Binary Vector Research Articles

Vector–Logic Synthesis of Deductive Matrices for Fault Simulation

The main idea is to create vector-logic computing that uses only read-write transactions on address memory to process large data. The main task is to implement new simple and reliable models and methods of vector computing based on primitive read-write transactions in the technology of vector flexible interpretive simulation of digital system faults. Vector-logic computing is a computational process based on read-write transactions over bits of a binary vector of functionality, where the input data is the addresses of the bits. A vector method for the synthesis of deductive matrices for transporting input fault lists is proposed, which has a quadratic computational complexity. The method is a development of the deductive vector synthesis algorithm based on the truth table. The deductive matrix is intended for the synthesis and verification of tests using parallel simulation of faults, as addresses, based on a read-write transaction of deductive vector cells in memory.

A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices.

Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.

New T-DNA binary vectors with NptII selection and RUBY reporter for efficient maize transformation and targeted mutagenesis.

New T-DNA binary vectors with NptII selection and RUBY reporter for efficient maize transformation and targeted mutagenesis.

A Method based on Evolutionary Algorithms and Channel Attention Mechanism to Enhance Cycle Generative Adversarial Network Performance for Image Translation.

A Generative Adversarial Network (GAN) can learn the relationship between two image domains and achieve unpaired image-to-image translation. One of the breakthroughs was Cycle-consistent Generative Adversarial Networks (CycleGAN), which is a popular method to transfer the content representations from the source domain to the target domain. Existing studies have gradually improved the performance of CycleGAN models by modifying the network structure or loss function of CycleGAN. However, these methods tend to suffer from training instability and the generators lack the ability to acquire the most discriminating features between the source and target domains, thus making the generated images of low fidelity and few texture details. To overcome these issues, this paper proposes a new method that combines Evolutionary Algorithms (EAs) and Attention Mechanisms to train GANs. Specifically, from an initial CycleGAN, binary vectors indicating the activation of the weights of the generators are progressively improved upon by means of an EA. At the end of this process, the best-performing configurations of generators can be retained for image generation. In addition, to address the issues of low fidelity and lack of texture details on generated images, we make use of the channel attention mechanism. The latter component allows the candidate generators to learn important features of real images and thus generate images with higher quality. The experiments demonstrate qualitatively and quantitatively that the proposed method, namely, Attention evolutionary GAN (AevoGAN) alleviates the training instability problems of CycleGAN training. In the test results, the proposed method can generate higher quality images and obtain better results than the CycleGAN training methods present in the literature, in terms of Inception Score (IS), Fréchet Inception Distance (FID) and Kernel Inception Distance (KID).

Targeting Disease Susceptibility Genes in Wheat Through wide Hybridization with Maize Expressing Cas9 and Guide RNA.

Two genes (TaHRC and Tsn1) conferring susceptibility to Fusarium head blight and tan spot, Septoria nodorum blotch, and spot blotch in wheat were targeted through wide hybridization with maize expressing Cas9 and guide RNA (gRNA). For each gene, two target sites were selected and corresponding gRNA expression cassettes were synthesized and cloned into a binary vector carrying the CRISPR/Cas9-mediated genome editing machinery. The constructed binary vectors were used to transform the hybrid maize Hi-II through an Agrobacterium-mediated approach to generate T0 and T1 plants, which were used to cross with wheat variety Dayn for targeting Tsn1 or the susceptible allele (TaHRC-S) of TaHRC as well as with the near-isogenic line (Day-Fhb1) of Dayn for targeting the resistant allele (TaHRC-R) of TaHRC. Haploid embryos were rescued in vitro from the wide crosses to generate haploid plants. PCR amplification and sequencing indicated that 15 to 33% of the haploid plants contained the target gene with mutations at the target sites. This wheat × maize hybridization combined with genome editing approach provides a useful alternative tool, not only for targeting susceptibility genes to improve disease resistance without regulatory issues, but also for understanding gene function in wheat. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

During the last three decades, evolutionary algorithms (EAs) have shown superiority in solving complex optimization problems, especially those with multiple objectives and non-differentiable landscapes. However, due to the stochastic search strategies, the performance of most EAs deteriorates drastically when handling a large number of decision variables. To tackle the curse of dimensionality, this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions. The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution, and provides a fast clustering method to highly reduce the dimensionality of the search space. More importantly, all the operations related to the decision variables only contain several matrix calculations, which can be directly accelerated by GPUs. While existing EAs are capable of handling fewer than 10 000 real variables, the proposed algorithm is verified to be effective in handling 1 000 000 real variables. Furthermore, since the proposed algorithm handles the large number of variables via accelerated matrix calculations, its runtime can be reduced to less than 10% of the runtime of existing EAs.

Wide-angle deep ultraviolet antireflective multilayers via discrete-to-continuous optimization

Abstract To date, various optimization algorithms have been used to design non-intuitive photonic structures with unconventional optical performance. Good training datasets facilitate the optimization process, particularly when an objective function has a non-convex shape containing multiple local optima in a continuous parametric space. Herein, we developed a discrete-to-continuous optimization algorithm and confirmed its validity by designing and fabricating deep-ultraviolet antireflective MgF2/LaF3 multilayers. For discrete optimization, a multilayer was encoded into a binary vector with multiple bits; a 10 nm thick MgF2 or LaF3 layer was assigned a binary digit of 0 or 1, respectively. Using the binary-based training datasets, a factorization machine formulated a surrogate function, which discovered the ground binary vector representing a near-optimal figure of merit. Then, the figure of merit was refined through continuous optimization (e.g., using an interior-point method) of the ground binary vector. MgF2/LaF3 multilayers with a variety of bit levels were created to attain a minimum average angular (0°–45°) reflectance at 193 nm. A MgF2/LaF3 multilayer optimized at ten bits (i.e., a total thickness of approximately 100 nm) yielded an average reflectance of 0.2%, which agreed well with the experimental results. Moreover, an integrated ray-wave optics simulation predicted that a single CaF2 plano-convex lens coated with the optimized multilayer could exhibit a transmittance of 99.7%. The developed optimization approach will be widely applicable to any photonic structures that can represent a binary vector with multiple bits, such as microwave metasurfaces, in addition to being useful for producing ideal optical multilayers.

A simple method for creating transgenic pea hairy roots using a Japanese pea cultivar and a Japanese Rhizobium radiobacter strain.

Pea (Pisum sativum) is an agriculturally important leguminous crop cultivated worldwide. It is also the plant from which phytoalexin was isolated for the first time. Several studies have investigated gene functions using pea hairy root culture systems. However, the procedures for producing hairy roots are relatively complicated and only a few pea cultivars and Rhizobium strains have been used. In this study, we established a simple method for generating transgenic hairy roots using a pea cultivar and a Rhizobium strain available in Japan. The transformation efficiency for the transgenic hairy roots (approximately 14%) was calculated on the basis of GFP fluorescence because the binary vector used in this study carried a GFP cassette as a marker. Furthermore, we confirmed that the production of the phytoalexin (+)-pisatin was induced by a copper dichloride treatment, indicating that this system can be used to characterize the biosynthesis of (+)-pisatin, which is a compound with a unique pterocarpan structure. Interestingly, some of the hairy roots turned into crown galls during the culture period. In summary, our simple method enables the production of transgenic pea hairy roots using biological materials accessible in Japan. The generated hairy roots can be used to elucidate the molecular mechanisms underlying (+)-pisatin biosynthesis as well as hairy root/crown gall formation.

Transformation of Nicotiana paniculata L., a recalcitrant species, using a T-DNA construct carrying two WUSCHEL-related homeobox genes.

A binary vector carrying two WUSCHEL-related homeobox (WOX) genes, WOX2 and WOX8, under the control of a chemical-inducible expression system, worked in the transformation in N. paniculata, a recalcitrant species of Nicotiana. The resulting transformants exhibited improved culture performance in regeneration from leaf segments and suspended cells. Multicellular masses generated from freely suspended cells showed a specific cell division pattern similar to that of somatic embryo, likely owing to the function of the two WOX genes.

Vector-deductive Memory-based Transactions for Fault-as-address Simulation

The main idea is to create logic-free vector computing, using only read-write transactions on address memory. The strategic goal is to create a deterministic vector-quantum computing using photons for read-write transactions on stable subatomic memory elements. The main task is to implement new vector computing models and methods based on primitive read-write transactions in vector flexible interpretive fault modeling and simulation technology, where data is used as addresses for processing the data itself. The essence of vector computing is read-write transactions on vector data structures in address memory. Vector computing is a computational process based on elementary read-write transactions over cells of binary vectors that are stored in address memory and form a functionality where the input data to be processed is the addresses of these cells. The advantages of a vector universal model for a compact description of ordered processes, phenomena, functions, and structures are defined for the purpose of their parallel analysis. Analytical expressions of logic, which require algorithmically complex calculators, are replaced by output state vectors of elements and digital circuits, focused on the parallelism of register logical procedures on regular data structures. A vector-deductive method for formula synthesis for propagating input lists (data) of faults is proposed, which has a quadratic computational complexity of register operations. A new matrix of deductive vectors has been synthesized, which is characterized by the following properties: compactness, parallel data processing based on a single read-write transaction in memory, elimination of traditional logic from fault simulation procedures, full automation of its synthesis process, and focus on technological solving all problems of technical diagnosis. In the work, the transition to vector logic in the organization of computing and the elimination of traditional logic presented in the form of tables and analytical expressions were carried out. The use of read-write transactions on memory in the absence of a command system focuses the new vector-logic computing towards deterministic quantum architectures based on stable subatomic memory particles.

Generation of a Triple-Shuttling Vector and the Application in Plant Plus-Strand RNA Virus Infectious cDNA Clone Construction.

Infectious cloning of plant viruses is a powerful tool for studying the reverse genetic manipulation of viral genes in virus-host plant interactions, contributing to a deeper understanding of the life history and pathogenesis of viruses. Yet, most of the infectious clones of RNA virus constructed in E. coli are unstable and toxic. Therefore, we modified the binary vector pCass4-Rz and constructed the ternary shuttle vector pCA4Y. The pCA4Y vector has a higher copy number in the E. coli than the conventional pCB301 vector, can obtain a high concentration of plasmid, and is economical and practical, so it is suitable for the construction of plant virus infectious clones in basic laboratories. The constructed vector can be directly extracted from yeast and transformed into Agrobacterium tumefaciens to avoid toxicity in E. coli. Taking advantage of the pCA4Y vector, we established a detailed large and multiple DNA HR-based cloning method in yeast using endogenous recombinase. We successfully constructed the Agrobacterium-based infectious cDNA clone of ReMV. This study provides a new choice for the construction of infectious viral clones.

Practical use of tobravirus-based vector to produce SARS-CoV-2 antigens in plants

A plant-based heterologous expression system is an attractive option for recombinant protein production because it is based on a eukaryotic system of high feasibility, and low biological risks. Frequently, binary vector systems are used for transient gene-expression in plants. However, plant virus vector-based systems offer advantages for higher protein yields due to their self-replicating machinery. In the present study, we show an efficient protocol using a plant virus vector based on a tobravirus, pepper ringspot virus, that was employed for transient expression of severe acute respiratory syndrome coronavirus 2 partial gene fragments of the spike (named S1-N) and the nucleocapsid (named N) proteins in Nicotiana benthamiana plants. Purified proteins yield of 40–60 µg/g of fresh leaves were obtained. Both proteins, S1-N and N, showed high and specific reactivities against convalescent patients’ sera by the enzyme-linked immunosorbent assay format. The advantages and critical points in using this plant virus vector are discussed.

Consecutive layer collaborative filter similarity for differentiable neural network pruning

Filter pruning is proven to be an effective strategy in model compression. However, convolutional filter pruning methods usually pay all attention to evaluating filters’ importance at a single layer, ignoring their collaborative relationship with corresponding filters of the next layer. In this paper, we propose novel consecutive layer collaborative filter similarity (CLCS) to make full use of the complete filter information and learn binary selection vectors to prune the redundant filters automatically. With learned selection vectors, the pruning ratio of each layer can be determined, and we can also calculate the FLOPs of the candidate pruned network at the current stage. Under the accuracy constraint and the FLOPs constraint, the selection vectors of each layer can be optimized to achieve a better trade-off between accuracy and efficiency. Extensive experiments on CIFAR-10 and ImageNet with multiple networks demonstrate the effectiveness of our proposed method. Specifically, we obtain 54.29% and 67.33% FLOPs reduction with 0.01% and 0.09% accuracy improvement for ResNet-56 and ResNet-110 on CIFAR-10, respectively. On ImageNet, we reduce FLOPs by nearly half compared to the ResNet-50 baseline with almost no loss of accuracy. Compared with state-of-the-art filter pruning methods, our approach also achieves superior results.

Editorial Issue 34.2

Editorial Issue 34.2

Semantic-based conditional generative adversarial hashing with pairwise labels

Hashing has been widely exploited in recent years due to the rapid growth of image and video data on the web. Benefiting from recent advances in deep learning, deep hashing methods have achieved promising results with supervised information. However, it is usually expensive to collect the supervised information. In order to utilize both labeled and unlabeled data samples, many semi-supervised hashing methods based on Generative Adversarial Networks (GANs) have been proposed. Most of them still need the conditional information, which is usually generated by the pre-trained neural networks or leveraging random binary vectors. One natural question about these methods is that how can we generate a better conditional information given the semantic similarity information? In this paper, we propose a general two-stage conditional GANs hashing framework based on the pairwise label information. Both the labeled and unlabeled data samples are exploited to learn hash codes under our framework. In the first stage, the conditional information is generated via a general Bayesian approach, which has a much lower dimensional representation and maintains the semantic information of original data samples. In the second stage, a semi-supervised approach is presented to learn hash codes based on the conditional information. Both pairwise based cross entropy loss and adversarial loss are introduced to make full use of labeled and unlabeled data samples. Extensive experiments have shown that the propose algorithm outperforms current state-of-the-art methods on three benchmark image datasets, which demonstrates the effectiveness of our method.

Analysis and identification of drug similarity through drug side effects and indications data

BackgroundThe measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects.MethodsThe model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity.ResultsAmong the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities.ConclusionBased on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.

A new simple and effective method for PLRV infection to screen for virus resistance in potato

Effective screening of plant germplasm collections for resistance to plant viruses requires that there is a rapid and efficient system in place to challenge individual plants with the virus. Potato leafroll virus (PLRV), a commercially important pathogen of potato, is able naturally to infect only the phloem-associated tissue of plants and is delivered to this tissue by feeding aphids. Mechanical (non-vector-mediated) infection by PLRV does not occur thus screening for PLRV resistance is currently laborious and time consuming. We constructed an infectious cDNA clone of a new (Hutton) isolate of PLRV in the binary vector pDIVA and transformed it into Agrobacterium tumefaciens strain LBA4404. Infiltration of this culture into leaves of Nicotiana benthamiana, a highly susceptible model plant, produced a systemic infection with PLRV, although this approach was not successful for potato. However, a very efficient and reproducible systemic infection of potato was achieved when we submerged cut stems of the plant into the agrobacterium cell suspension and then transplanted the stems into compost to grow roots and new apical leaves. Using a standardised protocol developed for this new PLRV inoculation method we have confirmed the previously described resistance to the virus in the JHI breeding line G8107(1) and identified 62 plant accessions from the Commonwealth Potato Collection in which no PLRV infection was detected.

Precession and polar alignment of accretion discs in triple (or multiple) stellar systems

ABSTRACTWe investigate the mechanism of polar alignment for accretion discs in hierarchical systems (HSs) with more than two stars. In eccentric binary systems, low-mass discs that are sufficiently tilted to the binary orbit align in a polar configuration with respect to the binary plane by aligning their angular momentum to the binary eccentricity vector. In HSs, secular evolution of the orbital parameters makes the eccentricity vector of the system precess with time. This precession undermines the stability of the polar orbit for accretion discs hosted in HSs. We analytically show that the binary criteria for polar alignment derived in the literature are necessary but not sufficient conditions for polar alignment in HSs. Then, we derive an analytical criterion for polar alignment in HSs. In general, we find that discs orbiting the innermost level of an HS can go polar. Conversely, radially extended discs orbiting the outer levels of an HS cannot polarly align and evolve as orbiting around a circular binary. We confirm our findings through detailed numerical simulations. Also, our results are compatible with the observed distribution of disc–orbit mutual inclination. Finally, we compare the observed distribution of disc inclinations in the binary and in the HS populations. Binaries host mainly coplanar discs, while HSs show a wide range of disc inclinations. We suggest that the wider range of inclinations in HSs results from the secular oscillation of their orbital parameters (such as Kozai–Lidov oscillations), rather than from a different initial condition or evolution between HSs and binaries.

Protocol for a high titer of BaEV-Rless pseudotyped lentiviral vector: Focus on syncytium formation and detachment

The development of hematopoietic stem cell (HSCs) gene therapy for DNA repair disorders, such as Fanconi anemia and Bloom syndrome, is challenging because of the induction of HSCs apoptosis by cytokine stimulation. Although the Baboon envelope pseudotyped lentiviral vector (BaEV-Rless-LV) has been reported as a non-stimulatory gene transfer tool, the virus titer of BaEV-Rless-LV is too low for use in clinical applications. Transfected 293 T cells with helper plasmids, including the BaEV-Rless plasmid, showed morphological changes, such as syncytium formation and detachment. To establish a novel protocol for producing a high titer of BaEV-Rless-LV, we optimized three aspects of a basic virus production protocol by focusing on modifying culture conditions and the use of reagents: the virus titer increased 3-fold when the amount of BaEV-Rless plasmid was increased 1.2-fold; the highest titer was obtained when the viral supernatant was harvested at 48-h post-transfection, despite complete syncytium formation and detachment of the 293 T cells; and the use of poly-L-lysine-coated culture plates to enhance the adhesion and proliferation of 293 T cells and prevent detachment doubled the titer. Collectively, our novel protocol resulted in a 10-fold titer increase compared to the basic protocol and may be useful in clinical applications for treating DNA repair disorders.

Transreal Explicit Construction of Universal Possible Worlds

In an earlier paper we supplied an indirect proof of the existence of universal possible worlds that have the topological property of being hypercyclic, which means they can access every world in sequences of worlds that approach arbitrarily closely to every possible world. That proof states that there are universal worlds but it does not exhibit such a world explicitly. We now explicitly construct two such universal worlds. A continuous universal world constructs possible worlds with transreal co-ordinates directly. A discrete world provides a binary hypercyclic vector which can be used to create transfloating-point co-ordinates that approximate transreal co-ordinates. We also discuss the philosophical implications of universal worlds for an omniscient observer and human science.