Coding Efficiency Research Articles

FP3D: A code for calculating 3D magnetic field and particle motion

An efficient numerical code FP3D has been developed to calculate particle orbits and evaluate particle confinement in 3D magnetic fields including stellarators and tokamaks with 3D fields. The magnetic field is either calculated from coils directly or obtained from equilibrium codes. FP3D has been verified with the 3D equilibrium code Variational Moments Equilibrium Code [Hirshman and Whitson, Phys. Fluids 26, 3553–3568 (1983)] for magnetic field calculation and with the drift-kinetic code Stellarator Fokker–Planck Iterative Neoclassical Conservative Solver [Landreman et al., Phys. Plasmas 21(4), 042503 (2014)] for neoclassical transport. The code has been applied successfully to the National Compact Stellarator Experiment stellarator [Nelson et al., Fusion Eng. Des. 66, 169–174 (2003)] for the calculation of neoclassical transport coefficient with the 3D magnetic field obtained directly from coils. FP3D is also used to calculate ripple losses in the tokamak Experimental Advanced Superconducting Tokamak [Y. Wan, Plasma Sci. Technol. 8(3) (2006)].

Numerical analysis for the free-boundary current reversal equilibrium in the AC plasma current operation in a tokamak

In recent decades, tokamak discharges with zero total toroidal current have been reported in tokamak experiments, and this is one of the key problems in alternating current (AC) operations. An efficient free-boundary equilibrium code is developed to investigate such advanced tokamak discharges with current reversal equilibrium configuration. The calculation results show that the reversal current equilibrium can maintain finite pressure and also has considerable effects on the position of the X-point and the magnetic separatrix shape, and hence also on the position of the strike point on the divertor plates, which is extremely useful for magnetic design, MHD stability analysis, and experimental data analysis etc. for the AC plasma current operation on tokamaks.

Fault protection of quasi-delay-insensitive pipeline models using efficient coding for asynchronous network-on-chip

<div align="center"><span>One promising approach for creating the chip-level connection of multiprocessing system on chip (MPSoC) is asynchronous logic. However, asynchronous systems are susceptible to errors. In this manuscript, the efficient fault-tolerant (FT) quasi-delay-insensitive (QDI) pipeline modules are designed using a delay-insensitive redundant check (DIRC) coding mechanism. The DIRC coding approach can tolerate single and multi-bit transient faults (TFs) in QDI-pipeline modules. The 4-phase 1-of-n coding approach incorporates DIRC-based QDI pipeline stages to strengthen the asynchronous links against TFs. The DIRC-based QDI pipeline stages are further used as asynchronous links in asynchronous network-on-chip ((NoC) for fault-free communication. The performance metrics like chip area, delay, and power parameters are evaluated in detail against different data widths for both basic unprotected and DIRC-based QDI pipeline modules. <br /> The DIRC-based QDI-pipeline module with 1-of-4 code uses only <2% chip area with a delay of 7.4 ns and power of 117 mW on Artix-7 chip for data width 128. The code rate of the proposed work decreased by 33.33% for both 1-of-2 and 1-of-4 codes in DIRC-based QDI-pipeline modules.</span></div>

Automated Buffer Sizing of Dataflow Applications in a High-level Synthesis Workflow

High-Level Synthesis (HLS) tools are mature enough to provide efficient code generation for computation kernels on FPGA hardware. For more complex applications, multiple kernels may be connected by a dataflow graph. Although some tools, such as Xilinx Vitis HLS, support dataflow directives, they lack efficient analysis methods to compute the buffer sizes between kernels in a dataflow graph. This article proposes an original method to safely approximate such buffer sizes. The first contribution computes an initial overestimation of buffer sizes without knowing the memory access patterns of kernels. The second contribution iteratively refines those buffer sizes, thanks to cosimulation. Moreover, the article introduces an open source framework using these methods to facilitate dataflow programming on FPGA using HLS. The proposed methods and framework have been tested on seven dataflow applications and outperform Vitis HLS cosimulation in five benchmarks, either in terms of BRAM and LUT usage, or in terms of exploration time. In the two other benchmarks, our best method gets results similar to Vitis HLS. Last but not least, our method admits directed cycles in the application graphs.

The Exploration of Modelling for The Student Achievement Predictor

The project is rooted in the analysis of historical student data. It encompasses a comprehensive approach involving data observation, meticulous analysis, thorough comparison, systematic processing, and efficient coding techniques. The ultimate goal is to harness the power of machine learning to predict students' academic achievements and discern the key features that exert the most significant influence on their learning outcomes. In terms of the machine learning model, this study explored and assessed several machine learning algorithms, including but not limited to the K-Neighbors Classifier, Logistic Regression, and Decision Tree Classifier. These models are scrutinized and fine-tuned to ensure their suitability for the task at hand. Furthermore, a pivotal aspect of this project is identifying the features that wield the greatest impact on students' learning achievements. By employing feature selection techniques, this study aims to uncover the critical factors that can make a difference in educational outcomes. This information can guide educational institutions in designing targeted interventions to support student success. The experimental results obtained from this study demonstrated the effectiveness of the employed machine learning methods.

Relative comparison of the benefits of wall envelope insulation materials in the South African energy zones, subject to the new national building energy efficiency standards

Introduction: South Africa is ranked among the 20 highest greenhouse gas (GHG) emitters worldwide, mainly due to its heavy reliance on coal for energy. The residential building industry can play a significant role towards the minimization of both GHG emissions and energy consumption. This research assesses the beneficial impacts of utilizing wall envelope insulation measures using five insulation materials, for a detached residential building, within the seven energy zones of South Africa in accordance with the South African National code for Building energy efficiency (SANS10400-XA).Methods: Lifecycle analysis (for 50 years), regression methods and sequential search techniques of building energy modelling were used to determine both the energy saving implications of adopting the insulation materials (at various levels of thickness), and their corresponding energy payback periods. The study area consisted of eight selected locations representing all the seven energy zones in South Africa (Welkom, Witbank, Thohoyandou, Cape Town, Pretoria, Ixopo, Sutherland, and Fraserburg).Results: Sutherland (zone 6), Cape Town (zone 4) and Fraserburg (zone 7) benefitted most from energy savings due to application of insulation measures. Witbank (Zone 2), Thohoyandou (zone 3) and Pretoria (zone 5) never benefitted from application of wall envelope insulation (in terms of energy savings). Cellulose and straw yielded the lowest payback periods. Generally, polyurethane yielded the highest net energy savings at lower insulation thickness levels, while cellulose was preferable at higher insulation thickness levels in Welkom (zone1) Cape Town (zone 4), Ixopo (zone 5H), Sutherland (zone 6) and Fraserburg (zone 7).Discussion: Lower optimal wall envelope insulation thicknesses (that maximized energy savings) appeared to correspond to locations with higher annual temperature ranges. Higher temperature ranges (hence, higher degree days) would ensure that more energy is saved in order to keep the indoor temperatures within the acceptable comfort limits, due to application of insulation. However, the selection of insulation material is also influenced by other factors such as resistance to insect and mould attack (which easily affect bio-insulation materials), fire retardancy, durability (which affects cellulose), sound proofing, structural strength, and resistance to water vapour.

A significance score for protein-protein interaction models through random docking.

Comparing accuracies of structural protein-protein interaction (PPI) models for different complexes on an absolute scale is a challenge, requiring normalization of scores across structures of different sizes and shapes. To help address this challenge, we have developed a statistical significance metric for docking models, called random-docking (RD) p-value. This score evaluates a PPI model based on how likely a random docking process is to produce a model of better or equal accuracy. The binding partners are randomly docked against each other a large number of times, and the probability of sampling a model of equal or greater accuracy from this reference distribution is the RD p-value. Using a subset of top predicted models from CAPRI (Critical Assessment of PRediction of Interactions) rounds over 2017-2020, we find that the ease of achieving a given root mean squared deviation or DOCKQ score varies considerably by target; achieving the same relative metric can be thousands of times easier for one complex compared to another. In contrast, RD p-values inherently normalize scores for models of different complexes, making them globally comparable. Furthermore, one can calculate RD p-values after generating a reference distribution that accounts for prior information about the interface geometry, such as residues involved in binding, by giving the random-docking process access the same information. Thus, one can decouple improvements in prediction accuracy that arise solely from basic modeling constraints from those due to the rest of the method. We provide efficient code for computing RD p-values at https://github.com/Grigoryanlab/RDP.

Thermal prediction of rotatory multiwall carbon nanotubes subject to convective boundary conditions and slip effects: Implicit finite difference simulations

This article presents the modeling and simulations 3D magneto-hydrodynamic rotating nanofluid flow through a stretched surface. Water is employed as the base liquid, and multiwall carbon nanotubes (MWCNTs) are used as nanosized materials. The unique structure of MWCNTs makes them stand out among the crowd. The principal concern is to enhance the thermal transport efficiency, chemical durability, and important optical and electronics properties. The classical Navier Stoke’s problem based upon the law of conservation of mass, momentum and energy with the Xue model’s implementation is transformed into an ordinary differential equation by applying similarity approach. Velocity slip and convective boundary conditions are also considered under the Lorentz force. The final form of nondimensional ODEs are, then, solved numerically using finite difference method with the assistance of MATLAB's powerful software. The impression of pertinent convergence flow constraints on fluid velocity, temperature, heat transfer rate, and friction factor are exposed through tables and graphs. A numerical comparison is used to evaluate the code efficiency, and the results show excellent agreement. The effect of the Biot number appears to be raising the temperature. Conversely, increasing the rotation and slip parameter parametric values results in a declining trend in the velocity profile.

Grid codes underlie multiple cognitive maps in the human brain

Grid cells fire at multiple positions that organize the vertices of equilateral triangles tiling a 2D space and are well studied in rodents. The last decade witnessed rapid progress in two other research lines on grid codes—empirical studies on distributed human grid-like representations in physical and multiple non-physical spaces, and cognitive computational models addressing the function of grid cells based on principles of efficient and predictive coding. Here, we review the progress in these fields and integrate these lines into a systematic organization. We also discuss the coordinate mechanisms of grid codes in the human entorhinal cortex and medial prefrontal cortex and their role in neurological and psychiatric diseases.

An Efficient Code for the Multi-Material Topology Optimization of 2D/3D Continuum Structures Written in Matlab

This paper presents an efficient and compact Matlab code for 2D and 3D topology optimization of multi-materials. We extend a classical 88-line-based educational code to the multi-material problem using the mapping-based interpolation function, with which each material is modeled equally and can obtain a clear 0, 1 result for each material of the optimized structures that can be manufactured easily. A solution of topology optimization of multi-materials with minimum compliance under volume constraints is documented, including the details of the optimization model, filtering, projection, and sensitivity analysis procedures. Several numerical examples are also conducted to demonstrate the effectiveness of the code, and the influence of the parameter on the optimized results is also analyzed. Complete 2D and 3D Matlab codes are provided.

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration

ANALYSIS OF FLUID-FLOW CHARACTERISTICS AND HEAT-TRANSFER PATTERNS AROUND A SQUARE BLUFF BODY

In this study, we investigate a numerical simulation of the flow and heat transfer characteristics around a single square cylinder subjected to incidence in a two-dimensional plane. The Reynolds and Prandtl numbers are set at <i>Re</i> = 100 and <i>Pr</i> = 0.71, respectively. The cylinder's surface is subjected to pressure and viscous forces due to the passing flow, with the magnitude of these forces influenced by the cross-sectional shape of the bluff body, the angle of attack, and flow velocity. The angle of orientation for the square cylinder varies from 0° to 45° in 5° increments. The study begins with comprehensively examining the governing equations, simulation procedures, and grid generation, employing an efficient and robust in-house finite volume code. Subsequently, we present and discuss the instantaneous streamlines, velocity components, vorticity, and isotherm patterns for different angles of attack. Additionally, global quantities such as viscous, pressure, total lift, drag coefficients, their root-mean-square, Strouhal, and Nusselt numbers are analyzed for various angles of attack. It is observed that the frequency of vortex shedding decreases with an increasing angle of attack. Furthermore, the values of global quantities and flow and temperature patterns remain relatively constant for angles of attack in the range of θ = 30°-45°. The numerical results show good agreement with experimental and numerical data in the existing literature.

Adaptation of the inferior temporal neurons and efficient visual processing.

Numerous studies examining the responses of individual neurons in the inferior temporal (IT) cortex have revealed their characteristics such as two-dimensional or three-dimensional shape tuning, objects, or category selectivity. While these basic selectivities have been studied assuming that their response to stimuli is relatively stable, physiological experiments have revealed that the responsiveness of IT neurons also depends on visual experience. The activity changes of IT neurons occur over various time ranges; among these, repetition suppression (RS), in particular, is robustly observed in IT neurons without any behavioral or task constraints. I observed a similar phenomenon in the ventral visual neurons in macaque monkeys while they engaged in free viewing and actively fixated on one consistent object multiple times. This observation indicates that the phenomenon also occurs in natural situations during which the subject actively views stimuli without forced fixation, suggesting that this phenomenon is an everyday occurrence and widespread across regions of the visual system, making it a default process for visual neurons. Such short-term activity modulation may be a key to understanding the visual system; however, the circuit mechanism and the biological significance of RS remain unclear. Thus, in this review, I summarize the observed modulation types in IT neurons and the known properties of RS. Subsequently, I discuss adaptation in vision, including concepts such as efficient and predictive coding, as well as the relationship between adaptation and psychophysical aftereffects. Finally, I discuss some conceptual implications of this phenomenon as well as the circuit mechanisms and the models that may explain adaptation as a fundamental aspect of visual processing.

Scalable Face Image Coding via StyleGAN Prior: Toward Compression for Human-Machine Collaborative Vision.

The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm's feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates (< 0.01 bpp), offering new insights for human-machine collaborative compression.

Perception-Based Prediction for Efficient Kinesthetic Coding

Perception-Based Prediction for Efficient Kinesthetic Coding

МЕТАПРОГРАММИРОВАНИЕ И ЕГО РОЛЬ В СОВРЕМЕННЫХ ЯЗЫКАХ ПРОГРАММИРОВАНИЯ

The article discusses the concept of metaprogramming and its importance in modern programming languages. Various approaches to metaprogramming, such as compiled and runtime, are described, as well as their applications in C++, Rust, Python, and Java. The benefits and risks associated with using metaprogramming are analyzed, including increased development efficiency and potential code complexity. The conclusion discusses the prospects for the further development of metaprogramming and its impact on the future of software and the IT industry in general.

Improving Learning-Based Semantic Coding Efficiency for Image Transmission via Shared Semantic-Aware Codebook

Improving Learning-Based Semantic Coding Efficiency for Image Transmission via Shared Semantic-Aware Codebook

Lithographically defined encoded magnetic heterostructures for the targeted screening of kidney cancer.

Renal cell carcinoma (RCC) is the 7th commonest cancer in the UK and the most lethal urological malignancy; 50% of all RCC patients will die from the condition. However, if identified early enough, small RCCs are usually cured by surgery or percutaneous procedures, with 95% 10 year survival. This study describes a newly developed non-invasive urine-based assay for the early detection of RCC. Our approach uses encoded magnetically controllable heterostructures as a substrate for immunoassays. These heterostructures have molecular recognition abilities and embedded patterned codes for a rapid identification of RCC biomarkers. The magnetic heterostructures developed for this study have a magnetic configuration designed for a remote multi axial control of their orientation by external magnetic fields, this control facilitates the code readout when the heterostructures are in liquid. Furthermore, the optical encoding of each set of heterostructures provides a multiplexed analyte capture platform, as different sets of heterostructures, specific to different biomarkers can be mixed together in a patient sample. Our results show a precise magnetic control of the heterostructures with an efficient code readout during liquid immunoassays. The use of functionalised magnetic heterostructures as a substrate for immunoassay is validated for urine specimen spiked with recombinant RCC biomarkers. Initial results of the newly proposed screening method on urine samples from RCC patients, and controls with no renal disorders are presented in this study. Comprehensive optimisation cycles are in progress to validate the robustness of this technology as a novel, non-invasive screening method for RCC.

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.

Effects of adiabatic focusing and free-escape boundaries in coronal shock acceleration

Solar energetic particles (SEPs) are considered a serious radiation threat to space technologies and humans in space. SEPs are accelerated to high energies by solar explosive phenomena such as solar flares and in particular by shocks driven by coronal mass ejections (CMEs). We aim to better understand the effects of magnetic field gradient-induced adiabatic focusing on the coronal acceleration of SEPs and to test whether free-escape boundaries produce the same effects as focusing. We present results from a one-dimensional oblique shock model with a mean free path similar to Bell’s (1978) theory using Monte Carlo simulations. We show that the momentum spectrum at a shock and far upstream will attain a steady state in a model with adiabatic focusing, whereas it does not in a non-focusing model. However, the effects of focusing can be mimicked in a non-focused simulation by introducing a free-escape boundary ahead of the shock close to the position where the particles will escape from the shock by focusing in a focused transport simulation. This provides a promising avenue for constructing computationally efficient codes that can model the particle emission from shocks.