Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses a computer vision model to automate the evaluation of residual plots. An accompanying Shiny application is provided for ease of use. Given a sample of residuals, the model predicts a visual signal strength (VSS) and offers supporting information to help analysts assess model fit.

Data encountered in practice are frequently contaminated by additive noise, and wavelet shrinkage remains a fundamental tool for recovering underlying signals in nonparametric estimation. Classical procedures such as hard and soft thresholding decide whether to retain a wavelet coefficient almost entirely from its magnitude. Although effective in many settings, these rules can be too rigid for coefficients whose magnitudes fall in an intermediate region where the distinction between signal and noise is uncertain. We propose MLShrink, a two-threshold wavelet denoising procedure that combines wavelet shrinkage with machine learning. Coefficients below a lower threshold are discarded, coefficients above an upper threshold are retained, and coefficients in the intermediate band are classified using local wavelet-domain features. In this way, MLShrink preserves the simplicity of classical thresholding away from the decision boundary while allowing data-adaptive decisions for ambiguous coefficients. The paper also develops a theoretical framework tailored to this architecture. We show that MLShrink is a nonexpansive support-selection rule, derive an oracle-based risk decomposition showing that excess denoising risk is determined by classification errors on the undecided band, and establish an oracle-consistency result under suitable assumptions on classifier performance. Simulation experiments on standard benchmark signals indicate that MLShrink is competitive with several established wavelet shrinkage methods and is especially effective for signals with irregular, edge-rich, or non-smooth structure. These findings suggest that learned decisions on the intermediate threshold band provide a useful and interpretable connection between classical wavelet denoising and modern statistical learning.

Flood of AI'garbage' is pushing open-source developers to the limit A viral cartoon about open-source software shows a teetering pile of boxes labelled "all modern digital infrastructure" and one tiny box right at the bottom, propping up the whole lot: "a project some random person in Nebraska has been thanklessly maintaining since 2003". That's the reality of open source: every website, application and operating system relies on it. Modern society couldn't function without it, and yet it's written by volunteers in their spare time. But the growing burden caused by a flood of AI-generated code is causing many to burn out and leave the community altogether, threatening the future of open-source software. 'Flashes of brilliance and frustration': I let an AI agent run my day AI models are making it easier and easier to generate code to build new features, fix bugs or create entire new projects at the click of a button.

MV Hondius, the Dutch cruise ship hit by a deadly hantavirus outbreak, has docked at its final destination in Rotterdam. Only the ship's crew were aboard for the last leg of the journey, as all passengers docked off the ship in the Canary Islands between 10 and 11 May. Rotterdam port harbour master René de Vries said 25 mobile homes kitted out with catering and satellite communications would be available for the crew to self-isolate in. Three people - a Dutch couple and a German woman - died after travelling on the ship, with two of them confirmed to have had the virus. The World Health Organization has so far reported 10 cases in total, eight confirmed and two suspected.

There are no confirmed cases in the US, but 41 people who were potentially exposed to the Andes virus are in quarantine or being monitored for symptoms. The US Centers for Disease Control and Prevention is monitoring 41 people in the US for the Andes hantavirus after a cruise ship was hit with a rare outbreak, but the risk to the public remains low, according to health officials. This includes a group of 18 passengers from the cruise ship who are now in quarantine facilities in Nebraska and Georgia. The agency is also monitoring passengers who returned home before the outbreak was identified and others who were exposed during travel, specifically on flights where a symptomatic case was present. "Most people under monitoring are considered high-risk exposures, and CDC recommends that everyone under monitoring stay at home and avoid being around people during their 42-day monitoring period," David Fitter, incident manager for the CDC's hantavirus response, told reporters during a media briefing on Thursday.

A lab at the University of Nebraska has developed a test that can detect the virus before symptoms become severe. Now, it's ready to start testing cruise ship passengers returning to the US. As passengers return to the US from the cruise that saw a rare hantavirus outbreak, much of the country is lacking a basic public health tool: a test to diagnose the illness in the earliest stages of infection. Nebraska may be the first state with the ability to do so. In just a few days, a lab at the University of Nebraska Medical Center in Omaha developed its own diagnostic test for the Andes virus in anticipation of receiving 16 American passengers from the ship. "I believe we might be the only lab in the nation that has this test available at the moment," Peter Iwen, director of the Nebraska Public Health Laboratory tells WIRED, referring to polymerase chain reaction (PCR) testing, which was important during the Covid-19 pandemic.

Here's what you need to know, from why the cruise ship outbreak won't spark the next pandemic to how hantavirus spreads. Now that more than 100 passengers aboard a hantavirus -stricken luxury cruise ship have been evacuated, with 18 Americans in biocontainment units in Nebraska and Georgia, health officials around the world are working to monitor more than two dozen individuals who left the cruise and anyone with whom they might have come in close contact. So far, all of the 11 reported hantavirus cases are among passengers or crew on the ship, the World Health Organization's director-general Tedros Adhanom Ghebreyesus said at a press conference in Madrid on Tuesday. That includes three deaths resulting from the virus. Typically, hantaviruses are spread when contaminated rodent droppings and urine are stirred up in the air and breathed in.

Palantir Employees Are Starting to Wonder if They're the Bad Guys Interviews with current and former Palantir employees, along with internal Slack messages obtained by WIRED, suggest a workforce in turmoil. It took just a few months of President Donald Trump's second term for Palantir employees to question their company's commitments to civil liberties . Last fall, Palantir seemed to become the technological backbone of Trump's immigration enforcement machinery, providing software identifying, tracking, and helping deport immigrants on behalf of the Department of Homeland Security (DHS), when current and former employees started ringing the alarm. Right as they picked up the call, one of them asked, "Are you tracking Palantir's descent into fascism?" "That was their greeting," the other former employee says.

Japanese scientist Makoto Kuroda faces felony charges after allegedly poisoning a colleague's water bottle with toxic chemicals at UW-Madison's Influenza Research Institute.

Abstract: We use the law of total variance to generate multiple expansions for the posterior predictive variance. These expansions are sums of terms involving conditional expectations and conditional variances and provide a quantification of the sources of predictive uncertainty. Since the posterior predictive variance is fixed given the model, it represents a constant quantity that is conserved over these expansions. The terms in the expansions can be assessed in absolute or relative sense to understand the main contributors to the length of prediction intervals. We quantify the term-wise uncertainty across expansions varying in the number of terms and the order of conditionates. In particular, given that a specific term in one expansion is small or zero, we identify the other terms in other expansions that must also be small or zero. We illustrate this approach to predictive model assessment in several well-known models. The Setting and Intuition Everyone uses prediction intervals (PI's) but few examine their structure or more precisely how they should be interpreted in the context of a model with multiple components. Often PI's seem overconfident (too narrow) or useless (too wide). Both frequentist and Bayesian practitioners routinely report PI's.