Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species.

The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023.

Neural Machine Translation (NMT) models have been shown to be vulnerable to adversarial attacks, wherein carefully crafted perturbations of the input can mislead the target model. In this paper, we introduce ACT, a novel adversarial attack framework against NMT systems guided by a classifier. In our attack, the adversary aims to craft meaning-preserving adversarial examples whose translations in the target language by the NMT model belong to a different class than the original translations. Unlike previous attacks, our new approach has a more substantial effect on the translation by altering the overall meaning, which then leads to a different class determined by an oracle classifier. To evaluate the robustness of NMT models to our attack, we propose enhancements to existing black-box word-replacement-based attacks by incorporating output translations of the target NMT model and the output logits of a classifier within the attack process. Extensive experiments, including a comparison with existing untargeted attacks, show that our attack is considerably more successful in altering the class of the output translation and has more effect on the translation. This new paradigm can reveal the vulnerabilities of NMT systems by focusing on the class of translation rather than the mere translation quality as studied traditionally.

In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data. Anomaly detection involves gradient descent to reconstruct energy sub-sequences, identifying the noise vector that closely generates them through the generator network. Soft-DTW is used as a differentiable alternative for the reconstruction loss and is found to be superior to Euclidean distance. Combining reconstruction loss and the latent space's prior probability distribution serves as the anomaly score. Our novel method accelerates detection by parallel computation of reconstruction of multiple points and shows promise in identifying anomalous energy consumption in buildings, as evidenced by performing experiments on hourly energy time series from 15 buildings.

Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work has largely been on truthfulness, in this paper we extend this framework to a richer set of concepts such as appropriateness, humor, creativity and quality, and explore to what degree current detection and guidance strategies work in these challenging settings. To facilitate evaluation, we develop a novel metric for concept guidance that takes into account both the success of concept elicitation as well as the potential degradation in fluency of the guided model. Our extensive experiments reveal that while some concepts such as truthfulness more easily allow for guidance with current techniques, novel concepts such as appropriateness or humor either remain difficult to elicit, need extensive tuning to work, or even experience confusion. Moreover, we find that probes with optimal detection accuracies do not necessarily make for the optimal guides, contradicting previous observations for truthfulness. Our work warrants a deeper investigation into the interplay between detectability, guidability, and the nature of the concept, and we hope that our rich experimental test-bed for guidance research inspires stronger follow-up approaches.

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.

As the space domain becomes increasingly congested, autonomy is proposed as one approach to enable small numbers of human ground operators to manage large constellations of satellites and tackle more complex missions such as on-orbit or in-space servicing, assembly, and manufacturing. One of the biggest challenges in developing novel spacecraft autonomy is mechanisms to test and evaluate their performance. Testing spacecraft autonomy on-orbit can be high risk and prohibitively expensive. An alternative method is to test autonomy terrestrially using satellite surrogates such as attitude test beds on air bearings or drones for translational motion visualization. Against this background, this work develops an approach to evaluate autonomous spacecraft behavior using a surrogate platform, namely a micro-quadcopter drone developed by the Bitcraze team, the Crazyflie 2.1. The Crazyflie drones are increasingly becoming ubiquitous in flight testing labs because they are affordable, open source, readily available, and include expansion decks which allow for features such as positioning systems, distance and/or motion sensors, wireless charging, and AI capabilities. In this paper, models of Crazyflie drones are used to simulate the relative motion dynamics of spacecraft under linearized Clohessy-Wiltshire dynamics in elliptical natural motion trajectories, in pre-generated docking trajectories, and via trajectories output by neural network control systems.

Nuclear facilities must routinely survey their infrastructure for radiation contamination. Generally, this is done by trained professionals, wearing personal protective equipment (PPE) that swipe potentially contaminated surfaces and test the wipes under detectors. This approach leaves personnel vulnerable to radiation exposure and is not comprehensive. Robots address these inadequacies, offering a cost-effective solution with negligible downtime. We present a Robot Radiation Survey System (RRSS): a heterogeneous robot team to perform comprehensive alpha/beta/gamma radiation surveys. The RRSS system members, core capabilities, and comprehensive survey plan are addresses in this paper.

We are interested in developing a data-driven method to evaluate race-induced biases in law enforcement systems. While the recent works have addressed this question in the context of police-civilian interactions using police stop data, they have two key limitations. First, bias can only be properly quantified if true criminality is accounted for in addition to race, but it is absent in prior works. Second, law enforcement systems are multi-stage and hence it is important to isolate the true source of bias within the "causal chain of interactions" rather than simply focusing on the end outcome; this can help guide reforms. In this work, we address these challenges by presenting a multi-stage causal framework incorporating criminality. We provide a theoretical characterization and an associated data-driven method to evaluate (a) the presence of any form of racial bias, and (b) if so, the primary source of such a bias in terms of race and criminality. Our framework identifies three canonical scenarios with distinct characteristics: in settings like (1) airport security, the primary source of observed bias against a race is likely to be bias in law enforcement against innocents of that race; (2) AI-empowered policing, the primary source of observed bias against a race is likely to be bias in law enforcement against criminals of that race; and (3) police-civilian interaction, the primary source of observed bias against a race could be bias in law enforcement against that race or bias from the general public in reporting against the other race. Through an extensive empirical study using police-civilian interaction data and 911 call data, we find an instance of such a counter-intuitive phenomenon: in New Orleans, the observed bias is against the majority race and the likely reason for it is the over-reporting (via 911 calls) of incidents involving the minority race by the general public.

We consider the task of causal imputation, where we aim to predict the outcomes of some set of actions across a wide range of possible contexts. As a running example, we consider predicting how different drugs affect cells from different cell types. We study the index-only setting, where the actions and contexts are categorical variables with a finite number of possible values. Even in this simple setting, a practical challenge arises, since often only a small subset of possible action-context pairs have been studied. Thus, models must extrapolate to novel action-context pairs, which can be framed as a form of matrix completion with rows indexed by actions, columns indexed by contexts, and matrix entries corresponding to outcomes. We introduce a novel SCM-based model class, where the outcome is expressed as a counterfactual, actions are expressed as interventions on an instrumental variable, and contexts are defined based on the initial state of the system. We show that, under a linearity assumption, this setup induces a latent factor model over the matrix of outcomes, with an additional fixed effect term. To perform causal prediction based on this model class, we introduce simple extension to the Synthetic Interventions estimator (Agarwal et al., 2020). We evaluate several matrix completion approaches on the PRISM drug repurposing dataset, showing that our method outperforms all other considered matrix completion approaches.