Computational Core Research Articles

Cellular and Molecular Mechanisms Regulating Retinal Synapse Development.

Synapse formation within the retinal circuit ensures that distinct neuronal types can communicate efficiently to process visual signals. Synapses thus form the core of the visual computations performed by the retinal circuit. Retinal synapses are diverse but can be broadly categorized into multipartner ribbon synapses and 1:1 conventional synapses. In this article, we review our current understanding of the cellular and molecular mechanisms that regulate the functional establishment of mammalian retinal synapses, including the role of adhesion proteins, synaptic proteins, extracellular matrix and cytoskeletal-associated proteins, and activity-dependent cues. We outline future directions and areas of research that will expand our knowledge of these mechanisms. Understanding the regulators moderating synapse formation and function not only reveals the integrated developmental processes that establish retinal circuits, but also divulges the identity of mechanisms that could be engaged during disease and degeneration.

Experimental validation of the 3-D neutron kinetics algorithm of RAPID using the JSI TRIGA Mark-II reactor

The high-fidelity simulation of reactor kinetics, with the ability of calculating detailed 3-D time-dependent distributions of the nuclear quantities of interest, is an important challenge in the field of nuclear engineering. As the industry moves towards innovative reactor designs and the financing and licensing of prototypes becomes increasingly complex, there is an increasing need of best-estimate and high-fidelity codes that are capable of replacing the computational tools that have been fine-tuned for LWRs using years of operation data and validation against benchmark facilities. Traditional neutron transport software either has to rely on significant modeling approximations or requires an impractical amount of computer time to calculate accurate and detailed solutions to the transport problems, failing to meet the need of the industry to simulate numerous scenarios over a relatively small time. RAPID is a software that relies on the Multi-stage Response-function Transport (MRT) methodology to achieve high-fidelity accurate solutions to the neutron transport problem by pre-calculating a database of response functions or coefficients that are then combined and interpolated to calculate the nuclear quantities of interest in seconds to minutes on a single computer core. In this article, the recently developed algorithms for 3-D time-dependent simulation of reactor kinetics and for control rods movement in the reactor core are validated using experiments performed at the Jožef Stefan Institute (JSI) TRIGA Mark-II reactor. The experiments were designed and performed with the goal of RAPID’s validation. In particular, time-dependent responses from four fission chambers located in the TRIGA core were collected and compared to RAPID-calculated values. The comparison of RAPID’s predictions to measured data shows very good agreement, demonstrating the ability of RAPID of calculating detailed 3-D time-dependent fission source distributions for a reactor kinetic problem that would be impractical to model with traditional neutron transport codes. In addition, it is demonstrated how RAPID is capable of obtaining a high level of accuracy within seconds to minutes on a single computer core, with a significant speedup with respect to state-of-the-art neutron transport methods.

A Study on the Key Applications of Malware

Malware, a portmanteau of "malicious software," represents a significant threat in today's interconnected digital landscape. This abstract explores the multifaceted impacts of malware applications on individuals, organizations, and society at large. Types of malware include computer viruses, worms, Trojan horses, ransomware and spyware. These malicious programs steal, encrypt and delete sensitive data; alter or hijack core computing functions; and monitor end users' computer activity. malware jeopardizes individual privacy and security by surreptitiously infiltrating personal devices, often leading to identity theft, financial loss, and unauthorized access to sensitive information. Beyond personal repercussions, malware poses substantial risks to organizational integrity. It can compromise corporate networks, disrupt operations, and inflict substantial financial losses through data breaches, ransomware attacks, and intellectual property theft

Confidential Computing in the Cloud: An Overview

Major financial institutions like Goldman Sachs and JP Morgan have employed these hardware-based trusted execution environments (TEEs) and reported a 50% reduction in data breaches and a 40% increase in customer trust. Daily, these companies do billions of transactions in the cloud, leveraging confidentiality computing to ensure the privacy and integrity of their sensitive data. Over the years, confidential computing has evolved significantly, and the emergence of technology to safeguard sensitive information from malicious insiders and external threats now encompasses advanced and complex cryptographic techniques and hardware innovations, offering robust security assurances for cloud-based operations. In this paper, we will talk about foundational technologies and implementation strategies for the core of confidential computing. We will explore benefits, including performance trade-offs and integration complexities. Furthermore, the paper will highlight real-world applications and use cases, showcasing how industries such as finance, healthcare, and government leverage confidential computing to enhance data security and complicate cloud environments.

Toward an efficient second‐order method for computing the surface gravitational potential on spherical‐polar meshes

AbstractAstrophysical accretion discs that carry a significant mass compared with their central object are subject to the effect of self‐gravity. In the context of circumstellar discs, this can, for instance, cause fragmentation of the disc gas, and—under suitable conditions—lead to the direct formation of gas‐giant planets. If one wants to study these phenomena, the disc's gravitational potential needs to be obtained by solving the Poisson equation. This requires to specify suitable boundary conditions. In the case of a spherical‐polar computational mesh, a standard multipole expansion for obtaining boundary values is not practicable. We hence compare two alternative methods for overcoming this limitation. The first method is based on a known Green's function expansion (termed “CCGF”) of the potential, while the second (termed “James' method”) uses a surface screening mass approach with a suitable discrete Green's function. We demonstrate second‐order convergence for both methods and test the weak scaling behavior when using thousands of computational cores. Overall, James' method is found superior owing to its favorable algorithmic complexity of compared with the scaling of the CCGF method.

Standard cell library design for edge computing chips

Abstract With the rapid development of big data, IoT, and other technologies, edge computing has emerged, and the core of edge computing is the edge computing chip. To meet the rapidly growing data computing speed and data computing volume and taking into account the short design cycle and low cost, this paper proposes a standard cell library for designing edge computing chips based on the PDK of 0.13 μm SOI process. Compared with the existing edge computing chips that operate at 100 MHz, the edge computing chip designed with this cell library can operate at 1 GHz. The design process includes selecting cell types and heights for the cell library, designing and drawing circuit logic and layout, extracting characteristic parameters of the cell library, and verifying the cell functions. The validation results show the designed cell can operate at 1 GHz with low delay. Compared with a commercial device, the designed device has a higher operating frequency and realizes a good balance between area and power consumption, and the standard cell library has a good application prospect.

Elastic response of trabecular bone under compression calculated using the firm and floppy boundary lattice element method

Micro-Finite Element analysis (μFEA) has become widely used in biomechanical research as a reliable tool for the prediction of bone mechanical properties within its microstructure such as apparent elastic modulus and strength. However, this method requires substantial computational resources and processing time. Here, we propose a computationally efficient alternative to FEA that can provide an accurate estimation of bone trabecular mechanical properties in a fast and quantitative way. A lattice element method (LEM) framework based on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) open-source software package is employed to calculate the elastic response of trabecular bone cores. A novel procedure to handle pore-material boundaries is presented, referred to as the Firm and Floppy Boundary LEM (FFB-LEM). Our FFB-LEM calculations are compared to voxel- and geometry-based FEA benchmarks incorporating bovine and human trabecular bone cores imaged by micro Computed Tomography (μCT). Using 14 computer cores, the apparent elastic modulus calculation of a trabecular bone core from a μCT-based input with FFB-LEM required about 15 min, including conversion of the μCT data into a LAMMPS input file. In contrast, the FEA calculations on the same system including the mesh generation, required approximately 30 and 50 min for voxel- and geometry-based FEA, respectively. There were no statistically significant differences between FFB-LEM and voxel- or geometry-based FEA apparent elastic moduli (+24.3% or +7.41%, and +0.630% or -5.29% differences for bovine and human samples, respectively).

The ADMORPH Approach for Adaptively Morphing Embedded Systems

Due to the increasing performance demands of missionand safety-critical Cyber-Physical Systems (of Systems), these systems exhibit a rapidly growing complexity, manifested by an increasing number of (distributed) computational cores and application components connected via complex networks. However, with these systems' growing complexity and interconnectivity, the chances of hardware failures and disruptions due to cyber-attacks will also quickly increase.

Ethraid: A Simple Method for Characterizing Long-period Companions Using Doppler, Astrometric, and Imaging Constraints

We present ethraid, an open-source Python package designed to measure the mass (m c ) and separation (a) of a bound companion from measurements covering a fraction of the orbital period. ethraid constrains m c and a by jointly modeling radial velocity, astrometric, and/or direct imaging data in a Bayesian framework. Partial orbit data sets, especially those with highly limited phase coverage, are represented well by a few method-specific summary statistics. By modeling these statistics rather than the original data, ethraid optimizes computational efficiency with minimal reduction in accuracy. ethraid uses importance sampling to efficiently explore the often broad posteriors that arise from partial orbits. The core computations of ethraid are implemented in Cython for speed. We validate ethraid's performance by using it to constrain the masses and separations of the planetary companions to HD 117207 and TOI-1694. We designed ethraid to be both fast and simple, as well as to give broad, “quick look” constraints on companion parameters using minimal data. ethraid is pip installable and available on Zenodo and GitHub.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Stereotyped goal-directed manifold dynamics in the insular cortex

The insular cortex is involved in diverse processes, including bodily homeostasis, emotions, and cognition. However, we lack a comprehensive understanding of how it processes information at the level of neuronal populations. We leveraged recent advances in unsupervised machine learning to study insular cortex population activity patterns (i.e., neuronal manifold) in mice performing goal-directed behaviors. We find that the insular cortex activity manifold is remarkably consistent across different animals and under different motivational states. Activity dynamics within the neuronal manifold are highly stereotyped during rewarded trials, enabling robust prediction of single-trial outcomes across different mice and across various natural and artificial motivational states. Comparing goal-directed behavior with self-paced free consumption, we find that the stereotyped activity patterns reflect task-dependent goal-directed reward anticipation, and not licking, taste, or positive valence. These findings reveal a core computation in insular cortex that could explain its involvement in pathologies involving aberrant motivations.

Interdisciplinary Dynamics in COVID-19 Research: Examining the Role of Computer Science and Collaboration Patterns

In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, theories, frameworks, data integration and analysis, and pertinent considerations. In the academic community, there is a widespread expectation that as science and technology continue to progress, the convergence of medicine with various other fields will gain momentum. Fields like computer science are anticipated to see expanded applications in domains such as medicine, vaccine research, disease diagnosis, and more. This study aims to examine interdisciplinary approaches in health-related research, particularly in the context of COVID-19. The goal is to analyze and comprehend the involvement and collaboration patterns of various disciplines in pandemic research, with a specific emphasis on the role and integration level of computer science. This study analyzed 240,509 COVID-19 related articles published from December 2019 to September 2022 using methods such as chord diagrams, modularity analysis, and eigenvector centrality analysis in Social Networking Analysis (SNA). The findings revealed an emerging trend of integration trend between Humanities & Social Sciences and Natural Sciences. Expectations that computer science would prominently feature in pandemic research during this technology-driven era haven’t materialized. While it maintains links with engineering, it hasn’t formed strong connections with medicine. This indicates a gap between computer science and core medical research in large-scale health crises, where COVID-19 research remains centered on medicine with varying interdisciplinary collaboration, and high-tech disciplines like computer science have not achieved their expected influence in these studies.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

Abstract TP141: Understanding Potential Limitations of CTP Imaging in Early Window Large Vessel Anterior Circulation Strokes

Background: Computerized tomography perfusion (CTP) imaging serves as a valuable modality for the assessment of individuals with a large vessel anterior circulation stroke. Current literature proposes that employing CTP imaging in patients within the initial time frame of less than 8 hours, may lead to an overestimation of the projected infarct core, specifically when utilizing cerebral blood flow (CBF) less than 30%. The hypoperfusion intensity ratio (HIR) may result in overestimation. We sought to further investigate the interplay of CTP parameters in patients presenting within 8 hours of symptom onset of stroke, to assess the accuracy of core infarct estimation. Methods: A retrospective cohort study analyzing patients with large vessel anterior occlusion (LVAO) who underwent CTP and mechanical thrombectomy within 24 hours of symptom onset between January 2017 to December 2022. RAPID software estimates the infarct core using CBF <30%. DWI-MRI determined final infarct volume. A multivariate logistic regression model was employed to assess the impact of HIR and reperfusion success on outcomes. Results: Ninety-seven patients were identified within an 8-hour time window. CBF<30% overestimated core infarct volume (mL) in 22 patients (22.6%) with a mean of 15.72 (range 4.4-37.7) and a median of 22.85. Of these, 2 patients experienced poor reperfusion (9%) while 20 had successful reperfusion (91%). Of the patients who did have successful reperfusion, an average of 13.57mL core was overpredicted compared to the DWI volumes. A multivariate logistic regression model adjusting for degree of reperfusion, administration of thrombolytic, and time from symptom onset showed that HIR strongly predicted the probability of core estimation with an odds ratio of 148.6, 95% CI [21.0-1048.8], (p<0.001). Conclusion: HIR exceeding 0.4 demonstrates a predictive capacity for core overestimation in early-window LVAO patients. Our study affirms this observation with statistical significance. The integration of HIR as a parameter for early-window LVAO core computation shows promise, and has potential ramifications for patient selection for thrombectomy. In this context, utilization of further databases is imperative for validation and broader application of HIR.

THz quantum gap: exploring potential approaches for generating and detecting non-classical states of THz light.

Over the past few decades, THz technology has made considerable progress, evidenced by the performance of current THz sources and detectors, as well as the emergence of several THz applications. However, in the realm of quantum technologies, the THz spectral domain is still in its infancy, unlike neighboring spectral domains that have flourished in recent years. Notably, in the microwave domain, superconducting qubits currently serve as the core of quantum computers, while quantum cryptography protocols have been successfully demonstrated in the visible and telecommunications domains through satellite links. The THz domain has lagged behind in these impressive advancements. Today, the current gap in the THz domain clearly concerns quantum technologies. Nonetheless, the emergence of quantum technologies operating at THz frequencies will potentially have a significant impact. Indeed, THz radiation holds significant promise for wireless communications with ultimate security owing to its low sensitivity to atmospheric disturbances. Moreover, it has the potential to raise the operating temperature of solid-state qubits, effectively addressing existing scalability issues. In addition, THz radiation can manipulate the quantum states of molecules, which are recognized as new platforms for quantum computation and simulation with long range interactions. Finally, its ability to penetrate generally opaque materials or its resistance to Rayleigh scattering are very appealing features for quantum sensing. In this perspective, we will discuss potential approaches that offer exciting prospects for generating and detecting non-classical states of THz light, thereby opening doors to significant breakthroughs in THz quantum technologies.

Rapid measurement of epidermal thickness in OCT images of skin

Epidermal thickness (ET) changes are associated with several skin diseases. To measure ET, segmentation of optical coherence tomography (OCT) images is essential; manual segmentation is very time-consuming and requires training and some understanding of how to interpret OCT images. Fast results are important in order to analyze ET over different regions of skin in rapid succession to complete a clinical examination and enable the physician to discuss results with the patient in real time. The well-known CNN-graph search (CNN-GS) methodology delivers highly accurate results, but at a high computational cost. Our objective was to build a computational core, based on CNN-GS, able to accurately segment OCT skin images in real time. We accomplished this by fine-tuning the hyperparameters, testing a range of speed-up algorithms including pruning and quantization, designing a novel pixel-skipping process, and implementing the final product with efficient use of core and threads on a multicore central processing unit (CPU). We name this product CNN-GS-skin. The method identifies two defined boundaries on OCT skin images in order to measure ET. We applied CNN-GS-skin to OCT skin images, taken from various body sites of 63 healthy individuals. Compared with CNN-GS, our described method reduced computation time by 130 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} with minimal reduction in ET determination accuracy (from 96.38 to 94.67%).

Generalization of One-Center Nonorthogonal Configuration Interaction Singles to Open-Shell Singlet Reference States: Theory and Application to Valence-Core Pump-Probe States in Acetylacetone.

We formulate a one-center nonorthogonal configuration interaction singles (1C-NOCIS) theory for the computation of core excited states of an initial singlet state with two unpaired electrons. This model, which we refer to as 1C-NOCIS two-electron open-shell (2eOS), is appropriate for computing the K-edge near-edge X-ray absorption spectra (NEXAS) of the valence excited states of closed-shell molecules relevant to pump-probe time-resolved (TR) NEXAS experiments. With the inclusion of core-hole relaxation effects and explicit spin adaptation, 1C-NOCIS 2eOS requires mild shifts to match experiment, is free of artifacts due to spin contamination, and can capture the high-energy region of the spectrum beyond the transitions into the singly occupied molecular orbitals (SOMOs). Calculations on water and thymine illustrate the different key features of excited-state NEXAS, namely, the core-to-SOMO transitions as well as shifts and spin-splittings in the transitions analogous to those of the ground state. Simulations of the TR-NEXAS of acetylacetone after excitation to its π → π* singlet excited state at the carbon K-edge, an experiment carried out recently, showcase the ability of 1C-NOCIS 2eOS to efficiently simulate NEXAS based on nonadiabatic molecular dynamics simulations.

High Fidelity Whole Core Reactor Eigenvalue Calculation using the Hybrid Monte Carlo and Deterministic RAPID Code System

This paper examines the accuracy and performance of the RAPID (Real-time Analysis for Particle transport and In-situ Detection) code system for whole core reactor simulation. The RAPID code is an implementation of the Multistage Response function Transport (MRT) methodology resulting in a hybrid Monte Carlo and deterministic technique. This paper utilizes the Watts Bar Unit 1 benchmark as the computational model to exam the accuracy and efficiency of RAPID compared to Serpent. The RAPID calculated eigenvalue and 3-D fission density distribution are compared to the results from Serpent. This paper shows that RAPID is able to obtain pin-wise, axially-dependent fission density in 7 minutes using one computer core, as opposed to assembly-wise, axially dependent fission density in 8 days on 40 computer cores.

Novel Ramanujan Digital Twin for Motor Periodic Fault Monitoring and Detection

The signal-processing and intelligent diagnostic and monitoring methods based on motor current signature analysis (MCSA) for induction motors (IM) usually depend on preset parameters. Moreover, many of them have difficulty in achieving ideal health monitoring effect with strong noise interference and switching working conditions. To overcome these limitations, a novel digital twin architecture called the Ramanujan digital twin (RDT) is composed. This architecture uses the Ramanujan periodic transform (RPT) as its computational core to detect the potential fault signatures in each monitoring frame. The quantity of interest from IM will be selected and calibrated based on the Bayesian-updated driven calibration mechanism to construct the phenomenal simulation signals with high fidelity to the potential fault signatures. These signals will provide guidance information. The effectiveness and robustness of the RDT are validated through experimental cases.

Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error

Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error