Research has suggested super-recognisers look at more areas across a face than typical people. Research has suggested super-recognisers look at more areas across a face than typical people. Research uses eye-tracking data to examine some people's extraordinary recognition ability They have been used in the search for the Salisbury novichok poisoners, finding murder suspects and even spotting sexual predators. Now, research has revealed fresh insights into why super-recognisers are so good at identifying faces. Previous research has suggested people with an extraordinary ability to recognise people look at more areas across a face than typical people.

Suspicious drone sightings in states across the Northeast in recent weeks may be coming from inside the United States, according to a military expert. Civilians and lawmakers have reported drone sightings in New Jersey, Pennsylvania, New York, Connecticut, Ohio and other states, with local and federal law enforcement offering little information about the aerial activity, most of which has been spotted at night. Some of the drones are as large as 6 feet in diameter, according to New Jersey state Rep. Dawn Fantasia, who was briefed on the matter last week. "The concern is definitely valid. One thing I do believe, I believe the government knows the source of these drones, and I believe the source of these drones is from inside the U.S., especially the larger drones," Col. William Dunn, president of Strategic Resilience Group, a government consulting group, told Fox News Digital.

On the evening of January 6, 2021, Brad Parscale texted Donald Trump adviser Katrina Pierson about the insurrectionist assault on the US Capitol that had finally been quashed by police. "This is about [T]rump pushing for uncertainty in our country," wrote Parscale, who ran digital and data operations for Trump's 2016 campaign and managed his 2020 reelection effort before being replaced. This week I feel guilty for helping him win." "You did what you felt right at the time and therefore it was right," Pierson replied. "Yeah," Parscale answered, "but a woman is dead." The conversation continued, with Pierson texting, "You do realize this was going to happen." Parscale responded that Trump's rhetoric had "killed someone." Pierson countered, "It wasn't the rhetoric." Parscale was obviously blaming Trump for the storming of the Capitol and the death of Trump supporter Ashli Babbitt. In these private texts--which were not made public until mid-2022 during the House investigation of January ...

While language is a complex adaptive system, most work on syntactic variation observes a few individual constructions in isolation from the rest of the grammar. This means that the grammar, a network which connects thousands of structures at different levels of abstraction, is reduced to a few disconnected variables. This paper quantifies the impact of such reductions by systematically modelling dialectal variation across 49 local populations of English speakers in 16 countries. We perform dialect classification with both an entire grammar as well as with isolated nodes within the grammar in order to characterize the syntactic differences between these dialects. The results show, first, that many individual nodes within the grammar are subject to variation but, in isolation, none perform as well as the grammar as a whole. This indicates that an important part of syntactic variation consists of interactions between different parts of the grammar. Second, the results show that the similarity between dialects depends heavily on the sub-set of the grammar being observed: for example, New Zealand English could be more similar to Australian English in phrasal verbs but at the same time more similar to UK English in dative phrases.

This paper measures variation in embedding spaces which have been trained on different regional varieties of English while controlling for instability in the embeddings. While previous work has shown that it is possible to distinguish between similar varieties of a language, this paper experiments with two follow-up questions: First, does the variety represented in the training data systematically influence the resulting embedding space after training? This paper shows that differences in embeddings across varieties are significantly higher than baseline instability. Second, is such dialect-based variation spread equally throughout the lexicon? This paper shows that specific parts of the lexicon are particularly subject to variation. Taken together, these experiments confirm that embedding spaces are significantly influenced by the dialect represented in the training data. This finding implies that there is semantic variation across dialects, in addition to previously-studied lexical and syntactic variation.

Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which Verbal and Nominal are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.

This paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of grammar induction and geospatial text classification produces robust dialect models, we do not know what influence both changing grammars and changing populations have on dialect models. This paper constructs a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. Syntactic representations are formulated within the usage-based Construction Grammar paradigm (CxG). The decay rate of classification performance for each dialect over time allows us to identify regions undergoing syntactic change. And the distribution of classification accuracy within dialect regions allows us to identify the degree to which the grammar of a dialect is internally heterogeneous. The main contribution of this paper is to show that a rigorous evaluation of dialect classification models can be used to find both variation over space and change over time.

With the abundance of available data, many enterprises seek to implement data-driven prescriptive analytics to help them make informed decisions. These prescriptive policies need to satisfy operational constraints, and proactively eliminate rule conflicts, both of which are ubiquitous in practice. It is also desirable for them to be simple and interpretable, so they can be easily verified and implemented. Existing approaches from the literature center around constructing variants of prescriptive decision trees to generate interpretable policies. However, none of the existing methods are able to handle constraints. In this paper, we propose a scalable method that solves the constrained prescriptive policy generation problem. We introduce a novel path-based mixed-integer program (MIP) formulation which identifies a (near) optimal policy efficiently via column generation. The policy generated can be represented as a multiway-split tree which is more interpretable and informative than a binary-split tree due to its shorter rules. We demonstrate the efficacy of our method with extensive experiments on both synthetic and real datasets.

LOS ANGELES, July 22 (Thomson Reuters Foundation) - Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data and forecasting to the world of firefighting.

Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.