Record cold for 235 million Americans starting in just HOURS as polar vortex brings'most extreme cold on Earth' Is this the END of Ozempic? Nashville neighbors can see what's REALLY going on with Nicole Kidman. Even I was once overweight. So trust me, this 30 DAY detox plan will get you thin WITHOUT Ozempic... but if you want to stay skinny, you'll have to make one major sacrifice: JILLIAN MICHAELS Mom who spent 10 years'gentle parenting' admits it was a mistake: 'My kids are anxious, insecure and entitled' Worrying side-effect of creatine you aren't being warned about: Cheap supplement is hailed as a'miracle' - but here's how to tell if YOUR brand is doing more harm than good Amazon warns 300 million shoppers of Cyber Monday scam... and how to avoid it'Murder for hire' housewife begs Bahamas judge to free her from GPS shackles so she can start a shocking new career Trump suffers fresh legal blow as Alina Habba's disqualification is upheld by appeals court Trump sparks fury as he frees $1.6 BILLION fraudster just days into seven-year-sentence I was drinking 130 units of alcohol a week and knew it was time to cut down. Then, I discovered this no-effort miracle solution.

Brit Award-winning singer Jorja Smith's record label has said it wants a share of the royalties for a song it claims was created using an artificial intelligence clone of the singer's voice. I Run by British dance act Haven went viral on TiKTok in October thanks, in part, to smooth soul vocals by an uncredited female singer. Although I Run has now been re-released with new vocals, Smith's label FAMM said it believes the track was made with AI trained on her work, and is seeking compensation. It's bigger than one artist or one song, FAMM wrote in a statement on Instagram . The label said it believes both versions of the track infringe on Jorja's rights and unfairly take advantage of the work of all the songwriters with whom she collaborates.

The question isn't whether the AI bubble will burst - but what the fallout will be Will the bubble ravage the economy when it bursts? What will it leave of value once it pops? The California Gold Rush left an outsized imprint on America. Some 300,000 people flocked there from 1848 to 1855, from as far away as the Ottoman Empire. Prospectors massacred Indigenous people to take the gold from their lands in the Sierra Nevada mountains. And they boosted the economies of nearby states and faraway countries from whence they bought their supplies.

'It's going much too fast': the inside story of the race to create the ultimate AI On the 8.49am train through Silicon Valley, the tables are packed with young people glued to laptops, earbuds in, rattling out code. As the northern California hills scroll past, instructions flash up on screens from bosses: fix this bug; add new script. There is no time to enjoy the view. These commuters are foot soldiers in the global race towards artificial general intelligence - when AI systems become as or more capable than highly qualified humans. Here in the Bay Area of San Francisco, some of the world's biggest companies are fighting it out to gain some kind of an advantage. And, in turn, they are competing with China. This race to seize control of a technology that could reshape the world is being fuelled by bets in the trillions of dollars by the US's most powerful capitalists. Passengers get off a train at Palo Alto station.

Drone video shows devastation from floods in Indonesia's Sumatra NewsFeed Drone video shows devastation from floods in Indonesia's Sumatra Drone video shows widespread destruction in part of Sumatra in Indonesia, where more than 440 people have died in flooding and landslides across the country. Hundreds of others are still missing. Pope Leo says two-state is'only solution' for Israel-Palestine Netanyahu requests Israel's president grant a pardon in corruption cases

Here's where things stand on Monday, December 1. The number of casualties from a Russian attack on Ukraine's Kyiv on Sunday rose to one person killed and 18 wounded, according to regional Governor Mykola Kalashnyk. In southern Kherson, at least two people were killed, and seven others were wounded in more Russian attacks, Governor Oleksandr Prokudin said on Telegram. In the Donetsk region, at least two people were killed, and five were injured in Russian attacks on Saturday, according to Governor Vadym Filashkin. In Russia, a Ukrainian drone attack killed two men in the Belgorod region, the region's operational headquarters said in a post on Telegram.

Bayesian computational strategies for inference can be inefficient in approximating the posterior distribution in models that exhibit some form of periodicity. This is because the probability mass of the marginal posterior distribution of the parameter representing the period is usually highly concentrated in a very small region of the parameter space. Therefore, it is necessary to provide as much information as possible to the inference method through the parameter prior distribution. We intend to show that it is possible to construct a prior distribution from the data by fitting a Gaussian process (GP) with a periodic kernel. More specifically, we want to show that it is possible to approximate the marginal posterior distribution of the hyperparameter corresponding to the period in the kernel. Subsequently, this distribution can be used as a prior distribution for the inference method. We use an adaptive importance sampling method to approximate the posterior distribution of the hyperparameters of the GP. Then, we use the marginal posterior distribution of the hyperparameter related to the periodicity in order to construct a prior distribution for the period of the parametric model. This workflow is empirical Bayes, implemented as a modular (cut) transfer of a GP posterior for the period to the parametric model. We applied the proposed methodology to both synthetic and real data. We approximated the posterior distribution of the period of the GP kernel and then passed it forward as a posterior-as-prior with no feedback. Finally, we analyzed its impact on the marginal posterior distribution.

Gambling disorder is a complex behavioral addiction that is challenging to understand and address, with severe physical, psychological, and social consequences. Early Risk Detection (ERD) on the Web has become a key task in the scientific community for identifying early signs of mental health behaviors based on social media activity. This work presents our participation in the MentalRiskES 2025 challenge, specifically in Task 1, aimed at classifying users at high or low risk of developing a gambling-related disorder. We proposed three methods based on a CPI+DMC approach, addressing predictive effectiveness and decision-making speed as independent objectives. The components were implemented using the SS3, BERT with extended vocabulary, and SBERT models, followed by decision policies based on historical user analysis. Although it was a challenging corpus, two of our proposals achieved the top two positions in the official results, performing notably in decision metrics. Further analysis revealed some difficulty in distinguishing between users at high and low risk, reinforcing the need to explore strategies to improve data interpretation and quality, and to promote more transparent and reliable ERD systems for mental disorders.

Given the constant growth and increasing sophistication of cyberattacks, cybersecurity can no longer rely solely on traditional defense techniques and tools. Proactive detection of cyber threats has become essential to help security teams identify potential risks and implement effective mitigation measures. Cyber Threat Intelligence (CTI) plays a key role by providing security analysts with evidence-based knowledge about cyber threats. CTI information can be extracted using various techniques and data sources; however, machine learning has proven promising. As for data sources, social networks and online discussion forums are commonly explored. In this study, we apply text mining techniques and machine learning to data collected from Dark Web forums in Brazilian Portuguese to identify malicious posts. Our contributions include the creation of three original datasets, a novel multi-stage labeling process combining indicators of compromise (IoCs), contextual keywords, and manual analysis, and a comprehensive evaluation of text representations and classifiers. To our knowledge, this is the first study to focus specifically on Brazilian Portuguese content in this domain. The best-performing model, using LightGBM and TF-IDF, was able to detect relevant posts with high accuracy. We also applied topic modeling to validate the model's outputs on unlabeled data, confirming its robustness in real-world scenarios.

Purpose: This study aims to develop and evaluate a three channel denoising diffusion probabilistic model (DDPM) for synthesizing single breast dual view mammograms and to assess the impact of channel representations on image fidelity and cross view consistency. Materials and Methods: A pretrained three channel DDPM, sourced from Hugging Face, was fine tuned on a private dataset of 11020 screening mammograms to generate paired craniocaudal (CC) and mediolateral oblique (MLO) views. Three third channel encodings of the CC and MLO views were evaluated: sum, absolute difference, and zero channel. Each model produced 500 synthetic image pairs. Quantitative assessment involved breast mask segmentation using Intersection over Union (IoU) and Dice Similarity Coefficient (DSC), with distributional comparisons against 2500 real pairs using Earth Movers Distance (EMD) and Kolmogorov Smirnov (KS) tests. Qualitative evaluation included a visual Turing test by a non expert radiologist to assess cross view consistency and artifacts. Results: Synthetic mammograms showed IoU and DSC distributions comparable to real images, with EMD and KS values (0.020 and 0.077 respectively). Models using sum or absolute difference encodings outperformed others in IoU and DSC (p < 0.001), though distributions remained broadly similar. Generated CC and MLO views maintained cross view consistency, with 6 to 8 percent of synthetic images exhibiting artifacts consistent with those in the training data. Conclusion: Three channel DDPMs can generate realistic and anatomically consistent dual view mammograms with promising applications in dataset augmentation.