The drones and programs will be fully paid for by the DOJ as part of the $850 million funding that has been allocated to help public safety departments fight the spread of COVID-19. This includes...

(PRWeb April 30, 2020)

Read the full story at https://www.prweb.com/releases/suntuity_airworks_offering_free_assistance_in_drone_acquisition_through_850mm_federal_grant_assistance_program_for_public_safety_agencies/prweb17090555.htm

Stephen M. S. Lee, Puyudi Yang.

Source: The Annals of Statistics, Volume 48, Number 1, 274--299.

Abstract:
Suppose that a confidence region is desired for a subvector $ heta $ of a multidimensional parameter $xi =( heta ,psi )$, based on an M-estimator $hat{xi }_{n}=(hat{ heta }_{n},hat{psi }_{n})$ calculated from a random sample of size $n$. Under nonstandard conditions $hat{xi }_{n}$ often converges at a nonregular rate $r_{n}$, in which case consistent estimation of the distribution of $r_{n}(hat{ heta }_{n}- heta )$, a pivot commonly chosen for confidence region construction, is most conveniently effected by the $m$ out of $n$ bootstrap. The above choice of pivot has three drawbacks: (i) the shape of the region is either subjectively prescribed or controlled by a computationally intensive depth function; (ii) the region is not transformation equivariant; (iii) $hat{xi }_{n}$ may not be uniquely defined. To resolve the above difficulties, we propose a one-dimensional pivot derived from the criterion function, and prove that its distribution can be consistently estimated by the $m$ out of $n$ bootstrap, or by a modified version of the perturbation bootstrap. This leads to a new method for constructing confidence regions which are transformation equivariant and have shapes driven solely by the criterion function. A subsampling procedure is proposed for selecting $m$ in practice. Empirical performance of the new method is illustrated with examples drawn from different nonstandard M-estimation settings. Extension of our theory to row-wise independent triangular arrays is also explored.

Björn Böttcher, Martin Keller-Ressel, René L. Schilling.

Source: The Annals of Statistics, Volume 47, Number 5, 2757--2789.

Abstract:
We introduce two new measures for the dependence of $nge2$ random variables: distance multivariance and total distance multivariance . Both measures are based on the weighted $L^{2}$-distance of quantities related to the characteristic functions of the underlying random variables. These extend distance covariance (introduced by Székely, Rizzo and Bakirov) from pairs of random variables to $n$-tuplets of random variables. We show that total distance multivariance can be used to detect the independence of $n$ random variables and has a simple finite-sample representation in terms of distance matrices of the sample points, where distance is measured by a continuous negative definite function. Under some mild moment conditions, this leads to a test for independence of multiple random vectors which is consistent against all alternatives.

Ye Zhang, Zhigang Yao, Patrik Forssén, Torgny Fornstedt.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2011--2042.

Abstract:
The means to obtain the rate constants of a chemical reaction is a fundamental open problem in both science and the industry. Traditional techniques for finding rate constants require either chemical modifications of the reactants or indirect measurements. The rate constant map method is a modern technique to study binding equilibrium and kinetics in chemical reactions. Finding a rate constant map from biosensor data is an ill-posed inverse problem that is usually solved by regularization. In this work, rather than finding a deterministic regularized rate constant map that does not provide uncertainty quantification of the solution, we develop an adaptive variational Bayesian approach to estimate the distribution of the rate constant map, from which some intrinsic properties of a chemical reaction can be explored, including information about rate constants. Our new approach is more realistic than the existing approaches used for biosensors and allows us to estimate the dynamics of the interactions, which are usually hidden in a deterministic approximate solution. We verify the performance of the new proposed method by numerical simulations, and compare it with the Markov chain Monte Carlo algorithm. The results illustrate that the variational method can reliably capture the posterior distribution in a computationally efficient way. Finally, the developed method is also tested on the real biosensor data (parathyroid hormone), where we provide two novel analysis tools—the thresholding contour map and the high order moment map—to estimate the number of interactions as well as their rate constants.

Adam Osękowski.

Source: Bernoulli, Volume 26, Number 3, 1912--1926.

Abstract:
Let $X=(X_{t})_{tgeq 0}in H^{1}$ and $Y=(Y_{t})_{tgeq 0}in{mathrm{BMO}} $ be arbitrary continuous-path martingales. The paper contains the proof of the inequality egin{equation*}mathbb{E}int _{0}^{infty }iglvert dlangle X,Y angle_{t}igrvert leq sqrt{2}Vert XVert _{H^{1}}Vert YVert _{mathrm{BMO}_{2}},end{equation*} and the constant $sqrt{2}$ is shown to be the best possible. The proof rests on the construction of a certain special function, enjoying appropriate size and concavity conditions.

Yating Liu, Gilles Pagès.

Source: Bernoulli, Volume 26, Number 2, 1171--1204.

Abstract:
We establish conditions to characterize probability measures by their $L^{p}$-quantization error functions in both $mathbb{R}^{d}$ and Hilbert settings. This characterization is two-fold: static (identity of two distributions) and dynamic (convergence for the $L^{p}$-Wasserstein distance). We first propose a criterion on the quantization level $N$, valid for any norm on $mathbb{R}^{d}$ and any order $p$ based on a geometrical approach involving the Voronoï diagram. Then, we prove that in the $L^{2}$-case on a (separable) Hilbert space, the condition on the level $N$ can be reduced to $N=2$, which is optimal. More quantization based characterization cases in dimension 1 and a discussion of the completeness of a distance defined by the quantization error function can be found at the end of this paper.

Christian Hirsch, Christian Mönch.

Source: Bernoulli, Volume 26, Number 2, 927--947.

Abstract:
This paper considers two asymptotic properties of a spatial preferential-attachment model introduced by E. Jacob and P. Mörters (In Algorithms and Models for the Web Graph (2013) 14–25 Springer). First, in a regime of strong linear reinforcement, we show that typical distances are at most of doubly-logarithmic order. Second, we derive a large deviation principle for the empirical neighbourhood structure and express the rate function as solution to an entropy minimisation problem in the space of stationary marked point processes.

Jing Lei.

Source: Bernoulli, Volume 26, Number 1, 767--798.

Abstract:
We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization can cover Euclidean spaces with large dimensionality, with the optimal dependence on the dimensionality. Our method also covers the important case of Gaussian processes in separable Hilbert spaces, with rate-optimal upper bounds for functional data distributions whose coordinates decay geometrically or polynomially. Moreover, our bounds of the expected value can be combined with mean-concentration results to yield improved exponential tail probability bounds for the Wasserstein error of empirical measures under Bernstein-type or log Sobolev-type conditions.

Xiucai Ding.

Source: Bernoulli, Volume 26, Number 1, 387--417.

Abstract:
We consider the recovery of a low rank $M imes N$ matrix $S$ from its noisy observation $ ilde{S}$ in the high dimensional framework when $M$ is comparable to $N$. We propose two efficient estimators for $S$ under two different regimes. Our analysis relies on the local asymptotics of the eigenstructure of large dimensional rectangular matrices with finite rank perturbation. We derive the convergent limits and rates for the singular values and vectors for such matrices.

Dissenters, Religious -- Great Britain.

Fox News contributor Jason Chaffetz and Andy McCarthy react to House Intelligence transcripts on Russia probe.

The New York City police department (NYPD) is conducting an internal investigation into a May 2 incident involving the violent arrests of multiple people, allegedly members of a group who were not social distancing

Laura Anderlucci, Angela Montanari, Cinzia Viroli.

Source: Statistical Science, Volume 34, Number 2, 280--300.

Abstract:
In this paper, we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: The Annals of Statistics , Biometrika , Journal of the American Statistical Association , Journal of the Royal Statistical Society, Series B and Statistical Science . The aim is to construct a kind of “taxonomy” of the statistical papers by organizing and clustering them in main themes. In this sense being identified in a cluster means being important enough to be uncluttered in the vast and interconnected world of the statistical research. Since the main statistical research topics naturally born, evolve or die during time, we will also develop a dynamic clustering strategy, where a group in a time period is allowed to migrate or to merge into different groups in the following one. Results show that statistics is a very dynamic and evolving science, stimulated by the rise of new research questions and types of data.

Víctor Elvira, Luca Martino, David Luengo, Mónica F. Bugallo.

Source: Statistical Science, Volume 34, Number 1, 129--155.

Abstract:
Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.

And it’s on sale at Nordstrom.

Sebastian Onasch
Apr 15, 2020; 40:3186-3202
Systems/Circuits

John D. Cahoy
Jan 2, 2008; 28:264-278
Cellular

Masaki Yamamoto
May 22, 2019; 39:4179-4192
Neurobiology of Disease

John D. Cahoy
Jan 2, 2008; 28:264-278
Cellular

Italian translation of the BIS press release about the BIS Quarterly Review, September 2017

French translation of the press release about the Basel Committee publishing a discussion paper on "The regulatory treatment of sovereign exposures" (7 December 2017)

French translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

02020-02-09: Yucatan Peninsula

Neurons and circuits each with a distinct balance of intrinsic and synaptic conductances can generate similar behavior but sometimes respond very differently to perturbation. Examining a large family of circuit models with non-identical neurons and synapses underlying rhythmic behavior, we analyzed the circuits' response to modifications in single and multiple intrinsic conductances in the individual neurons. To summarize these changes over the entire range of perturbed parameters, we quantified circuit output by defining a global stability measure. Using this measure, we identified specific subsets of conductances that when perturbed generate similar behavior in diverse individuals of the population. Our unbiased clustering analysis enabled us to quantify circuit stability when simultaneously perturbing multiple conductances as a nonlinear combination of single conductance perturbations. This revealed surprising conductance combinations that can predict the response to specific perturbations, even when the remaining intrinsic and synaptic conductances are unknown. Therefore, our approach can expose hidden variability in the balance of intrinsic and synaptic conductances of the same neurons across different versions of the same circuit solely from the circuit response to perturbations. Developed for a specific family of model circuits, our quantitative approach to characterizing high-dimensional degenerate systems provides a conceptual and analytic framework to guide future theoretical and experimental studies on degeneracy and robustness.

SIGNIFICANCE STATEMENT Neural circuits can generate nearly identical behavior despite neuronal and synaptic parameters varying several-fold between individual instantiations. Yet, when these parameters are perturbed through channel deletions and mutations or environmental disturbances, seemingly identical circuits can respond very differently. What distinguishes inconsequential perturbations that barely alter circuit behavior from disruptive perturbations that drastically disturb circuit output remains unclear. Focusing on a family of rhythmic circuits, we propose a computational approach to reveal hidden variability in the intrinsic and synaptic conductances in seemingly identical circuits based solely on circuit output to different perturbations. We uncover specific conductance combinations that work similarly to maintain stability and predict the effect of changing multiple conductances simultaneously, which often results from neuromodulation or injury.

For visually guided navigation, the use of environmental cues is essential. Particularly, detecting local boundaries that impose limits to locomotion and estimating their location is crucial. In a series of three fMRI experiments, we investigated whether there is a neural coding of navigational distance in the human visual cortex (both female and male). We used virtual reality software to systematically manipulate the distance from a viewer perspective to different types of a boundary. Using a multivoxel pattern classification employing a linear support vector machine, we found that the occipital place area (OPA) is sensitive to the navigational distance restricted by the transparent glass wall. Further, the OPA was sensitive to a non-crossable boundary only, suggesting an importance of the functional constraint of a boundary. Together, we propose the OPA as a perceptual source of external environmental features relevant for navigation.

SIGNIFICANCE STATEMENT One of major goals in cognitive neuroscience has been to understand the nature of visual scene representation in human ventral visual cortex. An aspect of scene perception that has been overlooked despite its ecological importance is the analysis of space for navigation. One of critical computation necessary for navigation is coding of distance to environmental boundaries that impose limit on navigator's movements. This paper reports the first empirical evidence for coding of navigational distance in the human visual cortex and its striking sensitivity to functional constraint of environmental boundaries. Such finding links the paper to previous neurological and behavioral works that emphasized the distance to boundaries as a crucial geometric property for reorientation behavior of children and other animal species.

Malnutrition isn’t just a problem of hunger in developing countries—it exists in all regions and across socio-economic classes. The effects on human health Besides undernutrition, malnutrition also includes micronutrient-deficient diets and overweight and obesity. Chronic malnutrition can have serious, often life-threatening, health consequences, especially for children. Undernutrition can lead to physical and cognitive stunting, and makes children more susceptible to infectious diseases. Micronutrient deficiencies [...]

“Children subjected to child labour need our support and action so they can enjoy their right to education and health and become productive farmers and workers as adults to escape poverty and hunger.” - José Graziano da Silva, FAO Director- General  Child labour is not unique to a particular country, ethnicity, culture, or ideology. Today, there are about 100 million boys [...]

An estimated 700 000 people die each year from antimicrobial resistant (AMR) infections and an untold number of sick animals may not be responding to treatment. AMR is a significant global threat to public health, food safety and security, as well as to livelihoods, animal production and economic and agricultural development. The intensification of agricultural production has led to a rising use of antimicrobials – a use that is expected to more than double by 2030. Antimicrobials are important for the treatment of animal and plant diseases [...]

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