We therefore pursue a data-driven alternative based on complex-valued V AEs and latent-space OOD scores. In recent years, data-driven approaches have emerged to alleviate the need for precise clutter modeling. Among them, V AEs [4] have demonstrated promising capabilities for anomaly and OOD detection in diverse applications, including radar detection [5], speech enhancement [6], medical imaging [7], industrial monitoring [8], and acoustic signal analysis [9]. These models learn a latent representation of the training data and use reconstruction or probabilistic criteria to detect deviations. Despite their effectiveness, most V AE-based detectors operate in the real domain and often treat complex-valued radar data by separating real and imaginary components into distinct channels. Recent advances in Complex-V alued Neural Networks (CVNNs) have shown the benefits of directly modeling complex-valued signals [10, 11].

This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between Metropolis-Hastings and Gibbs sampling. While such approaches are often considered advantageous for enabling data-driven regularization, we show that their performance critically depends on the sharpness of the employed likelihood form. By introducing a sharpness parameter and exploring alternative likelihood formulations proportional to the target objective function, we demonstrate how likelihood curvature governs both in-sample performance and the degree of regularization inferred by the training data. Empirical applications are conducted on reinforcement learning tasks: including a navigation problem and the game of tic-tac-toe. The study concludes with a separate analysis examining the implications of extreme likelihood sharpness on arbitrary objective functions stemming from the classic game of blackjack, where the first block of the two-block MCMC framework is replaced with an iterative optimization step. The resulting hybrid approach achieves performance nearly identical to the original MCMC framework, indicating that excessive likelihood sharpness effectively collapses posterior mass onto a single dominant mode.

Existing methods are either: (i) DNN-based, which are inherently coupled with network-specific parameters, inevitably introducing architectural bias and compromising generalization; or (ii) DNN-free, which utilize heuristics that lack rigorous theoretical guarantees for stability and accuracy. Neither approach explicitly constrains distributional equivalence of the representative subsets, largely because continuous distribution matching is broadly considered inapplicable to discrete dataset sampling. Furthermore, prevalent distribution metrics (e.g., MSE, KL, MMD, and CE) are often incapable of accurately capturing higher-order moments differences. These deficiencies lead to suboptimal coreset performance, preventing the selected coreset from being truly equivalent to the original dataset. W e propose F AST (Frequency-domain Aligned Sampling via T opology), the first DNN-free distribution-matching coreset selection framework that formulates coreset selection task as a graph-constrained optimization problem grounded in spectral graph theory and employs the Characteristic Function Distance (CFD) to capture full distributional information (i.e., all moments and intrinsic correlations) in the frequency domain. W e further discover that naive CFD suffers from a "vanishing phase gradient" issue in medium and high-frequency regions; to address this, we introduce an Attenuated Phase-Decoupled CFD.

During generation, we use cuboidal control domains of varying dimensionality, location, and shape, to slice out relevant substructures. These local substructures are used to compute differentiable penalty functions that steer the sample towards target constraints. W e control geometric features such as size, shape, and position through voxel-wise moments, while topological features such as connected components, loops, and voids are enforced through persistent homology. Lastly, we implement Anatomica for latent diffusion models, where neural field decoders partially extract substructures, enabling the efficient control of anatomical properties. Anatomica applies flexibly across diverse anatomical systems, composing constraints to control complex structures over arbitrary dimensions and coordinate systems, thereby enabling the rational design of synthetic datasets for virtual trials or machine learning workflows.

Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text labelled with corresponding sentiment classes. One reason for that is that manually labelling text data is time-consuming and expensive. Consequently, automatic and rapid processes are needed to reduce the manual effort as much as possible making the labelling process as efficient as possible. In this paper, we present and analyze an automatic language-independent sentiment labelling method that leverages information from sentiment-bearing emojis and words. Our experiments are conducted with tweets in the languages English, Sepedi and Setswana from SAfriSenti, a multilingual sentiment corpus for South African languages. We show that our sentiment labelling approach is able to label the English tweets with an accuracy of 66%, the Sepedi tweets with 69%, and the Setswana tweets with 63%, so that on average only 34% of the automatically generated labels remain to be corrected.

A driver takes his hands off the wheel of an Isuzu self-driving truck during a test run in Mukawa, Hokkaido, on Nov. 18. In the face of a serious shortage of drivers in the logistics industry, Japan's government and commercial vehicle-makers are accelerating experiments aimed at putting self-driving trucks into practical use. They are aiming to attain Level 4 autonomous driving, or driving without human intervention under certain conditions. In addition to carrying out the trials, they will also make efforts to gain the public's understanding for self-driving trucks to ease concerns. Autonomous driving for large vehicles carries a risk of being rejected by the public with a single accident, a senior official of a commercial vehicle-maker said. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses'I fell for Joan the moment I saw her': The emotional love letter Sir Richard Branson penned to his'rock' on their anniversary - as he announces her death after 50 years together Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Emmy-winning CBS anchor reveals her devastating health battle: 'I've been silently struggling' Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train Celebrities are known for living life in the fast lane - but being famous really can prove deadly, according to a new study. Researchers have discovered that being in the limelight comes with a higher mortality risk compared to those who never quite'make it'. It could explain why some singers such as Janis Joplin, Whitney Houston and Jimi Hendrix died so young. And it suggests that fame comes with'unique psychosocial stress' that leads to'harmful coping behaviours' like substance abuse, they said.

Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses'I fell for Joan the moment I saw her': The emotional love letter Sir Richard Branson penned to his'rock' on their anniversary - as he announces her death after 50 years together Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Emmy-winning CBS anchor reveals her devastating health battle: 'I've been silently struggling' Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train Scientists have captured the first-ever direct evidence for dark matter, the elusive substance that makes up more than a quarter of the universe. Using NASA's Fermi telescope, researchers have detected powerful gamma-ray radiation emerging from a'halo-like' structure surrounding the Milky Way. Its frequency and intensity suggest that this could be dark matter. According to the study's author, Professor Tomonori Totani of the University of Tokyo, this eerie image is the first time that humanity has been able to'see' the mysterious substance.

Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train Couple left red-faced after buying $25K'dirt alley' at auction thinking it was bargain San Francisco home LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Troubled 350lb son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Doctors appalled by North West's new body modification warn parents to stop children from chasing the dangerous fad Alex appeared to have the dream Manhattan mom life. But she was hiding a dark secret... and it almost killed her Shocking extent America has turned on ICE is revealed as Joe Rogan breaks from conservatives still cheering Trump's army of masked men Sir Richard Branson's wife Joan dies: 'Heartbroken' Virgin tycoon pays tribute to his'best friend' after she passed away Trump gives Thanksgiving turkeys scathing nicknames and calls Pritzker a'fat slob' in fiery White House holiday speech How to tell if a man is using'therapy speak' to manipulate you: If he says any of these 15 toxic phrases, run for the hills... I'll tell you what he REALLY means: JANA HOCKING I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY A comet has been predicted to strike the Earth by the end of the year, on what a controversial religious leader called'the last day of this world.' The doomsday warning came from the writings of Riaz Ahmed Gohar Shahi, a Pakistani spiritual leader and mystic, who claimed that God was sending a comet to collide with Earth because humanity had strayed too far from spiritual truths . He founded several organizations to spread his teachings of'divine love,' including the spiritual movement called Anjuman Serfaroshan-e-Islam and the Messiah Foundation International (MFI).

Hollywood director James Cameron said he can remain friends with Tesla owner Elon Musk, despite their political differences, over shared goals in AI and space travel.