We estimate fair graphs from graph-stationary nodal observations such that connections are not biased with respect to sensitive attributes. Edges in real-world graphs often exhibit preferences for connecting certain pairs of groups. Biased connections can not only exacerbate but even induce unfair treatment for downstream graph-based tasks. We therefore consider group and individual fairness for graphs corresponding to group- and node-level definitions, respectively. To evaluate the fairness of a given graph, we provide multiple bias metrics, including novel measurements in the spectral domain. Furthermore, we propose Fair Spectral Templates (FairSpecTemp), an optimization-based method with two variants for estimating fair graphs from stationary graph signals, a general model for graph data subsuming many existing ones. One variant of FairSpecTemp exploits commutativity properties of graph stationarity while directly constraining bias, while the other implicitly encourages fair estimates by restricting bias in the graph spectrum and is thus more flexible. Our methods enjoy high probability performance bounds, yielding a conditional tradeoff between fairness and accuracy. In particular, our analysis reveals that accuracy need not be sacrificed to recover fair graphs. We evaluate FairSpecTemp on synthetic and real-world data sets to illustrate its effectiveness and highlight the advantages of both variants of FairSpecTemp.

Whoopi Goldberg and'View' guest Charlamagne Tha God argued over President Biden's pardon of Hunter and whether he was a liar. "The View" co-host Ana Navarro admitted that she relied on information given by ChatGPT after she was mocked for sharing false information about presidential pardons in her defense of President Biden. Biden came under fire this week for issuing a sweeping pardon to his son Hunter Biden on Sunday after repeatedly insisting he would not do so. Navarro, who identifies as a Republican but is an ardent supporter of Democrats who reliably offers liberal commentary on "The View" and on CNN, came out swinging against Biden's critics. She wrote on X, "Woodrow Wilson pardoned his brother-in-law, Hunter deButts. Bill Clinton pardoned his brother, Roger. Donald Trump pardoned his daughter's father-in-law, Charlie Kushner. And just appointed him Ambassador to France. But tell me again how Joe Biden'is setting precedent'?" Navarro's bizarre claim about Woodrow Wilson's pardon of a fictional brother-in-law named "Hunter deButts" instantly raised eyebrows.

Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with or even superior to their highly optimized CNN-based counterparts operating on 2D natural images, their success is closely coupled to access to a vast amount of training data. This, however, restricts the feasibility of employing Detection Transformers in the medical domain, as access to annotated data is typically limited. To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder. Focused Decoder leverages information from an anatomical region atlas to simultaneously deploy query anchors and restrict the cross-attention's field of view to regions of interest, which allows for a precise focus on relevant anatomical structures. We evaluate our proposed approach on two publicly available CT datasets and demonstrate that Focused Decoder not only provides strong detection results and thus alleviates the need for a vast amount of annotated data but also exhibits exceptional and highly intuitive explainability of results via attention weights. Our code is available at https://github.com/bwittmann/transoar.

Artificial intelligence has found disparities in the amount of airtime women and men were given on CNN, FOX News and MSNBC - females had a 10 percent less chance of speaking during political discussions because male speakers constantly interrupted them. The discovery was made by researchers at Rochester Institute of Technology who analyzed 625,409 dialogues hosted on the three news cable networks from January 2000 through July 2021. The technology revealed women received an average of 72.8 words per chance to speak compared to 81.4 for male speakers and women were interrupted 39.4 percent of the time during discussions - this is compared to the 35.9 percent of the time for men. The team believes their AI could be used during talk shows, interviews and political debates to identify a serial interrupter in real-time, but the study also reinforces previous research that found men interrupt women more to show their dominance. AI analyzed thousands of dialogues from news segments on the three networks and found woman are given a 10 percent less chance at speaking because men interrupt them.

The Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of popular lossless compression programs (such as bzip2) as well as recent powerful compressed indexes (such as the r-index7), central in modern bioinformatics. The compressibility of BWT is quantified by the number r of equal-letter runs in the output. Despite the practical significance of BWT, no nontrivial upper bound on r is known. By contrast, the sizes of nearly all other known compression methods have been shown to be either always within a poly-log n factor (where n is the length of the text) from z, the size of Lempel–Ziv (LZ77) parsing of the text, or much larger in the worst case (by an nε factor for ε 0). In this paper, we show that r (z log2 n) holds for every text. This result has numerous implications for text indexing and data compression; in particular: (1) it proves that many results related to BWT automatically apply to methods based on LZ77, for example, it is possible to obtain functionality of the suffix tree in (z polylog n) space; (2) it shows that many text processing tasks can be solved in the optimal time assuming the text is compressible using LZ77 by a sufficiently large polylog n factor; and (3) it implies the first nontrivial relation between the number of runs in the BWT of the text and of its reverse. In addition, we provide an (z polylog n)-time algorithm converting the LZ77 parsing into the run-length compressed BWT. To achieve this, we develop several new data structures and techniques of independent interest. In particular, we define compressed string synchronizing sets (generalizing the recently introduced powerful technique of string synchronizing sets11) and show how to efficiently construct them. Next, we propose a new variant of wavelet trees for sequences of long strings, establish a nontrivial bound on their size, and describe efficient construction algorithms. Finally, we develop new indexes that can be constructed directly from the LZ77 parsing and efficiently support pattern matching queries on text substrings. Lossless data compression aims to exploit redundancy in the input data to represent it in a small space.

Cosmologist Francisco "Paco" Villaescusa-Navarro has a problem. "We are spending billions of dollars in ground and space telescopes to decipher the mysteries of the universe," he explains, "but we are missing most of the information that the surveys contain." The issue is that in any survey, most of the information is at the very smallest scales. For example, if you look at a picture of a forest, you'll get some information, like a rough idea of how many trees are in there. Once you zoom in a bit, you can see the individual trees and get more information – say, the different species and their heights.

The'Outnumbered' panel reacts to Sunny Hostin and Ana Navarro being pulled from the set moments before the vice president was set to arrive Ana Navarro, one of two co-hosts who were pulled from ABC's "The View" live on air Friday due to positive COVID-19 tests, has since revealed the results that caused the chaos were false positives. Producers informed Navarro and Sunny Hostin in their earpieces halfway through Friday's broadcast that they would have to leave the Hot Topics table, leaving Joy Behar and Sara Haines to conduct the rest of the show on their own. The remaining hosts often struggled to kill time, at one point taking questions from the audience, but often not being able to hear the questions that were muffled by their masks. Friday's drama was even more pronounced considering Navarro and Hostin were pulled just as Vice President Kamala Harris was on her way to the studio for an in-person interview. Even though Harris made it to the building, producers explained her appearance would end up taking place remotely from a separate room out of precaution.

In addition to new outfits and backpacks, face masks are now an essential addition to kids' back-to-school gear. According to new guidelines released by the Centers for Disease Control and Prevention, all students and staff should wear masks inside schools, regardless of vaccination status. But kids used to virtual learning may not have much experience interacting or communicating with their peers or teachers while masked. And parents and child development experts alike are wondering how that will affect children as they return to school. For instance, to assess whether kids can accurately interpret a masked person's emotions, researchers from the University of Wisconsin-Madison's Child Emotion Lab showed children ages seven to 13 pictures of people displaying different emotions.

Technology evolution is no longer keeping pace with the growth of data. We are facing problems storing and processing the huge amounts of data produced every day. People rely on data-intensive applications and new paradigms (for example, edge computing) to try to keep computation closer to where data is produced and needed. Thus, the need to store and query data in devices where capacity is surpassed by data volume is routine today, ranging from astronomy data to be processed by supercomputers, to personal data to be processed by wearable sensors. The scale is different, yet the underlying problem is the same.

On Fox Nation's "Maria Bartiromo's Insiders", White House trade adviser Peter Navarro pointed the finger at the Federal Reserve amid concerns of a potential U.S. economic recession. "The problem here, the thing that worries me is that we've got a Federal Reserve playing checkers in a chess world," said Navarro. Despite signs of slowing U.S. job growth and global economic uncertainty, Navarro insists that the U.S. economy is "solid", and he blamed the U.S. central bank for failing to properly react to financial moves made by global actors. "In the world of central banking, the Federal Reserve has to pay very close attention to what the European Central Bank is doing and other central banks. If they lower, we have to lower otherwise we lose exports and we slow our growth," said Navarro, adding that the high cost of borrowing is damaging to the U.S. economy.