In this paper, we introduce a physics and geometry informed neural operator network with application to the forward simulation of acoustic scattering. The development of geometry informed deep learning models capable of learning a solution operator for different computational domains is a problem of general importance for a variety of engineering applications. To this end, we propose a physics-informed deep operator network (DeepONet) capable of predicting the scattered pressure field for arbitrarily shaped scatterers using a geometric parameterization approach based on non-uniform rational B-splines (NURBS). This approach also results in parsimonious representations of non-trivial scatterer geometries. In contrast to existing physics-based approaches that require model re-evaluation when changing the computational domains, our trained model is capable of learning solution operator that can approximate physically-consistent scattered pressure field in just a few seconds for arbitrary rigid scatterer shapes; it follows that the computational time for forward simulations can improve (i.e. be reduced) by orders of magnitude in comparison to the traditional forward solvers. In addition, this approach can evaluate the scattered pressure field without the need for labeled training data. After presenting the theoretical approach, a comprehensive numerical study is also provided to illustrate the remarkable ability of this approach to simulate the acoustic pressure fields resulting from arbitrary combinations of arbitrary scatterer geometries. These results highlight the unique generalization capability of the proposed operator learning approach.

We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. But, in contrast to query-based dense attack counterparts against black-box models, constructing sparse adversarial perturbations, even when models serve confidence score information to queries in a score-based setting, is non-trivial. Because, such an attack leads to: i) an NP-hard problem; and ii) a non-differentiable search space. Our artifacts and DIY attack samples are available on GitHub. Importantly, our work facilitates faster evaluation of model vulnerabilities and raises our vigilance on the safety, security and reliability of deployed systems. We are amidst an increasing prevalence of deep neural networks in real-world systems. So, our ability to understand the safety and security of neural networks is critical to our trust in machine intelligence. We have heightened awareness of adversarial attacks (Szegedy et al., 2014)--crafting imperceptible perturbations in inputs to manipulate deep perception systems to produce erroneous decisions. Only, access to model decisions (labels) or confidence scores are possible. Thus, crafting adversarial examples in black-box query-based interactions with a model is both interesting and practical to consider. Since confidence scores expose more information compared to model decisions, we can expect fewer queries to elicit effective attacks and, consequently, the potential for developing attacks at scale under score-based settings. While dense attacks are widely explored, the success of sparse-attacks, especially under score-based settings, has drawn much less attention and remains less understood (Croce et al., 2022). This leads to our lack of knowledge of model vulnerabilities to sparse perturbation regimes. Why are Score-Based Sparse Attacks Hard? An image with ground-truth label Minibus is misclassified as a Warplane.

The main challenge of large-scale numerical simulation of radiation transport is the high memory and computation time requirements of discretization methods for kinetic equations. In this work, we derive and investigate a neural network-based approximation to the entropy closure method to accurately compute the solution of the multi-dimensional moment system with a low memory footprint and competitive computational time. We extend methods developed for the standard entropy-based closure to the context of regularized entropy-based closures. The main idea is to interpret structure-preserving neural network approximations of the regularized entropy closure as a two-stage approximation to the original entropy closure. We conduct a numerical analysis of this approximation and investigate optimal parameter choices. Our numerical experiments demonstrate that the method has a much lower memory footprint than traditional methods with competitive computation times and simulation accuracy.

Large AI models trained on audio data may have the potential to rapidly classify patients, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets using expensive recording equipment in high-income, English-speaking countries. This challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. This application ultimately results in an audio electronic health record (voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and language with semantic meaning - compensating for the typical limitations of unimodal clinical datasets. This report introduces a consortium of partners for global work, presents the application used for data collection, and showcases the potential of informative voice EHR to advance the scalability and diversity of audio AI.

Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extrapolation capabilities. In this paper, we systematically explore anchoring as a general protocol for training vision models, providing fundamental insights into its training and inference processes and their implications for generalization and safety. Despite its promise, we identify a critical problem in anchored training that can lead to an increased risk of learning undesirable shortcuts, thereby limiting its generalization capabilities. To address this, we introduce a new anchored training protocol that employs a simple regularizer to mitigate this issue and significantly enhances generalization. We empirically evaluate our proposed approach across datasets and architectures of varying scales and complexities, demonstrating substantial performance gains in generalization and safety metrics compared to the standard training protocol.

We consider a weakly supervised learning problem called Learning from Label Proportions (LLP), where examples are grouped into ``bags'' and only the average label within each bag is revealed to the learner. We study various learning rules for LLP that achieve PAC learning guarantees for classification loss. We establish that the classical Empirical Proportional Risk Minimization (EPRM) learning rule (Yu et al., 2014) achieves fast rates under realizability, but EPRM and similar proportion matching learning rules can fail in the agnostic setting. We also show that (1) a debiased proportional square loss, as well as (2) a recently proposed EasyLLP learning rule (Busa-Fekete et al., 2023) both achieve ``optimistic rates'' (Panchenko, 2002); in both the realizable and agnostic settings, their sample complexity is optimal (up to log factors) in terms of $\epsilon, \delta$, and VC dimension.

Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing (in size and importance) portion of our lives, including, but not limited to, recommendation systems, autonomous driving technologies, healthcare diagnostics, financial services, and personalized marketing. On the one hand, the outsized influence of these systems imposes a high standard of quality, particularly in the data used to train them. On the other hand, establishing and maintaining standards of Data Quality (DQ) becomes more challenging due to the proliferation of Edge Computing and Internet of Things devices, along with their increasing adoption for training and deploying ML models. The nature of the edge environment -- characterized by limited resources, decentralized data storage, and processing -- exacerbates data-related issues, making them more frequent, severe, and difficult to detect and mitigate. From these observations, it follows that DQ research for edge ML is a critical and urgent exploration track for the safety and robust usefulness of present and future AI systems. Despite this fact, DQ research for edge ML is still in its infancy. The literature on this subject remains fragmented and scattered across different research communities, with no comprehensive survey to date. Hence, this paper aims to fill this gap by providing a global view of the existing literature from multiple disciplines that can be grouped under the umbrella of DQ for edge ML. Specifically, we present a tentative definition of data quality in Edge computing, which we use to establish a set of DQ dimensions. We explore each dimension in detail, including existing solutions for mitigation.

The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and fairness, and reliability. In this survey paper, we focus on a critical concern for adaptation of ML in risk-sensitive applications, namely understanding and quantifying uncertainty. Our paper approaches this topic in a structured way, providing a review of the literature in the various facets that uncertainty is enveloped in the ML process. We begin by defining uncertainty and its categories (e.g., aleatoric and epistemic), understanding sources of uncertainty (e.g., data and model), and how uncertainty can be assessed in terms of uncertainty quantification techniques (Ensembles, Bayesian Neural Networks, etc.). As part of our assessment and understanding of uncertainty in the ML realm, we cover metrics for uncertainty quantification for a single sample, dataset, and metrics for accuracy of the uncertainty estimation itself. This is followed by discussions on calibration (model and uncertainty), and decision making under uncertainty. Thus, we provide a more complete treatment of uncertainty: from the sources of uncertainty to the decision-making process. We have focused the review of uncertainty quantification methods on Deep Learning (DL), while providing the necessary background for uncertainty discussion within ML in general. Key contributions in this review are broadening the scope of uncertainty discussion, as well as an updated review of uncertainty quantification methods in DL.

Google announced on Thursday that it would refine and retool its summaries of search results generated by artificial intelligence, posting a blog explaining why the feature was returning bizarre and inaccurate answers that included telling people to eat rocks or add glue to pizza sauce. The company will reduce the scope of searches that will return an AI-written summary. Google has added several restrictions on the types of searches that would generate AI Overview results, the company's head of search, Liz Reid, said, as well as "limited the inclusion of satire and humor content". The company is also taking action against what it described as a small number of AI Overviews that violate its content policies, which it said occurred in fewer than 1 in 7m unique search queries where the feature appeared. The AI Overviews feature, which Google released in the US this month, quickly produced viral examples of the tool misinterpreting information and appearing to use satirical sources like the Onion or joke Reddit posts to generate answers.

In the largest global election year yet, generative AI is already being used to trick and manipulate voters around the world. Will this growing trend have real impact? Today on WIRED Politics Lab, we talk about a new online project that will be tracking the use of AI in elections around the world. Plus, Nilesh Christopher dives into the lucrative industry of deepfakes, and how politicians are using them to bombard Indian voters. Be sure to subscribe to the WIRED Politics Lab newsletter here.