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At least 667 records · Page 37

Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles

Abstract We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators. The constructed loss functions ensure that the applied transformations are symmetries and the corresponding set of generators forms a closed (sub)algebra. Our procedure is validated with several examples illustrating different types of conserved quantities preserved by symmetry. In the process of deriving the full set of symmetries, we analyze the complete subgroup structure of the rotation groups SO (2), SO (3), and SO (4), and of the Lorentz group S O ( 1 , 3 ) . Other examples include squeeze mapping, piecewise discontinuous labels, and SO (10), demonstrating that our method is completely general, with many possible applications in physics and data science. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.

97 MATHEMATICS AND COMPUTING↗

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

Preservation of lunar core samples - Preparation and interpretation of three-dimensional stratigraphic sections

Preservation and analysis of core samples collected from the lunar regolith are discussed. The objectives of core sampling are described together with the core-sampling equipment used in the Apollo program. The effects of disaggregation in core samples are summarized, and ways to preserve the stratigraphy in these samples are reviewed, particularly partial impregnation of samples with polybutyl methacrylate. It is shown that stratigraphic sections preserved in this way reveal individual fragments of the regolith in their original positions, primary and secondary depositional structures related to lunar processes, and the nature and extent of deformations caused by sampling equipment. It is concluded that since the core sedimentary structures indicate complex three-dimensional variations in the regolith, quantitative models of regolith evolution must allow for distinctive local histories as well as irregular reworking and deposition events due to impact processes.

Fryxell, R.↗

Designing structures for large space systems

A questionnaire was compiled to identify technology deficiencies and point to research and development activities required in support of the large space structures program. Problem areas identified in setting criteria for such structures include improved flightworthiness, satisfying cost constraints, assessing new loads and environments, improved mission performance and life, and preservation of the ecology.

Christensen, R. H.↗

Role of coherent structures in turbulent shear flows

A definition for the large-scale coherent structure is presented, and the nature and role of coherent structures in turbulent shear flows are examined. The equations governing the coherent motions and the experimental considerations as well as constraints in the investigations of coherent structures in wall-bounded and free turbulent shear flows are discussed. Results from a few of our recent and on-going studies of coherent structures in excited and unexcited free turbulent shear flows are reviewed. These results show that coherent structures are dominant in transport in the early stages of their formation, but not in the self-preserving regions of turbulent shear flows.

Fazle Hussain, A. K. M.↗

A model for the biological precipitation of Precambrian iron-formation

A biological model for the precipitation of Precambrian iron formations is presented. Assuming an oxygen deficient atmosphere and water column to allow sufficient Fe solubility, it is proposed that local oxidizing environments, produced biologically, led to precipitation of iron formations. It is further suggested that spheroidal structures about 30 mm in diameter, which are widespread in low grade cherty rion formations, are relict forms of the organic walled microfossil Eosphaera tylerii. The presence of these structures suggests that the organism may have had a siliceous test, which allowed sufficient rigidity for accumulation and preservation. The model involves precipitation of ferric hydrates by oxidation of iron in the photic zone by a variety of photosynthetic organisms. Silica may have formed in the frustules of silica secreting organisms, including Eosphaera tylerii. Iron formates formed, therefore, by a sediment rain of biologically produced ferric hydrates and silica and other organic material. Siderite and hematite formed diagenetically on basin floors, and subsequent metamorphism produced magnetite and iron silicates.

Laberge, G. L.↗

Near Optimality of Matched Filter Detection for Cyclic Prefix Direct Sequence Spread Spectrum

Abstract—Cyclic Prefix Direct Sequence Spread Spectrum -DSSS) has been presented as a potential solution for ultrareliable low latency communications (URLLC) and massive machine type communication (mMTC), where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show that when operating in the low SNR regime, CP-DSSS achieves near optimum performance when using a matched filter (MF) detector at the receiver. Time reversal (TR) precoding at the transmitter is also analyzed. These results also carry forward into multi-antenna scenarios where array gain is preserved. With nearly optimal performance of MF detection, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed networks.

5G and Beyond Communications↗

Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging

Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗

A method of order reduction for structural dynamics

This paper presents a new method of order reduction based on recent work of a similar nature applicable to system dynamics and control. This method provides a practical computational procedure for producing a condensed model which exactly preserves the slowest n1 modes (where n1 is the number of 'slow' eigenvalues) of the total n modes of the original model for almost any set of n1 degrees of freedom retained in the condensed model. The method can also be used to compute the eigensolutions corresponding to the n1 slowest modes of the original structural dynamics problem. The accuracy of the condensed model and the speed/accuracy performance of the eigensolver are compared with standard methods for a 90 DOF cantilevered plate.

Anderson, L. R.↗

Hydrocarbons in the Haughton Impact Structure, Devon Island, Nunavut, Canada

The occurrence of organic matter in terrestrial impact craters is important to astrobiology, as it may offer insight into possible relationships between impact events and the genesis, distribution and preservation of biologically relevant materials on planets. In particular, the processing and mobilization of preexisting organic material in planetary targets is of interest for studies of pathways to chemical complexity. Observations in old (Palaeozoic, Precambrian) craters indicate that organic carbon can survive large impacts. However, limited exposure and superimposed geological events can make the detailed history of organic matter in old craters difficult to decipher. We present here the first identification of hydrocarbons in the young (23 Ma, Miocene) Haughton impact structure.

Parnell, J.↗

Transverse structure of the pion beyond leading twist with basis light-front quantization

We investigate the twist-3transverse-momentum-dependent parton distribution functions (TMDs) of the pion with basis light-front quantization. The twist-3 TMDs are not independent and can be decomposed into twist-2and genuine twist-3 terms from the equations of motion (EOM). We compute the TMDs from the resulting light-front wave functions obtained by diagonalizing the light-front QCD Hamiltonian, determined for pion’s constituent quark-antiquark and quark-antiquark-gluon Fock sectors, with three-dimensional confinement. We also obtain the twist-3 parton distribution functions (PDFs) and show that they preserve the sum rule, which affirms the robustness of our approach. This is the first time that theoretical predictions are made for subleading twist structures of the pion containing interference terms between two light-front Fock sectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A Privacy-Preserving Strategy for the Trust Layer of the Energy Grid of Things Distributed Energy Resource Management System

Emergent from the shadows of the traditional grid flaws, the Smart Grid (SG) idea was born and led by government mandates toward cleaner energy production. The SG represents the next generation of electricity distribution systems that subsume recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. Unfortunately, this relatively new concept is not flawless; the intrinsic reliance on increased digital communication spreads open attack paths for adversaries. Therefore, finding solutions that address information exchange vulnerabilities has become imperative. The Energy Grid of Things (EGoT) is Portland State University’s (PSU’s) implementation of a Distributed Energy Resource Management System (DERMS). The EGoT DERMS requires access to customers’ information to achieve operational objectives. The system’s access to customers’ information needs to be restricted such that it does not violate customers’ privacy. Applying privacy protection models such as K-anonymity to EGoT DERMS sub-components safeguards that privacy. This thesis work proposes a strategy to ensure communication in the EGoT DERMS is privacy-preserving and secure. Specifically, it provides an approach to applying the Mondrian Algorithm to ensure data within the system excludes Personally Identifiable Information (PII) and provides means for securing the communication according to industry standards (IEEE 2030.5). Results suggest that the generalization hierarchy derived for the EGoT DERMS exhibits an Identical Generalization Hierarchy structure. Guarantees of sameness manifested in the test feeder topology would not hold in real-world scenarios. This thesis work proposes a strategy to ensure communication in the EGoT DERMS is privacy-preserving and secure. Specifically, it provides an approach to applying the Mondrian Algorithm to ensure data within the system excludes Personally Identifiable Information (PII) and provides means for securing the communication according to industry standards (IEEE 2030.5). Results suggest that the generalization hierarchy derived for the EGoT DERMS exhibits an Identical Generalization Hierarchy structure. Guarantees of sameness manifested in the test feeder topology would not hold in real-world scenarios.

Alsiad, Mohammed↗

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

Quantum chemistry is a key application area for noisy-intermediate scale quantum (NISQ) devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on the performance of computing ground states for alkali-hydrides under an array of error mitigation methods. State-of-the-art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM are outlined here. Here it is demonstrated how to extend the reach of variational eigensolvers with symmetry preserving Ansätze. Next, it is outlined how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, a new error mitigation method is demonstrated which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Bioinspired mechanically interlocking holey graphene@SiO 2 anode

Mechanically interlocking structures that can enhance adhesion at the interface and regulate the stress distribution have been widely observed in biological systems. Inspired by the biological structures in the wings of beetles, we synthesized a holey graphene@SiO 2 anode with strong mechanical interlocking, characterized it electrochemically, and explained its performance by finite element analysis and density functional calculations. The mechanically interlocking structure enhances lithium-ion (Li + ) storage by transmitting the strain from SiO 2 to the holey graphene and by a mechano-electrochemical coupling effect. The interlocking fit hinders the abscission of SiO 2 and the distinctive structure reduces the stress and strain of SiO 2 during (de)lithiation. The positive mechano-electrochemical coupling effect preserves the amount of electrochemically active phase (Li x Si) during cycles and facilitates Li + diffusion. Therefore, the capacity shows only a slight attenuation after 8000 cycles (cycling stability), and the specific capacity is ~1200 mA h g –1 at 5 A/g (rate-performance). This study furnishes a novel way to design high-performance Li + /Na + /K + /Al 3+ anodes with large volume expansion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗