Making science more accessible remains a challenge even with much effort devoted on the producer and publisher side. As content producers, researchers are encouraged to engage directly with the public, either through social media (Davies, 2008; Hara et al., 2019; Knox and Hara, 2021) or by crafting more digestible manuscripts in research (Maurer et al., 2021) and practice (Grene et al., 2017). Funding agencies and renowned journals also encourage the communication of scientific findings in accessible language. For instance, the National Institutes of Health (NIH) advocate "clear and simple" principles when communicating with audiences with limited health literacy, and the Proceedings of the National Academy of Sciences of the United States of America (PNAS) requires authors to submit a significance statement accessible to non-experts (Berenbaum, 2021; Pool et al., 2021). As scientific research progresses with increased specialization and interdisciplinarity, it is acknowledged that the use of jargon effectively reduces communication costs among domain experts, particularly those responsible for reviewing submissions. This specialized language, however, can become incomprehensible to those without a similar research background. While efforts to share scientific findings in more accessible language from the producer side are gaining traction, widespread adoption is unlikely in the near future due to the inherent conflicts between the specialized nature of scholarly communication and the public-oriented dissemination of scientific findings. Within this effort to create understandable research findings and open science to broader communities, libraries--and our digital libraries in particular--have a role to play. Driven by this idea, we propose to start by improving the readability of abstracts from scholarly works through automated rewriting.

Deep neural networks are vulnerable to adversarial examples, and adversarial attacks that generate adversarial examples have been studied in this context. Existing studies imply that increasing the diversity of model outputs contributes to improving the attack performance. This study focuses on the Auto Conjugate Gradient (ACG) attack, which is inspired by the conjugate gradient method and has a high diversification performance. We hypothesized that increasing the distance between two consecutive search points would enhance the output diversity. To test our hypothesis, we propose Rescaling-ACG (ReACG), which automatically modifies the two components that significantly affect the distance between two consecutive search points, including the search direction and step size. ReACG showed higher attack performance than that of ACG, and is particularly effective for ImageNet models with several classification classes. Experimental results show that the distance between two consecutive search points enhances the output diversity and may help develop new potent attacks.

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In this work, we give an information-theoretic analysis of the generalization error and excess risk of transfer learning algorithms. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence $D(\mu\|\mu')$ plays an important role in the characterizations where $\mu$ and $\mu'$ denote the distribution of the training data and the testing data, respectively. Specifically, we provide generalization error and excess risk upper bounds for learning algorithms where data from both distributions are available in the training phase. Recognizing that the bounds could be sub-optimal in general, we provide improved excess risk upper bounds for a certain class of algorithms, including the empirical risk minimization (ERM) algorithm, by making stronger assumptions through the \textit{central condition}. To demonstrate the usefulness of the bounds, we further extend the analysis to the Gibbs algorithm and the noisy stochastic gradient descent method. We then generalize the mutual information bound with other divergences such as $\phi$-divergence and Wasserstein distance, which may lead to tighter bounds and can handle the case when $\mu$ is not absolutely continuous with respect to $\mu'$. Several numerical results are provided to demonstrate our theoretical findings. Lastly, to address the problem that the bounds are often not directly applicable in practice due to the absence of the distributional knowledge of the data, we develop an algorithm (called InfoBoost) that dynamically adjusts the importance weights for both source and target data based on certain information measures. The empirical results show the effectiveness of the proposed algorithm.

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, plus a simultaneously learnt surrogate model with inputs on this manifold. The proposed dimensionality reduction framework can then be applied to perform outer loop many-query tasks, like sensitivity analysis and uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy, and highlight its advantages with respect to existing approaches in the literature.

We outline the principles of classical assurance for computer-based systems that pose significant risks. We then consider application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML). A key element in this "dependability" perspective is a requirement to have near-complete understanding of the behavior of critical components, and this is considered infeasible for AI and ML. Hence the dependability perspective aims to minimize trust in AI and ML elements by using "defense in depth" with a hierarchy of less complex systems, some of which may be highly assured conventionally engineered components, to "guard" them. This may be contrasted with the "trustworthy" perspective that seeks to apply assurance to the AI and ML elements themselves. In cyber-physical and many other systems, it is difficult to provide guards that do not depend on AI and ML to perceive their environment (e.g., other vehicles sharing the road with a self-driving car), so both perspectives are needed and there is a continuum or spectrum between them. We focus on architectures toward the dependability end of the continuum and invite others to consider additional points along the spectrum. For guards that require perception using AI and ML, we examine ways to minimize the trust placed in these elements; they include diversity, defense in depth, explanations, and micro-ODDs. We also examine methods to enforce acceptable behavior, given a model of the world. These include classical cyber-physical calculations and envelopes, and normative rules based on overarching principles, constitutions, ethics, or reputation. We apply our perspective to autonomous systems, AI systems for specific functions, generic AI such as Large Language Models, and to Artificial General Intelligence (AGI), and we propose current best practice and an agenda for research.

There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning. We introduce CoverBench, a challenging benchmark focused on verifying LM outputs in complex reasoning settings. Datasets that can be used for this purpose are often designed for other complex reasoning tasks (e.g., QA) targeting specific use-cases (e.g., financial tables), requiring transformations, negative sampling and selection of hard examples to collect such a benchmark. CoverBench provides a diversified evaluation for complex claim verification in a variety of domains, types of reasoning, relatively long inputs, and a variety of standardizations, such as multiple representations for tables where available, and a consistent schema. We manually vet the data for quality to ensure low levels of label noise. Finally, we report a variety of competitive baseline results to show CoverBench is challenging and has very significant headroom. The data is available at https://huggingface.co/datasets/google/coverbench .

This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.

This is the author's version of the article that has been published in the proceedings of IEEE Visualization conference. The final version of this record is available at: xx.xxxx/TVCG.201x.xxxxxxx/ This metric also provides no visibility into how particular Reconstruction of 3D scenes from 2D images is a technical challenge images, lighting conditions, camera positions, or details of the that impacts domains from Earth and planetary sciences and morphology of the remote environment might interact to create inaccuracies space exploration to augmented and virtual reality. The impact of these unknowns algorithms first identify common features across images compounds in domains where high accuracy terrain reconstruction and then minimize reconstruction errors after estimating the is critical to outcomes, like science or space exploration where there shape of the terrain. This bundle adjustment (BA) step optimizes is no ground truth and inaccurate reconstruction can lead to false around a single, simplifying scalar value that obfuscates many possible results or risking billion-dollar spacecraft.

Software vulnerabilities, caused by unintentional flaws in source code, are a primary root cause of cyberattacks. Static analysis of source code has been widely used to detect these unintentional defects introduced by software developers. Large Language Models (LLMs) have demonstrated human-like conversational abilities due to their capacity to capture complex patterns in sequential data, such as natural languages. In this paper, we harness LLMs' capabilities to analyze source code and detect known vulnerabilities. To ensure the proposed vulnerability detection method is universal across multiple programming languages, we convert source code to LLVM IR and train LLMs on these intermediate representations. We conduct extensive experiments on various LLM architectures and compare their accuracy. Our comprehensive experiments on real-world and synthetic codes from NVD and SARD demonstrate high accuracy in identifying source code vulnerabilities.

Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which struggles to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, we propose a novel evaluation framework referred to as StructEval. Starting from an atomic test objective, StructEval deepens and broadens the evaluation by conducting a structured assessment across multiple cognitive levels and critical concepts, and therefore offers a comprehensive, robust and consistent evaluation for LLMs. Experiments on three widely-used benchmarks demonstrate that StructEval serves as a reliable tool for resisting the risk of data contamination and reducing the interference of potential biases, thereby providing more reliable and consistent conclusions regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.