Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is not possible to train networks that are accurate in all situations, models must recognize situations where they cannot operate confidently. Bayesian deep learning methods sample the model parameter space to estimate uncertainty, but these parameters are often subject to the same vulnerabilities, which can be exploited by adversarial attacks. We propose a novel ensemble approach based on feature decorrelation and Fourier partitioning for teaching networks diverse complementary features, reducing the chance of perturbation-based fooling. We test our approach on electrocardiogram classification, demonstrating superior accuracy confidence measurement, on a variety of adversarial attacks. For example, on our ensemble trained with both decorrelation and Fourier partitioning scored a 50.18% inference accuracy and 48.01% uncertainty accuracy (area under the curve) on {\epsilon} = 50 projected gradient descent attacks, while a conventionally trained ensemble scored 21.1% and 30.31% on these metrics respectively. Our approach does not require expensive optimization with adversarial samples and can be scaled to large problems. These methods can easily be applied to other tasks for more robust and trustworthy models.

Data-to-text (D2T) and text-to-data (T2D) are dual tasks that convert structured data, such as graphs or tables into fluent text, and vice versa. These tasks are usually handled separately and use corpora extracted from a single source. Current systems leverage pre-trained language models fine-tuned on D2T or T2D tasks. This approach has two main limitations: first, a separate system has to be tuned for each task and source; second, learning is limited by the scarcity of available corpora. This paper considers a more general scenario where data are available from multiple heterogeneous sources. Each source, with its specific data format and semantic domain, provides a non-parallel corpus of text and structured data. We introduce a variational auto-encoder model with disentangled style and content variables that allows us to represent the diversity that stems from multiple sources of text and data. Our model is designed to handle the tasks of D2T and T2D jointly. We evaluate our model on several datasets, and show that by learning from multiple sources, our model closes the performance gap with its supervised single-source counterpart and outperforms it in some cases.

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

Random Fourier Features (RFF) is among the most popular and broadly applicable approaches for scaling up kernel methods. In essence, RFF allows the user to avoid costly computations on a large kernel matrix via a fast randomized approximation. However, a pervasive difficulty in applying RFF is that the user does not know the actual error of the approximation, or how this error will propagate into downstream learning tasks. Up to now, the RFF literature has primarily dealt with these uncertainties using theoretical error bounds, but from a user's standpoint, such results are typically impractical -- either because they are highly conservative or involve unknown quantities. To tackle these general issues in a data-driven way, this paper develops a bootstrap approach to numerically estimate the errors of RFF approximations. Three key advantages of this approach are: (1) The error estimates are specific to the problem at hand, avoiding the pessimism of worst-case bounds. (2) The approach is flexible with respect to different uses of RFF, and can even estimate errors in downstream learning tasks. (3) The approach enables adaptive computation, so that the user can quickly inspect the error of a rough initial kernel approximation and then predict how much extra work is needed. Lastly, in exchange for all of these benefits, the error estimates can be obtained at a modest computational cost.

Recent advances in multi and many-core processors have led to significant improvements in the performance of scientific computing applications. However, the addition of a large number of complex cores have also increased the overall power consumption, and power has become a first-order design constraint in modern processors. While we can limit power consumption by simply applying software-based power constraints, applying them blindly will lead to non-trivial performance degradation. To address the challenge of improving the performance, power, and energy efficiency of scientific applications on modern multi-core processors, we propose a novel Graph Neural Network based auto-tuning approach that (i) optimizes runtime performance at pre-defined power constraints, and (ii) simultaneously optimizes for runtime performance and energy efficiency by minimizing the energy-delay product. The key idea behind this approach lies in modeling parallel code regions as flow-aware code graphs to capture both semantic and structural code features. We demonstrate the efficacy of our approach by conducting an extensive evaluation on $30$ benchmarks and proxy-/mini-applications with $68$ OpenMP code regions. Our approach identifies OpenMP configurations at different power constraints that yield a geometric mean performance improvement of more than $25\%$ and $13\%$ over the default OpenMP configuration on a 32-core Skylake and a $16$-core Haswell processor respectively. In addition, when we optimize for the energy-delay product, the OpenMP configurations selected by our auto-tuner demonstrate both performance improvement of $21\%$ and $11\%$ and energy reduction of $29\%$ and $18\%$ over the default OpenMP configuration at Thermal Design Power for the same Skylake and Haswell processors, respectively.

The multi-sentential long sequence textual data unfolds several interesting research directions pertaining to natural language processing and generation. Though we observe several high-quality long-sequence datasets for English and other monolingual languages, there is no significant effort in building such resources for code-mixed languages such as Hinglish (code-mixing of Hindi-English). In this paper, we propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i.e., MUTANT. We propose a token-level language-aware pipeline and extend the existing metrics measuring the degree of codemixing Figure 1: Example MCT and the corresponding article's to a multi-sentential framework and title form two multilingual data sources: (A) automatically identify MCT in the multilingual Dainik Jagran news article and (B) Man-ki-baat speech articles. The MUTANT dataset comprises transcript. We color code the tokens as: English, Hindi, 67k articles with 85k identified Hinglish and language independent.

The use of AI in healthcare is designed to improve care delivery and augment the decisions of providers to enhance patient outcomes. When deployed in clinical settings, the interaction between providers and AI is a critical component for measuring and understanding the effectiveness of these digital tools on broader health outcomes. Even in cases where AI algorithms have high diagnostic accuracy, healthcare providers often still rely on their experience and sometimes gut feeling to make a final decision. Other times, providers rely unquestioningly on the outputs of the AI models, which leads to a concern about over-reliance on the technology. The purpose of this research was to understand how reliant drug shop dispensers were on AI-powered technologies when determining a differential diagnosis for a presented clinical case vignette. We explored how the drug dispensers responded to technology that is framed as always correct in an attempt to measure whether they begin to rely on it without any critical thought of their own. We found that dispensers relied on the decision made by the AI 25 percent of the time, even when the AI provided no explanation for its decision.

In the last few months, artificial intelligence has gotten a lot of attention, especially with the arrival of ChatGPT. The program, launched in late November, has amazed people with its ability to answer questions and write stories while mimicking natural speech. In the progression of AI, many see ChatGPT as a big step. But as the new technologies increase, they are likely to raise questions. AI has long been a curiosity -- and a fear -- straight out of science fiction books and films, from the HAL 9000 to M3gan.

A collaboration led by an Oregon State University College of Forestry researcher has used very-high-resolution satellite imagery to develop a machine learning model that aims to improve climate scientists' ability to estimate aboveground carbon stocks in the Amazon. Findings of the study were published in the journal Carbon Balance and Management. Covering more than 2.5 million square miles in South America, the Amazon is the largest of the world's tropical forests, which play huge ecological roles for the planet despite covering less than 10% of the Earth's land area. More than half of all carbon stored in aboveground biomass is sequestered in tropical rain forests, which are also home to greater than 60% of all terrestrial species. Second growth and degraded forests now cover more area than intact forests, but scientists say the full extent of tropical forest degradation is not completely known.

ORNL Weinberg Fellow Addis Fuhr uses quantum chemistry and machine learning methods to advance new materials. When Addis Fuhr was growing up in Bakersfield, California, he enjoyed visiting the mall to gaze at crystals and rocks in the gem store. "I was always fascinated and loved how the different crystals looked and how they would get their different colors," he said. "I now know it's from impurities." It was impossible to see at the time, but the future Alvin M. Weinberg Fellow at the Department of Energy's Oak Ridge National Laboratory had identified a potential career option.