Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible particles (SvPs), which can compromise their efficacy and trigger immunological responses, emphasizing the critical need for robust monitoring techniques. Flow Imaging Microscopy (FIM) has been a significant advancement in detecting SvPs, evolving from monochrome to more recently incorporating color imaging. Complementing SvP images obtained via FIM, deep learning techniques have recently been employed successfully for stress source identification of monochrome SvPs. In this study, we explore the potential of color FIM to enhance the characterization of stress sources in SvPs. To achieve this, we curate a new dataset comprising 16,000 SvPs from eight commercial monoclonal antibodies subjected to heat and mechanical stress. Using both supervised and self-supervised convolutional neural networks, as well as vision transformers in large-scale experiments, we demonstrate that deep learning with color FIM images consistently outperforms monochrome images, thus highlighting the potential of color FIM in stress source classification compared to its monochrome counterparts.

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0

Existing research primarily evaluates the values of LLMs by examining their stated inclinations towards specific values. However, the "Value-Action Gap," a phenomenon rooted in environmental and social psychology, reveals discrepancies between individuals' stated values and their actions in real-world contexts. To what extent do LLMs exhibit a similar gap between their stated values and their actions informed by those values? This study introduces ValueActionLens, an evaluation framework to assess the alignment between LLMs' stated values and their value-informed actions. The framework encompasses the generation of a dataset comprising 14.8k value-informed actions across twelve cultures and eleven social topics, and two tasks to evaluate how well LLMs' stated value inclinations and value-informed actions align across three different alignment measures. Extensive experiments reveal that the alignment between LLMs' stated values and actions is sub-optimal, varying significantly across scenarios and models. Analysis of misaligned results identifies potential harms from certain value-action gaps. To predict the value-action gaps, we also uncover that leveraging reasoned explanations improves performance. These findings underscore the risks of relying solely on the LLMs' stated values to predict their behaviors and emphasize the importance of context-aware evaluations of LLM values and value-action gaps.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

As probabilistic models continue to permeate various facets of our society and contribute to scientific advancements, it becomes a necessity to go beyond traditional metrics such as predictive accuracy and error rates and assess their trustworthiness. Grounded in the competence-based theory of trust, this work formalizes I-trustworthy framework -- a novel framework for assessing the trustworthiness of probabilistic classifiers for inference tasks by linking local calibration to trustworthiness. To assess I-trustworthiness, we use the local calibration error (LCE) and develop a method of hypothesis-testing. This method utilizes a kernel-based test statistic, Kernel Local Calibration Error (KLCE), to test local calibration of a probabilistic classifier. This study provides theoretical guarantees by offering convergence bounds for an unbiased estimator of KLCE. Additionally, we present a diagnostic tool designed to identify and measure biases in cases of miscalibration. The effectiveness of the proposed test statistic is demonstrated through its application to both simulated and real-world datasets. Finally, LCE of related recalibration methods is studied, and we provide evidence of insufficiency of existing methods to achieve I-trustworthiness.

Elon Musk, right, has cast doubt on whether there is enough funding available to follow through on a 500 billion AI infrastructure project announced by President Donald Trump on Tuesday. OpenAI CEO Sam Altman, left, pushed back on Musk's claims. EMPTY COFFERS?: Business magnate and X CEO Elon Musk has cast doubt on whether there is enough funding available to follow through on a massive 500 billion artificial intelligence (AI) infrastructure project announced by President Donald Trump on Tuesday. SpaceX and Tesla founder Elon Musk speaks during an America PAC town hall on Oct. 26, 2024 in Lancaster, Pa. ( Samuel Corum/Getty Images) ON THE BRINK: Walter Isaacson, author of "Elon Musk," discusses the Trump administration's collaboration with tech giants to drive AI innovation and technological advancement on "America's Newsroom." CONTROVERSIAL TECH: Artificial intelligence (AI) tools are now available for future medical professionals at one Texas university to navigate the complexities of pregnancy and abortion--a development that further blurs the line between technology, politics and healthcare.

Dozens of patients have been killed in a drone attack on one of the last functioning hospitals in el-Fasher in Sudan's Darfur region. While it was not immediately clear who targeted the Saudi Hospital on Friday, medical sources quoted by AFP news agency said the same building was hit by a Rapid Support Forces (RSF) drone "a few weeks ago". Friday's attack killed at least 30 patients in the emergency department, the report added. Regional governor Mini Minawi posted graphic images of bloodied bodies on his X account on Saturday, saying that the attack "exterminated" more than 70 patients, including women and children. The Sudanese army has been at war with the paramilitary RSF, who have seized nearly the entire vast western region of Darfur, since April 2023.

On Inauguration Day, fans of the All-In Podcast gathered in a billiards room in Washington DC to watch Donald Trump's swearing-in – and a few miles away, the podcast co-host and PayPal Mafia alum David Sacks prepared to ascend to his role as Trump's AI and crypto czar. Very popular in Silicon Valley, All-In is fiercely pro-capitalism and enthusiastic about the world of tech start-ups and investments. Last summer, its co-hosts, Sacks and Jason Calacanis in particular, became vocal in their support for Trump and attempted to rally other tech leaders, including their listeners, behind the candidate. Now, Sacks has a seat at the table in the White House, as do many others in tech, including a former Uber executive, a senior adviser at Palantir, and a PayPal co-founder, who was picked to be ambassador to Denmark (Greenland, a territory Trump wants to seize, is part of Denmark). It's a watershed moment for relationships between Silicon Valley and Washington and, more broadly, what's often described as the tech right.

This story originally appeared on Grist and is part of the Climate Desk collaboration. What's shaping up to be one of the worst wildfire disasters in US history had many causes. Before the blazes raged across Los Angeles last week, eight months with hardly any rain had left the brush-covered landscape bone-dry. Santa Ana winds blew through the mountains, their gusts turning small fires into infernos and sending embers flying miles ahead. As many as 12,000 buildings have burned down, some hundred thousand people have fled their homes, and at least two dozen people have died.

For years, conservative media, lawmakers and talking heads have been sounding the alarm about President Joe Biden's cognitive free fall. And for years, left-wing media, lawmakers and their loyal mouthpieces waved it off with the same condescending dismissal -- accusing us of lying, fear-mongering or worse. Some even went so far as to say they couldn't keep up with Biden's supposed brilliance and jam-packed schedule of what was mostly just one morning briefing and two mid-afternoon naps. Now that Biden has shuffled out of office, left-wing media seems to be waking up to the glaringly obvious. The New York Times of all places -- yes, the same paper that acted as Biden's PR firm -- has revealed that he relied on teleprompters during intimate fundraisers in private homes.