Shadow or no shadow, the rodents have a special set of ecological skills. Breakthroughs, discoveries, and DIY tips sent six days a week. Despite advances in meteorological technology, we still keep a close eye on a rodent's burrow every February 2 for a weather forecast. While groundhogs --also called woodchucks--have been associated with the end of winter and beginning of spring for centuries, there's more to know about our rodent friends than their amateur Al Roker'ing. Unlike bears, groundhogs are true hibernators.

Unlike its land cousin the box turtle, painted turtles do not have a hinged bottom shell that closes when the head is pulled in. Breakthroughs, discoveries, and DIY tips sent every weekday. Winter has officially arrived in the Northern Hemisphere. With today's winter solstice, the days will start to get a little bit longer, but the cold will stick around. We humans typically handle the dipping temperatures by staying inside, sleeping more, and dressing in layers . Here are some unique ways that animals survive winter's deep freeze.

Tech expert Kurt Knutsson discusses how robots can milk, feed and clean cows on dairy farms, boosting efficiency and comfort. A threat to American livestock – the New World Screwworm (NWS) fly, which has been considered eradicated from the country since 1966 -- has reemerged as a potential danger following an outbreak in Mexico. The news triggered a shutdown of cattle, horse and bison imports along the southern border, as U.S. Department of Agriculture (USDA) Secretary Brooke Rollins announced in an X post on Sunday. "Due to the threat of New World Screwworm I am announcing the suspension of live cattle, horse, & bison imports through U.S. southern border ports of entry effective immediately," she wrote in the post. "The last time this devastating pest invaded America, it took 30 years for our cattle industry to recover.

In the humanities, it is rarely possible to resort to proof. Humanities are not built on the formulation of hypotheses and their proof or refutation. It is a field where different ways of describing its material (e.g., artistic culture) compete [Harpham, 2013]. Therefore, the question of text authorship is so important for humanists; it remains one of the few questions in the humanities that can be formulated as falsifiable and sometimes verifiable hypotheses. This is an area where humanists find themselves in a situation very similar to that in which representatives of the sciences usually exist. Consequently, this is the rhetorical resource that humanists can use in the struggle for resources in science and for symbolic capital in the scientific field.

Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation (ABC). Our curated dataset includes 3 benchmark brown dwarfs (Gl 570D, {\epsilon} Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat and Sonora Cholla model grids tend to predict logg ~3-4 (cgs units) even after data blueward of 1.2 {\mu}m have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.

In this paper, I introduce a simple method of computing relative word frequencies for authorship attribution and similar stylometric tasks. Rather than computing relative frequencies as the number of occurrences of a given word divided by the total number of tokens in a text, I argue that a more efficient normalization factor is the total number of relevant tokens only. The notion of relevant words includes synonyms and, usually, a few dozen other words in some ways semantically similar to a word in question. To determine such a semantic background, one of word embedding models can be used. The proposed method outperforms classical most-frequent-word approaches substantially, usually by a few percentage points depending on the input settings.

As companies increasingly involve AI in their hiring processes, advocates, lawyers, and researchers have continued to sound the alarm. Algorithms have been found to automatically assign job candidates different scores based on arbitrary criteria like whether they wear glasses or a headscarf or have a bookshelf in the background. Hiring algorithms can penalize applicants for having a Black-sounding name, mentioning a women's college, and even submitting their résumé using certain file types. They can disadvantage people who stutter or have a physical disability that limits their ability to interact with a keyboard. All of this has gone widely unchecked.

The Biden administration announced Thursday that employers who use algorithms and artificial intelligence to make hiring decisions risk violating the Americans with Disabilities Act if applicants with disabilities are disadvantaged in the process. The majority of American employers now use the automated hiring technology -- tools such as resume scanners, chatbot interviewers, gamified personality tests, facial recognition and voice analysis. The ADA is supposed to protect people with disabilities from employment discrimination, but just 19 percent of disabled Americans were employed in 2021, according to the Bureau of Labor Statistics. Kristen Clarke, the assistant attorney general for civil rights at the Department of Justice, which made the announcement jointly with the Equal Employment Opportunity Commission, told NBC News there is "no doubt" that increased use of the technologies is "fueling some of the persistent discrimination." "We hope this sends a strong message to employers that we are prepared to stand up for people with disabilities who are locked out of the job market because of increased reliance on these bias-fueled technologies," she said.

The technical assistance is a follow up to EEOC's announcement last fall that it would address the implications of hiring technologies for bias. In October 2021, Chair Charlotte Burrows said the agency would reach out to stakeholders as part of an initiative to learn about algorithmic tools and identify best practices around algorithmic fairness and the use of AI in employment decisions. Other EEOC members, including Commissioner Keith Sonderling, have previously spoken about the necessity of evaluating algorithm-based tools. A confluence of factors have led the agencies to address the topic, Burrows and Clarke said during Thursday's press call. One is the persistent issue of unemployment for U.S. workers with disabilities.

The use of artificial intelligence ("AI") and machine learning in the workplace is growing exponentially – and specifically in hiring. Over the last two decades, web-based applications and questionnaires have made paper applications nearly obsolete. As employers seek to streamline recruitment and control costs, they have jumped to use computer-based screening tools such as "chatbots" to communicate with job applicants, to schedule interviews, ask screening questions, and even conduct video conference interviews and presentations in the selection process. Employers of all sizes are creating their own systems, or hiring vendors who will design and implement keyword searches, predictive algorithms and even facial recognition algorithms to find the best-suited candidates. The algorithms in these computer models make inferences from data about people, including their identities, their demographic attributes, their preferences, and their likely future behaviors.