Let's nitpick about the physics of Stranger Things, not its ending Feedback has seen all the fuss about the finale of Stranger Things, but would like to point out that if we're going to dissect the plot, we have bigger things to worry about In common, it seems, with a substantial fraction of the human species, Feedback spent part of our holiday watching the final episodes of Stranger Things . We laughed, we cried, we wondered if it would have even more endings than The Return of the King (it did). As is almost inevitable these days, a group of fans vocally disliked the finale, and went so far as to create a conspiracy theory about it. According to "Conformity Gate" (don't blame us, we didn't name it), the finale wasn't the real finale - despite lasting more than 2 hours, costing an enormous amount of money and being shown in cinemas. No, a super-secret final episode was going to air in January, which would reveal the true ending.

US military poised to seize ports and airfields in Venezuela as Trump strikes a fourth'narco-terrorist' boat Robert Griffin III involved in'scary' car crash with wife and kids as shocking photos emerge I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied His daughter was warped into an ultra-woke monster and set fire to his life. Now, GOP state senator Jay Block fights back... and reveals the dark secrets she was desperate to hide The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Realtor with expensive ex-wife arrested over shocking $11.6m claims about how he was funding Palm Beach lifestyle Trump dollar coin design released by Treasury... and its inspired by the most iconic political photo of the century Fired CNN host Don Lemon's delivers expletive-filled rant at Megyn Kelly for comments about his husband Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish Warning as pasta salad is recalled due to risk of'fatal infections' Multiple'UFOs' caught on camera flying over erupting volcano claimed to be a'wormhole' for aliens A swarm of UFOs was seen flying over an active volcano in Mexico this week, reviving a wild theory that the natural landmark could be an alien portal to space .

We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein (SW) distances. Specifically, we leverage both standard SW distances, which provide lower bounds, and lifted SW distances, which provide upper bounds, as predictors of the true Wasserstein distance. To ensure parsimony, we introduce two linear models: an unconstrained model with a closed-form least-squares solution, and a constrained model that uses only half as many parameters. We show that accurate models can be learned from a small number of distribution pairs. Once estimated, the model can predict the Wasserstein distance for any pair of distributions via a linear combination of SW distances, making it highly efficient. Empirically, we validate our approach on diverse tasks, including Gaussian mixtures, point-cloud classification, and Wasserstein-space visualizations for 3D point clouds. Across various datasets such as MNIST point clouds, ShapeNetV2, MERFISH Cell Niches, and scRNA-seq, our method consistently provides a better approximation of Wasserstein distance than the state-of-the-art Wasserstein embedding model, Wasserstein Wormhole, particularly in low-data regimes. Finally, we demonstrate that our estimator can also accelerate Wormhole training, yielding \textit{RG-Wormhole}.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' In 2019, gravitational wave detectors on Earth picked up a signal that left scientists baffled. Gravitational waves are ripples in the fabric of space and time, usually created when massive, dense objects like black holes collide. But at less than a tenth of a second long, this sudden burst was far shorter than the drawn-out chirps normally produced by merging black holes. Now, researchers think this strange signal, dubbed GW190521, could have arrived from a parallel universe.

Elon Musk called on Thursday for the United States to "delete entire agencies" from the federal government as part of his push under President Donald Trump to radically cut spending and restructure its priorities. Musk offered a wide-ranging survey via a videocall to the World Governments Summit in Dubai, United Arab Emirates, of what he described as the priorities of the Trump administration interspersed with multiple references to "thermonuclear warfare" and the possible dangers of artificial intelligence. "We really have here rule of the bureaucracy as opposed to rule of the people -- democracy," Musk said, wearing a black T-shirt that read: "Tech Support." He also joked that he was the "White House's tech support," borrowing from his profile on the social platform X, which he owns. "I think we do need to delete entire agencies as opposed to leave a lot of them behind," Musk said.

Previous studies on question generation from videos have mostly focused on generating questions about common objects and attributes and hence are not entity-centric. In this work, we focus on the generation of entity-centric information-seeking questions from videos. Such a system could be useful for video-based learning, recommending ``People Also Ask'' questions, video-based chatbots, and fact-checking. Our work addresses three key challenges: identifying question-worthy information, linking it to entities, and effectively utilizing multimodal signals. Further, to the best of our knowledge, there does not exist a large-scale dataset for this task. Most video question generation datasets are on TV shows, movies, or human activities or lack entity-centric information-seeking questions. Hence, we contribute a diverse dataset of YouTube videos, VideoQuestions, consisting of 411 videos with 2265 manually annotated questions. We further propose a model architecture combining Transformers, rich context signals (titles, transcripts, captions, embeddings), and a combination of cross-entropy and contrastive loss function to encourage entity-centric question generation. Our best method yields BLEU, ROUGE, CIDEr, and METEOR scores of 71.3, 78.6, 7.31, and 81.9, respectively, demonstrating practical usability. We make the code and dataset publicly available. https://github.com/thePhukan/ECIS-VQG

Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity logs. These concepts provide valuable insights into the underlying structures and behaviors of sequential data, enabling better decision-making and forecasting. This paper introduces Wormhole, a novel deep representation learning framework that is concept-aware and designed for co-evolving time sequences. Our model presents a self-representation layer and a temporal smoothness constraint to ensure robust identification of dynamic concepts and their transitions. Additionally, concept transitions are detected by identifying abrupt changes in the latent space, signifying a shift to new behavior - akin to passing through a wormhole. This novel mechanism accurately discerns concepts within co-evolving sequences and pinpoints the exact locations of these wormholes, enhancing the interpretability of the learned representations. Experiments demonstrate that this method can effectively segment time series data into meaningful concepts, providing a valuable tool for analyzing complex temporal patterns and advancing the detection of concept drifts.

Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology.

The experience of an ACL2 user generally includes many failed proof attempts. A key to successful use of the ACL2 prover is the effective use of tools to debug those failures. We focus on changes made after ACL2 Version 8.5: the improved break-rewrite utility and the new utility, with-brr-data.

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This integration equips LLMs with the capability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. The AI community has witnessed the emergence of powerful pre-trained Large Language Models (LLMs) (Brown et al., 2020; Chowdhery et al., 2022; OpenAI, 2023; Touvron et al., 2023), which leads to the pursuit of the potential realization of Artificial General Intelligence (AGI). Inspired by the fact that an intelligent agent, like the human brain, processes information of diverse types, there is a trend towards empowering LLMs to understand various forms of data, such as audio (Huang et al., 2023) and images (Alayrac et al., 2022). Despite significant strides in interpreting multimodal information (Yin et al., 2023), empowering LLMs to understand graph data remains relatively unexplored. Graphs, which represent entities as nodes and relationships as edges, are ubiquitous in numerous fields, e.g. An intelligent agent is expected to reason with graph data to facilitate many tasks such as drug discovery (Stokes et al., 2020) and chip design (Mirhoseini et al., 2021).