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The emergence of the dark web has enabled hackers to anonymously exchange information and trade malware worldwide, exposing organizations to an unprecedented number of threats. Without visibility into this offensive base, defenders are often left to mitigate damage. While prior cyber-threat intelligence research has been valuable, it has been constrained by incomplete, outdated, and noisy datasets. In this paper, we detail our efforts to build a comprehensive repository that illuminates the current plans of cyber-attackers. We achieve this by designing and deploying DarkMiner, a system that regularly scrapes the Tor network to populate the DarkMiner Database (DMDb). DMDb offers researchers a structured criminal hacking data collection enhanced with non-textual fields and object change tracking capabilities. To show its potential, we present three case studies analyzing: 1) cyber threat market fluctuations, 2) image-based vendor attribution, and 3) software vulnerability targeting. 

Harmful algal blooms threaten ecosystems and water safety, necessitating rapid treatment strategies. This study developed an electrochemical ozonation process to realize simultaneous cyanobacteria inactivation and microcystin removal. 

Abstract Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-establishedd-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. 

Abstract Building upon thed-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites withd-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials. 

Abstract As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys ( candidates) that were generated from thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from ‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.