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Abstract Redox, a native modality in biology involving the flow of electrons, energy, and information, is used for energy‐harvesting, biosynthesis, immune‐defense, and signaling. Because electrons (in contrast to protons) are not soluble in the medium, electron‐flow through the redox modality occurs through redox reactions that are sometimes organized into pathways and networks (e.g., redox interactomes). Redox is also accessible to electrochemistry, which enables electrodes to receive and transmit electrons to exchange energy and information with biology. In this Perspective, efforts to develop electrochemistry as a tool for redox‐based bio‐information processing: to interconvert redox‐based molecular attributes into interpretable electronic signals, are described. Using a series of Case Studies, how the information‐content of the measurements can be enriched using: diffusible mediators; tuned electrical input sequences; and cross‐modal measurements (e.g., electrical plus spectral), is shown. Also, theory‐guided feature engineering approaches to compress the information in the electronic signals into quantitative metrics (i.e., features) that can serve as correlating variables for pattern recognition by data‐driven analysis are described. Finally, how redox provides a modality for electrogenetic actuation is illustrated. It is suggested that electrochemistry's capabilities to provide real‐time, low‐cost, and high‐content data in an electronic format allow the feedback‐control needed for autonomous learning and deployable sensing/actuation. 

Abstract Hybrid perovskites are interesting optoelectronic materials. The perovskite ABX₃structure offers a vast compositional space, and we have identified over 300 perovskite ions. This flexibility enables tuneable properties and has significantly contributed to the success of perovskite optoelectronics. However, this diversity also leads to confusion, ambiguity, and inconsistencies causing challenges for data mining and machine learning applications. To address this issue, we propose guidelines and a JSON schema to standardize the reporting of perovskite compositions. The schema adheres to IUPAC recommendations and is designed to make data both human- and machine-readable. It captures key descriptors such as perovskite composition, molecular formula, SMILES representation, IUPAC name, and CAS number for each ion. To facilitate adoption, we have developed utilities to automatically generate comprehensive and standardized perovskite descriptions from standard ion abbreviations and stoichiometric coefficients. Additionally, we provide a curated database of all identified perovskite ions with associated descriptive data. 

Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives. 

Epidemic containment has long been a crucial task in many high-stake application domains, ranging from public health to misinformation dissemination. Existing studies for epidemic containment are primarily focused on undirected networks, assuming that the infection rate is constant throughout the contact network regardless of the strength and direction of contact. However, such an assumption can be unrealistic given the asymmetric nature of the real-world infection process. To tackle the epidemic containment problem in directed networks, simply grafting the methods designed for undirected network can be problematic, as most of the existing methods rely on the orthogonality and Lipschitz continuity in the eigensystem of the underlying contact network, which do not hold for directed networks. In this work, we derive a theoretical analysis on the general epidemic threshold condition for directed networks and show that such threshold condition can be used as an optimization objective to control the spread of the disease. Based on the epidemic threshold, we propose an asymptotically greedy algorithm DINO (DIrected NetwOrk epidemic containment) to identify the most critical nodes for epidemic containment. The proposed algorithm is evaluated on real-world directed networks, and the results validate its effectiveness and efficiency.