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Systems and methods for binary code analysis

Human-readable (HR) code may be derived from a binary. The HR code may be configured to have statistical properties suitable for machine-learned (ML) translation. The HR code may comprise source code, intermediate code, assembly code, or the like. A machine-learned translator may be configured to translate the HR code into labels comprising semantic information pertaining to respective functions of the binary, such as a function name, role, or the like. Execution of the binary may be blocked in response to translating the HR code to a label associated with malware, such as cryptocurrency mining malware or the like. Conversely, the binary may be permitted to proceed to execution in response to determining that the translation is free from labels indicative of malware.

Anderson, Matthew W.

Comparing quantum annealing and spiking neuromorphic computing for sampling binary sparse coding QUBO problems

We consider the problem of computing a sparse binary representation of an image. Given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when added together best reconstruct the given input. We formulate this problem with an L 2 loss on the reconstruction error, and an L 0 loss on the binary vector enforcing sparsity. First, we solve the sparse representation QUBOs by solving them both on a D-Wave quantum annealer with Pegasus chip connectivity, as well as on the Intel Loihi 2 spiking neuromorphic processor using a stochastic Non-equilibrium Boltzmann Machine (NEBM). Second, using Quantum Evolution Monte Carlo with Reverse Annealing and iterated warm starting on Loihi 2 to evolve the solution quality from the respective machines. We demonstrate that both quantum annealing and neuromorphic computing are suitable for solving binary sparse coding QUBOs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC

The Santa Barbara Binary-disk Code Comparison

We have performed numerical calculations of a binary interacting with a gas disk, using 11 different numerical methods and a standard binary-disk setup. The goal of this study is to determine whether all codes agree on a numerically converged solution and to determine the necessary resolution for convergence and the number of binary orbits that must be computed to reach an agreed-upon relaxed state of the binary-disk system. We find that all codes can agree on a converged solution (depending on the diagnostic being measured). The zone spacing required for most codes to reach a converged measurement of the torques applied to the binary by the disk is roughly 1% of the binary separation in the vicinity of the binary components. For our disk model to reach a relaxed state, codes must be run for at least 200 binary orbits, corresponding to about a viscous time for our parameters, 0.2(a 2 Ω B /v) is the kinematic viscosity. The largest discrepancies between codes resulted from the dimensionality of the setup (3D vs. 2D disks). We find good agreement in the total torque on the binary between codes, although the partition of this torque between the gravitational torque, orbital accretion torque, and spin accretion torque depends sensitively on the sink prescriptions employed. In agreement with previous studies, we find a modest difference in torques and accretion variability between 2D and 3D disk models. We find cavity precession rates to be appreciably faster in 3D than in 2D.

79 ASTRONOMY AND ASTROPHYSICS

Assurance of Reasoning Enabled Systems (ARES)

ARES was in part motivated by the determination of President’s Council of Advisors on Science and Technology (PCAST) on May 13th, 2023 that published a set of inquiries: In an era in which convincing images, audio, and text can be generated with ease on a massive scale, how can we ensure reliable access to verifiable, trustworthy information? How can we be certain that a particular piece of media is genuinely from the claimed source? What technologies, policies, and infrastructure can be developed to detect and counter AI-generated disinformation? In an effort to automatically analyze and patch/optimize code the work in this report describes various neural Machine Learning (ML) analysis engine implementations to assist in situations where source code is deficient or completely lacking to decompile (lift) binary code to ’C’. The goal is to gradually reduce human intervention. To this end, two Large Language Model (LLM) variants (Code LLama 2, LLama 3.1 and Starcoder1, Starcoder 2) where finetuned with ’before/after’ code pairs on the OpenBLAS library. LLama trained on the lowering process, Starcoder trained on the lifting process with National Security Agency’s (NSA) open-source Ghidra decompiler assist. The inferencing test results indicate correctness for only very short sequences for Starcoder 2. Moving forward, the experiments conclude with a set of recommendations of required resources and technologies

97 MATHEMATICS AND COMPUTING

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke

Moment-preserving Monte-Carlo Coulomb collision method for particle codes

Binary-pairing Monte-Carlo methods are widely used in particle-in-cell codes to capture effects of small angle Coulomb collisions. These methods preserve momentum and energy exactly when the simulation particles have equal weights. However, when the interacting particles are of varying weight, these physical conservation laws are only preserved on average. Here, we 1) extend these methods to weighted particles such that the scattering physics is correct on average, and 2) describe a new method for adjusting the particle velocities post scatter to restore exact conservation of momentum and energy. In conclusion, the efficacy of the model is illustrated with various test problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs

Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Rodriguez, Alex [Argonne National Laboratory (ANL)

Error quantification and comparison of binary neutron star gravitational waveforms from numerical relativity codes

Future gravitational wave detections of merging binary neutron star systems have the possibility to tightly constrain the equation of state of dense nuclear matter. In order to extract such constraints, gravitational waveform models need to be calibrated to accurate numerical relativity simulations of the late inspiral and merger. In this work, we take an essential step toward classifying the error and potential systematics in current generation numerical relativity simulations of merging binary neutron stars. To this end, we perform a direct comparison of two codes (fil, spec), which differ in many aspects, including the numerical methods and discretizations used and equations solved. We find that despite these different approaches, the codes are—within current numerical resolution bounds—fully consistent, and broadly comparable in cost for a given accuracy level. Our results indicate that the error in the waveforms is primarily dominated by the hydrodynamic evolution, consistent with earlier findings in the literature. Finally, we also discuss current limitations and cost estimates for numerical relativity simulations to reach the accuracies required in the era of next-generation gravitational detectors.

gravitational waves

Noisy quantum trees: infinite protection without correction

We study quantum networks with tree structures, in which information propagates from a root to leaves. At each node in the network, the received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by the delocalization of information. In the classical setting, where each node simply copies the input bit into multiple output bits, this model has been studied as the broadcasting or reconstruction problem on trees, which has broad applications. In this work, we study the quantum version of this problem. We consider a Clifford encoder at each node that encodes the input qubit in a stabilizer code, along with a single qubit Pauli noise channel at each edge. Such noisy quantum trees describe a scenario in which one has access to a stream of fresh (low-entropy) ancilla qubits, but cannot perform error correction. Therefore, they provide a different perspective on quantum fault tolerance. Furthermore, they provide a useful model for describing the effect of noise within the encoders of concatenated codes. We prove that above certain noise thresholds, which depend on the properties of the code such as its distance, as well as the properties of the encoder, information decays exponentially with the depth of the tree. On the other hand, by studying certain efficient decoders, we prove that for codes with distance d ≥ 2 and for sufficiently small (but non-zero) noise, classical information and entanglement propagate over a noisy tree with infinite depth. Indeed, we find that this remains true even for binary trees with certain 2-qubit encoders at each node, which encodes the received qubit in the binary repetition code with distance d = 1.

Quantum information

Evaluation of Hardware and Software Bill of Materials (HBOMs/SBOMs) Extraction Methods

Hardware and software bills of materials (HBOMs and SBOMs) provide important visibility into the components, dependencies, and supply chain relationships within programmable digital devices. This visibility is critical for advanced nuclear reactor applications, where use of common or shared hardware components, software libraries, suppliers, or manufacturing processes may create common cause failure (CCF) vulnerabilities despite apparent diversity. This paper evaluates current approaches for obtaining and analyzing HBOMs and SBOMs in support of CCF, diversity and defense-in-depth (D3) assessments, and begins to explore potential methods for artificial intelligence/machine learning-based analysis. The availability of BOM information from advanced reactor manufacturers and vendors, representative hardware and software categories found in advanced reactor systems continues to limit research [13]. This paper compares commonly used BOM formats, including CycloneDX, SPDX, and SWID. It also surveys publicly available tools for generating BOMs from source code, compiled binaries, and hardware-related information, noting limitations in language coverage, system age, and format interoperability. Finally, this paper evaluates methods for correlating BOM data with vulnerability and exploitability information, including VEX, CVE, and CWE resources. The findings indicate that publicly available nuclear-vendor BOMs are limited, making third-party extraction and research into novel analysis techniques necessary.

Cybersecurity

Using the COSMIC Population Synthesis Code to Investigate How Metallicity Affects the Rates of Interacting Binaries

We use COSMIC, a galaxy population synthesis code, to investigate how metallicity affects the rate of formation of massive stars with a closely orbiting compact object companion. Metallicity—a crucial component to stellar evolution and binary system formation—can affect how and when these systems form. We present the formation time of these systems at different metallicities, and the anti-correlation the rates have with metallicity. In particular, these systems occur about 10 times more frequently at metallicities between Z = 2 × 10 −4 and 2 × 10 −3 , compared to those between Z = 2 × 10 −3 and 2 × 10 −2 . This work serves as a prerequisite to predicting global rates of these systems as a function of redshift, ultimately giving crucial insight into our understanding of the progenitors of long gamma-ray bursts and their evolution over cosmic time.

79 ASTRONOMY AND ASTROPHYSICS

serial-log-replayer

The code records or logs serial input along with a timestamp to a binary file. The code can then read this log file and replay the serial data with the same relative timings.

Sikkema, Isaac [Oak Ridge National Laboratory (ORN

VHClass

The code is used to predict the taxonomic source of an antibody heavy chain sequence. The code assigns a binary label to the input set of sequences - camelid or human. This prediction is generated using a random-forest based classification algorithm which is the backbone of the code. A complementary code splits the antibody sequence into antibody features - framework regions and CDR regions.

Davis, Anastasiia

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION

Probability Density Function for the spatial and intensity distribution of neutron-induced defects in Silicon

The ability to model semiconductor device degradation under neutron irradiation depends upon having a robust modeling capability for the neutron-induced collision cascades as well as a means to analytically fit the resulting probability distributions of defect production and ionizing energy deposition for purposes of extrapolation to low-probability, high-consequence scenarios. In this paper, the widely-utilized binary collision approximation codes MARLOWE and SRIM are deployed in conjunction with a critical examination of their parameterizations as benchmarked against higher-fidelity molecular dynamics simulations. A simple 3-parameter form described by the Generalized Logistic Distribution is shown to be a good fit to Frenkel pair and ionization intensity distributions in bulk silicon. The BCA codes are then applied to simulate cascades in 5 nm layers of a representative gate-all-around nanosheet transistor, where joint probability distributions of threshold levels of damage to multiple layers are evaluated.

36 MATERIALS SCIENCE

Value of Information App (Value of Information App for Binary Geothermal Decisions and Binary Geothermal Possibilities) (Negative/Positive) [SWR-25-15]

Code base to run Streamlit Value of Information App for binary decision with geothermal techno economics. An open-source VOI app that models binary decisions (e.g. do something (drill) or walk away (do nothing)) and binary geothermal scenarios (positive or negative) has been developed. Users can input their anticipated economic values (profits or losses) directly into the value matrix to represent all four combinations of these actions and geothermal possibilities. VOI in general requires probabilities to be assigned for “probability of success”, or probability of experiencing a positive geothermal scenario versus negative. The users of the App can toggle this probability of success both in the demo problem and in the Value of Imperfect Information problem. The VOI App allows users to upload their own labeled data to evaluate how well it allows them to distinguish between positive versus negative sites. We have been using IGNENIOUS data to test and demonstrate; industry members have prepared their own labeled data, and have present their examples from diverse use cases at a conference workshop. The VOI App is open to the public at: https://voigeothermalrising.streamlit.app

Trainor-Guitton, Whitney [National Renewable Energ