Classical Implementation Research Articles

Leveraging analog quantum computing with neutral atoms for solvent configuration prediction in drug discovery

We introduce an approach to sampling equilibrium solvent water molecule configurations within proteins that leverages analog quantum computing. We present a complete end-to-end study from the molecular biology application to the development of the quantum algorithm to the implementation on a neutral atom quantum processing unit (QPU). To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model, an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an antiferromagnetic Ising model. Its solution is embodied into a Rydberg atom array QPU. Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM algorithms open a route towards the application of analog quantum computing in molecular modeling and drug design. Published by the American Physical Society 2024

A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation

This study proposes a novel factorization method for the DCT IV algorithm that allows for breaking it into four or eight sections that can be run in parallel. Moreover, the arithmetic complexity has been significantly reduced. Based on the proposed new algorithm for DCT IV, the speed performance has been improved substantially. The performance of this algorithm was verified using two different GPU systems produced by the NVIDIA company. The experimental results show that the novel proposed DCT algorithm achieves an impressive reduction in the total processing time. The proposed method is very efficient, improving the algorithm speed by more than 4-times—that was expected by segmenting the DCT algorithm into four sections running in parallel. The speed improvements are about five-times higher—at least 5.41 on Jetson AGX Xavier, and 10.11 on Jetson Orin Nano—if we compare with the classical implementation (based on a sequential approach) of DCT IV. Using a parallel formulation with eight sections running in parallel, the improvement in speed performance is even higher, at least 8.08-times on Jetson AGX Xavier and 11.81-times on Jetson Orin Nano.

Secure two-party computation via measurement-based quantum computing

Secure multiparty computation (SMC) provides collaboration among multiple parties, ensuring the confidentiality of their private information. However, classical SMC implementations encounter significant security and efficiency challenges. Resorting to the entangled Greenberger–Horne–Zeilinger (GHZ) state, we propose a quantum-based two-party protocol to compute binary Boolean functions, with the help of a third party. We exploit a technique in which a random Z-phase rotation on the GHZ state is performed to achieve higher security. The security and complexity analyses demonstrate the feasibility and improved security of our scheme compared to other SMC Boolean function computation methods. Additionally, we implemented the proposed protocol on the IBM QisKit and found consistent outcomes that validate the protocol’s correctness.

Distributionally robust disaster relief planning under the Wasserstein set

We study a two-stage natural disaster management problem modeled as a stochastic program, where the first stage consists of a facility location problem, deciding where to open facilities and pre-allocate resources such as medical and food kits, and the second stage is a fixed-charge transportation problem, routing resources to affected areas after observing a disaster. Our model has binary variables present in both stages. Due to the lack of data, classical stochastic programming approaches may be ill-suited, and we propose a two-stage distributionally robust formulation with a Wasserstein ambiguity set, where we consider distributions consistent with historical data and a tunable parameter to control the level of risk aversion. We develop a tailored column-and-constraint generation (CCG) algorithm to solve an extensive reformulation, where scenarios are iteratively generated. We handle the presence of binary variables in the second stage by leveraging the structure of our support set and second-stage problem, and provide conditions under which the optimal value of the latter is concave with respect to the intensity of the disaster, leading to an efficient scenario generation procedure. We also show that our results extend to the case where the second stage is a fixed-charge network flow problem. We perform extensive computational experiments demonstrating the computational advantage of our method over classical CCG implementations on synthetic instances, and illustrate the benefits of our approach on a popular case study from the literature of hurricane threats on the Gulf of Mexico in the United States.

Adaptive Deep Fourier Residual method via overlapping domain decomposition

The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural network (VPINN). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on minimizing the dual norm of the weak residual of a PDE, which is equivalent to minimizing the energy norm of the error. To compute the dual norm of the weak residual, the DFR method employs an orthonormal spectral basis of the test space, known for rectangles or cuboids for multiple function spaces.In this work, we extend the DFR method with ideas of traditional domain decomposition (DD). This enables two improvements: (a) to solve problems in more general polygonal domains and (b) to develop an adaptive refinement technique in the test space using a Döfler marking algorithm. In the former case, we retain the desirable equivalence between the employed loss function and the H1 error under non-restrictive assumptions, numerically demonstrating adherence to explicit bounds in the case of the L-shaped domain problem. In the latter, we show how refinement strategies lead to potentially significant improvements against a reference, classical DFR implementation with a test function space of significantly lower dimensionality, allowing us to better approximate singular solutions at a more reasonable computational cost.

Fish age reading using deep learning methods for object-detection and segmentation

Abstract Determination of individual age is one essential step in the accurate assessment of fish stocks. In non-tropical environments, the manual count of ring-like growth patterns in fish otoliths (ear stones) is the standard method. It relies on visual means and individual judgment and thus is subject to bias and interpretation errors. The use of automated pattern recognition based on machine learning may help to overcome this problem. Here, we employ two deep learning methods based on Convolutional Neural Networks (CNNs). The first approach utilizes the Mask R-CNN algorithm to perform object detection on the major otolith reading axes. The second approach employs the U-Net architecture to perform semantic segmentation on the otolith image in order to segregate the regions of interest. For both methods, we applied a simple postprocessing to count the rings on the output masks returned, which corresponds to the age prediction. Multiple benchmark tests indicate the promising performance of our implemented approaches, comparable to recently published methods based on classical image processing and traditional CNN implementation. Furthermore, our algorithms showed higher robustness compared to the existing methods, while also having the capacity to extrapolate missing age groups and to adapt to a new domain or data source.

Modified Seasonal Decomposition Variations of Earth Magnetic Field Induction Module

In this paper, we present a modification of the classic method of seasonal decomposition of the time series, in particular its application for the analysis of geomagnetic data. Seasonal decomposition is a powerful tool for time series analysis, but its classic implementation does not always provide accurate results when the time series contains amplitude outliers and prolonged gaps. We propose a modified approach to solve this task of seasonal decomposition, by applying an average daily profile. This ensures the extraction of various anomalies in the residual component of the decomposition, in particular, global and contextual outliers, as well as disturbances due to magnetic storms in the variations of geomagnetic field induction module. Keywords: geomagnetic field, seasonal decomposition, data gaps, autocorrelation function, residual component, outliers, magnetic storm, DST index

Quantifying the performance enhancement facilitated by fractional-order implementation of classical control strategies for nanopositioning

For most nanopositioning systems, maximizing positioning bandwidth to accurately track periodic and aperiodic reference signals is the primary performance goal. Closed-loop control schemes are employed to overcome the inherent performance limitations such as mechanical resonance, hysteresis and creep. Most reported control schemes are integer-order and combine both damping and tracking actions. In this work, fractional-order controllers from the positive position feedback family namely: the Fractional-Order Integral Resonant Control (FOIRC), the Fractional-Order Positive Position Feedback (FOPPF) controller, the Fractional-Order Positive Velocity and Position Feedback (FOPVPF) controller and the Fractional-Order Positive, Acceleration, Velocity and Position Feedback (FOPAVPF) controller are designed and analysed. Compared with their classical integer-order implementation, the fractional-order damping and tracking controllers furnish additional design (tuning) parameters, facilitating superior closed-loop bandwidth and tracking accuracy. Detailed simulated experiments are performed on recorded frequency-response data to validate the efficacy, stability and robustness of the proposed control schemes. The results show that the fractional-order versions deliver the best overall performance.

Dataset meta-level and statistical features affect machine learning performance

What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset’s different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learning Repository (53) and developed ML classification models (through classification implementation and hyperparameter tuning) for each dataset. Then, this study developed multiple regression models to explore the impact of dataset features on ML performance. We found that kurtosis has a statistically significant negative effect on the accuracy of the three non-tree-based ML algorithms of the Support vector machine (SVM), Logistic regression (LR) and K-nearest neighbour (KNN) for their classical implementation with both uniform and weighted aggregations. This study observed similar findings in most cases for ML implementations through hyperparameter tuning, except for SVM with weighted aggregation. Meta-level and statistical features barely show any statistically significant impact on the accuracy of the two tree-based ML algorithms (Decision tree and Random forest), except for implementation through hyperparameter tuning for the weighted aggregation. When we excluded some datasets based on the imbalanced statistics and a significantly higher contribution of one attribute compared to others to the classification performance, we found a significant effect of the meta-level ratio feature and statistical mean and standard deviation features on SVM, LR and KNN accuracy in many cases. Our findings open a new research direction in understanding how dataset characteristics affect ML performance and will help researchers select appropriate ML algorithms for a possible optimal accuracy outcome.

Semi-active vibro-acoustic control of vehicle transmission systems using a metal rubber-based isolator

This work is concerned about employing a semi-active metal rubber (MR)-based isolator to suppress noise and vibration for vehicle transmission systems. The adaptive stiffness and damping properties of MR material play a key role in the semi-active control effect. In this regard, a particular automotive transmission component—precisely, a plate dedicated to accommodating bearings—has been selected as the focus of research to control its noise and vibration. A microcomputer-driven control system is applied, featuring characteristics such as high performance, compact size, lightweight design, and energy efficiency, which make it ideal for testing in the automotive sector. The efficacy of the devised control scheme is assessed through the deployment of accelerometers and sound intensity probes to quantify the vibrations and the radiated acoustic power, respectively. Interesting noise and vibration reduction rates are achieved using both open-loop and closed-loop strategies. This research opens the horizons to employ MR material in a semi-active configuration for industrial requirements surpassing its classical passive implementation, notably in terms of broadband attenuation performance.

Monitoring and evaluation of provincial classical swine fever immunization implementation with an E2 subunit vaccine in Jeju Island, South Korea.

Accidental vaccination with a live attenuated low-virulence strain of Miyagi (LOM) vaccine led to the reemergence of classical swine fever virus (CSFV) in Jeju province, South Korea in 2014. To control the continual outbreaks of LOM-derived CSFV, the provincial government launched a provincial mass vaccination project using a CSF-E2 subunit vaccine. We conducted this study to assess the herd immunity level and outcomes of E2 vaccine-based immunization in breeding and growing herds on Jeju Island during 2020-2021. A large-scale vaccination trial using the Bayovac CSF-E2 vaccine investigated its efficacy in breeding and growing herds under farm application conditions (10 CSFV-affected and three CSFV-naïve swine farms). The level of herd immunity in each farm was classified into three (S1-S3) and six (G1-G6) profiles in breeding and growing herds, respectively. Immunity monitoring revealed a remarkable improvement in the herd immunity status in all farms. The majority (10/13) of farms, including CSFV-free farms, showed the S1G1 immunity profile in 2021, indicating the appropriate implementation of the advised vaccination regime. Moreover, there were significant decreases in Erns seropositivity from 100% to 50% and 25.9% to 4.3% at farm and pig levels, respectively. In particular, all farms were confirmed as CSFV free in the growing-finishing herds. Our large-scale trial demonstrated the effectiveness of the E2 subunit vaccine in establishing herd immunity stabilization and eliminating CSFV circulation in the affected farms and highlighted the need for a provincial vaccination policy to regain the CSF-free status on Jeju Island.

Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.

An Efficient Design of a Parallel Prefix Adder based on QCA Technology

This paper presents an optimized Parallel Prefix Adders (PPA) based on Quantum dot Cellular Automata (QCA) technology. PPA presents the most used block in Arithmetic Logic Units (ALU) which presents a fundamental part in several digital integrated circuits. The classical implementation method of PPA based on traditional Complementary Metal Oxide Semiconductor (CMOS) Technology presents several problems in terms of frequency, power consumption, surface and others. The QCA technology offers several features such as ultralow power consumption, small size, and can operate up to 1 THz. In this work, a 4-bit Parallel Prefix Adder with a new prefix low area and high speed is proposed. The proposed design is based on a half-adder circuit and other logic gates. These designs were tested and simulated using the freely known QCA Designer Tool version 2.0.3. and QCA pro. The proposed Half-adder presents a reduction of 23% 46% and 36% in terms of cell count, latency and power consumption, respectively compared to existing designs. The proposed PPA design shows almost an optimization of 5% in area and 58% in latency compared to the RCA design. The proposed circuit present an optimization of 20% in term of energy consumption compared to the existing PPA 4-bit Brent Kung Adder design.

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds.

Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (Grid-robust Efficient Neural Interface for Universal Solvent-Excluded Surface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance.

Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study

Spiking Neural Networks (SNNs) hold the promise of lower energy consumption in embedded hardware due to their spike-based computations compared to traditional Artificial Neural Networks (ANNs). The relative energy efficiency of this emerging technology compared to traditional digital hardware has not been fully explored. Many studies do not consider memory accesses, which account for an important fraction of the energy consumption, use naive ANN hardware implementations, or lack generality. In this paper, we compare the relative energy efficiency of classical digital implementations of ANNs with novel event-based SNN implementations based on variants of the Integrate and Fire (IF) model. We provide a theoretical upper bound on the relative energy efficiency of ANNs, by computing the maximum possible benefit from ANN data reuse and sparsity. We also use the Eyeriss ANN accelerator as a case study. We show that the simpler IF model is more energy-efficient than the Leaky IF and temporal continuous synapse models. Moreover, SNNs with the IF model can compete with efficient ANN implementations when there is a very high spike sparsity, i.e. between 0.15 and 1.38 spikes per synapse per inference, depending on the ANN implementation. Our analysis shows that hybrid ANN-SNN architectures, leveraging a SNN event-based approach in layers with high sparsity and ANN parallel processing for the others, are a promising new path for further energy savings.

A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution

Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.

Switch-Averaged Frequency Domain Simulation of Photovoltaic Systems

This paper pushes forward frequency domain (FD) modeling of switched networks aimed at transient simulation, with particular interest in photovoltaic (PV) systems. The PV system simulation is performed via the numerical Laplace transform (NLT) in a sequential (partitioned-time) fashion by using a set of time-windows. The proposed technique enhances existing FD PV models by a) averaging switching functions and b) using sample overlapping to alleviate numerical oscillations due to rise-time phenomenon at time-window interfaces. The proposed enhancements provide a more efficient dynamic simulation compared to both classical single-window full-sample NLT implementation and non-averaged FD PV models. Verification is performed via prevalent electromagnetic transient (EMT) software tools.

Quantum privacy‐preserving service for secure lane change in vehicular networks

AbstractSecure Multiparty Computation (SMC) enables multiple parties to cooperate securely without compromising their privacy. SMC has the potential to offer solutions for privacy obstacles in vehicular networks. However, classical SMC implementations suffer from efficiency and security challenges. To address this problem, two quantum communication technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution were utilised. These technologies supply symmetric and oblivious keys respectively, allowing fast and secure inter‐vehicular communications. These quantum technologies are integrated with the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to form a Quantum Secure Multiparty Computation (QSMC) platform. A lane change service is implemented in which vehicles broadcast private information about their intention to exit the highway. The proposed QSMC approach provides unconditional security even against quantum computer attacks. Moreover, the communication cost of the quantum approach for the lane change use case has decreased by 97% when compared to the classical implementation. However, the computation cost has increased by 42%. For open space scenarios, the reduction in communication cost is especially important, because it conserves bandwidth in the free‐space radio channel, outweighing the increase in computation cost.

Versatile OSCAT time-domain THz spectrometer.

We report on a compact and versatile time-domain spectrometer operating in the THz spectral region from 0.2 to 2.5 THz based on ultrafast Yb:CALGO laser and photo-conductive antennas. The spectrometer operates with the optical sampling by cavity tuning (OSCAT) method based on laser repetition rate tuning, which allows at the same time the implementation of a delay-time modulation scheme. The whole characterization of the instrument is presented and compared to the classical THz time-domain spectroscopy implementation. THz spectroscopic measurements on a 520-μm thick GaAs wafer substrate together with water vapor absorption measurements are also reported to further validate the instrument capabilities.

A Review of the Applicability of Classical and Extension of Fuzzy Logic Approaches to Project Decision-Making using Real Options

Introduction: In this study, a review of fuzzy implementation to Real Options Approach (ROA) theory where the applicability of classical and extended theories of “fuzziness” studied. Background: ROA allows taking into account the value of some sources of managerial flexibility and therefore assessing a more accurately project value. The positive value of flexibility results from limiting the impacts of adverse events while taking advantage of positive ones. One of the main lessons is that uncertainty adds value in the presence of flexibility. Ambiguous parameters that have a significant effect on the project value are usually represented as fuzzy sets using Zadeh's classical theory of Fuzzy logic (also termed "type-1"). However, there have been so many derivatives, and expansions of the fuzzy set theories developed by different researchers. Dealing with uncertainty can be manifested in the different mechanism of fuzziness. Objective: The objective of this review is to identify the research gap as well as provide an elementary guide to the applicability of different varieties of classical and extended applicability of fuzziness to ROA when evaluating project investment. Methods: After a generic review of the progress of ROA theory and fuzzy approaches by researchers This paper reviews the applicability of ROA to fuzzy sets (classical and extended) implementation to decision-making for large projects where project timing and uncertainty are key parameters affecting the project value Results: After reviewing the applicability of each of the classical and extended theories of fuzzy logic to ROA, a tabular format shows the result of this study summarizing the scenario, showing the applicability of different techniques. Conclusion: Most of the reviewed techniques of fuzzy implementation to ROA approach, still based on the classical theory of fuzzy logic. Implementation of more extended techniques has a potential of enhancing the outcome of such research.