US military'war room' jet spotted leaving Washington DC as unrest grips the nation A simple trick cured my tinnitus after a long-haul flight left me in misery for months. Here's the miracle method I wish everyone knew Insidious secret life of promiscuous neurosurgeon found dead in his $2.5m mansion Impeachment whistleblower launches Senate campaign in Trump's home state: 'Our country is in chaos' Gavin Newsom's ballyhooed'care first' $236 million mental health push helps ONLY 22 people in four years Lawyer, 44, who died on flight to London after falling asleep on her mother's shoulder had undiagnosed cardiac condition, inquest hears Heart stopping video shows toddler fly out of mother's Mercedes SUV at busy LA intersection Food Network star Valerie Bertinelli's heartbreaking struggles laid bare after confession about shock firing My perfect life at $2m Manchester-by-the-Sea mansion took nasty turn when neighbors tried to ban me from getting a gun because of my HUSBAND - now I've had the last laugh ICE agents WILL be present at Milan Cortina Winter Olympics, officials confirm... despite furious response from locals Winter Storm Fern death toll climbs to 34 after brutal freeze batters the US... and meteorologists warn even colder weather is on the way Coco Gauff's behind-the-scenes meltdown at the Australian Open: World No 3 smashes racket in a rage after losing in just 59 minutes - and it was all caught on camera Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Brandi Glanville debuts dramatic new look after facial disfigurement caused by'parasite'... see RHOBH alum now Martha Stewart breaks political silence after being urged by teenage granddaughter: 'Things must change' US military'war room' jet spotted leaving Washington DC as unrest grips the nation The US Air Force's Boeing C-40B Clipper is currently flying west after departing Washington DC Tuesday morning on an undisclosed mission. The aircraft, often described as an airborne'war room' or'flying office,' is used to transport high-priority personnel, including Cabinet members, combatant commanders and senior military leaders. It also provides secure, global communications capabilities for VIP transport and special missions.

Somalis plunge to bottom of America's favorability rankings after fraud scandal as poll reveals immigrant group voters like most Trump declares himself'Acting President of Venezuela' in cryptic social media post Tucker Carlson is pushing a dangerous'globo-homo' conspiracy. I lost 3st and still couldn't get rid of my stubborn double chin. Then I found an astonishing, permanent knife-free cure few know about... so can you guess my secret? Lip reader deciphers argument they tried to hide behind smiles: 'You must hate me' Seattle's woke new mayor proudly poses for photo with Antifa activist holding placard calling for arson attacks My husband only wants sex once a month... but I know he masturbates in the shower every day. Kylie Jenner and Timothée Chalamet's VERY unlikely pairing is illustrated in'iconic' Golden Globes snap - as reality star pulls'classless' pose while indie actor is stony-faced We went from £100 a month to £1MILLION in five years and retired: We weren't high earners and these were our game-changing tricks Truth about'wonder supplement' creatine: It can boost middle-aged muscles, help sleep, brain health and even ease women's hormone problems.

Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors.

Abstract-- Global data association is an essential prerequisite for robot operation in environments seen at different times or by different robots. Repetitive or symmetric data creates significant challenges for existing methods, which typically rely on maximum likelihood estimation or maximum consensus to produce a single set of associations. However, in ambiguous scenarios, the distribution of solutions to global data association problems is often highly multimodal, and such single-solution approaches frequently fail. In this work, we introduce a data association framework that leverages approximate Bayesian inference to capture multiple solution modes to the data association problem, thereby avoiding premature commitment to a single solution under ambiguity. Our approach represents hypothetical solutions as particles that evolve according to a deterministic or randomized update rule to cover the modes of the underlying solution distribution. Furthermore, we show that our method can incorporate optimization constraints imposed by the data association formulation and directly benefit from GPU-parallelized optimization. Extensive simulated and real-world experiments with highly ambiguous data show that our method correctly estimates the distribution over transformations when registering point clouds or object maps. I. INTRODUCTION Data association is essential in many robotic applications, enabling key perception technologies such as dynamic object tracking [1]-[3] and simultaneous localization and mapping (SLAM) [4]-[6]. In these scenarios, robots must recognize when an object or feature they are currently observing is the same as something they (or another robot) may have seen from a different perspective. Without correct data association, the environment representation may be inconsistent, leading to undesirable behaviors in downstream tasks (e.g., incorrect associations in loop closure detection can lead to dramatically distorted maps [6]).

LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a compression-based approach for generating synthetic data tailored to narrative claim verification - a task that requires reasoning over a book to verify a given claim. Instead of generating claims directly from the raw text of the book, which results in artifact-riddled claims, CLIPPER first compresses the book into chapter outlines and book summaries and then uses these intermediate representations to generate complex claims and corresponding chain-of-thoughts. Compared to naive approaches, CLIPPER produces claims that are more valid, grounded, and complex. Using CLIPPER, we construct a dataset of 19K synthetic book claims paired with their source texts and chain-of-thought reasoning, and use it to fine-tune three open-weight models. Our best model achieves breakthrough results on narrative claim verification (from 28% to 76% accuracy on our test set) and sets a new state-of-the-art for sub-10B models on the NoCha leaderboard. Further analysis shows that our models generate more detailed and grounded chain-of-thought reasoning while also improving performance on other narrative understanding tasks (e.g., NarrativeQA).

On a recent Wednesday night in Los Angeles, I was ready to buy a hot dog with my face. I was at the Intuit Dome, a 2 billion entertainment complex that opened earlier this month. Soon, it will be the home of the L.A. Clippers, but I was there to watch Olivia Rodrigo, queen of teen angst, perform a sold-out show. The arena was filled with people wearing purple cowboy hats and the same silver sequin miniskirt, all of us ready to scream-sing for two hours straight. But first, we needed food.

One upshot of the current glut of streaming platforms is a flood of programming to fill them: something for every attention span, something to plug every potential gap of viewer inactivity that might render a certain streaming service irrelevant while some other service pulls ahead. And so stories get told and retold. The romantic comedies begin to feel the same. The dating reality shows rely (often successfully, it must be said) on the same dramatic tricks. Another consequence of this, for better or worse, is that the stories being told are pulling from more immediate memory.

Surgical triplet recognition is an essential building block to enable next-generation context-aware operating rooms. The goal is to identify the combinations of instruments, verbs, and targets presented in surgical video frames. In this paper, we propose DiffTriplet, a new generative framework for surgical triplet recognition employing the diffusion model, which predicts surgical triplets via iterative denoising. To handle the challenge of triplet association, two unique designs are proposed in our diffusion framework, i.e., association learning and association guidance. During training, we optimize the model in the joint space of triplets and individual components to capture the dependencies among them. At inference, we integrate association constraints into each update of the iterative denoising process, which refines the triplet prediction using the information of individual components. Experiments on the CholecT45 and CholecT50 datasets show the superiority of the proposed method in achieving a new state-of-the-art performance for surgical triplet recognition. Our codes will be released.

Knowing the locations of nearby moving objects is important for a mobile robot to operate safely in a dynamic environment. Dynamic object tracking performance can be improved if robots share observations of tracked objects with nearby team members in real-time. To share observations, a robot must make up-to-date estimates of the transformation from its coordinate frame to the frame of each neighbor, which can be challenging because of odometry drift. We present Multiple Object Tracking with Localization Error Elimination (MOTLEE), a complete system for a multi-robot team to accurately estimate frame transformations and collaboratively track dynamic objects. To accomplish this, robots use open-set image-segmentation methods to build object maps of their environment and then use our Temporally Consistent Alignment of Frames Filter (TCAFF) to align maps and estimate coordinate frame transformations without any initial knowledge of neighboring robot poses. We show that our method for aligning frames enables a team of four robots to collaboratively track six pedestrians with accuracy similar to that of a system with ground truth localization in a challenging hardware demonstration. The code and hardware dataset are available at https://github.com/mit-acl/motlee.

Identifying correspondences in noisy data is a critically important step in estimation processes. When an informative initial estimation guess is available, the data association challenge is less acute; however, the existence of a high-quality initial guess is rare in most contexts. We explore graph-theoretic formulations for data association, which do not require an initial estimation guess. Existing graph-theoretic approaches optimize over unweighted graphs, discarding important consistency information encoded in weighted edges, and frequently attempt to solve NP-hard problems exactly. In contrast, we formulate a new optimization problem that fully leverages weighted graphs and seeks the densest edge-weighted clique. We introduce two relaxations to this problem: a convex semidefinite relaxation which we find to be empirically tight, and a fast first-order algorithm called CLIPPER which frequently arrives at nearly-optimal solutions in milliseconds. When evaluated on point cloud registration problems, our algorithms remain robust up to at least 95% outliers while existing algorithms begin breaking down at 80% outliers. Code is available at https://mit-acl.github.io/clipper.