Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.

Sylvia Frühwirth-Schnatter.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 706--733.

Abstract:
Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised Domain Adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from Electronic Health Records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection, are discussed.

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

Johann Gagnon-Bartsch, Yotam Shem-Tov.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1464--1483.

Abstract:
The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not control the assignment mechanism and instead may rely upon natural experiments or matching methods as a substitute to experimental randomization. The standard testable implication of random assignment is covariate balance between the treated and control units. Covariate balance is commonly used to validate the claim of as good as random assignment. We propose a new nonparametric test of covariate balance. Our Classification Permutation Test (CPT) is based on a combination of classification methods (e.g., random forests) with Fisherian permutation inference. We revisit four real data examples and present Monte Carlo power simulations to demonstrate the applicability of the CPT relative to other nonparametric tests of equality of multivariate distributions.

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

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.

Robbyn Plumb tried to balance a career in the public service with looking after two kids, one with special needs. When the pandemic hit she finally hit a wall and decided to stop working. She writes about how hard it is for parents trying to do it all during COVID-19.

A fine and delicate balance in contemporary jazz  Telegraph India

The African Union Development Agency says countries should think about how they choose the lesser of the two evils because strict lockdowns have severe consequences.

Oil headed for its first back-to-back weekly gain since February as output cuts from the biggest producers and a nascent recovery in demand began to rebalance a market awash with crude.

Having student board members with voting clout on school boards poses a number of logistical challenges, readers say in response to a recent Education Week feature.

Economic growth prospects are better and Finance Minister Malusi Gigaba has made the right choices about where to spend the money, says ANC national chair Gwede Mantashe.

Grace McStravock, who will graduate Saturday, May 9 with a 3.98 GPA in actuarial science and minors in mathematics and statistics, has been selected as Smeal’s spring 2020 risk management student marshal.

Conglomerate hopes to become debt-free this year

Gold ETFs investors can exit only by selling to secondary market participants on the stock exchange.

How the retail pharmacy chain uses customer data to make its marketing messages relevant

Attackers are pivoting their existing infrastructure for the distribution of ransomware, phishing emails, and other malware, leveraging COVID-19 keywords that get us to click on links or open emails.

Angela Ruch has a full-time ride in the NASCAR Truck Series, a growing family and a docuseries on Facebook. Now, she'd love to get back to racing and get a win.

Hi Folks, A question that I've often received from designers, "Is there a method to determine the amount of memory required before I submit a job? I use distributed processing and need to provide an estimate before submitting jobs." The answer...(read more)

hi everyone, 

i'm trying to create a netlist for aging simulation. i would like to simulate how power, Gain and PAE (efficiency) are inlfuenced after 3 hours

i would be grateful if someone can correct my syntax in the netlist since i'm trying to make a sweep HB  simulation where the input power is the parameter.

i did it without any error for the sp (S parameters)  simulation.

you can see the images for both sp and hb simulation netlists. (from left to right: sp aging netlist; hb aging netlist)

i will be grateful if someone can provide me some syntax advices.

thanks,

best regards

visit News18 Urdu for latest news, breaking news, news headlines and updates from Balangir on politics, sports, entertainment, cricket, crime and more.

Zen Load Balancer version 3.10.1 suffers from a directory traversal vulnerability. This finding was originally discovered by Cody Sixteen.

This whitepaper is a quick tutorial on pentesting the Zen load balancer.

This Metasploit module exploits an authenticated directory traversal vulnerability in Zen Load Balancer version 3.10.1. The flaw exists in index.cgi not properly handling the filelog= parameter which allows a malicious actor to load arbitrary file path.

Stevens Mokgalapa talks about foreign investment opportunities and challenges in South Africa’s administrative capital, and the balancing act of development and environmental needs in the developing world.

The chairman of the board of the National Bank of the Republic of Belarus talks to fDi about preserving financial stability and diversifying the country’s export split.

U.S. President Donald Trump tapped Energy Department staffer Bernard McNamee to join the nation’s top energy regulator, potentially smoothing the path for a brewing bailout of money-losing coal and nuclear plants.

California’s rapid renewable energy expansion has led to a boom in large-scale solar and wind projects and a huge expansion of rooftop solar. As costs have fallen, solar energy has become much less expensive, and utility-scale solar and wind are now less expensive than new gas plants, and far less expensive than existing coal.

U.S. Export-Import Bank Chairman Fred Hochberg mounted a defense of the 80-year-old agency as Republicans weigh eliminating the lender they say backs major corporations with political connections.

...

The volume of home mortgage loans made out by commercial banks is falling at a rapid rate after the government's measure to keep the housing marketing from overheating on August 2. According to figures made public by the nation's top-five banks including Kookmin, Shinhan, Woori, KEB Hana, and Nonghyup on August 21, the balance of home mortgage loans as of August 18 was 368,370.2 billion won, up by 1,834.3 billion won from the end of July (366,535.9 billion won). In monthly rate, this is up ...

The household debt balance has rapidly increased to approach 1,400 trillion won (US$1.24 trillion) as of the end of June this year. It is estimated the balance may have surpassed the 1,400-trillion-won level by mid-August. As the amount of household debt reaches 90 percent of the gross domestic products (1,637 trillion won at the end of last year), economists began warning the high debt level may pose an obstacle to further growth.According to the Bank of Korea on August 23, the balance of house...

This year's tourism balance of payments is estimated to surpass US$15 billion. This is up more than 40 percent of $10.8 billion recorded in 2007 in the wake of the global financial crisis. According to the Bank of Korea, the tourism balance of payments in the month of June this year was a deficit of $1.39 billion. The cumulative deficit for the first six months of the year has been $7.74 billion, the second all-time highest after the second half of 2007. Last year, the tourism spending by K...

Vidya Balan, in a recent interview, shed light on the many realisations she has been having during the lockdown

The transmission channels of perishables like fruits and vegetables from producers to consumers in developing countries need a revamp to provide benefits to all involved.

From Northridge, California to Garching, Germany and everywhere in between, HARMAN has been recognizing the achievements and accomplishments of women in recognition of International Women’s Day. Inspired by the campaign’s theme of #BalanceforBetter, the...

Title: Balanitis (Inflammation of the Head of the Penis)
Category: Diseases and Conditions
Created: 6/24/2013 12:00:00 AM
Last Editorial Review: 11/22/2019 12:00:00 AM

Title: Is Balanitis Contagious?
Category: Diseases and Conditions
Created: 5/28/2015 12:00:00 AM
Last Editorial Review: 12/4/2019 12:00:00 AM

Manganese (Mn) is an essential micronutrient required for the normal development of many organs, including the brain. Although its roles as a cofactor in several enzymes and in maintaining optimal physiology are well-known, the overall biological functions of Mn are rather poorly understood. Alterations in body Mn status are associated with altered neuronal physiology and cognition in humans, and either overexposure or (more rarely) insufficiency can cause neurological dysfunction. The resultant balancing act can be viewed as a hormetic U-shaped relationship for biological Mn status and optimal brain health, with changes in the brain leading to physiological effects throughout the body and vice versa. This review discusses Mn homeostasis, biomarkers, molecular mechanisms of cellular transport, and neuropathological changes associated with disruptions of Mn homeostasis, especially in its excess, and identifies gaps in our understanding of the molecular and biochemical mechanisms underlying Mn homeostasis and neurotoxicity.