Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Gift Ideas For Everyone On Your List Mashable @Home In My Bag Safety Net Versus Trending Now Say More Creator Playbook Mashable Selects Getting Technical with Mashable All Series Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said Trump has been waffling at his rallies again, and Jimmy Kimmel wants you to know just how ridiculous things can get. For a man that is very proud he picked out all the marble in the White House, he certainly seems to be losing his, the host says in the monologue above, before playing a 40-second montage of some of Trump's more ridiculous and nonsensical moments. He's almost incoherent enough to be the next White House press secretary, says Kimmel.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Look Up Trending Now Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series Jimmy Kimmel calls out Fox's reaction to a'joke' from him versus one from Trump I'm going to report him to the FCC. Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' 'Kimmel' guest host Ike Barinholtz examines Trump's reaction to Lindsey Graham's death Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said Trump recently made a comment about LA and San Francisco that sparked backlash followed by a swift defence from the White House and Fox News, all of which has Jimmy Kimmel questioning the network's reaction to what it classifies as a joke. It's a small price to pay for keeping the world safe, keeping our country safe, Trump said about the Iran conflict during a rally.

Creator Playbook Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Say More Trending Now Mashable Selects Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net All Series It's so perfect, the guy who spent four years screaming'Sleepy Joe, sleepy Joe,' constantly falling asleep on camera, Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said Jimmy Kimmel has a brutal joke about Trump's memorial pool turning green Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' Need a reminder of the many times Trump has been accused of falling asleep on camera? Jimmy Kimmel has you covered. Inspired by Trump apparently napping during the recent 9/11 remembrance event at the Pentagon (He was sleeping so much on camera the White House had to put out a statement that said he wasn't asleep, says Kimmel), the host plays a montage of the president appearing to doze off at various events.

Look Up Back to School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Switch Off Mashable Voices Safety Net Versus All Series Jimmy Kimmel recaps Trump's summer with a montage of the weirdest things he's said Donald Trump and I have one thing in common: We both accomplished nothing all summer long. Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Rosie O'Donnell calls out'Mango Mussolini' Trump on'Kimmel' Seth Meyers' brutal montage proves that Trump has no idea how percentages work Jimmy Kimmel has the perfect joke to sum up Trump's Iran deal Jimmy Kimmel has been on vacation since mid-June, which means there was a lot to catch up on during his return to late night. Donald Trump and I have one thing in common, the host says in the monologue above.

Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large-scale models is hampered by topological heterogeneity: each public EEG data defines its own electrode layout, limiting generalization. We introduce LUNA (Latent Unified Network Architecture), a self-supervised foundation model that reconciles disparate electrode geometries while scaling linearly--not quadratically--with channel count. LUNA compresses multi-channel EEG into a fixed-size, topology-agnostic latent space via learned queries and cross-attention. Downstream transformer blocks then operate exclusively on this latent representation using patch-wise temporal self-attention, decoupling computation from electrode count.

In expressing emotions, as an expression form separate from natural language, we propose an alternative form that complements natural language, acting as a proxy or window for emotional states. First, we set up an expression form "Effect of Contradictory Structure." "Effect of Contradictory Structure" is not static but dynamic. Effect in "Effect of Contradictory Structure" is unpleasant or pleasant, and the orientation to avoid that unpleasantness is considered pseudo-expression of will. Second, "Effect of Contradictory Structure" can be overlapped with each other. This overlapping operation is called "montage." A broader "Structure" that includes related "Effect of Contradictory Structure" and "Effect of Structure" are set up. Montage produces "Effect of Structure". In montage, it is necessary to set something like "strength," so we adopted Deleuze and Deleuze/Guattari's word "intensity" and set it as an element of our model. We set up a general theoretical framework - Word Import Between Systems (Models) and justified the import of "intensity" through Austin's use of the word "force." "Effect of Structure" process is demonstrated using the example of proceeding to the next level of education.

Long-sequence electroencephalogram (EEG) modeling is essential for developing generalizable EEG representation models. This need arises from the high sampling rate of EEG data and the long recording durations required to capture extended neurological patterns in brain activity. Transformer-based models have shown promise in modeling short sequences of a few seconds; however, their quadratic complexity limits scalability to longer contexts. Moreover, variability in electrode montage across available datasets, along with inter-subject differences in brain signals, pose significant challenges to developing a generalizable and robust foundation model. We propose \textit{SAMBA}, a self-supervised learning framework with a Mamba-based U-shaped encoder-decoder architecture, which effectively captures long-range temporal dependencies and spatial variability in EEG data. Leveraging the inherent ability of Mamba in processing long context sizes, we introduce: (1) \textit{Temporal Semantic Random Masking} for semantic-level sequence reconstruction, (2) a \textit{Multi-Head Differential Mamba} module to suppress redundancy and emphasize salient temporal structures, and (3) a \textit{Spatial-Adaptive Input Embedding} that learns unified embeddings in a three-dimensional Euclidean space, enabling robustness across devices. Experiments on thirteen EEG datasets across diverse tasks, electrode configurations, and sequence durations demonstrate that SAMBA consistently outperforms state-of-the-art methods while maintaining low memory consumption and inference time. We also show the learned spatial weight maps from our embedding module align closely with task-relevant neurophysiological regions, demonstrating the learnability and interpretability of SAMBA. These results highlight SAMBA's scalability and practical potential as a foundation model for real-time brain-computer interface applications.

Inspired by the needle in a haystack test widely used by LLMs, we introduce a novel task of Ne edle in a Mo ntage (NeMo), designed to assess VideoLLMs' critical reasoning capabilities, including long-context recall and temporal grounding. To generate video question answering data for our task, we develop a scalable automated data generation pipeline that facilitates high-quality data synthesis. Built upon the proposed pipeline, we present NeMoBench, a video-language benchmark centered on our task. Specifically, our full set of NeMoBench features 31,378 automatically generated question-answer (QA) pairs from 13,486 videos with various durations ranging from seconds to hours. Experiments demonstrate that our pipeline can reliably and automatically generate high-quality evaluation data, enabling NeMoBench to be continuously updated with the latest videos. We evaluate 20 state-of-the-art models on our benchmark, providing extensive results and key insights into their capabilities and limitations.

This paper introduces AI Blob!, an experimental system designed to explore the potential of semantic cataloging and Large Language Models (LLMs) for the retrieval and recontextualization of archival television footage. Drawing methodological inspiration from Italian television programs such as Blob (RAI Tre, 1989-), AI Blob! integrates automatic speech recognition (ASR), semantic embeddings, and retrieval-augmented generation (RAG) to organize and reinterpret archival content. The system processes a curated dataset of 1,547 Italian television videos by transcribing audio, segmenting it into sentence-level units, and embedding these segments into a vector database for semantic querying. Upon user input of a thematic prompt, the LLM generates a range of linguistically and conceptually related queries, guiding the retrieval and recombination of audiovisual fragments. These fragments are algorithmically selected and structured into narrative sequences producing montages that emulate editorial practices of ironic juxtaposition and thematic coherence. By foregrounding dynamic, content-aware retrieval over static metadata schemas, AI Blob! demonstrates how semantic technologies can facilitate new approaches to archival engagement, enabling novel forms of automated narrative construction and cultural analysis. The project contributes to ongoing debates in media historiography and AI-driven archival research, offering both a conceptual framework and a publicly available dataset to support further interdisciplinary experimentation.

--Personalised music-based interventions offer a powerful means of supporting motor rehabilitation by dynamically tailoring auditory stimuli to provide external timekeeping cues, modulate affective states, and stabilise gait patterns. Gener-alisable Brain-Computer Interfaces (BCIs) thus hold promise for adapting these interventions across individuals. However, inter-subject variability in EEG signals, further compounded by movement-induced artefacts and motor planning differences, hinders the generalisability of BCIs and results in lengthy calibration processes. We propose Individual T angent Space Alignment (ITSA), a novel pre-alignment strategy incorporating subject-specific recentering, distribution matching, and supervised rotational alignment to enhance cross-subject generalisation. Using leave-one-subject-out cross-validation, 'ITSA' demonstrates significant performance improvements across subjects and conditions. The parallel fusion approach shows the greatest enhancement over its sequential counterpart, with robust performance maintained across varying data conditions and electrode configurations. The code will be made publicly available at the time of publication. Brain-computer interfaces (BCI) are effective tools for motor rehabilitation and understanding musical stimulus effects on motor function [1]-[4]. In stroke rehabilitation, BCIs decode the user's intention from brain electrical activity to provide sensorimotor feedback and enable control of external devices or motor functions [5], [6]. The use of these BCI strategies for motor rehabilitation has been grouped into either assistive or rehabilitative. The former focuses on bypassing the damaged neuronal pathways to provide alternative control of the external devices, whereas the latter aims to exploit neuro-plasticity by promoting the recovery of damaged pathways and therefore restoring impaired motor functions [5]. Electroen-cephalography signals are often used for the input of BCIs as they provide portable, non-invasive, low-cost solutions and have high temporal resolution [7].