Liquid democracy with ranked delegations is a novel voting scheme that unites the practicability of representative democracy with the idealistic appeal of direct democracy: Every voter decides between casting their vote on a question at hand or delegating their voting weight to some other, trusted agent. Delegations are transitive, and since voters may end up in a delegation cycle, they are encouraged to indicate not only a single delegate, but a set of potential delegates and a ranking among them. Based on the delegation preferences of all voters, a delegation rule selects one representative per voter. Previous work has revealed a trade-off between two properties of delegation rules called anonymity and copy-robustness. To overcome this issue we study two fractional delegation rules: MIXEDBORDA BRANCHING, which generalizes a rule satisfying copy-robustness, and the RANDOMWALKRULE, which satisfies anonymity. Using the Markov chain tree theorem, we show that the two rules are in fact equivalent, and simultaneously satisfy generalized versions of the two properties. Combining the same theorem with Fulkerson's algorithm, we develop a polynomial-time algorithm for computing the outcome of the studied delegation rule. This algorithm is of independent interest, having applications in semi-supervised learning and graph theory.

Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same. The fact that domain shifts often occur in practice necessitates equipping FL methods with a domain generalization (DG) capability. However, existing DG algorithms face fundamental challenges in FL setups due to the lack of samples/domains in each client's local dataset. In this paper, we propose StableFDG, a style and attention based learning strategy for accomplishing federated domain generalization, introducing two key contributions. The first is style-based learning, which enables each client to explore novel styles beyond the original source domains in its local dataset, improving domain diversity based on the proposed style sharing, shifting, and exploration strategies. Our second contribution is an attention-based feature highlighter, which captures the similarities between the features of data samples in the same class, and emphasizes the important/common characteristics to better learn the domain-invariant characteristics of each class in data-poor FL scenarios. Experimental results show that StableFDG outperforms existing baselines on various DG benchmark datasets, demonstrating its efficacy.

We present the HOH (Human-Object-Human) Handover Dataset, a large object count dataset with 136 objects, to accelerate data-driven research on handover studies, human-robot handover implementation, and artificial intelligence (AI) on handover parameter estimation from 2D and 3D data of two-person interactions. HOH contains multi-view RGB and depth data, skeletons, fused point clouds, grasp type and handedness labels, object, giver hand, and receiver hand 2D and 3D segmentations, giver and receiver comfort ratings, and paired object metadata and aligned 3D models for 2,720 handover interactions spanning 136 objects and 20 giver-receiver pairs--40 with role-reversal--organized from 40 participants. We also show experimental results of neural networks trained using HOH to perform grasp, orientation, and trajectory prediction. As the only fully markerless handover capture dataset, HOH represents natural human-human handover interactions, overcoming challenges with markered datasets that require specific suiting for body tracking, and lack high-resolution hand tracking. To date, HOH is the largest handover dataset in terms of object count, participant count, pairs with role reversal accounted for, and total interactions captured.

Fecal vandal's nearly weeklong crime spree comes to an end when police catch her in the act Catching the horny landlady teaching your boyfriend mouth-to-mouth is not a sign that it's time to move MAGA bikini congresswoman sends a message to big brother, Dale Earnhardt turns 75 & MLB fan gets pulverized! Wait... Who is actually using highway rest stop BBQ grills? Hilary Duff's latest Instagram content has suburban millennial moms gasping, a tennis match turns nasty & MEAT Opening day at Six Flags St. Louis ended in chaos after brawl with as many as 100 people broke out Mountain climber survives terrifying 500-foot fall in California's Sierra Nevada, night stranded on ledge Ella Langley's brand deal with American Eagle shows Bud Light how it could've been in 2023, fan fight & MEAT Shannon Elizabeth, to nobody's surprise, cashes in on OnlyFans with reported 7-figure payday in her first week Airline doesn't buy couple's claim that they were praying, bans them for attempting to join mile high club Bill Maher & David Cross get into heated war of words over'looney left' & trans rights, including 3-year-old'Map wars': Brit Hume says redistricting battle is'as bitter' as he's ever seen it Candidates make their case as California governor's race intensifies Hegseth, Caine defend Pentagon's budget request on Capitol Hill Greg Gutfeld: Walz tries to appear'above it all,' but is'drowning' in corruption Ukraine is'militarily' defeated: Trump Trump posts AI image of himself with a gun, says Iran'better get smart soon' Trump calls Comey a'dirty cop' and a'crooked man' Sen. Rand Paul backs White House ballroom after WHCA shooting Steven Hilton says voter ID push could boost GOP turnout in California governor's race There's no question that robots are going to be coming for some folks' jobs sooner rather than later, and it looks like baggage handlers could be one of the first on the robo-chopping block. Japan Airlines is going to start rolling out its humanoid robots to help with baggage at Tokyo's Haneda Airport. Now, while I'm usually not one to celebrate something like this -- I feel it's just one step closer to all of us having to pay our respects to robot overlords -- I was excited about it.

"I believe the technology was deployed too quickly in too vast amounts, with hundreds of vehicles, when it wasn't really ready," one police official told federal regulators last month. Emergency first-responder leaders told federal regulators in a private meeting last month that they were frustrated with the performance of autonomous vehicles on their streets--that city firefighters, police officers, EMTs, and paramedics are forced to spend time during emergencies resolving issues with frozen or stuck cars. One fire official called them "a safety issue for our crews as well as the victims." WIRED obtained an audio recording of the meeting. Officials from San Francisco and Austin, where Waymo has been ferrying passengers without drivers for more than a year, said the vehicles' performance is getting worse.

Musk accuses Altman of betraying OpenAI's nonprofit founding mission Tech billionaire Elon Musk has taken the stand for a second day in a landmark United States trial against Sam Altman, a fellow OpenAI co-founder whom he accuses of betraying promises to keep the company a nonprofit dedicated to humanity's benefit. The trial centres on OpenAI's 2015 founding as a nonprofit that later evolved into a for-profit venture. The world's richest man, Musk gave testimony in the case on Wednesday, telling jurors that he lost confidence that Altman would maintain the company's nonprofit mission. Musk, who left the company in 2018, said that by late 2022, he was concerned that Altman was trying to "steal the charity" and alleged that "it turned out to be true". Altman was present at the proceedings in a California federal court, but did not testify.

Aleksandra Mendoza, aka Alorah Ziva, alleges that the 20-year-old influencer injected her with drugs on a livestream and had nonconsensual sex with her while she was underage. An 18-year-old woman who promotes herself as the "#1 female looksmaxxer" is suing the highly controversial streamer Braden Eric Peters, aka Clavicular, for fraud, battery, and alleged sexual assault. In the suit, which was filed in Miami-Dade County court and obtained by WIRED, Aleksandra Mendoza, who goes by the name @zahloria, or Alorah Ziva, on Instagram, alleges that she first encountered Peters in May 2025, when she was just 16 years old. According to the complaint, Peters promised Mendoza he could make her "the female face of looksmaxxing," the online trend of using surgery or drugs to enhance one's facial features. Eager to grow her social media following, Mendoza agreed to make four looksmaxxing videos for Peters in exchange for a $1,000 payment, court documents say.