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1. Low Spatial Proximity Between Text and Illustrations Improves Children’s Comprehension and Attention: An Eye Tracking Study.

   Boyd, M; Godwin, K.E.; Gurchiek, E.; Fisher, A.V.; Eng, C.M. (July 2022, Proceedings of the Annual Conference of the Cognitive Science Society)

   Culbertson, J.; Perfors, A; Rabagliati, H.; Ramenzoni, V. (Ed.)

   Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children. 

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2. Optimal Mechanism Design for Fresh Data Acquisition

   https://doi.org/10.1109/ISIT45174.2021.9517951  

   Zhang, Meng; Arafa, Ahmed; Wei, Ermin; Berry, Randall A. (July 2021, IEEE International Symposium on Information Theory)

   In this paper, we study a fresh data acquisition problem to acquire fresh data and optimize the age-related performance when strategic data sources have private market information. We consider an information update system in which a destination acquires, and pays for, fresh data updates from a source. The destination incurs an age-related cost, modeled as a general increasing function of the age-of-information (AoI). The source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. To this end, we design an optimal (economic) mechanism for timely information acquisition by generalizing Myerson's seminal work. The goal is to minimize the sum of the destination's age-related cost and its payment to the source, while ensuring that the source truthfully reports its private information and will voluntarily participate in the mechanism. Our results show that, under some distributions of the source's cost, our proposed optimal mechanism can lead to an unbounded benefit, compared against a benchmark that naively trusts the source's report and thus incentivizes its maximal over-reporting. 

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3. Dual-comb mid-infrared spectromicroscopy with photothermal fluorescence detection

   https://doi.org/10.1364/OE.557991  

   Turner, Gwendylan_A; Herman, Daniel_I; Razumtcev, Aleksandr; Mangold, Markus; Li, Minghe; Cao, Ziyi; Rong, Jiayue; Bouman, Charles_A; Buzzard, Gregery; Simpson, Garth_J (June 2025, Optics Express)

   An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables the acquisition of infrared spectra at high speed (<1 millisecond) through the generation of optical beat patterns and radio-frequency detection. The high-speed nature of the spectral acquisition is shown to support spectral mapping in microscopy measurements. Direct detection of the transmitted infrared beam yields high signal-to-noise spectral information, but long infrared wavelengths impose low diffraction-limited spatial resolution. The use of fluorescence detected photothermal infrared (F-PTIR) imaging provides high spatial resolution tied directly to the integrated IR absorption. Computational imaging using a multi-agent consensus equilibrium (MACE) approach combines the high spatial resolution of F-PTIR and the high spectral information of dual-comb infrared transmission in a single optimized equilibrium hyperspectral data cube. 

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4. A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning

   https://doi.org/10.3390/e24101469  

   Hössjer, Ola; Díaz-Pachón, Daniel Andrés; Rao, J. Sunil (October 2022, Entropy)

   Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ rule. The degree of true belief is quantified by means of active information I+: a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent’s strength of belief in a true proposition has increased in comparison with the ignorant person (I+>0), or the strength of belief in a false proposition has decreased (I+<0). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds that correspond to parameters of a statistical model. This makes it possible to interpret learning as a hypothesis test for such a model, whereas knowledge acquisition additionally requires estimation of a true world parameter. Our framework of learning and knowledge acquisition is a hybrid between frequentism and Bayesianism. It can be generalized to a sequential setting, where information and data are updated over time. The theory is illustrated using examples of coin tossing, historical and future events, replication of studies, and causal inference. It can also be used to pinpoint shortcomings of machine learning, where typically learning rather than knowledge acquisition is in focus. 

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5. Towards Cost Sensitive Decision Making

   Li, Yang; Oliva, Junier (May 2025, Proceedings of The 28th International Conference on Artificial Intelligence and Statistics)

   Li, Yingzhen; Mandt, Stephan; Agrawal, Shipra; Khan, Emtiyaz (Ed.)

   Many real-world situations allow for the acquisition of additional relevant information when making decisions with limited or uncertain data. However, traditional RL approaches either require all features to be acquired beforehand (e.g. in a MDP) or regard part of them as missing data that cannot be acquired (e.g. in a POMDP). In this work, we consider RL models that may actively acquire features from the environment to improve the decision quality and certainty, while automatically balancing the cost of feature acquisition process and the reward of task decision process. We propose the Active-Acquisition POMDP and identify two types of the acquisition process for different application domains. In order to assist the agent in the actively-acquired partially-observed environment and alleviate the exploration-exploitation dilemma, we develop a model-based approach, where a deep generative model is utilized to capture the dependencies of the features and impute the unobserved features. The imputations essentially represent the beliefs of the agent. Equipped with the dynamics model, we develop hierarchical RL algorithms to resolve both types of the AA-POMDPs. Empirical results demonstrate that our approach achieves considerably better performance than existing POMDP-RL solutions 

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