Generation Scheme Research Articles

How Accurate Are QM/MM Models?

Despite the success and widespread use of QM/MM methods in modeling (bio)chemically important processes, their accuracy is still not well understood. A key reason is because these methods are ultimately approximations to direct QM calculations of very large systems, which are impractical to perform in most cases. We highlight recent progress toward the development of realistic model systems where it is possible to obtain full QM reference data to directly and systematically evaluate the effectiveness of different QM/MM generation schemes. These model systems are highly flexible and can be tailored to probe the sensitivity of a QM/MM model to different reaction types and simulation parameters such as pairing of QM and MM potentials, QM region size, and composition. It is envisaged that this strategy could be used to directly validate different QM/MM generation schemes and spur the development of more robust models in the future.

Hybrid Source of Quantum Light for Generation of Frequency Tunable Fock States.

We propose a scheme for quantum-light generation in a nonlinear cavity hybridized with a two-level system and theoretically show that, when excited by a series of controlled pump pulses, the hybrid source generates Fock states with high probabilities, e.g., one- and two-photon states can be generated near on demand, and Fock states with up to seven photons with a probability above 50%. The tailorable nature of the nonlinear cavity allows for generating Fock states with arbitrary frequencies, even with a fixed two-level system, creating fundamentally new opportunities in all areas of quantum technologies.

Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation

The stochastic FitzHugh-Nagumo (FHN) model is a two-dimensional nonlinear stochastic differential equation with additive degenerate noise, whose first component, the only one observed, describes the membrane voltage evolution of a single neuron. Due to its low-dimensionality, its analytical and numerical tractability and its neuronal interpretation, it has been used as a case study to test the performance of different statistical methods in estimating the underlying model parameters. Existing methods, however, often require complete observations, non-degeneracy of the noise or a complex architecture (e.g., to estimate the transition density of the process, ‘‘recovering’’ the unobserved second component) and they may not (satisfactorily) estimate all model parameters simultaneously. Moreover, these studies lack real data applications for the stochastic FHN model. The proposed method tackles all challenges (non-globally Lipschitz drift, non-explicit solution, lack of available transition density, degeneracy of the noise and partial observations). It is an intuitive and easy-to-implement sequential Monte Carlo approximate Bayesian computation algorithm, which relies on a recent computationally efficient and structure-preserving numerical splitting scheme for synthetic data generation and on summary statistics exploiting the structural properties of the process. All model parameters are successfully estimated from simulated data and, more remarkably, real action potential data of rats. The presented novel real-data fit may broaden the scope and credibility of this classic and widely used neuronal model.

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea.

A Novel Solution Approach Based on Dominance Evaluation Measure for Project Scheduling in Multi-Project Environments

The widely recognized measure for resources called resource strength (RS) does not fully capture the resources complexity of a project. Therefore, it cannot be used as a standalone measure to distinguish the complexity of various instances of project scheduling problems. Consequently, additional resource measures such as total amount of overflow (TAO) have been introduced, which should be used in conjunction with the RS. Extensive experimental studies have shown that as the value of TAO increases in a project, scheduling schemes with higher dimensional scheduling schemes such as bi-directional and tri-directional result in schedules with shorter makespans. In this study, an effective approach is proposed for integrating projects in multi-project environments, called the integrated project approach (IPA), taking into account the influence of TAO and building upon the relation between the TAO and the scheduling generation schemes. To assess the performance of IPA, we develop a new random multi-project generator based on the well-known benchmark sets, which utilizes TAO as a control tool to generate instances. The findings indicate that prioritizing the projects and frequency of the projects integration, facilitated by the proposed IPA, have a positive impact on the quality of multi-project schedules.

Optomechanical entanglement induced by backward stimulated Brillouin scattering

We propose a scheme to generate robust optomechanical entanglement, i.e., the entanglement between a mechanical and an optical modes. This scheme is based on a Backward Stimulated Brillouin Scattering (BSBS) process, which is hosted within an optomechanical structure. Our benchmark system consists of an acoustic (mechanical) mode coupled to two optical modes through an electrostrictive (radiation pressure) effect. After determining the optimal acoustic parameters allowing the entanglement in our system, we have shown that both the acoustic coupling and the decay rate require a certain threshold from where the optomechanical entanglement is generated. For instance, to generate an optomechanical entanglement in our proposal, the strength of the used acoustic decay rate most exceed both the mechanical and optical decay rates, which is the figure of merit of our proposal. The generated entanglement is robust enough against thermal fluctuation. Our work provides a new scheme for entanglement generation based on BSBS effect, and can be extended to microwaves and hybrid optomechanical structures. Such a generated entangled states can be used for quantum information processing, quantum sensing, and quantum computing.

Multi-Mode Data Organization and File Retrieval Based on a Primer Library in Large-Scale Digital DNA Storage

At present, the polymerase chain reaction (PCR) amplification-based file retrieval method is the most commonly used and effective means of DNA file retrieval. The number of orthogonal primers limits the number of files that can be accurately accessed, which in turn affects the density in a single oligo pool of digital DNA storage. In this paper, a multi-mode DNA sequence design method based on PCR file retrieval in a single oligonucleotide pool is proposed for high-capacity DNA data storage. Firstly, by analyzing the maximum number of orthogonal primers at each predicted primer length, it was found that the relationship between primer length and the maximum available primer number does not increase linearly, and the maximum number of orthogonal primers is on the order of 104. Next, this paper analyzes the maximum address space capacity of DNA sequences with different types of primer binding sites for file mapping. In the case where the capacity of the primer library is R (where R is even), the number of address spaces that can be mapped by the single-primer DNA sequence design scheme proposed in this paper is four times that of the previous one, and the two-level primer DNA sequence design scheme can reach [R2∙(R2-1)]2 times. Finally, a multi-mode DNA sequence generation method is designed based on the number of files to be stored in the oligonucleotide pool, in order to meet the requirements of the random retrieval of target files in an oligonucleotide pool with large-scale file numbers. The performance of the primers generated by the orthogonal primer library generator proposed in this paper is verified, and the average Gibbs free energy of the most stable heterodimer formed between the orthogonal primers produced is –1 kcal∙(mol∙L−1)−1 (1 kcal = 4.184 kJ). At the same time, by selectively PCR-amplifying the DNA sequences of the two-level primer binding sites for random access, the target sequence can be accurately read with a minimum of 103 reads, when the primer binding site sequences at different positions are mutually different. This paper provides a pipeline for orthogonal primer library generation and multi-mode mapping schemes between files and primers, which can help achieve precise random access to files in large-scale DNA oligo pools.

A Lightweight and Efficient Key Generation Scheme From OFDM Subcarriers’ Channel Responses

A Lightweight and Efficient Key Generation Scheme From OFDM Subcarriers’ Channel Responses

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

One-dimensional bin packing with pattern-dependent processing time

In this paper the classical one-dimensional bin packing problem is integrated with scheduling elements: a due date is assigned to each item and the time required to process each bin depends on the pattern being used. The objective is to minimize a convex combination of the material waste and the delay costs, both significant in many real-world contexts. We present a novel pattern-based mixed integer linear formulation suitable for different classical scheduling objective functions, and focus on the specific case where the delay cost corresponds to the maximum tardiness. The formulation is tackled by a branch-and-price algorithm where the pricing of the column generation scheme is a quadratic problem solved by dynamic programming. A sequential value correction heuristic (SVC) is used to feed with warm starting solutions the column generation which, in turn, feeds the SVC with optimal prices so as to compute refined feasible solutions during the enumeration. Computational tests show that both column generation and branch-and-price substantially outperform standard methods in computing dual bounds and exact solutions. Additional tests are presented to analyze the sensitivity to parameters’ changes.

Optimal current reconstruction strategy utilizing various PWM switching generation schemes in DC-link single shunt resistor

Optimal current reconstruction strategy utilizing various PWM switching generation schemes in DC-link single shunt resistor

A new Q-band comb-based multi-channel microwave Doppler backward scattering diagnostic developed on the HL-3 tokamak

Abstract The Doppler backscattering (DBS) diagnostic is widely used to measure the localized density fluctuations and the propagation velocity of turbulent structures. Microwave is launched at a frequency that approaches a cutoff layer in the plasma and at an angle that is oblique to the cutoff layer. A new Q-band multichannel DBS system based on a comb generator has been designed and tested for application on the HL-3 tokamak. With the comb generator and heterodyne scheme, the stability and flexibility of the new DBS system are improved. The new DBS diagnostic has a high output power (~ 10 dBm), good power flatness (< 5 dB in Q-band) and frequency stability, and the inter-frequency separation is tunable remotely. This article introduces the system design, laboratory test results, and initial experimental results from the HL-3 tokamak. With the help of the newly developed multichannel DBS, the turbulence information can be studied with high temporal and spatial resolution.

Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising.

Diffusion models (DM) built from a hierarchy of denoising autoencoders have achieved remarkable progress in image generation, and are increasingly influential in the field of image restoration (IR) tasks. In the meantime, its backbone of autoencoders also evolved from UNet to vision transformer, e.g. Restormer. Therefore, it is important to disentangle the contribution of backbone networks and the additional generative learning scheme. Notably, DM shows varied performance across IR tasks, and the performance of recent advanced transformer-based DM on PET denoising is under-explored. In this study, we further raise an intuitive question, "{if we have a sufficiently powerful backbone, whether DM can be a general add-on generative learning scheme to further boost PET denoising}". Specifically, we investigate one of the best-in-class IR models, i.e., DiffIR, which is a latent DM based on the Restormer backbone. We provide a qualitative and quantitative comparison with UNet, SR3 (UNet+pixel DM), and Restormer, on the 25% low dose 18F-FDG whole-body PET denoising task, aiming to identify the best practices. We trained and tested on 93 and 12 subjects, and each subject has 644 slices. It appears that Restormer outperforms UNet in terms of PSNR and MSE. However, additional latent DM over Restormer does not contribute to better MSE, SSIM, or PSNR in our task, which is even inferior to the conventional UNet. In addition, SR3 with pixel space DM is not stable to synthesize satisfactory results. The results are consistent with the natural image super-resolution tasks, which also suffer from limited spatial information. A possible reason would be the denoising iteration at latent feature space cannot well support detailed structure and texture restoration. This issue is more crucial in the IR tasks taking inputs with limited details, e.g., SR and PET denoising.

Experimental Demonstration of a Versatile and Scalable Scheme for Iterative Generation of Non-Gaussian States of Light.

Non-Gaussian states of light, such as Gottesman-Kitaev-Preskill states, are essential resources for optical continuous-variable quantum computing. The ability to efficiently produce these states would open up tremendous prospects for quantum technologies in general and fault-tolerant quantum computing in particular. This letter demonstrates a versatile method using a quantum-memory cavity to mitigate the consequences of the probabilistic nature of the breeding protocols and generate non-Gaussian states at high rates with scalability perspectives. The performances of our experimental setup are illustrated with the generation of squeezed cat states of amplitude α=1.63 with a fidelity of more than 60% at a generation rate in the kHz range, which is higher than the state of the art for such states.

Simultaneous generation of dual 16-QAM-modulated millimeter-wave signals enabled by precoding-based optical I/Q modulation and single photodetector detection

Integration of quadrature-amplitude-modulation (QAM) modulation and millimeter-wave (mm-wave) technology ensures a concise enhancement of transmission capacity and peak data rates in radio-over-fiber (RoF) systems. Nevertheless, with the continuous development of new-generation mobile applications, higher demands on access flexibility have also been formulated. We propose a novel, to the best of our knowledge, dual 16-QAM-modulated mm-wave signal generation scheme for the simultaneous generation and transmission of two independent 16-QAM signals with different carrier frequencies, enhancing the channel capacity, spectrum efficiency, and access flexibility of the RoF systems. In our proposed scheme, the generation of the optical dual 16-QAM-modulated mm-wave signals is implemented by a precoding-based optical in-phase/quadrature (I/Q) modulator operating at optical carrier suppression (OCS) mode, and their detection is implemented by a single photodetector (PD). At the transmitter side, two independent driving QAM vector radio-frequency (RF) signals with precoding, generated via digital signal process (DSP), are fed into two parallel Mach–Zehnder modulators (MZMs) of an I/Q modulator to implement OCS modulation. The I/Q modulator generates optical carriers carrying both information from different QAM signals simultaneously. After square-law detection in a single PD, we generate the desired dual 16-QAM-modulated mm-wave signals with photonic frequency-doubling and recover the original 16-QAM signals without information distortion. Based on our proposed scheme, we experimentally demonstrate the simultaneous generation and transmission of 2-Gbaud dual 16-QAM-modulated mm-wave signals with carrier frequencies of 26 GHz and 40 GHz, respectively. After transmission over 10-km single-mode fiber (SMF) link and 1.2-m wireless link, the dual 16-QAM signals can be successfully separated and demodulated by a common DSP. Bit-error ratios (BERs) of both recovered 16-QAM signals can reach the hard-decision forward-error-correction (HD-FEC) threshold of 3.8×10−3. Compared with the existing schemes, our scheme only requires one-half of the driving frequency to generate dual 16-QAM-modulated mm-wave signals with the same carrier frequency. The result indicates that our proposed scheme can lower bandwidth requirements for optoelectronics devices, along with implementing better spectral efficiency and receiver sensitivity, which is more applicable to the demands for increased system capacity and multi-frequency access flexibility in future systems.

IPv6 addressing strategy with improved secure duplicate address detection to overcome denial of service and reconnaissance attacks

With technology development, the growing self-communicating devices in IoT networks require specific naming and identification, mainly provided by IPv6 addresses. The IPv6 address in the IoT network is generated by using the stateless auto address configuration (SLAAC) mechanism, and its uniqueness is ensured by the DAD protocol. Recent research suggests that IPv6 deployment can be a risky decision due to the existing SLAAC-based addressing scheme and the DAD protocol being prone to reconnaissance and denial of service (DoS) attacks. This research paper proposes a new IPv6 generation scheme with an improved secure DAD mechanism to address these problems. The proposed addressing scheme generates IPv6 addresses by taking a hybrid approach based on vendor id of medium access control (MAC) address, physical location, and arbitrary random numbers, which mitigates reconnaissance attacks by malicious nodes. To prevent the DAD process from DoS attacks, hybrid values of interface identifier (IID) are multicast instead of actual values. The proposed scheme is evaluated under reconnaissance and DoS attacks in the presence of malicious nodes. The evaluation results demonstrate that the proposed method effectively mitigates reconnaissance and DoS attacks, outperforming the EUI-64 and SEUI-64 schemes in terms of address success rate (ASR), energy consumption, and communication overhead. Specifically, the proposed method significantly reduces the average probing rate for scanning the existence of an IPv6 address, with only a 1% probing rate compared to SEUI-64’s 5% and EUI-64’s 100%. Furthermore, the additional communication overhead introduced by the proposed method is less than 13% and 11% compared to EUI-64 and SEUI-64, respectively. Additionally, the energy consumption required to assign an IPv6 address using the proposed method is lower by 12% and 5% when compared to EUI-64 and SEUI-64, respectively. These findings highlight the effectiveness of the proposed method in enhancing security and optimizing resource utilization in IPv6 addressing.

Parallel high-speed random bit generation based on wideband chaotic microcomb and wavelet high-pass filtering.

We propose and experimentally demonstrate a parallel ultra-fast random bit generation (RBG) scheme based on wideband chaotic microcomb, which utilizes a phase modulation and dispersive component broadening spectrum. The effective bandwidth of each comb tooth is increased by over 10-fold. Wavelet high-pass filtering (WHPF) is employed to make the probability density functions (PDFs) of the chaotic signal's amplitude unbiased, achieving high symmetry with a skewness coefficient |S| of 0.0026, and the RBG rate of a single channel reaches 200 Gbps. Furthermore, the autocorrelation properties of the random sequences from each comb tooth and the cross-correlation properties between different comb teeth are analyzed, confirming both true randomness and orthogonality. This scheme can simultaneously generate dozens of wideband chaotic combs in the wavelength range of 1500-1600 nm.

Evolving Paradigms in Automated Program Repair: Taxonomy, Challenges, and Opportunities

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. The software bug has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software bug problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques’ complete development and future opportunities, we review the evolution of APR techniques and discuss in depth the latest advances in APR research. In this article, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool and then discuss the current state of APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.

Research on generative artificial intelligence technology scheme of shore-based power digital twin in scenario with incomplete information

Research on generative artificial intelligence technology scheme of shore-based power digital twin in scenario with incomplete information

Comparison of methods for changing the spectrum of radiation of a pulse x-ray source to determine the most effective two-energy image processing

For detecting substances with similar chemical composition and density, one of the promising methods of non-destructive testing is dual-energy processing of X-ray images. In particular, dual-energy transformation algorithms can be used to search for minerals hidden within barren rock. This method is most effective when the conditions for registering X-ray images and energy levels are properly selected. This study compares the effectiveness of image processing using the dual-energy method for three cases of spectral composition variation: firstly, as a result of adjusting the voltage on the X-ray tube; secondly, by attenuating low-energy radiation through the use of a copper filter; and thirdly, by combining these two methods. Beryl particles embedded in ground muscovite are used as samples for detection. The study utilizes a pulsed X-ray radiation source that generates radiation pulses of nanosecond duration. An original high-voltage generator scheme has been implemented for the method of regulating radiation energy by changing the peak voltage on the X-ray tube. The use of X-ray sources of this type enables the acquisition of high-resolution X-ray images of moving objects.