ILS Bermuda added a science-focused session to Convergence 2024, developed with BIOS and ASU, featuring an address by Peter Schlosser and panels on science and innovation in the ILS industry. A spokesperson said, “ILS Bermuda is excited to announce the addition of a special afternoon of innovative science-focused content to its Convergence 2024 agenda. Scheduled […]

Here’s the good news: Experts predict that within the next four or five years, there will be more than 40 billion IoT devices hard at work — improving the safety, efficiency, reliability and productivity of the world’s enterprises.

In X-ray diffraction imaging (XDI), electron density maps of a targeted particle are reconstructed computationally from the diffraction pattern alone using phase-retrieval (PR) algorithms. However, the PR calculations sometimes fail to yield realistic electron density maps that approximate the structure of the particle. This occurs due to the absence of structure amplitudes at and near the zero-scattering angle and the presence of Poisson noise in weak diffraction patterns. Consequently, the PR calculation becomes a bottleneck for XDI structure analyses. Here, a protocol to efficiently yield realistic maps is proposed. The protocol is based on the empirical observation that realistic maps tend to yield low similarity scores, as suggested in our prior study [Sekiguchi et al. (2017), J. Synchrotron Rad. 24, 1024–1038]. Among independently and concurrently executed PR calculations, the protocol modifies all maps using the electron-density maps exhibiting low similarity scores. This approach, along with a new protocol for estimating particle shape, improved the probability of obtaining realistic maps for diffraction patterns from various aggregates of colloidal gold particles, as compared with PR calculations performed without the protocol. Consequently, the protocol has the potential to reduce computational costs in PR calculations and enable efficient XDI structure analysis of non-crystalline particles using synchrotron X-rays and X-ray free-electron laser pulses.

The convergence of voice, video and data is old news today, but when it happened it changed the world.  New possibilities became available for the delivery of services like phone, movies and entertainment. All this was enhanced with the flood of small mobile devices.  And now, we love being able to use our smartphone to [...]

In 2024, the North American air-to-water heat pump market saw significant expansion, with nine new offerings introduced at the January AHR show by companies new to this category. By year's end, at least 18 companies either have or plan to offer these pumps in the U.S. Read the list of these companies as of mid-2024.

This online exhibition introduced was not a one-time business event, but provides regular business services based on various events and promotions.

Convergence: Crypto and 3D Printing The convergence of cryptocurrency and 3D printing represents a natural evolution driven by the shared principles of decentralization, democratization, and innovation. Several key areas illustrate how these technologies are complementing each other: Digital Ownership with…

Resolution 60 - (Rev. Geneva, 2022) - Responding to the challenges of the evolution of the identification/numbering system and its convergence with IP-based systems/networks

Capability exposure enhancement for supporting fixed mobile convergence in IMT-2020 networks

Overview of convergence of artificial intelligence and blockchain

ILS Bermuda announced that Paul Craven,  an international speaker and specialist in behavioural science, will be the keynote speaker at ILS Bermuda’s Convergence 2024. A spokesperson said, “The event will take place from October 7–9, 2024, bringing together industry leaders and innovators to explore the latest trends and opportunities in the insurance-linked securities and alternative risk […]

In recent weeks, we’ve seen multiple examples of women on the political right straddling two kinds of womanhood: the girlboss and the tradwife. The visibility of these women exposes a hidden link between conservative womanhood and girlboss feminism that deserves our attention.  Katie Britt broadcast her response to the State of the Union from her kitchen. Michelle […]

Zhaonan Wang and Yingpu Deng
Math. Comp. 93 (), 2921-2942.
Abstract, references and article information

Haiyong Wang
Math. Comp. 93 (), 2861-2884.
Abstract, references and article information

Abdessamad Dehaj and Mohamed Guessous
Theor. Probability and Math. Statist. 111 (), 1-8.
Abstract, references and article information

In this webinar, Martijn de Vries, CTO at Bright Computing and Robert Stober, Director of Product Management at Bright Computing, discuss the convergence of HPC and AI in the context […]

The post The Convergence of HPC and AI appeared first on HPCwire.

Despite earlier income convergence among nations, many low-income countries (LICs) and people are falling further behind. Worse, the number of poor and hungry has been increasing again after declining for decades. After the post-Second World War ‘Golden Age’ ended over half a century ago, the world has seen unequal and uneven economic growth, industrialisation, and […]

Growth model finds evidence of agricultural convergence among Asian countries, but changes in factors including foreign aid may make this impossible to realize.

The strategy is to create consumer agents, but then use those to inform how they train agents in the enterprise.

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The switch to renewable energy requires technology upgradation, funding and a collective push from public and private stakeholders

In the fifth and final part of Future Cities: Holy Land, WIRED explores how Israel can continue to grow as a tech powerhouse.

National Bureau of Economic Research

Mobile providers have garnered a very large share of traditional services, such as telephony, over the past decade. Nevertheless, mobile networks are dependent on fixed networks and could not efficiently meet the rapidly expanding demand of users without the contributions made by fixed broadband networks.

Connected television allows the provision of certain new and valuable services to end-users that will also have implications for the activities of all players in the content distribution ecosystem. In addition to identifying the new services that connected TV enables, this report analyses their effects and includes a discussion of policy implications raised for the actual connected television devices and for network infrastructure.

Although Lithuania’s growth has been impressive, inequality is high, the risk of poverty is one of the highest of European countries, and life expectancy is comparatively low and strongly dependent on socio-economic background.

Although Lithuania’s growth has been impressive, inequality is high, the risk of poverty is one of the highest of European countries, and life expectancy is comparatively low and strongly dependent on socio-economic background.

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information exchange. We first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to the stationary point if each local cost function is smooth and linearly to the global optimum under an additional condition that the global cost function satisfies the Polyak-{L}ojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving the linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique or finite. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations.

In this work, we study dynamic properties of classical solutions to a homogenous Neumann initial-boundary value problem (IBVP) for a two-species and two-stimuli chemotaxis model with/without chemical signalling loop in a 2D bounded and smooth domain. We successfully detect the product of two species masses as a feature to determine boundedness, gradient estimates, blow-up and $W^{j,infty}(1leq jleq 3)$-exponential convergence of classical solutions for the corresponding IBVP. More specifically, we first show generally a smallness on the product of both species masses, thus allowing one species mass to be suitably large, is sufficient to guarantee global boundedness, higher order gradient estimates and $W^{j,infty}$-convergence with rates of convergence to constant equilibria; and then, in a special case, we detect a straight line of masses on which blow-up occurs for large product of masses. Our findings provide new understandings about the underlying model, and thus, improve and extend greatly the existing knowledge relevant to this model.

In one embodiment, interference suppression is improved by improving convergence criteria. For some embodiments, convergence is improved by employing non-constant alpha-beta-weighting. For other embodiments, convergence is improved by employing successive interference suppression methods that have guaranteed convergence properties.

M. M. Kapustei and P. V. Slyusarchuk
Theor. Probability and Math. Statist. 99 (2020), 101-111.
Abstract, references and article information

Alberto Arenas, Óscar Ciaurri and Edgar Labarga
Proc. Amer. Math. Soc. 148 (2020), 2539-2550.
Abstract, references and article information

Ruofei Zhao, Yuanzhi Li, Yuekai Sun.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 632--660.

Abstract:
We study the convergence behavior of the Expectation Maximization (EM) algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by at least $Omega (sqrt{min {M,d}})$, where $M$ is the number of components and $d$ is the dimension, the EM algorithm converges locally to the global optimum of the log-likelihood. Further, we show that the convergence rate is linear and characterize the size of the basin of attraction to the global optimum.

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We first investigate regularized algorithms adapted to a projection operator on a closed subspace of the Hilbert space. We prove convergence results with respect to variants of norms, under a capacity assumption on the hypothesis space and a regularity condition on the target function. As a result, we obtain optimal rates for regularized algorithms with randomized sketches, provided that the sketch dimension is proportional to the effective dimension up to a logarithmic factor. As a byproduct, we obtain similar results for Nystr"{o}m regularized algorithms. Our results provide optimal, distribution-dependent rates that do not have any saturation effect for sketched/Nystr"{o}m regularized algorithms, considering both the attainable and non-attainable cases, in the well-conditioned regimes. We then study stochastic gradient methods with projection over the subspace, allowing multi-pass over the data and minibatches, and we derive similar optimal statistical convergence results.

Daniel Ahlberg.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 397--401.

Abstract:
We study the rate of convergence in the celebrated Shape Theorem in first-passage percolation, obtaining the precise asymptotic rate of decay for the probability of linear order deviations under a moment condition. Our results are presented from a temporal perspective and complement previous work by the same author, in which the rate of convergence was studied from the standard spatial perspective.

Bastien Mallein.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 356--373.

Abstract:
Consider a supercritical branching random walk on the real line. The consistent maximal displacement is the smallest of the distances between the trajectories followed by individuals at the $n$th generation and the boundary of the process. Fang and Zeitouni, and Faraud, Hu and Shi proved that under some integrability conditions, the consistent maximal displacement grows almost surely at rate $lambda^{*}n^{1/3}$ for some explicit constant $lambda^{*}$. We obtain here a necessary and sufficient condition for this asymptotic behaviour to hold.

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which extit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $mathcal{O}(1/sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed extit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $mathcal{O}(1/sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $sqrt{ ho^star} > 1$, where $ ho^star - 1$ is the minimum $chi^2$-divergence between the target and an exponential-family proposal.

As an important type of reinforcement learning algorithms, actor-critic (AC) and natural actor-critic (NAC) algorithms are often executed in two ways for finding optimal policies. In the first nested-loop design, actor's one update of policy is followed by an entire loop of critic's updates of the value function, and the finite-sample analysis of such AC and NAC algorithms have been recently well established. The second two time-scale design, in which actor and critic update simultaneously but with different learning rates, has much fewer tuning parameters than the nested-loop design and is hence substantially easier to implement. Although two time-scale AC and NAC have been shown to converge in the literature, the finite-sample convergence rate has not been established. In this paper, we provide the first such non-asymptotic convergence rate for two time-scale AC and NAC under Markovian sampling and with actor having general policy class approximation. We show that two time-scale AC requires the overall sample complexity at the order of $mathcal{O}(epsilon^{-2.5}log^3(epsilon^{-1}))$ to attain an $epsilon$-accurate stationary point, and two time-scale NAC requires the overall sample complexity at the order of $mathcal{O}(epsilon^{-4}log^2(epsilon^{-1}))$ to attain an $epsilon$-accurate global optimal point. We develop novel techniques for bounding the bias error of the actor due to dynamically changing Markovian sampling and for analyzing the convergence rate of the linear critic with dynamically changing base functions and transition kernel.

In this paper we obtain the limit distribution for partial sums with a random number of terms following a class of mixed Poisson distributions. The resulting weak limit is a mixing between a normal distribution and an exponential family, which we call by normal exponential family (NEF) laws. A new stability concept is introduced and a relationship between {alpha}-stable distributions and NEF laws is established. We propose estimation of the parameters of the NEF models through the method of moments and also by the maximum likelihood method, which is performed via an Expectation-Maximization algorithm. Monte Carlo simulation studies are addressed to check the performance of the proposed estimators and an empirical illustration on financial market is presented.

Monika Bhattacharjee, Arup Bose.

Source: The Annals of Statistics, Volume 47, Number 6, 3470--3503.

Abstract:
Consider a high-dimensional linear time series model where the dimension $p$ and the sample size $n$ grow in such a way that $p/n o 0$. Let $hat{Gamma }_{u}$ be the $u$th order sample autocovariance matrix. We first show that the LSD of any symmetric polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$ exists under independence and moment assumptions on the driving sequence together with weak assumptions on the coefficient matrices. This LSD result, with some additional effort, implies the asymptotic normality of the trace of any polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$. We also study similar results for several independent MA processes. We show applications of the above results to statistical inference problems such as in estimation of the unknown order of a high-dimensional MA process and in graphical and significance tests for hypotheses on coefficient matrices of one or several such independent processes.

Kyoungjae Lee, Jaeyong Lee, Lizhen Lin.

Source: The Annals of Statistics, Volume 47, Number 6, 3413--3437.

Abstract:
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [ Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero elements in the Cholesky factor. Furthermore, our result does not require the irrepresentable condition, which is necessary for Lasso-type methods. We also derive the posterior convergence rates for precision matrices and Cholesky factors with respect to various matrix norms. The obtained posterior convergence rates are the fastest among those of the existing Bayesian approaches. In particular, we prove that our posterior convergence rates for Cholesky factors are the minimax or at least nearly minimax depending on the relative size of true sparseness for the entire dimension. The simulation study confirms that the proposed method outperforms the competing methods.

Qian Qin, James P. Hobert.

Source: The Annals of Statistics, Volume 47, Number 4, 2320--2347.

Abstract:
The use of MCMC algorithms in high dimensional Bayesian problems has become routine. This has spurred so-called convergence complexity analysis, the goal of which is to ascertain how the convergence rate of a Monte Carlo Markov chain scales with sample size, $n$, and/or number of covariates, $p$. This article provides a thorough convergence complexity analysis of Albert and Chib’s [ J. Amer. Statist. Assoc. 88 (1993) 669–679] data augmentation algorithm for the Bayesian probit regression model. The main tools used in this analysis are drift and minorization conditions. The usual pitfalls associated with this type of analysis are avoided by utilizing centered drift functions, which are minimized in high posterior probability regions, and by using a new technique to suppress high-dimensionality in the construction of minorization conditions. The main result is that the geometric convergence rate of the underlying Markov chain is bounded below 1 both as $n ightarrowinfty$ (with $p$ fixed), and as $p ightarrowinfty$ (with $n$ fixed). Furthermore, the first computable bounds on the total variation distance to stationarity are byproducts of the asymptotic analysis.

Qiyang Han, Jon A. Wellner.

Source: The Annals of Statistics, Volume 47, Number 4, 2286--2319.

Abstract:
We study the performance of the least squares estimator (LSE) in a general nonparametric regression model, when the errors are independent of the covariates but may only have a $p$th moment ($pgeq1$). In such a heavy-tailed regression setting, we show that if the model satisfies a standard “entropy condition” with exponent $alphain(0,2)$, then the $L_{2}$ loss of the LSE converges at a rate [mathcal{O}_{mathbf{P}}igl(n^{-frac{1}{2+alpha}}vee n^{-frac{1}{2}+frac{1}{2p}}igr).] Such a rate cannot be improved under the entropy condition alone. This rate quantifies both some positive and negative aspects of the LSE in a heavy-tailed regression setting. On the positive side, as long as the errors have $pgeq1+2/alpha$ moments, the $L_{2}$ loss of the LSE converges at the same rate as if the errors are Gaussian. On the negative side, if $p<1+2/alpha$, there are (many) hard models at any entropy level $alpha$ for which the $L_{2}$ loss of the LSE converges at a strictly slower rate than other robust estimators. The validity of the above rate relies crucially on the independence of the covariates and the errors. In fact, the $L_{2}$ loss of the LSE can converge arbitrarily slowly when the independence fails. The key technical ingredient is a new multiplier inequality that gives sharp bounds for the “multiplier empirical process” associated with the LSE. We further give an application to the sparse linear regression model with heavy-tailed covariates and errors to demonstrate the scope of this new inequality.