Binary Encoding Research Articles

Gray Encoded Harmonics Power Line Interference Cancelling Structure Using LMS and NLMS Adaptive Algorithms

This paper proposes the implementation of a Gray encoded structure for harmonics power line interference cancelling. The structure uses dedicated hardware architecture for the Least Mean Square (LMS) adaptive filtering algorithm, as well as its normalized version (NLMS). In the used scheme, from a 60Hz reference signal, the algorithms are able to estimate the superior harmonics, using after these results for the cancelling of interferences related to the signal of interest. In this work, the proposed adaptive filtering architectures and the harmonics generator block use a Hybrid encoding in its data buses, which is a compromise between the minimal input dependency presented by the Binary encoding and the low switching characteristic of the Gray encoding. For the Hybrid structures, new Hybrid multipliers were proposed, and the results showed that those multipliers are more efficient than the Binary ones, by presenting less power consumption in some cases. The implemented harmonic cancelling structure with the LMS and NLMS adaptive filtering architectures were validated and compared by using both Binary and Hybrid encoding. The efficiency of the implemented Hybrid structure for the cancelling of interferences was proved by reducing more power than the Binary one. By the results, we conclude that it could be practicable to implement a harmonic cancelling structure, based on LMS and NLMS adaptive filtering, operating on Hybrid encoding.

Evaluating Binary Encoding Techniques in The Presence of Missing Values in Privacy-Preserving Record Linkage

IntroductionApplications in domains ranging from healthcare to national security increasingly require records about individuals in sensitive databases to be linked in privacy-preserving ways. Missing values make the linkage process challenging because they can affect the encoding of attribute values. No study has systematically investigated how missing values affect the outcomes of different encoding techniques used in privacy-preserving linkage applications.
 Objectives and ApproachBinary encodings, such as Bloom filters, are popular for linking sensitive databases. They are now employed in real-world linkage applications. However, existing encoding techniques assume the quasi-identifying attributes used for encoding to be complete. Missing values can lead to incomplete encodings which can result in decreased or increased similarities and therefore to false non-matches or false matches. In this study we empirically evaluate three binary encoding techniques using real voter databases, where pairs of records that correspond to the same voter (with name or address changes) resulted in files of 100,000 and 500,000 records containing from 0% to 50% missing values.
 ResultsWe encoded between two and four of the attributes first and last name, street, and city into three record-level binary encodings: Cryptographic long-term key (CLK) [Schnell et al. 2009], record-level Bloom filter (RBF) [Durham et al. 2014], and tabulation Min-hashing (TBH) [Smith 2017]. Experiments showed a 10% to 25% drop on average in both precision and recall for all encoding techniques when missing values are increasing. CLK resulted in the highest decrease in precision, while TBH resulted in the highest decrease in recall compared to the other encoding techniques.
 ConclusionBinary encodings such as Bloom filters are now used in practical applications for linking sensitive databases. Our evaluation shows that such encoding techniques can result in lower linkage quality if there are missing values in quasi-identifying attributes. This highlights the need for novel encoding techniques that can overcome the challenge of missing values.

Highly multiplexed spatial mapping of microbial communities.

Mapping the complex biogeography of microbial communities in situ with high taxonomic and spatial resolution poses a major challenge because of the high density1 and rich diversity2 of species in environmental microbiomes and the limitations of optical imaging technology3–6. Here, we introduce High Phylogenetic Resolution microbiome mapping by Fluorescence in situ Hybridization (HiPR-FISH), a versatile technology that uses binary encoding, spectral imaging, and machine learning based decoding to create micron-scale maps of the locations and identities of hundreds of microbial species in complex communities. We demonstrate the ability of 10-bit HiPR-FISH to distinguish 1023 E. coli isolates, each fluorescently labeled with a unique binary barcode. HiPR-FISH, in conjunction with custom algorithms for automated probe design and single-cell image analysis, reveals the disruption of spatial networks in the mouse gut microbiome in response to antibiotic treatment and the longitudinal stability of spatial architectures in the human oral plaque microbiome. Combined with super-resolution imaging, HiPR-FISH reveals the diverse ribosome organization strategies of human oral microbial taxa. HiPR-FISH provides a framework for analyzing the spatial ecology of environmental microbial communities at single-cell resolution.

Zero-Shot Learning to Index on Semantic Trees for Scalable Image Retrieval.

In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set. Unlike existing image index methods, our proposed LTI-ST method has the following two unique characteristics. First, it does not need to analyze the test images in the query database to construct the index structure. Instead, it is directly predicted by a network learnt from the training set. This zero-shot capability is critical for flexible, distributed, and scalable implementation and deployment of the image indexing and retrieval services at large scales. Second, unlike the existing distance-based index methods, our index structure is learnt using the LTI-ST deep neural network with binary encoding and decoding on a hierarchical semantic tree. Our extensive experimental results on benchmark datasets and ablation studies demonstrate that the proposed LTI-ST method outperforms existing index methods by a large margin while providing the above new capabilities which are highly desirable in practice.

Polynomiality for Bin Packing with a Constant Number of Item Types

We consider the bin packing problem with d different item sizes s i and item multiplicities a i , where all numbers are given in binary encoding. This problem formulation is also known as the one-dimensional cutting stock problem . In this work, we provide an algorithm that, for constant d , solves bin packing in polynomial time. This was an open problem for all d\ge 3 . In fact, for constant d our algorithm solves the following problem in polynomial time: Given two d -dimensional polytopes P and Q , find the smallest number of integer points in P whose sum lies in Q . Our approach also applies to high multiplicity scheduling problems in which the number of copies of each job type is given in binary encoding and each type comes with certain parameters such as release dates, processing times, and deadlines. We show that a variety of high multiplicity scheduling problems can be solved in polynomial time if the number of job types is constant.

Amino Acid Encoding Methods for Protein Sequences: A Comprehensive Review and Assessment.

As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different from the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining them with different algorithms. However, it has not attracted enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods. Those methods are grouped into five categories according to their information sources and information extraction methodologies, including binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding, and machine-learning encoding. Then, 16 representative methods from five categories are selected and compared on protein secondary structure prediction and protein fold recognition tasks by using large-scale benchmark datasets. The results show that the evolution-based position-dependent encoding method PSSM achieved the best performance, and the structure-based and machine-learning encoding methods also show some potential for further application, the neural network based distributed representation of amino acids in particular may bring new light to this area. We hope that the review and assessment are useful for future studies in amino acid encoding.

Feature selection based bee swarm meta-heuristic approach for combinatorial optimisation problems: a case-study on MaxSAT

Meta-heuristics are high-level methods widely used in different fields of applications. To enhance their performance, they are often combined to concepts borrowed from machine learning and statistics in order to improve the quality of solutions and/or reduce the response time. In this paper, we investigate the use of feature selection to speed-up the search process of Bee Swarm Optimisation (BSO) meta-heuristic in solving the MaxSAT problem.The general idea is to extract a subset of the most relevant features that describe an instance of a problem in order to reduce its size. We propose to translate a MaxSAT instance into a dataset following one of several representations proposed in this study, and then apply a FS technique to select the most relevant variables or clauses. Two data organizations are proposed depending on whether we want to remove variables or clauses. In addition, two data encodings can be used: binary encoding if we are only interested by the presence or not of a variable in a clause, and ternary encoding if we consider the information that it appears as a positive or negative literal. Moreover, we experiment two feature evaluation approaches: subset evaluation approach which returns the optimal subset, and individual evaluation which ranks the features and lets the user choose the number of features to remove. All possible combinations of data organization, data encoding and features evaluation approach lead to eight (08) variants of the hybrid algorithm, named FS-BSO. BSO and all the variants of FS-BSO have been applied to several instances of different benchmarks. The analysis of experimental results showed that in terms of solution quality, BSO gives the best results. However, FS-BSO algorithms achieve very good results and are statistically equivalent to BSO for some instances. In terms of execution time, all hybrid variants of FS-BSO are faster. In addition, results showed that removing clauses is slightly more advantageous in terms of solution quality whereas removing variables gives better execution times. Concerning data encoding, the results did not show any difference between the binary and ternary encodings. In this paper, we investigated the possibility to speed-up BSO meta-heuristic in solving an instance of the MaxSAT problem by extracting a priori knowledge. Feature selection has been used as a preprocessing technique in order to reduce the instance size by selecting a subset of the most relevant vaiables/clauses. Results showed that there is a strong link between the reduction rate and solution quality, and that FS-BSO offers a better quality-time trade off.

A presentation and retrieval hash scheme of images based on principal component analysis

Image representation and approximate query is always a research challenge and is affected greatly by the dimension and size of images. Since hash-based methods and binary encodings in combination with other techniques, such as kernel tricks, a longer binary code and mapping vectors rotation, can maintain a linear query time and query accuracy, they have been used in this area broadly. This paper develops principal component analysis hashing (PCAH) and unequal length of binary coding to divide images into more categories, denoted as PCA-MD, to improve accuracy of the representation and lookup of images. This paper firstly proves that the eigenvector mapping is locality sensitive, which is the basis for more classes division. For the anisotropy of the eigenvectors, PCA-MD utilizes an unequal length of binary coding and fewer eigenvectors, rather than an equal code, to divide the images mapped on every eigenvector to more categories. Moreover, L1-norm distance is applied to measure the distances of images to avoid the enormous computation of Euclidean distance. Theoretical analysis and extensive experimental results demonstrate that the PCA-MD has a higher query performance and a slight longer run time than the state-of-the-art approaches based on the Hamming distance. This in turn verifies that PCAH is a locality sensitive hash and that partitioning into more categories rather than only two categories is reasonable.

Fast algorithms for single and multiple pattern Cartesian tree matching

Cartesian tree matching is the problem of finding every substring of a given text which has the same Cartesian tree as that of a given pattern. In this paper we propose fast algorithms for single and multiple pattern Cartesian tree matching by introducing new representations and encodings. For single pattern Cartesian tree matching, we present the framework of a binary filtration method and an efficient verification technique. Any exact string matching algorithm can be used as a filtration for Cartesian tree matching in our framework. For multiple pattern Cartesian tree matching, we present two fingerprinting methods, i.e., the parent-distance encoding and the binary encoding. By combining an efficient fingerprinting method and a conventional multiple string matching algorithm, we can efficiently solve multiple pattern Cartesian tree matching. By experiments we show that our matching algorithms provide good performances for both single and multiple pattern Cartesian tree matching.

I6mA-stack: A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome

DNA N6-methyladenine (6 mA) is an epigenetic modification that plays a vital role in a variety of cellular processes in both eukaryotes and prokaryotes. Accurate information of 6 mA sites in the Rosaceae genome may assist in understanding genomic 6 mA distributions and various biological functions such as epigenetic inheritance. Various studies have shown the possibility of identifying 6 mA sites through experiments, but the procedures are time-consuming and costly. To overcome the drawbacks of experimental methods, we propose an accurate computational paradigm based on a machine learning (ML) technique to identify 6 mA sites in Rosa chinensis (R.chinensis) and Fragaria vesca (F.vesca). To improve the performance of the proposed model and to avoid overfitting, a recursive feature elimination with cross-validation (RFECV) strategy is used to extract the optimal number of features (ONF) subset from five different DNA sequence encoding schemes, i.e., Binary Encoding (BE), Ring-Function-Hydrogen-Chemical Properties (RFHC), Electron-Ion-Interaction Pseudo Potentials of Nucleotides (EIIP), Dinucleotide Physicochemical Properties (DPCP), and Trinucleotide Physicochemical Properties (TPCP). Subsequently, we use the ONF subset to train a double layers of ML-based stacking model to create a bioinformatics tool named ‘i6mA-stack’. This tool outperforms its peer tool in general and is currently available at http://nsclbio.jbnu.ac.kr/tools/i6mA-stack/

Multitemporal image encoding for monitoring spatiotemporal variations of water bodies using Landsat 8 Operational Land Imager (OLI) data

Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the presence of water at different dates is mapped applying the normalized difference water index using two encoding methods. The first one is based on the cumulative analysis of water in the pixel along time, and the second one uses the principle of binary encoding. The cumulative analysis helps to visualize more humid and dry areas, while binary encoding indicates the monthly variations of the lake surface, storing information about the dynamics of the phenomenon. The methods are compared using Landsat time series of Lake Poopó obtained between 2013 and 2019. The results showed that binary encoding allows detecting when and where severe droughts affect the water body and its recovery. In addition, it was possible to monitor the severe drought that affected the lake in 2016 and it was also noticed that its surface is still below the level registered before the drought in 2013.

Using Chou’s 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods

N 6-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biological processes and has attracted attention as a target for treating diseases. Here, an ensemble predictor named as TL-Methy, was developed to identify m6A sites across the genome. TL-Methy is a 2-level machine learning method developed by combining the support vector machine model and multiple features extraction methods, including nucleic acid composition, di-nucleotide composition, tri-nucleotide composition, position-specific trinucleotide propensity, Bi-profile Bayes, binary encoding, and accumulated nucleotide frequency. For Homo sapiens, TL-Methy method reached the accuracy of 91.68% on jackknife test and of 92.23% on 10-fold cross validation test; For Mus musculus, TL-Methy method achieved the accuracy of 93.66% on jackknife test and of 97.07% on 10-fold cross validation test; For Saccharomyces cerevisiae, TL-Methy method obtained the accuracy of 81.57% on jackknife test and of 82.54% on 10-fold cross validation test; For rice genome, TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification. Communicated by Ramaswamy H. Sarma

Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net

Lysine crotonylation is an important protein post-translational modification, which plays an important role in the process of chromosome organization and nucleic acid metabolism. Recognition of crotonylation sites is important to understand the function and mechanism of proteins. Traditional experimental methods are time-consuming and expensive, and can't predict crotonylation sites quickly and accurately. Therefore, this paper proposes a novel crotonylation sites prediction method called LightGBM-CroSite. First, binary encoding (BE), position weight amino acid composition (PWAA), encoding based on grouped weight (EBGW), k nearest neighbors (KNN), pseudo-position specific scoring matrix (PsePSSM) are used to extract features of protein sequences and obtain the original feature space. Second, the elastic net is used to remove redundant information and select the optimal feature subset. Third, the synthetic minority oversampling technique (SMOTE) is used to balance the samples. Finally, the balanced feature vectors are input into LightGBM to predict the crotonylation sites. According to the result of jackknife test, the Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 98.99%, 0.9798 and 0.9996, respectively. Compared with other state-of-the-art methods, the results show that our method has a better model performance on the crotonylation sites prediction. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LightGBM-CroSite/.

A binary encoding of spinors and applications

AbstractWe present a binary code for spinors and Clifford multiplication using non-negative integers and their binary expressions, which can be easily implemented in computer programs for explicit calculations. As applications, we present explicit descriptions of the triality automorphism of Spin(8), explicit representations of the Lie algebras 𝔰𝔭𝔦𝔶 (8), 𝔰𝔭𝔦𝔶 (7) and 𝔤2, etc.

Augmentation Invariant and Instance Spreading Feature for Softmax Embedding.

Deep embedding learning plays a key role in learning discriminative feature representations, where the visually similar samples are pulled closer and dissimilar samples are pushed away in the low-dimensional embedding space. This paper studies the unsupervised embedding learning problem by learning such a representation without using any category labels. This task faces two primary challenges: mining reliable positive supervision from highly similar fine-grained classes, and generalizing to unseen testing categories. To approximate the positive concentration and negative separation properties in category-wise supervised learning, we introduce a data augmentation invariant and instance spreading feature using the instance-wise supervision. We also design two novel domain-agnostic augmentation strategies to further extend the supervision in feature space, which simulates the large batch training using a small batch size and the augmented features. To learn such a representation, we propose a novel instance-wise softmax embedding, which directly perform the optimization over the augmented instance features with the binary discrmination softmax encoding. It significantly accelerates the learning speed with much higher accuracy than existing methods, under both seen and unseen testing categories. The unsupervised embedding performs well even without pre-trained network over samples from fine-grained categories. We also develop a variant using category-wise supervision, namely category-wise softmax embedding, which achieves competitive performance over the state-of-of-the-arts, without using any auxiliary information or restrict sample mining.

Efficient Time-Bin Encoding for Practical High-Dimensional Quantum Key Distribution

High-dimensional quantum key distribution (QKD) allows achievement of information-theoretic secure communications, providing high key-generation rates, which cannot, in principle, be obtained by QKD protocols with binary encoding. Nonetheless, the amount of experimental resources needed increases as the quantum states to be detected belong to a larger Hilbert space, thus raising the costs of practical high-dimensional systems. Here, we present an alternative scheme for fiber-based four-dimensional QKD, with time and phase encoding and one-decoy state technique. Quantum state transmission is tested over different channel lengths up to 145 km of standard single-mode fiber, evaluating the enhancement of the secret key rate in comparison to the three-state two-dimensional BB84 protocol, which is tested with the same experimental setup. Our scheme allows measurement of the four-dimensional states with a simplified and compact receiver, where only two single-photon detectors are necessary, thus making it a cost-effective solution for practical and fiber-based QKD.

Prediction of 2-hydroxyisobutyrylation sites by integrating multiple sequence features with ensemble support vector machine

Lysine 2-hydroxyisobutyrylation (Khib) is a new type of histone mark, which has been found to affect the association between histone and DNA. To better understand the molecular mechanism of Khib, it is important to identify 2-hydroxyisobutyrylated substrates and their corresponding Khib sites accurately. In this study, a novel bioinformatics tool named KhibPred is proposed to predict Khib sites in human HeLa cells. Three kinds of effective features, the composition of k-spaced amino acid pairs, binary encoding and amino acid factors, are incorporated to encode Khib sites. Moreover, an ensemble support vector machine is employed to overcome the imbalanced problem in the prediction. As illustrated by 10-fold cross-validation, the performance of KhibPred achieves a satisfactory performance with an area under receiver operating characteristic curve of 0.7937. Therefore, KhibPred can be a useful tool for predicting protein Khib sites. Feature analysis shows that the polarity factor features play significant roles in the prediction of Khib sites. The conclusions derived from this study might provide useful insights for in-depth investigation into the molecular mechanisms of Khib.

Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey

The number of publications related to complex-valued encoding metaheuristic optimization research is increasing the area of metaheuristic optimization is gaining in popularity. In this paper, we aim to provide researchers with a comprehensive and extensive overview of complex-valued encoding metaheuristic algorithms and applications for function optimization, engineering optimization design, and combination optimization. Compared with the basic metaheuristic algorithm, which are based on real-valued encoding or binary encoding, the complex-valued encoding metaheuristic algorithm expands the dimension of the search region and efficiently avoids the problem of falling into the local minimum. Finally, eight complex-valued encoding metaheuristic algorithms were used for 29 benchmark test functions and five engineering optimization design problems. Through the analysis and comparison of the results with statistical significance, the superiority of complex-value encoding was proved, and the complex-value encoding metaheuristic algorithm with the best performance was obtained. The purpose of this review is to present a relatively comprehensive list of all the complex-value encoding metaheuristic algorithms in the literature to inspire further research.

Cancer molecular subtype classification from hypervolume-based discrete evolutionary optimization

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification.

Binary Ostensibly‐Implicit Trees for Fast Collision Detection

AbstractWe present a simple, efficient and low‐memory technique, targeting fast construction of bounding volume hierarchies (BVH) for broad‐phase collision detection. To achieve this, we devise a novel representation of BVH trees in memory. We develop a mapping of the implicit index representation to compact memory locations, based on simple bit‐shifts, to then construct and evaluate bounding volume test trees (BVTT) during collision detection with real‐time performance. We model the topology of the BVH tree implicitly as binary encodings which allows us to determine the nodes missing from a complete binary tree using the binary representation of the number of missing nodes. The simplicity of our technique allows for fast hierarchy construction achieving over 6× speedup over the state‐of‐the‐art. Making use of these characteristics, we show that not only it is feasible to rebuild the BVH at every frame, but that using our technique, it is actually faster than refitting and more memory efficient.