Binary Matrix Research Articles

How to compute the minimum diagonal length of recurrence quantifiers to optimize their sensitivity to deterministic and stochastic properties

Recurrence Plots (RP) are binary matrices that quantify the recurrent and non-recurrent states of a trajectory. Each element of an RP matrix, denoted by ai,j, consists of an one or a zero, indicating a recurrent or non-recurrent pair of elements (i,j) of the trajectory, respectively. RP represents a graphical mosaic of the recurrent and non-recurrent states of a particular trajectory. The graphical properties of Recurrence Plots can be quantified using tools known as Recurrence Quantification Analysis (RQA), which rely on several structures embedded in an RP, such as diagonal, vertical, or horizontal line lengths. All of these quantifiers depend on free parameters, with the main ones being the threshold used to determine whether two points are recurrent. The recurrence threshold parameter may be computed using the concept of maximum recurrence entropy, which turns it into a self-adjustable parameter. In a similar way, we propose a new method for automatically select the minimum diagonal line length (ℓmin), a critical parameter for recurrence quantifiers such as Determinism. Based on a cost-function, we show how to choose an adequate minimum recurrent diagonal line length to accurately compute diagonal-based recurrences. Our analysis reveals a well-defined limit for the credible applicability of recurrence analysis based on the number of data points. Moreover, we demonstrate a clear dependence of ℓmin on the number of dynamical epochs (pseudo-periods) sampled. We also demonstrate how our method can maximize the sensitivity of determinism with respect to changes in the stationary character of the time series. Finally, we provide two experimental examples to illustrate our approach.

Application of Image Processing in Evaluation of Hydraulic Fracturing with Liquid Nitrogen: A Case Study of Coal Samples from Karaganda Basin

Research of microstructure and permeability evolution of coal following LN2 treatment elucidate the process of cryogenic fracturing due to environmentally friendly behavior in comparison with conventional hydraulic fracturing. The evolution of the 2D microstructure of bituminous coal before and after LN2 treatment was examined using a high-resolution camera. The image processing was implemented using functions from the OpenCV Python library that are sequentially applied to digital images of original coal samples. The images were converted into binary pixel matrices to identify cracks and to evaluate the number of cracks, crack density, total crack area, and average crack length. Results were visualized using Seaborn and Matplotlib Python libraries. There were calculations of total crack area (TCA), total number of cracks (TNC), crack density (CD), the average length of cracks (Q2), first (Q1) and third (Q3) quartiles in fracture length statistics. Our findings demonstrate a progressive increase in the Total Crack Area (δTCA) with longer freezing times and an increased number of freezing–thawing cycles. In contrast, the change in crack density (δCD) was generally unaffected by freezing time alone but exhibited a significant increase after several freezing–thawing cycles. Among the freezing times investigated, the highest crack density (CD) value of 300 m−1 was achieved in FT60, while the lowest CD value of 31.25 m−1 was observed in FT90 after liquid nitrogen (LN2) treatment. Additionally, the FTC4 process resulted in a 50% augmentation in the number of cracks, whereas the FTC5 process tripled the number of small cracks.

An RGB Color Image Steganography Scheme by Binary Lower Triangular Matrix

Steganography is an area where researchers focus on how to develop novel hiding techniques that are aided by the mathematical or computational model. In order to conceal the secret bits in a cover image, the imperceptible property and embedding payload capacity must be maintained. In most of the developed methods, some selective least significant bits (LSBs) of each pixel of the cover image is modified to hide the data which may lead to substantial distortion in the stego-image especially when the payload size is large. Therefore, to attain the trade-off between payload capacity and image quality, the authors have devised a binary lower triangular matrix-based secret message embedding process in which data can be hidden in the color pixel. In this secret message embedding process, the secret data is embedded aided by the binary lower triangular matrix. The formulated mathematical model for the secret message embedding process has ensured a secure embedding technique with reduced aberration on the stego-image. The proposed steganography scheme is preserving significant visual quality while emphasizing the security aspect by adopting an indirect data-hiding mechanism in contrast to the conventional mechanism. Furthermore, for the betterment of the visual properties of the stego-image, we have generically extended the formulation and tested it to check the visual quality. The empirical results of the proposed steganography scheme depict outcomes satisfactorily for the RGB color images. Researchers can explore and apply this novel data-hiding scheme in different data communication applications.

3D Human Pose Lifting with Grid Convolution

Existing lifting networks for regressing 3D human poses from 2D single-view poses are typically constructed with linear layers based on graph-structured representation learning. In sharp contrast to them, this paper presents Grid Convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. GridConv is based on a novel Semantic Grid Transformation (SGT) which leverages a binary assignment matrix to map the irregular graph-structured human pose onto a regular weave-like grid pose representation joint by joint, enabling layer-wise feature learning with GridConv operations. We provide two ways to implement SGT, including handcrafted and learnable designs. Surprisingly, both designs turn out to achieve promising results and the learnable one is better, demonstrating the great potential of this new lifting representation learning formulation. To improve the ability of GridConv to encode contextual cues, we introduce an attention module over the convolutional kernel, making grid convolution operations input-dependent, spatial-aware and grid-specific. We show that our fully convolutional grid lifting network outperforms state-of-the-art methods with noticeable margins under (1) conventional evaluation on Human3.6M and (2) cross-evaluation on MPI-INF-3DHP. Code is available at https://github.com/OSVAI/GridConv.

Identification of single- and double-well coherence–incoherence patterns by the binary distance matrix

The study of chimera states or, more generally, coherence–incoherence patterns has led to the development of several tools for their identification and characterization. In this work, we extend the eigenvalue decomposition method to distinguish between single-well (SW) and double-well (DW) patterns. By applying our method, we are able to identify the following four types of dynamical patterns in a ring of nonlocally coupled Chua circuits and nonlocally coupled cubic maps: SW cluster, SW coherence–incoherence pattern, DW cluster, and DW coherence–incoherence. In a ring-star network of Chua circuits, we investigate the influence of adding a central node on the spatio-temporal patterns. Our results show that increasing the coupling with the central node favors the occurrence of SW coherence–incoherence states. We observe that the boundaries of the attraction basins resemble fractal and riddled structures.

Differentiable learning of matricized DNFs and its application to Boolean networks

Boolean networks (BNs) are well-studied models of genomic regulation in biology where nodes are genes and their state transition is controlled by Boolean functions. We propose to learn Boolean functions as Boolean formulas in disjunctive normal form (DNFs) by an explainable neural network Mat_DNF and apply it to learning BNs. Directly expressing DNFs as a pair of binary matrices, we learn them using a single layer NN by minimizing a logically inspired non-negative cost function to zero. As a result, every parameter in the network has a clear meaning of representing a conjunction or literal in the learned DNF. Also we can prove that learning DNFs by the proposed approach is equivalent to inferring interpolants in logic between the positive and negative data. We applied our approach to learning three literature-curated BNs and confirmed its effectiveness. We also examine how generalization occurs when learning data is scarce. In doing so, we introduce two new operations that can improve accuracy, or equivalently generalizability for scarce data. The first one is to append a noise vector to the input learning vector. The second one is to continue learning even after learning error becomes zero. The first one is explainable by the second one. These two operations help us choose a learnable DNF, i.e., a root of the cost function, to achieve high generalizability.

Simultaneous adsorption of ciprofloxacin and Cu2+ using Fe and N co-doped biochar: Competition and selective separation

The treatment of combined antibiotics and heavy metals pollution is a critical challenge. Herein, iron and nitrogen co-doped biochar (Fe/N-BC) was synthesized using rape straw as precursor, and applied for the adsorption of ciprofloxacin (CIP) and Cu2+ in single and binary systems. The qmax for CIP and Cu2+ were 46.45 mg g−1 and 30.77 mg g−1, respectively. Adsorption decreased in a binary matrix, indicating that there was a competitive effect between CIP and Cu2+, which might be due to CIP and Cu2+ sharing similar active adsorption sites on Fe/N-BC. Interestingly, CIP and Cu2+ co-adsorption was a pH-dependent process. Fe/N-BC has potential to highly selectively separate CIP/Cu2+ from mixed solutions through adjusting pH values. Furthermore, adsorption mechanisms were systematically investigated in this research. This research could help to provide a deeper understanding of the synchronously removing specific antibiotics and heavy metals by biochar adsorbents.

Connections between Linear Complementary Dual Codes, Permanents and Geometry

Linear codes with complementary duals, or LCD codes, have recently been applied to side-channel and fault injection attack-resistant cryptographic countermeasures. We explain that over characteristic two fields, they exist whenever the permanent of any generator matrix is non-zero. Alternatively, in the binary case, the matroid represented by the columns of the matrix has an odd number of bases. We explain how Grassmannian varieties as well as linear and quadratic complexes are connected with LCD codes. Accessing the classification of polarities, we relate the binary LCD codes of dimension k to the two kinds of symmetric non-singular binary matrices, to certain truncated Reed–Muller codes, and to the geometric codes of planes in finite projective space via the self-orthogonal codes of dimension k.

Hybrid binary grey wolf naked mole-rat algorithm for fragment-type UWB antenna optimization using time-varying transfer functions

This paper introduces a novel hybrid binary variant of the Grey Wolf Optimizer (GWO) and the Naked Mole-Rat Algorithm (NMRA), called HbGNMR. The primary objective of this hybridization is to overcome the poor exploration and local optima stagnation problems of the basic NMRA as well as the poor exploitation of GWO. HbGNMR incorporates the global search strategy of GWO into the worker phase to enhance exploration, and a mating factor based on simulated annealing-inertia weight is added to the breeder phase to improve exploitation. Moreover, fourteen new time-varying binary transfer functions of two families: seven S-shaped and seven V-shaped functions have been analysed. Among these transfer functions, TVV5 was identified as the most effective in converting solutions from continuous search space to binary search space and maintaining a balance between exploration and exploitation phases. The hybrid performance of HbGNMR is evaluated on twenty-three CEC’05, thirty CEC’17, and eighty CEC’21 benchmark functions. The statistical results demonstrate that HbGNMR outperforms nine well-known state-of-the-art algorithms according to Friedman-test and Wilcoxon rank-sum test. To test the effectiveness of the proposed algorithm for real-world applications, HbGNMR is employed to optimize a large dimensional planar-monopole ultrawideband (UWB) fragment-type antenna structure of an 18 × 18 binary matrix cells. Experimental testing of the prototype is conducted on a vector-network-analyser-based experimental-testbed, which shows good agreement with the simulated results. Furthermore, the optimized structure achieves large bandwidth (3.1-12.6 GHz), excellent gain, and high directivity for the 10 × 15 mm2 compact size UWB antenna geometry. Therefore, the optimized antenna can be utilized for modern wireless applications.

Contributions of leaf morphoanatomy to the taxonomy of Bronwenia (Malpighiaceae Juss.)

Bronwenia has about 11 species whose current delimitation is the result of a complicated taxonomic history marked by recent evolutionary hypotheses in Malpighiaceae. Therefore, there is a need for approaches that contribute to circumscribing the group and distinguishing the species. Thus, the aim of this work was to prove the effectiveness of leaf anatomy in this purpose and point out the characters that distinguish the species of Bronwenia. Plant anatomy techniques were used and the coded anatomical characters were plotted in a binary matrix and interpreted in a cluster analysis, using UPGMA. The relevant characters to distinguish the taxa were: petiole contour, petiole/midrib vascular system conformation, presence or absence malpighiaceous trichomes and laticifers. While the remaining characters occur widely in the group and can be used in future approaches to distinguish Bronwenia from other related genera, including Diplopterys and Banisteriopsis, which previously constituted a single genus. Therefore, leaf anatomy represents relevance in the taxonomy of Bronwenia and can direct evolutionary hypotheses for Malpighiaceae.

An enhanced noise-tolerant hashing for drone object detection

Drone, a.k.a. Unmanned aerial vehicle (UAV), has been pervasively applied in geological hazard monitoring, smart agriculture, and urban planning in the past decade. In this work, we fuse multiple attributes into a noise-tolerant hashing framework that can detect objects from drone pictures extremely fast. Our method can intrinsically and flexibly encode various topological structures from each target object, based on which multi-scale objects can be discovered in a view- and altitude-invariant way. Moreover, by leveraging lF and l1 norms collaboratively, the calculated hash codes are robust to low quality drone pictures and noisy semantic labels. More specifically, for each drone-borne picture, we extract visually/semantically salient object parts inside it. To characterize their topological structure, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to hierarchically exploit the semantics of these graphlets, wherein three attributes: i) deep binary hash codes learning, ii) contaminated pictures/labels denoising, and iii) adaptive data graph updating are seamlessly incorporated. Such multi-attribute binary MF can be solved iteratively, and in turn each graphlet is transformed into the binary hash codes. Finally, the hash codes corresponding to graphlets within each drone photo are utilized for ranking-based object discovery. Comprehensive experiments on the DAC-SDC, MOHR, and our self-compiled data set have demonstrated the competitively speed and accuracy of our method. As a byproduct, we employ an elaborately-designed FPGA architecture to calculate our hash codes. On average, a 57 frames per second (fps) object detection speed is achieved on 4K drone videos (without temporal modeling).

Conception of efficient key-dependent binary diffusion matrix structures for dynamic cryptographic algorithms

The existing static cryptographic algorithms suffer from different performance and security challenges. These limitations are attributed to their fixed structure, where the substitution and diffusion primitives maintain the same values throughout the process. In this paper, we present a new framework of a dynamic binary diffusion matrix with flexible dimensions (n×n). The proposed solution replaces the static diffusion primitives, in existing symmetric and un-keyed cryptographic algorithms, with dynamic primitives. We define six different dynamic and flexible binary diffusion forms, four that are invertible, and two that are non-invertible, and hence, they could be used for different security services. However, using a single diffusion form does not always guarantee the required cryptographic properties. To that effect, we propose a binary multiplication scheme of a dynamic primary matrix and its transposed form, which yields the desired efficiency, and provides good resistance against recent implementation attacks, yet without degrading the system performance. We conduct security and performance analyses to validate the effectiveness of the proposed solution. The results confirm the cryptographic performance in terms of the linear branch number and the number of fixed points. In this context, the best-obtained branch number is 4 for n=8 and 5 for n=12 for the invertible forms, while for the non-invertible ones, the best branch number is 3 for n=8 and 4 for n=12. Moreover, in terms of the number of fixed points, the obtained numbers are very close to 0 for the invertible and non-invertible forms.

Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.

Increasing evidence indicates that metabolites are closely related to human diseases. Identifying disease-related metabolites is especially important for the diagnosis and treatment of disease. Previous works have mainly focused on the global topological information of metabolite and disease similarity networks. However, the local tiny structure of metabolites and diseases may have been ignored, leading to insufficiency and inaccuracy in the latent metabolite-disease interaction mining. To solve the aforementioned problem, we propose a novel metabolite-disease interaction prediction method with logical matrix factorization and local nearest neighbor constraints (LMFLNC). First, the algorithm constructs metabolite-metabolite and disease-disease similarity networks by integrating multi-source heterogeneous microbiome data. Then, the local spectral matrices based on these two networks are established and used as the input of the model, together with the known metabolite-disease interaction network. Finally, the probability of metabolite-disease interaction is calculated according to the learned latent representations of metabolites and diseases. Extensive experiments on the metabolite-disease interaction data were conducted. The results show that the proposed LMFLNC method outperformed the second-best algorithm by 5.28 and 5.61% in the AUPR and F1, respectively. The LMFLNC method also exhibited several potential metabolite-disease interactions, such as "Cortisol" (HMDB0000063), relating to "21-Hydroxylase deficiency," and "3-Hydroxybutyric acid" (HMDB0000011) and "Acetoacetic acid" (HMDB0000060), both relating to "3-Hydroxy-3-methylglutaryl-CoA lyase deficiency." The proposed LMFLNC method can well preserve the geometrical structure of original data and can thus effectively predict the underlying associations between metabolites and diseases. The experimental results show its effectiveness in metabolite-disease interaction prediction.

Multi-functional graphene periodic patterns

By describing an idea based on binary matrices, a meta-surface design methodology has been introduced in the THz spectrum window. Independence of the numerical values of the geometrical parameters, the shape of graphene patterns, as well as the mode of the bias network for the patterns, have been investigated. The possibility of describing meta-surfaces by circuit equivalents has made it possible to predict the behavior of meta-surfaces at least in the THz band with acceptable accuracy by exploiting the idea of impedance matching. Considering the significant effect of changing graphene patterns and applying multiple biases, this work tries to optimize the pattern's shapes and external bias application. In this regard, two simple binary matrices are introduced which present pattern types and corresponding biasing schemes. Leveraging developed equivalent circuit models for known periodic graphene patterns, a systematic procedure is suggested to consider pattern diversity and simultaneous multiple biases for single layers. Exploiting patterns of graphene disks, rings, and ribbons besides a back reflector and polymer substrate, a reconfigurable and adjustable wave absorber is suggested. Two sets of external stimulations via chemical potentials force the device to show wide-band and multi-band absorption while appealing tuning capability is achieved for multi-band mode. Considering both operational modes, it seems that the proposed absorber can cover almost all of the THz spectrum with excellent shifting possibilities. The validity of impedance matching description is verified by full-wave simulations while ample analysis is shown to investigate reliability against oblique radiation.

Machine learning-based multimodal prediction of prognosis in patients with resected intrahepatic cholangiocarcinoma.

4121 Background: Intrahepatic cholangiocarcinoma (iCCA) is an aggressive malignancy and the second most common primary liver cancer. About a third of patients may benefit from surgery but recurrence is common and the overall survival is low. Accurate prognosis modeling that can predict response to treatment and outcome remains an unmet clinical need. Deep learning methods provide a new opportunity to better predict prognosis by extracting distinguishing characteristics interrogating multiple data sources, which appears to be superior to unimodal modeling. This study aimed to construct a predictive model of patient outcome and identify predictive biomarkers based on a combination of clinical, genomic, histological and radiological data. Methods: We analyzed 83 patients with iCCA that underwent surgery and designed a multi-modal model to predict overall survival (OS) and progression-free survival (PFS). Among these patients, each had one to three hematoxylin/eosin histology slides. As a pre-processing step, each histology slide was decomposed into patches, each patch was fed into a ResNet feature extractor to extract a feature representation of the patch, and the features across patches were aggregated with maximum pooling to yield a 2048-dimensional vector representation for each slide. Among these 83 patients, 76 patients had their tumors profiled for somatic genomic alterations using MSK-IMPACT, a deep targeted-sequencing assay. A binary matrix across altered genes and samples was created as an input to the prediction model. Cox proportional hazard models specific to each modality (clinical data, histological slides, and altered genes) were then designed and their predictions were averaged to produce the final log-risk score. The models were validated using a 5-fold, 5-repeat cross-validation with patient-level splits. Results: A model using only clinical and routine histological data achieved a concordance index of 0.74 (95% confidence interval, 0.64-0.82) for OS and 0.73 (0.64-0.78) for PFS. Adding information about genetic alterations improved performance for OS (0.80, 0.70-0.88) and similar performance for PFS (0.72, 0.64-0.79). Both models outperform a staging-based patient stratification. Conclusions: This study demonstrates that machine learning models can improve survival prediction using multi-modal data after resection of intrahepatic cholangiocarcinoma. Such models have the potential to improve risk stratification and treatment recommendations. [Table: see text]

Strategic management, scenario analysis and competitive advantage analysis: New opportunities for anti-money laundering system reform

The article identifies and mathematically substantiates vectors of reforming the financial monitoring system based on the synergistic approach in the cross-country context by developing scenarios by selecting internal and external factors that stimulate money laundering. The key external and internal factors of the money laundering process intensification are indicated. Competitive advantages of the anti-money laundering processes, the achievement of which is possible in terms of key external and internal factors for activation of money laundering process are formed. The relationship between the key internal and external factors of money laundering intensification and competitive advantages by building a binary characteristic matrix is formed. The synergy effect made by the mutual influence of simultaneously acting internal and external factors – a quantitative feature of the further strategy of the financial monitoring system reforming in terms of cross-country analysis is calculated. An economic-mathematical model for evaluating and interpreting strategy for improving the financial monitoring system in terms of cross-country context based on integer optimization is developed. In the article, there is calculation of the number of competitive advantages received by each observed country, without considering the possible synergy effect of mutual influence of simultaneously acting internal and external factors on each other. Moreover, there is quantitative assessment of the synergy effect made by the emergence of additional competitive advantages due to a certain combination of simultaneous influence made by external and internal factors of the money laundering intensification. The “ideal” situation of the possibility of gaining all possible competitive advantages in reforming the financial monitoring system in terms of cross-country context by overcoming internal and external factors stimulating the money laundering is studied. The results of the cross-country analysis form a basis for the further formalization of the limits for quantitative evaluation of the developed strategies through a uniform distribution.

Securing LSB steganography using bite substitution and image blocking

Protecting secret messages is a vital issue, in this paper’s research, a simplified, highly secure method of message steganography will be introduced. The proposed method will use a complicated PK, which contains information to select a secret block from the color image to be used as a covering block; also it will contain the values of the chaotic logistic map model to run this model to generate the indices key needed to substitute the message binary matrix. The PK will provide a huge keyspace capable to resist hacking attacks, the extracted message will be very sensitive to any minor changes in the PK, and any changes in this key during the extraction phase will be considered a hacking attempt by producing a damaged extracted message. It will be shown that the proposed method will be always efficient when changing the message length and changing the covering images. The proposed method will be implemented using various messages and various covering images, the obtained results will be analyzed using various types of data analysis methods to prove the improvements provided by the proposed method (quality, security, and efficiency).

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

A Physical Theory of Information Vs. A Mathematical Theory of Communication

This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principle are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Bekenstein, and mass-energy equivalence are derived.

Adaptive Android APKs Reverse Engineering for Features Processing in Machine Learning Malware Detection

The key component that makes the detection of android malware possible is the availability of the right triggers and pointers, which are found in the Android application packages, known as features or attributes. These are fundamental in the training of the different machine learning algorithms to produce the required detection model. The process of extracting these attributes or features, from the Android application packages, is known as reverse engineering. This paper delved into the experimental detail processes of applying reverse engineering procedure, using Sublime Text 2 and Androguard Plugin, on Android Application packages for the extraction of, particularly permissions, which are the targeted features. The study further discussed the cleaning stages, using NotePad++, Microsoft Excel Worksheet, and MS Word, to sort out all the relevant and important features by removing all the noisy ones. A total of 1500 Android apps were downloaded from both benign and malicious sources and used for the experiment. The cleaned or important features extracted from these application packages at the end of the reverse engineering processes are 162 in total and these were further used to form a feature binary matrix of size 1500 by 163 (including the class features).