Template parameter initialization
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C++, templates, classes↗
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ROSE Castor
ROSE Castor is a tool enabling automated verification of C++, built off of the ROSE compiler framework and the Why3 framework. Castor defines a verification language for providing specifications of C++ code, letting users perform automated functional formal verification of their C++ code. Castor is designed to target C++17, and supports a subset of the language, including classes, functions, templates, integers and booleans, pointers and references, and single inheritance. Castor currently does not support multiple or virtual inheritance, virtual functions, floating-point, threading, lambda functions, or the C++ STL, though some of these are planned in future updates. Castor ships with an in-house parser for parsing verification conditions.
Lane, PhillipA [Lawrence Livermore National Labora↗
Discovery of an Interlocked and Interwoven Molecular Topology in Nanocarbons via Dynamic C–C Bond Formation
Topologically complex carbon nanostructures are an exciting but largely unexplored class of materials due to their challenging synthesis. Previous methods are low yielding because they rely on irreversible C sp 2 −C sp 2 bond formation, which necessitates complex templating strategies to enforce entanglement. Here, reversible zirconocene coupling of alkynes is developed as a new method to access complex molecular topologies, where dynamic C−C bond formation facilitates entanglement under thermodynamic control, allowing the use of simple precursors without the need for preassembly. This strategy enables the scalable, high-yield synthesis of three topologically distinct nanocarbons, including the serendipitous discovery of a structure containing a new topological motif that was not previously identified or realized synthetically. This motif, consisting of an unusual combination of interlocking and interweaving, was recognized to be generalizable to a new topological class of molecules, introduced here as perplexanes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
CRISPR-GRIT: Guide RNAs with Integrated Repair Templates Enable Precise Multiplexed Genome Editing in the Diploid Fungal Pathogen Candida albicans
Candida albicans, an opportunistic fungal pathogen, causes severe infections in immunocompromised individuals. Limited classes and overuse of current antifungals have led to the rapid emergence of antifungal resistance. Thus, there is an urgent need to understand fungal pathogen genetics to develop new antifungal strategies. Genetic manipulation of C. albicans is encumbered by its diploid chromosomes requiring editing both alleles to elucidate gene function. Although the recent development of CRISPR-Cas systems has facilitated genome editing in C. albicans, large-scale and multiplexed functional genomic studies are still hindered by the necessity of cotransforming repair templates for homozygous knockouts. Here, we present CRISPR-GRIT (Guide RNAs with Integrated Repair Templates), a repair template-integrated guide RNA design for expedited gene knockouts and multiplexed gene editing in C. albicans. Here, we envision that this method can be used for high-throughput library screens and identification of synthetic lethal pairs in both C. albicans and other diploid organisms with strong homologous recombination machinery.
60 APPLIED LIFE SCIENCES↗
Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site
The HIV-1 fusion peptide (FP) represents a promising vaccine target, but global FP sequence diversity among circulating strains has limited anti-FP antibodies to ~60% neutralization breadth. Here we evolve the FP-targeting antibody VRC34.01 in vitro to enhance FP-neutralization using site saturation mutagenesis and yeast display. Successive rounds of directed evolution by iterative selection of antibodies for binding to resistant HIV-1 strains establish a variant, VRC34.01_mm28, as a best-in-class antibody with 10-fold enhanced potency compared to the template antibody and ~80% breadth on a cross-clade 208-strain neutralization panel. Structural analyses demonstrate that the improved paratope expands the FP binding groove to accommodate diverse FP sequences of different lengths while also recognizing the HIV-1 Env backbone. These data reveal critical antibody features for enhanced neutralization breadth and potency against the FP site of vulnerability and accelerate clinical development of broad HIV-1 FP-targeting vaccines and therapeutics.
60 APPLIED LIFE SCIENCES↗
The primordial black holes that disappeared: connections to dark matter and MHz-GHz gravitational Waves
In the post-LIGO era, there has been a lot of focus on primordial black holes (PBHs) heavier than ~10 15 g as potential dark matter (DM) candidates. We point out that the branch of the PBH family that disappeared — PBHs lighter than ~10 9 g that ostensibly Hawking evaporated away in the early Universe — also constitute an interesting frontier for DM physics. Hawking evaporation itself serves as a portal through which such PBHs can illuminate new physics, for example by emitting dark sector particles. Here, taking a simple DM scalar singlet model as a template, we compute the abundance and mass of PBHs that could have provided, by Hawking evaporation, the correct DM relic density. We consider two classes of such PBHs: those originating from curvature perturbations generated by inflation, and those originating from false vacuum collapse during a first-order phase transition. For PBHs of both origins we compute the gravitational wave (GW) signals emanating from their formation stage: from second-order effects in the case of curvature perturbations, and from sound waves in the case of phase transitions. The GW signals have peak frequencies in the MHz-GHz range typical of such light PBHs. We compute the strength of such GWs compatible with the observed DM relic density, and find that the GW signal morphology can in principle allow one to distinguish between the two PBH formation histories.
79 ASTRONOMY AND ASTROPHYSICS↗
A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint
The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Accessibility and Mechanical Stability of Nanoporous Zinc Oxide and Aluminum Oxide Coatings Synthesized via Infiltration of Polymer Templates
The conformal nanoporous inorganic coatings with accessible pores that are stable under applied thermal and mechanical stresses represent an important class of materials used in the design of sensors, optical coatings, and biomedical systems. Here, we synthesize porous AlO x and ZnO coatings by the sequential infiltration synthesis (SIS) of two types of polymers that enable the design of porous conformal coatings—polymers of intrinsic microporosity (PIM) and block co-polymer (BCP) templates. Using quartz crystal microbalance (QCM), we show that alumina precursors infiltrate both polymer templates four times more efficiently than zinc oxide precursors. Using the quartz crystal microbalance (QCM) technique, we provide a comprehensive study on the room temperature accessibility to water and ethanol of pores in block copolymers (BCPs) and porous polymer templates using polystyrene-block-poly-4-vinyl pyridine (PS75-b-P4VP25) and polymers of intrinsic microporosity (PIM-1), polymer templates modified by swelling, and porous inorganic coatings such as AlO x and ZnO synthesized by SIS using such templates. Importantly, we demonstrate that no structural damage occurs in inorganic nanoporous AlO x and ZnO coatings synthesized via infiltration of the polymer templates during the water freezing/melting cycling tests, suggesting excellent mechanical stability of the coatings, even though the hardness of the inorganic nanoporous coating is affected by the polymer and precursor selections. We show that the hardness of the coatings is further improved by their annealing at 900 °C for 1 h, though for all the cases except ZnO obtained using the BCP template, this annealing has a negligible effect on the porosity of the material, as is confirmed by the consistency in the optical characteristics. These findings unravel new potential for the materials being used across various environment and temperature conditions.
36 MATERIALS SCIENCE↗
Multiplicity and event-scale dependent flow and jet fragmentation in pp collisions at $\sqrt{s}$ = 13 TeV and in p–Pb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV
Long- and short-range correlations for pairs of charged particles are studied via two-particle angular correlations in pp collisions at $\sqrt{s}$ = 13 TeV and p–Pb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV. The correlation functions are measured as a function of relative azimuthal angle Δφ and pseudorapidity separation Δη for pairs of primary charged particles within the pseudorapidity interval |η| < 0.9 and the transverse-momentum interval 1 < p T < 4 GeV/c. Flow coefficients are extracted for the long-range correlations (1.6 < |Δη| < 1.8) in various high-multiplicity event classes using the low-multiplicity template ft method. The method is used to subtract the enhanced yield of away-side jet fragments in high-multiplicity events. These results show decreasing flow signals toward lower multiplicity events. Furthermore, the flow coefficients for events with hard probes, such as jets or leading particles, do not exhibit any significant changes compared to those obtained from high-multiplicity events without any specific event selection criteria. The results are compared with hydrodynamic-model calculations, and it is found that a better understanding of the initial conditions is necessary to describe the results, particularly for low-multiplicity events.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Keck Infrared Transient Survey. I. Survey Description and Data Release 1
Abstract We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01 ( D ≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete to z = 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction using PypeIt , which requires minimal human interaction to ensure reproducibility.
Tinyanont, S. (ORCID:0000000214814676)↗
Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”
This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.
13C↗