Conditional generative models represent a significant advancement in the field of machine learning, allowing for the controlled synthesis of data by incorporating additional information into the generation process. In this work we introduce a novel Schr\"odinger bridge based deep generative method for learning conditional distributions. We start from a unit-time diffusion process governed by a stochastic differential equation (SDE) that transforms a fixed point at time $0$ into a desired target conditional distribution at time $1$. For effective implementation, we discretize the SDE with Euler-Maruyama method where we estimate the drift term nonparametrically using a deep neural network. We apply our method to both low-dimensional and high-dimensional conditional generation problems. The numerical studies demonstrate that though our method does not directly provide the conditional density estimation, the samples generated by this method exhibit higher quality compared to those obtained by several existing methods. Moreover, the generated samples can be effectively utilized to estimate the conditional density and related statistical quantities, such as conditional mean and conditional standard deviation.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Whether it's your 10th time playing or your first, the Masters at Augusta National Golf Club is a daunting task for every golfer. It's the only major of the golfing season that's continuously played at the same course, yet golfers sometimes take weeks off between tournaments just to prepare for it. Like any sport, analytics factor into a golfer's preparation, with statisticians used by almost everyone on Tour, helping them track previous rounds on any given course to figure out a game plan each week.

Large language models (LLMs) can store a vast amount of world knowledge, often extractable via question-answering (e.g., "What is Abraham Lincoln's birthday?"). However, do they answer such questions based on exposure to similar questions during training (i.e., cheating), or by genuinely learning to extract knowledge from sources like Wikipedia? In this paper, we investigate this issue using a controlled biography dataset. We find a strong correlation between the model's ability to extract knowledge and various diversity measures of the training data. $\textbf{Essentially}$, for knowledge to be reliably extracted, it must be sufficiently augmented (e.g., through paraphrasing, sentence shuffling) $\textit{during pretraining}$. Without such augmentation, knowledge may be memorized but not extractable, leading to 0% accuracy, regardless of subsequent instruction fine-tuning. To understand why this occurs, we employ (nearly) linear probing to demonstrate a strong connection between the observed correlation and how the model internally encodes knowledge -- whether it is linearly encoded in the hidden embeddings of entity names or distributed across other token embeddings in the training text. This paper provides $\textbf{several key recommendations for LLM pretraining in the industry}$: (1) rewrite the pretraining data -- using small, auxiliary models -- to provide knowledge augmentation, and (2) incorporate more instruction-finetuning data into the pretraining stage before it becomes too late.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The PGA Tour is making major changes to its schedule and how several of its events are played as LIV Golf's second season gets underway. The PGA Tour ratified a motion Tuesday that reduces fields for eight designated events in 2024 to between 70 and 80 golfers with no 36-hole cuts. The tour has not announced which events will be affected, but the majors, the FedEx Cup Playoffs and the Players Championship will not be included in the changes.

In Virginia, the statewide mortality rate for sepsis was 13.2% in 2016. Sepsis is the body's life-threatening response to infection that can lead to tissue damage and organ failure. In the U.S., 1.5 million people develop sepsis each year, and about 17% of those die. Early detection of sepsis is critical to decrease mortality. Clinical and IT staffs at Augusta Health, an independent, community-owned, not-for-profit hospital in Virginia, knew that studies have shown that though treatments are available in a general hospital setting, they are rarely completed in a timely manner.

Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in contemporary settings where item responses are being continuously collected, such as Massive Open Online Courses (MOOCs), interest will naturally be on the dynamics of ability, thus complicating usage of traditional IRT models. We propose DynAEsti, an augmentation of the traditional IRT Expectation Maximization algorithm that allows ability to be a continuously varying curve over time. In the process, we develop CurvFiFE, a novel non-parametric continuous-time technique that handles the curve-fitting/regression problem extended to address more general probabilistic emissions (as opposed to simply noisy data points). Furthermore, to accomplish this, we develop a novel technique called grafting, which can successfully approximate distributions represented by graphical models when other popular techniques like Loopy Belief Propogation (LBP) and Variational Inference (VI) fail. The performance of DynAEsti is evaluated through simulation, where we achieve results comparable to the optimal of what is observed in the static ability scenario. Finally, DynAEsti is applied to a longitudinal performance dataset (80-years of competitive golf at the 18-hole Masters Tournament) to demonstrate its ability to recover key properties of human performance and the heterogeneous characteristics of the different holes. Python code for CurvFiFE and DynAEsti is publicly available at github.com/chausies/DynAEstiAndCurvFiFE. This is the full version of our ICDM 2019 paper.

The U.S. Army may establish an artificial intelligence task force over the next 90 days in an effort to help develop needed expertise and better prepare for the service for the future of warfare, says Lt. Gen. Bruce Crawford, USA, Army chief information officer. The service also is creating a cloud computing advisory board. Gen. Crawford mentioned the potential task force during the AFCEA TechNet Augusta conference in Augusta, Georgia. He tied the task force to the Defense Department's creation of an Joint Artificial Intelligence Center (JAIC), an effort being led by the department's CIO, Dana Deasy. "Our leadership across the department, in response to the National Defense Strategy, is in the process of standing up a joint AI center," told the conference audience.

PGA golfers such as four-time major champion Rory McIlroy embrace the tens of thousands of data points -- roughly 32,000 per event -- that the tour's ShotLink System has offered since 2001. "I made the decision at the end of last year to really look at my stats," McIlroy said after last week's Travelers Championship. "I think they've become very important, and I think the strokes-gained stats, whether it's tee to green or putting or around the green or whatever, I think that's been one of the biggest changes for good that we've seen in golf, because it really just lets you see how your game stacks up against everyone else." For the first time Thursday at the Quicken Loans National at TPC Potomac at Avenel Farm, three fixed, high-resolution cameras, part of the tour's upgraded ShotLink ball-tracking system, replaced the human-operated laser on every green of every hole, capturing the ball in motion as opposed to only the ball at rest. "It's the next phase of how we get the data without having to have human interaction on everything that happens," said Matt Troka, senior vice president of product and partner management of CDW, a technology partner of the PGA Tour.

PGA golfers such as four-time major champion Rory McIlroy embrace the tens of thousands of data points -- roughly 32,000 per event -- that the tour's ShotLink System has offered since 2001. "I made the decision at the end of last year to really look at my stats," McIlroy said after last week's Travelers Championship. "I think they've become very important, and I think the strokes-gained stats, whether it's tee to green or putting or around the green or whatever, I think that's been one of the biggest changes for good that we've seen in golf, because it really just lets you see how your game stacks up against everyone else." For the first time Thursday at the Quicken Loans National at TPC Potomac at Avenel Farm, three fixed, high-resolution cameras, part of the tour's upgraded ShotLink ball-tracking system, replaced the human-operated laser on every green of every hole, capturing the ball in motion as opposed to only the ball at rest. "It's the next phase of how we get the data without having to have human interaction on everything that happens," said Matt Troka, senior vice president of product and partner management of CDW, a technology partner of the PGA Tour.

The human brain is the most sophisticated organ in the human body. The things that the brain can do, and how it does them, have even inspired a model of artificial intelligence (AI). Now, a recent study published in the journal Frontiers in Systems Neuroscience shows how human intelligence may be a product of a basic algorithm. This algorithm is found in the Theory of Connectivity, a "relatively simple mathematical logic underlies our complex brain computations," according to researcher and author Joe Tsien, neuroscientist at the Medical College of Georgia at Augusta University, co-director of the Augusta University Brain and Behavior Discovery Institute and Georgia Research Alliance Eminent Scholar in Cognitive and Systems Neurobiology. He first proposed the theory in October 2015.