Understanding how decision makers balance operational efficiency with environmental and ecological risks is central to vessel navigation. We model vessel speed as a control variable in a constrained optimization framework in which vessel operators balance multiple competing objectives, including transit efficiency, ice related navigational risk, and whale related ecological risk. The underlying risk parameters are estimated using over 14 million Automatic Identification System (AIS) observations from the United States Arctic (2010-2019), together with environmental covariates and spatially explicit whale density estimates. The framework incorporates a nonlinear risk objective, vessel heterogeneity, and regularization to ensure stable and interpretable results.The inferred trade offs reveal distinct decision making patterns across vessel groups and navigational statuses. Vessel types such as Tug Tow and Cargo balance operational speed with environmental and ecological considerations. In contrast, several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk, while Pleasure Craft and Tankers exhibit higher sensitivity to whale related risk. Across navigational status categories, similar heterogeneity is observed. The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints. Statuses including restricted maneuverability and engaged in fishing exhibit higher estimated sensitivity to whale related risk, though with substantial uncertainty.Sensitivity analysis indicates that increasing whale-related risk weighting produces limited changes in model-implied optimal speed, whereas increasing ice-related risk leads to more consistent reductions.

Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice.

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.

Estimating the covariance of asset returns, i.e., the risk model, is a key component of financial portfolio construction and evaluation. Most risk modeling approaches produce a factor model that decomposes the asset variability into two components: the first attributed to a small number of factors that are common among the assets and the second attributed to the idiosyncratic behavior of each asset. Third-party providers typically provide risk models to investors, and while these models are typically of high quality, they may fail to capture important information, e.g., changing market regimes and transient factors. To overcome these limitations, we propose a systematic method based on maximum likelihood estimation to enhance an existing factor model by both refining the given model and adding new statistical factors. Our approach relies only on the observed sequence of realized returns and on the choice of two hyperparameters: the number of additional factors and the half-life parameter that determines the weights assigned to returns in the log-likelihood objective. Importantly, our methodology applies to the situation where asset returns may be missing, making it suitable for typical equity datasets. We demonstrate our approach on the Barra short-term US risk model, a high-quality risk model used in practice, for a universe of US high-capitalization equities. We show that the proposed extension captures structure in the returns that is missed by the original model.

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.

Could Contact-Tracing Apps Help With the Hantavirus? Contact-tracing apps were widely deployed during the Covid pandemic. After three people died on a cruise ship struck by a hantavirus, authorities are actively tracking down 29 people who had left the ship. They're trying to trace the spread of the virus. It's a long, arduous, global process to find and notify people who might be at risk of infection.

Companies like Lovable, Base44, Replit, and Netlify use AI to let anyone build a web app in seconds--and in thousands of cases, spill highly sensitive data onto the public internet. As AI increasingly takes over the work of modern programmers, the cybersecurity world has warned that automated coding tools are sure to introduce a new bounty of hackable bugs into software. When those same vibe-coding tools invite anyone to create applications hosted on the web with a click, however, it turns out the security implications go beyond bugs to a total absence of any security--even, sometimes, for highly sensitive corporate and personal data. Security researcher Dor Zvi and his team at the cybersecurity firm he cofounded, RedAccess, analyzed thousands of vibe-coded web applications created using the AI software development tools Lovable, Replit, Base44, and Netlify and found more than 5,000 of them that had virtually no security or authentication of any kind. Many of these web apps allowed anyone who merely finds their web URL to access the apps and their data.

Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses this issue by introducing an additional loss that encourages low-confidence predictions on OoD data during training. While outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers.