Large language models (LLMs) are increasingly applied to clinical decision-making. However, their potential to exhibit bias poses significant risks to clinical equity. Currently, there is a lack of benchmarks that systematically evaluate such clinical bias in LLMs. While in downstream tasks, some biases of LLMs can be avoided such as by instructing the model to answer "I'm not sure...", the internal bias hidden within the model still lacks deep studies. We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in Large Language Models), a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks. Notably, for intrinsic bias, we introduce a novel metric, AssocMAD, to assess the disparities of LLMs across multiple demographic groups. Additionally, we leverage counterfactual intervention to evaluate extrinsic bias in a task of clinical diagnosis prediction. Our experiments across popular and medically adapted LLMs, particularly from the Mistral and LLaMA families, unveil prevalent behaviors with both intrinsic and extrinsic bias. This work underscores the critical need to mitigate clinical bias and sets a new standard for future evaluations of LLMs' clinical bias.

CNN chief medical correspondent Dr. Sanjay Gupta called for President Biden to undergo cognitive testing so that the American people can find out if he has dementia ahead of the 2024 election CNN chief medical correspondent Dr. Sanjay Gupta called for President Biden to undergo neurological testing so that the American people can find out if he has dementia ahead of the 2024 election. In an article published to CNN.com on Friday, Gupta said that he and his medical colleagues became concerned about Biden's cognitive ability during last Thursday's presidential debate and advised that he be tested to see if his sluggish performance was just due to a "bad night" or a more serious underlying concerns. "The consensus from the doctors reaching out to me, however, was that the president should be encouraged to undergo detailed cognitive and movement disorder testing," Gupta wrote, noting that he agreed. CNN Chief Medical Correspondent Dr. Sanjay Gupta argued that President Biden needs to take a cognitive test following his debate performance last week. The physician and media pundit spelled out what concerned him and his colleagues about Biden during that debate.

The Russian navy's Black Sea Fleet has been forced to rebase nearly all its combat-ready warships from occupied Crimea to other locations, and its main naval hub is becoming ineffectual because of attacks by Kyiv, Ukraine's navy chief said. Vice-Admiral Oleksiy Neizhpapa said Ukrainian missile and naval drone strikes had caused heavy damage to the Sevastopol base, a logistics hub for repairs, maintenance, training and ammunition storage among other important functions for Russia. "They were established over many decades, possibly centuries. And clearly they are now losing this hub," Neizhpapa told Reuters in a rare interview in the port city of Odesa ahead of Ukraine Navy Day on Sunday. More than 28 months since Russia's full-scale invasion, Kyiv has dealt a series of stinging blows to Moscow in the Black Sea although Ukrainian ground troops are on the back foot across a sprawling front.

The Japanese government will announce a package of comprehensive measures to help other countries remove landmines, an informed source said Friday. Foreign Minister Yoko Kamikawa will make the announcement Saturday during her trip to Cambodia, according to the source. The aim is to utilize the know-how of Japan and the Southeast Asian country in removing mines and help other nations struggling with the issue, including Ukraine. The package will include education to avoid the risk of mines, provision of mine detectors, support for victims and the development of an artificial intelligence-powered system to identify possible mine locations. In Cambodia, a civil war continued for more than 20 years from 1970, with 4 million to 6 million mines believed to have been buried.

Perhaps unsurprisingly, July 4th was a quiet day for news, but we've still got editorials on e-ink writing, the most-delayed video game ever and more bad news from the makers of ChatGPT. Earlier this week, engineer and Swift developer Pedro José Pereira Vieito dug into OpenAI's Mac ChatGPT app and found that it was storing user conversations locally in plain text, rather than encrypting them. Because that app is only available from OpenAI's website, and since it's not available on the App Store, it doesn't have to follow Apple's sandboxing requirements. OpenAI released an update that added encryption to locally stored chats. Then, more bad news stemmed from issues in 2023. Last spring, a hacker obtained information about OpenAI after illicitly accessing the company's internal messaging systems.

Keir Starmer will be Britain's new prime minister, as his centre-left opposition Labour Party swept to a landslide victory, ending 14 years of Conservative rule. At a triumphant party rally in central London on Friday, Starmer, 61, told cheering activists that "change begins here" and promised a "decade of national renewal", putting "country first, party second". We will continue the work begun with the UK for our bilateral cooperation, for peace and security in Europe, for the climate and for AI," Macron posted on X. We will continue the work begun with the UK for our bilateral cooperation, for peace and security in Europe, for the climate and for AI. "Keir Starmer has brought the Labour Party a comprehensive victory … The relationship between Ireland and the UK is deeply consequential for all people across these islands," Harris said in a statement. "I look forward to early engagement with the incoming Prime Minister." "Ukraine and the United Kingdom have been and will continue to be reliable allies through thick and thin.

Indian Prime Minister Narendra Modi and other ruling party leaders have been rattled by India's newly emboldened opposition, which now plans to use its expanded presence in parliament to challenge the Bharatiya Janata Party on multiple fronts, a senior opposition leader said. Shashi Tharoor, a senior member of the Indian National Congress and 15-year veteran of the lower house of parliament, the Lok Sabha, said BJP leaders appeared "shaken" by a fiery parliamentary speech this week by opposition leader Rahul Gandhi. He said the speech signals the presence of a more forceful opposition in India for the first time since Modi took power a decade ago. "They're not used to it. They have to get used to it," Tharoor said of the BJP during an interview in New Delhi on Wednesday.

Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies to ensure seamless connectivity across different altitudes and platforms. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance these networks. We outline the current architecture of ISATNs and highlight the significant role LLMs can play in optimizing data flow, signal processing, and network management to advance 5G/6G communication technologies through advanced predictive algorithms and real-time decision-making. A comprehensive analysis of ISATN components is conducted, assessing how LLMs can effectively address traditional data transmission and processing bottlenecks. The paper delves into the network management challenges within ISATNs, emphasizing the necessity for sophisticated resource allocation strategies, traffic routing, and security management to ensure seamless connectivity and optimal performance under varying conditions. Furthermore, we examine the technical challenges and limitations associated with integrating LLMs into ISATNs, such as data integration for LLM processing, scalability issues, latency in decision-making processes, and the design of robust, fault-tolerant systems. The study also identifies key future research directions for fully harnessing LLM capabilities in ISATNs, which is crucial for enhancing network reliability, optimizing performance, and achieving a truly interconnected and intelligent global network system.

We propose a randomized physics-informed neural network (PINN) or rPINN method for uncertainty quantification in inverse partial differential equation (PDE) problems with noisy data. This method is used to quantify uncertainty in the inverse PDE PINN solutions. Recently, the Bayesian PINN (BPINN) method was proposed, where the posterior distribution of the PINN parameters was formulated using the Bayes' theorem and sampled using approximate inference methods such as the Hamiltonian Monte Carlo (HMC) and variational inference (VI) methods. In this work, we demonstrate that HMC fails to converge for non-linear inverse PDE problems. As an alternative to HMC, we sample the distribution by solving the stochastic optimization problem obtained by randomizing the PINN loss function. The effectiveness of the rPINN method is tested for linear and non-linear Poisson equations, and the diffusion equation with a high-dimensional space-dependent diffusion coefficient. The rPINN method provides informative distributions for all considered problems. For the linear Poisson equation, HMC and rPINN produce similar distributions, but rPINN is on average 27 times faster than HMC. For the non-linear Poison and diffusion equations, the HMC method fails to converge because a single HMC chain cannot sample multiple modes of the posterior distribution of the PINN parameters in a reasonable amount of time.

The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three general scales of abstractions of what a material is - atomistic, physical, and design. At each, an efficient materials informatics infrastructure is being built from the ground up based on (1) the fundamental understanding of the underlying prior knowledge, including the data, (2) deployment routes that take advantage of it, and (3) pathways to extend it in an autonomous or semi-autonomous fashion, while heavily relying on artificial intelligence (AI) to guide well-established DFT-based ab initio and CALPHAD-based thermodynamic methods. The resulting multi-level discovery infrastructure is highly generalizable as it focuses on encoding problems to solve them easily rather than looking for an existing solution. To showcase it, this dissertation discusses the design of multi-alloy functionally graded materials (FGMs) incorporating ultra-high temperature refractory high entropy alloys (RHEAs) towards gas turbine and jet engine efficiency increase reducing CO2 emissions, as well as hypersonic vehicles. It leverages a new graph representation of underlying mathematical space using a newly developed algorithm based on combinatorics, not subject to many problems troubling the community. Underneath, property models and phase relations are learned from optimized samplings of the largest and highest quality dataset of HEA in the world, called ULTERA. At the atomistic level, a data ecosystem optimized for machine learning (ML) from over 4.5 million relaxed structures, called MPDD, is used to inform experimental observations and improve thermodynamic models by providing stability data enabled by a new efficient featurization framework.