Doctor's husband'was watching X-rated videos in his house while daughter, 2, died in roasting car outside' Florida's housing market is flashing a warning for the rest of the US Now scientists redefine'obese' - and they've made up to 60% more people'fat' Bella Hadid's health battle takes dark turn: Loved ones reveal hellish new details about'missing' model... as ominous texts emerge America's saddest lost soul can no longer SPEAK and spends days hitting herself'after years of unspeakable abuse by gangs of men' Shocking moment brazen gunman opens fire at Michigan businessman's Land Rover in daylight attack'You will DIE if you do not remove your breasts', doctors screamed at me. I refused and tried a new experimental therapy instead... now I'm cancer-free The world's most powerful passport revealed - as UK and USA both drop to record lows Police say they have FOUND woman seen in viral'kidnapping' video and reveal what happened to her after harrowing footage emerged Will Trump's Gaza peace deal fail? Policy expert MARK DUBOWITZ breaks down all the forces at play... and how the president can actually pull this off America's most renowned'prophet' makes startling prediction about alien'mothership' Kim Kardashian says she wasn't'emotionally or financially safe' during'toxic' marriage to Kanye West as she claims rapper hasn't contacted their children for MONTHS and has destroyed her dating life Astonishing interactive map lays bare where MILLIONS of homes will be submerged by water within a few years... are YOU at risk? Outrageous reason LA County CEO was awarded $2m payout for'hurt feelings' that'll see her take months off taxpayer-funded $570,000-a-year job Ugly divorce war between Mitt Romney's wealthy brother and estranged wife before she was found dead Full horrors of torture suffered by Noa Argamani's commando boyfriend are revealed - including how 6ft 5in hostage was beaten and kept chained in 6ft cell for a year after he tried to escape from Hamas Mother, 52, and daughter, 21, die after eating'poisoned birthday cake delivered by relative who owed them money' in Brazil Astonishing interactive map lays bare where MILLIONS of homes will be submerged by water within a few years... are YOU at risk? Millions of buildings and even more Americans could be at risk of sinking underwater by the end of the century. Researchers from McGill University in Canada warned rising sea levels, resulting from continued greenhouse gas emissions, threaten to wipe out coastal cities worldwide. Sea level rise measures the ocean's surface height over time.

We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work. To help designers better understand model predictions and to provide more actionable design feedback than predictions alone, we additionally use ML interpretability techniques to help explain the output of our model. We use XRAI to highlight areas in the input screenshot that most strongly influence the tappability prediction for the selected region, and use k-Nearest Neighbors to present the most similar mobile UIs from the dataset with opposing influences on tappability perception.

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Emotion detection in older adults is crucial for understanding their cognitive and emotional well-being, especially in hospital and assisted living environments. In this work, we investigate an edge-based, non-obtrusive approach to emotion identification that uses only physiological signals obtained via wearable sensors. Our dataset includes data from 40 older individuals. Emotional states were obtained using physiological signals from the Empatica E4 and Shimmer3 GSR+ wristband and facial expressions were recorded using camera-based emotion recognition with the iMotion's Facial Expression Analysis (FEA) module. The dataset also contains twelve emotion categories in terms of relative intensities. We aim to study how well emotion recognition can be accomplished using simply physiological sensor data, without the requirement for cameras or intrusive facial analysis. By leveraging classical machine learning models, we predict the intensity of emotional responses based on physiological signals. We achieved the highest 0.782 r2 score with the lowest 0.0006 MSE on the regression task. This method has significant implications for individuals with Alzheimer's Disease and Related Dementia (ADRD), as well as veterans coping with Post-Traumatic Stress Disorder (PTSD) or other cognitive impairments. Our results across multiple classical regression models validate the feasibility of this method, paving the way for privacy-preserving and efficient emotion recognition systems in real-world settings.

Over the weekend, the Federal Aviation Administration officially designated the airspace above the Caesars Superdome as a "No Drone Zone" during and ahead of the big game. Drone operators who do fly their devices into the restricted area, accidentally or otherwise, could have their drones confiscated or receive hefty fines up to 75,000. The decision comes just weeks after a hobbyist drone collided with a plane helping combat wildfires in California and amid an uptick in drone sightings around the country. Starting at 1:30 p.m. CST on game day (Sunday, February 9) the FAA will prohibit drones from flying within a 1.5 nautical miles radius and 2,000 feet in altitude of the Caesars Superdome. The restricted area space will expand to a 30 nautical-mile radius and 18,000 feet in altitude between 4:30 and 10:30 p.m CST that same day.

The idea of entire cities being plunged underwater might sound like the plot of the latest science fiction blockbuster. But it could become a reality in just 75 years, according to a terrifying new study. Scientists from Nanyang Technological University (NTU), Singapore, have predicted that global sea levels could rise by a staggering 6.2 feet (1.9 metres) by 2100 if carbon dioxide (CO2) emissions continue to increase. 'The high-end projection of 1.9 metres underscores the need for decision-makers to plan for critical infrastructure accordingly,' said Dr Benjamin Grandey, lead author of the study. If global sea levels were to rise by 6.2ft (1.9 metres), towns and cities around the world could be plunged underwater - including several in the UK.

Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes providing a few demonstrations and optimizing image descriptions to be more detailed and logical. However, as demand for more complex and flexible image descriptions grows, enhancing comprehension of input text within the ICL paradigm remains a critical yet underexplored area. In this work, we extend this line of research by constructing parallel multilingual prompts aimed at harnessing the multilingual capabilities of LMMs. More specifically, we translate the input text into several languages and provide the models with both the original text and the translations. Experiments on two LMMs across 3 benchmarks show that our method, PMT2I, achieves superior performance in general, compositional, and fine-grained assessments, especially in human preference alignment. Additionally, with its advantage of generating more diverse images, PMT2I significantly outperforms baseline prompts when incorporated with reranking methods. Our code and parallel multilingual data can be found at https://github.com/takagi97/PMT2I.

As a former military intelligence officer, serving in the Defense Intelligence Agency (DIA), I tracked foreign threats to the U.S. homeland, identifying adversaries' plans, intentions and capabilities that could harm Americans. I predicted Russia's invasion of Ukraine more than a year before it took place. In March, in my Fox News Digital article titled "Ignore FBI director's urgent warning about terrorist threats at our own peril," I predicted terrorist attacks striking inside the U.S. homeland, the kind that took place on New Year's Day in New Orleans and in Las Vegas. Here are the top three reasons why we will likely face more terrorism in America this year. This time, it will be something we haven't seen before.

Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, there is no clear conclusion as to which part of the model these hallucinations originate from. In this paper, we present an in-depth investigation into the object hallucination problem specifically within the CLIP model, which serves as the backbone for many state-of-the-art vision-language systems. We unveil that even in isolation, the CLIP model is prone to object hallucinations, suggesting that the hallucination problem is not solely due to the interaction between vision and language modalities. To address this, we propose a counterfactual data augmentation method by creating negative samples with a variety of hallucination issues. We demonstrate that our method can effectively mitigate object hallucinations for CLIP model, and we show the the enhanced model can be employed as a visual encoder, effectively alleviating the object hallucination issue in LVLMs.

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.