining the biological function of cellular systems. Quantum correlation microscopy (QCM) is a promising technique for providing diffraction unlimited emitter localisation that can be used with either confocal or widefield modalities. However, so far, QCM has not been applied to three dimensional localisation problems. Here we show that QCM provides diffraction-unlimited three-dimensional localisation for two emitters within a single diffraction-limited spot. By introducing a two-stage maximum likelihood estimator, our modelling shows that the localisation precision scales as 1 / t where t is the total detection time. Diffraction unlimited localisation is achieved using both intensity and photon correlation from Hanbury Brown and Twiss measurements at as few as four measurement locations. We also compare the results of (MC) simulations with the Cramér–Rao lower bound.

We provide statistical guarantees for a family of variational approximations to Bayesian posterior distributions, called α-VB, which has close connections with variational approximations of tempered posteriors in the literature. The standard variational approximation is a special case of α-VB with α = 1. When α ∈ (0, 1], a novel class of variational inequalities are developed for linking the Bayes risk under the variational approximation to the objective function in the variational optimization problem, implying that maximizing the evidence lower bound in variational inference has the effect of minimizing the Bayes risk within the variational density family. Operating in a frequentist setup, the variational inequalities imply that point estimates constructed from the α-VB procedure converge at an optimal rate to the true parameter in a wide range of problems. We illustrate our general theory with a number of examples, including the mean-field variational approximation to (low)-highdimensional Bayesian linear regression with spike and slab priors, Gaussian mixture models and latent Dirichlet allocation.

The identification of both closed frequent high average utility itemsets (CFHAUIs) and generators of frequent high average utility itemsets (GFHAUIs) has substantial significance because they play an essential and concise role in representing frequent high average utility itemsets (FHAUIs). These concise summaries offer a compact yet crucial overview that can be much smaller. In addition, they allow the generation of non-redundant high average utility association rules, a crucial factor for decision-makers to consider. However, difficulty arises from the complexity of discovering these representations, primarily because the average utility function does not satisfy both monotonic and anti-monotonic properties within each equivalence class, that is for itemsets sharing the same subset of transactions. To tackle this challenge, this paper proposes an innovative method for efficiently extracting CFHAUIs and GFHAUIs. This approach introduces novel bounds on the average utility, including a weak lower bound called wlbau and a lower bound named auvlb. Efficient pruning strategies are also designed with the aim of early elimination of non-closed and/or non-generator FHAUIs based on the wlbau and auvlb bounds, leading to quicker execution and lower memory consumption. Additionally, the paper introduces a novel algorithm, CG-FHAUI, designed to concurrently discover both GFHAUIs and CFHAUIs. Empirical results highlight the superior performance of the proposed algorithm in terms of runtime, memory usage, and scalability when compared to a baseline algorithm.

The presence of hypothetical bias (HB) associated with stated preference methods has garnered frequent attention in the broad literature trying to describe and understand human behavior, often seen in environmental valuation, marketing studies, transportation choices, medical research, and others. This study presents an updated meta-analysis to explore the source of HB and methods to mitigate it. While previous meta-analysis on this topic often involves a few dozen articles, this analysis includes 131 studies after reviewing over 500 published and unpublished articles. This enables the inclusion of several important factors that have not been investigated before. These include relatively recent willingness to pay elicitation methods such as choice experiments and the Turnbull lower bound estimator. Newly emerged HB reduction techniques such as consequentiality and certainty follow-up treatments are also included. For explanatory variables that have been examined in previous studies, this analysis does not always report consistent findings. In particular, holding everything constant and contrary to commonlyheld beliefs, the method of auction does not offer much reduction to HB compared to more conventional methods such as a referendum vote. However, choice experiment, cheap talk, consequentiality and certainty follow-up all significantly contributed to explaining and mitigating the magnitude of HB. These results help practitioners to understand HB’s presence and choose appropriate methods for amelioration. The framework established through this study also enables future analyses targeted at understanding variations built upon one or multiple HB mitigation techniques.

Given a symmetric social network, we are interested in testing whether it has only one community or multiple communities. The desired tests should (a) accommodate severe degree heterogeneity, (b) accommodate mixed memberships, (c) have a tractable null distribution and (d) adapt automatically to different levels of sparsity, and achieve the optimal phase diagram. How to find such a test is a challenging problem. We propose the Signed Polygon as a class of new tests. Fixing m ≥ 3, for each m-gon in the network, define a score using the centered adjacency matrix. The sum of such scores is then the mth order Signed Polygon statistic. The Signed Triangle (SgnT) and the Signed Quadrilateral (SgnQ) are special examples of the Signed Polygon. We show that both the SgnT and SgnQ tests satisfy (a)–(d), and especially, they work well for both very sparse and less sparse networks. Our proposed tests compare favorably with existing tests. For example, the EZ and GC tests behave unsatisfactorily in the less sparse case and do not achieve the optimal phase diagram. Also, many existing tests do not allow for severe heterogeneity or mixed memberships, and they behave unsatisfactorily in our settings. The analysis of the SgnT and SgnQ tests is delicate and extremely tedious, and the main reason is that we need a unified proof that covers a wide range of sparsity levels and a wide range of degree heterogeneity. For lower bound theory, we use a phase transition framework, which includes the standard minimax argument, but is more informative. The proof uses classical theorems on matrix scaling.

The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain.

We revisit lower bounds on the regret in the case of multiarmed bandit problems. We obtain nonasymptotic, distribution-dependent bounds and provide simple proofs based only on well-known properties of Kullback–Leibler divergences. These bounds show in particular that in the initial phase the regret grows almost linearly, and that the well-known logarithmic growth of the regret only holds in a final phase. The proof techniques come to the essence of the information-theoretic arguments used and they involve no unnecessary complications.

The Ritz upper bound to eigenvalues of Hermitian operators is essential for many applications in science. It is a staple of quantum chemistry and physics computations. The lower bound devised by Temple in 1928 [G. Temple, Proc. R. Soc. A Math. Phys. Eng. Sci. 119, 276–293 (1928)] is not, since it converges too slowly. The need for a good lower-bound theorem and algorithm cannot be overstated, since an upper bound alone is not sufficient for determining differences between eigenvalues such as tunneling splittings and spectral features. In this paper, after 90 y, we derive a generalization and improvement of Temple’s lower bound. Numerical examples based on implementation of the Lanczos tridiagonalization are provided for nontrivial lattice model Hamiltonians, exemplifying convergence over a range of 13 orders of magnitude. This lower bound is typically at least one order of magnitude better than Temple’s result. Its rate of convergence is comparable to that of the Ritz upper bound. It is not limited to ground states. These results complement Ritz’s upper bound and may turn the computation of lower bounds into a staple of eigenvalue and spectral problems in physics and chemistry.

This paper studies the optimal time decay to the Oldroyd-B model in R d ( d ≥ 2 ). By appealing to a refined pure energy method, we prove the lower and upper bounds of decay estimates of the global solutions. In particular, the lower bound of decay estimates is established by fully exploiting the structure of this system and by introducing a new combined quantity.