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Optical and synthetic aperture radar (SAR) image registration is a challenging task due to significant geometric and radiometric differences. In particular, the strong scattering phenomenon in SAR images can seriously affect the registration results. Accordingly, to solve the low repeatability of the key points in optical and SAR images, a complexity analysis scheme is proposed. In the first phase, the complexity distribution diagram is calculated by the threshold sliding window in the edge images obtained from the maximum moment of the phase congruency. Then morphological operation and connected regions are used to obtain the high-complexity regions and mask them to avoid the extraction of the interference points. Next to solve the limitations of the local self-similarity (LSS) descriptor in the optical and SAR image registration, such as poor discrimination, expensive computational complexity, and sensitivity to large geometric and radiometric difference, we propose a binary LSS descriptor (BLSS). We replace the correlation surface of the LSS with the local gradient orientation histogram. Furthermore, we construct descriptors based on the XY-coordinate system and convert the correlation of the regions to binary descriptors. Finally, the fast sample consensus (FSC) is used to remove false correspondences. The experiments conducted on several optical and SAR image pairs verify the effectiveness of the proposed algorithm.

Facing the challenge of small open labeled data sets in remote sensing change detection, this letter proposes a novel supervised change detection method by taking advantages of capsule network which can reach the same performance as traditional convolutional neural networks (CNNs) but with less training data. To achieve this aim, we propose a pseudo-Siamese capsule network which takes both rotational invariance and spatial hierarchies between features into account for aerial images change detection. First, the features of image pairs are extracted by two identical nonshared weights convolutional capsule networks. Second, the extracted features are directly concatenated and sent to another convolutional capsule layer. The change probability map is obtained by calculating the length of the capsule vectors in the final layer. Additionally, to reduce the influence of imbalance samples when we optimize our network, we design a margin-focal loss function to pay more attention to the misclassified samples. Finally, binary change map can be produced by a simple threshold. Experimental results carried out on the SZTAKI AirChange Benchmark Set show that the proposed method achieves comparable and even better results with existing state-of-the-art methods in terms of F-measure.

In this study application of convolutional neural networks (CNNs) preceded by synthetic aperture radar (SAR), image segmentation for sea ice concentration (SIC) estimation over the Baltic Sea from dual-polarized C-band SAR imagery is studied. Three algorithm variants were studied and trained using FMI ice chart SIC or a synthetic SIC dataset with different SIC values generated by combining pure open water and sea ice blocks by applying binary masks. The first two algorithm variants were trained using only open water and fully ice-covered patches, based on the FMI ice charts, and they had a similar CNN structure. These two algorithm variants differ only in deriving the segmentwise SIC from the CNN output. In the third algorithm, variant synthetic SIC data derived as a mixture of open water and fully ice-covered ice patches according to the ice charts were used in training. The estimation results were evaluated with respect to the FMI ice chart SIC for an independent test dataset. The results were very encouraging for operational purposes and significantly better than for our earlier SAR-based SIC estimation algorithms. The algorithm version trained with synthetic SIC data clearly outperformed the two other algorithm versions and our earlier SIC estimation results based on dual-polarized SAR, using independent FMI ice chart SIC as a reference.

The study of refractive index of liquids becoming increasingly popular in diverse areas to monitor chemical, pharmaceutical, food, scientific, biochemical, medical, etc. processes and procedures. In the present paper a low cost, simple, compact, reliable, robust, miniaturized and sturdy Refractive Index sensor is reported. It is configured by using a set of two multimode PCS fiber of 50 cm length in each, connected one to the source of wavelength 660 nm and other to a light detector. A glass rod drawn in the form of ‘U’, of compatible dimensions to that of two multimode fibers, was connected between source and detector by jointing to the remaining ends of the fibers, thus creating a sensing zone. Maintaining the binary liquids of Toluene and t-Butanol at the sensing zone at various temperatures, the light launched from source was monitored in the detector at the detector end. By forming relationship between index of refraction and power output at the temperature range of 10 °C to 60 °C, the sensor expected to be used to detect the index of refraction of liquids with maximum resolution of the order of 10-5 in the operating range from 1.36312nD to 1.50915nD.

This paper investigates the consensus of a multiagent system (MAS) under binary-valued communication with directed topology. An improved two-time-scale algorithm that alternates between control and estimation is proposed. Compared with the existing two-time-scale algorithm for the undirected topology case, we relax the conditions on the algorithm parameters, thereby providing more choices for practical implementations. The asymptotic property of the algorithm is analyzed by analyzing the convergence of the explicit closed-loop solution. To reduce the effect of binary communication on the consensus value for a single-sample path, the selection method for obtaining suitable parameters is given. To demonstrate their effectiveness, the selection method and algorithm are applied to the reputation management of a public welfare platform.

Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI.

The quasi-lattice theory based on the joint effect of size ratio and the energetic (or enthalpic) contribution as well as entropic effect has been applied to study the concentration dependence of the thermodynamic and structural properties of AgCu liquid alloys at 1423 K. The analytical expressions for the thermodynamic properties such as excess free energy of mixing, free energy of mixing, entropy of mixing, heat of mixing and the structural properties like the concentration fluctuations in the long wavelength limit and short range order parameter for a binary liquid alloy have been derived in the framework of quasi-lattice theory. The analytical expressions have been used to compute the various thermodynamic and structural properties of liquid AgCu alloys at 1423 K. The theoretical data of thermodynamic and structural properties have been compared with the available experimental data. The present theoretical investigation successfully explains the observed symmetries in the properties of mixing of liquid AgCu alloys at 1423 K.Further the energy parameter is found to be temperature dependent.

The small object semantic segmentation task is aimed at automatically extracting key objects from high-resolution remote sensing (HRS) imagery. Compared with the large-scale coverage areas for remote sensing imagery, the key objects, such as cars and ships, in HRS imagery often contain only a few pixels. In this article, to tackle this problem, the foreground activation (FA)-driven small object semantic segmentation (FactSeg) framework is proposed from perspectives of structure and optimization. In the structure design, FA object representation is proposed to enhance the awareness of the weak features in small objects. The FA object representation framework is made up of a dual-branch decoder and collaborative probability (CP) loss. In the dual-branch decoder, the FA branch is designed to activate the small object features (activation) and suppress the large-scale background, and the semantic refinement (SR) branch is designed to further distinguish small objects (refinement). The CP loss is proposed to effectively combine the activation and refinement outputs of the decoder under the CP hypothesis. During the collaboration, the weak features of the small objects are enhanced with the activation output, and the refined output can be viewed as the refinement of the binary outputs. In the optimization stage, small object mining (SOM)-based network optimization is applied to automatically select effective samples and refine the direction of the optimization while addressing the imbalanced sample problem between the small objects and the large-scale background. The experimental results obtained with two benchmark HRS imagery segmentation datasets demonstrate that the proposed framework outperforms the state-of-the-art semantic segmentation methods and achieves a good tradeoff between accuracy and efficiency. Code will be available at: http://rsidea.whu.edu.cn/FactSeg.htm

With the help of measured values of ultrasonic Velocity (U), density (ρ) and viscosity (η) excess acoustic and thermodynamic properties like velocity (UE), density (ρE), viscosity (ηE), adiabatic compressibility (βEa), free length (LEf), free volume (VEf), acoustic impedance (ZE), internal pressure (πEi), relaxation time (τE), Gibb’s free energy (GE) have been calculated for the binary mixture of isopropyl alcohol with CCl4, at various temperatures at a fixed frequency of 2 MHz for different concentration ranges. The graphs were plotted against the concentration of the mixture at different temperatures, from that the strength of interaction between like and unlike molecules has been discussed.

A procedure is developed to generate theoretical receiver operating characteristic (ROC) curves for the two-stage change detector proposed by Cha et al., in order to compare its detection performance with other detectors. A modification, however, to the first stage of the two-stage detector is proposed, which introduces a symmetry that significantly simplifies analysis. The probability density function (pdf) for this symmetric two-stage detector is derived, and using this pdf, an approach for determining thresholds is proposed. The symmetric two-stage detector introduces a new parameter that effectively weights each statistic making up its two individual stages. Detection performance is explored versus this new parameter. The two-stage detector is shown to yield overall better detection performance than the sample variance ratio, Berger's alternative coherence estimator, and the classical coherence estimator. When applied to real synthetic aperture radar (SAR) data, the two-stage detector and Berger's estimator yield binary change images with more easily visible areas of change. Finally, we derive an expression for the pdf of the optimal log-likelihood ratio statistic that is simpler than that given by Preiss et al.

This paper presents the study of binary component analysis subjected to distillation process using Aspen Plus. Binary components namely Isopropyle alcohol and water were considered for the study and their characteristics are analyzed at certain temperature and pressure. Aspen Plus provides virtual exposure of physical process, gives quantitative measure of mole fraction of liquid phase, vapor phase with changes in temperature, pressure and activity coefficients. Steady state and dynamic simulation results depicts the behavior of the system and enables the user to understand how the system reacts in virtual environment, to realize the system behavior in real-time environment.

We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative example, the series circuit of a binary memristor and capacitor is considered in detail. Some analytical solutions are found. Our work offers a novel analytical/numerical tool for modeling complex stochastic networks, which may find a broad range of applications.

In this work, we introduce the average top-k (ATk) loss, which is the average over the k largest individual losses over a training data, as a new aggregate loss for supervised learning. We show that the ATk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss. Yet, the ATk loss can better adapt to different data distributions because of the extra flexibility provided by the different choices of k. Furthermore, it remains a convex function over all individual losses and can be combined with different types of individual loss without significant increase in computation. We then provide interpretations of the ATk loss from the perspective of the modification of individual loss and robustness to training data distributions. We further study the classification calibration of the ATk loss and the error bounds of ATk-SVM model. We demonstrate the applicability of minimum average top-k learning for supervised learning problems including binary/multi-class classification and regression, using experiments on both synthetic and real datasets.

The simulation-based artificial neural networks (ANN) program is one of the suitable candidates from artificial intelligence simulation which can work to predict important ultrasonic and mechanical parameters in the glass field. This research is focused on exploring the validity of this system by comparing the prediction values from ANN with the experimental measurements and other theoretical models. The ANN simulation was effectively applied to a binary zinc-borate glass system with the composition of zZnO−(100-z)B2O3 where z = 0, 40, 45, 50, 55, and 60 mol%, which was fabricated by using the melt-quenching techniques. The increase of ZnO content caused the ultrasonic velocity and elastic moduli of the glasses to exhibit a decreasing trend. The bond compression theoretical calculation compared with the experimental measurement was considered to be satisfactory with the value of the coefficient R2 being around 0.92452 to 0.98492. The Makishima-Mackenzie calculation model concerning the experimental measurement of the elastic moduli and Poisson's ratio were between 0.86628 to 0.99786. The coefficient R2 value from the ANN simulation displayed on the density, ultrasonic velocity, and elastic moduli graph is around 0.9999 to 1.0000, which is considered to be very reasonable. The values predicted by this remarkable model proved that ANN simulation is suitable for use in glass research.

We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.

Due to the difficulty and time-consuming of labeling ground truth map in practical situations, unsupervised multiple change detection (MCD) for multispectral images (MSIs) have attracted much attention in recent years. One possible strategy to obtain multiple changes is to assign labels to the binary change result. However, some methods are difficult to obtain the accurate binary result because of the complexity of backgrounds; moreover, assigning labels is also a challenge owing to the limitation of the number of spectral channels in MSIs. Therefore, we propose a novel unsupervised MCD framework based on auto-updating multitemporal matrix factorization (AMMF) and spatiospectral channel augmentation (SSCA). In AMMF, the accurate binary change result can be detected based on joint matrix factorization, during which the distribution and subspace information of each temporal image are regularized to encode the spatiotemporal correlation. In SSCA, some novel augmentation strategies are introduced to increase the number of channels in MSIs to form the normalized high-dimensional maps for each temporal image based on nonlinear operations and convolutional sparse analysis, respectively. MCD can be achieved by integrating the binary change result and directional information that can be calculated by high-dimensional maps of different temporal images. Experiments are conducted on two real MSIs, indicating that the proposed framework performs well in detecting multiple changes.

Face recognition is one the most interesting topic in the field in computer vision and image processing. Face recognition is a processing system that recognizes and identifies individuals human by their faces. Automatic face recognition is powerful way to provide, authorized access to control their system. Face recognition has many challenging problems (like face pose, face expression variation, illumination variation, face orientation and noise) in the field of image analysis and computer vision. This method is work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image. Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction, only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works on different type image like illuminated and expression variation image.

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in \( \mathcal {O} \mathopen {}\left( n\right) \) time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields \((1 - \epsilon )\) approximation in \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) \) time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) \) for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.

Publisher Summary The basic objectives of object measurement are application dependent. It can be used simply to provide a measure of the object morphology or structure by defining its properties in terms of area, perimeter, intensity, color, shape, etc. It can also be used to differentiate between objects by measuring and comparing their properties. Object measurements can be broadly classified as geometric measures, ones based on the histogram of the object image, and those based on the intensity of the object. Geometric measures, including those that quantify object structure, can be computed for both binary and grayscale objects. In contrast, histogram- and intensity-based measures are applicable to grayscale objects. Another category of measures, which are distance based, can be used for computing the distance between objects or between two or more components of objects.

In this study, it was claimed that ROC analysis, which is used to determine to what extent medical diagnosis tests can be differentiated between patients and non-patients, can also be used to examine the discrimination of binary scored items in cognitive tests. In order to obtain various evidence for this claim, the 2x2 contingency table used in the ROC analysis was adapted in accordance with the logic of item discrimination. It was suggested in the article that the areas under the ROC curves (AUC) obtained by using the sensitivity and specificity values calculated with the adapted contingency table can be considered as a measure of item discrimination. The results of the statistical analyses made on the simulation data showed that the AUC values were positively and highly correlated with the D, 𝑟bis and a parameter values of the items, and the AUC values from different sized samples were consistent. Additionally, ROC analysis was more stable against range narrowing than other methods. In this respect, it was concluded that very large groups were not needed to examine item discrimination with the proposed method.

Using chaotic system for image encryption is a feasible and popular research interest. In this paper, a compound Sine-Piecewise Linear Chaotic Map is proposed to improve the chaotic characteristics of the original chaotic map. Compared with the original chaotic system, the proposed compound chaotic system has better dynamics performances, larger parameter space, which is more suitable for cryptographic applications. Furthermore, an image encryption algorithm based on this chaotic model and varying DNA coding is proposed. In this encryption algorithm, a novel method is provided to convert decimal numbers to binary numbers. Several numerical experiments show that the proposed encryption algorithm has good security performances, and is competitive with other chaos-based image encryption algorithms.

Recently, the combination of hybrid nanofluids with microchannel heat sinks (MCHSs) has led to superior heat removal from high heat flux electronics chips. The present work aimed to evaluate numerically the first and second law performances of MCHSs using the new cost‐effective binary/ternary hybrid nanofluids. Water‐based binary and ternary hybrid nanofluids include the MgO/TiO2 nanocomposite and the CuO/MgO/TiO2 nanocomposite, respectively. The effects of the hybrid nanofluid volume concentration (vol.%) and Reynolds number (Re) on the heat transfer, pressure drop, combined thermohydraulic characteristics, and entropy generation characteristics of the MCHSs are discussed. The results show that higher values of the convective heat transfer coefficient, pressure drop, and frictional entropy generation rate are obtained when using hybrid nanofluids with high Re and vol.% values. Furthermore, by increasing the Re and vol.% values of the hybrid nanofluids, the bottom wall temperature, total thermal resistance, and thermal entropy generation rate decreased, and an increased temperature uniformity was obtained on the bottom surface of the MCHSs. In conclusion, the applied hybrid nanofluids are considered to be more promising heat transfer fluids when compared with conventional fluids such as water. Particularly, the CuO/MgO/TiO2‐water ternary hybrid nanofluid exhibited a better heat transfer efficiency than did the MgO/TiO2‐water binary hybrid nanofluid.

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$ . Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

Supernova explosion and the associated neutron star natal kicks are important events on a pathway of a binary to become a gravitational wave source, an X-ray binary or a millisecond radio pulsar. Weak natal kicks often lead to binary survival, while strong kicks frequently disrupt the binary. In this article, we aim to further constrain neutron star natal kicks in binaries. We explore binary population synthesis models by varying prescription for natal kick, remnant mass and mass accretion efficiency. We introduce a robust statistical technique to analyse combined observations of different nature. Using this technique, we further test different models using parallax and proper motion measurements for young isolated radio pulsars and similar measurements for Galactic Be X-ray binaries. Our best model for natal kicks is consistent with both measurements and contains a fraction of $w=0.2\pm 0.1$ weak natal kicks with $\sigma_1 = 45^{+25}_{-15}$ km/s, the remaining natal kicks are drawn from the high-velocity component, same as in previous works: $\sigma_2 = 336$ km/s. We found that currently used models for natal kicks of neutron stars produced by electron capture supernova (combination of maxwellian $\sigma=265$ km/s and $\sigma = 30$ km/s for electron capture) are inconsistent or marginally consistent with parallaxes and proper motions measured for isolated radio pulsars. We suggest a new model for natal kicks of ecSN, which satisfy both observations of isolated radio pulsars and Be X-ray binaries., Comment: 21 page, 36 figures, accepted to MNRAS on September 21

This work investigates a novel design to realize 3-bit binary to gray code and gray to binary code converter utilizing all-optical XOR gates on a photonic crystal platform. An ultra-compact and high contrast ratio XOR gate working at 1550 nm is implemented using silicon rods on a hexagonal lattice with air substrate. The operation principle is the linear interference effect which leads to power efficient operation. The proposed designs are aimed at reducing the dimensions and enabling flexible and compatible operation with an Optical Integrated Circuit. The designs are optimized with respect to structural parameters such as lattice constant and radius to achieve the maximum contrast ratio and minimum response time. The proposed structures exhibit compact footprints of $100.04~\mu \text{m}^{2}$ and $105.6~\mu \text{m}^{2}$ for the all-optical gray to binary code converter and binary to gray code converter respectively. The corresponding values of contrast ratio are attained as 11.24 dB and 13.46 dB respectively. To analyse the structures and to calculate the required performance metrics, the Finite Difference Time Domain Method is used. This is the first time that a design for an all-optical gray to binary code converter has been attempted on a photonic crystal platform. The proposed design is successfully implemented with cascaded layout using interference effect without employing any additional phase control mechanisms. Gray codes are widely employed in optical communication systems for several applications such as improved switching, error correction and encryption. All-optical code converters form crucial elements in all-optical computing to enable efficient code conversions for high-speed photonic integrated circuits.

This paper presents a simple, yet effective method for dynamic beamforming that can be readily realized by integrating a tunable binary Huygens' metasurface with a leaky-waveguide antenna. Heretofore, dynamic beamforming has been difficult to achieve owing to the challenges in attaining full 360 of dynamic phase tunability, while also independently controlling the local aperture amplitudes from zero to unity. In contrast, the hereby proposed method only requires 90 of dynamic phase tunability (with arbitrary transmission amplitudes), thereby significantly alleviating the stringent tuning requirements. In particular, the proposed method is obtained by leveraging the antenna-array and holography theories, and utilizing the idea of ‘virtual’ electric line sources. Based on full-wave simulations, we demonstrate the versatility of the proposed method through the designs of a Huygens'-metasurface-assisted leaky-waveguide antenna that generates various complex far-field patterns such as sector beams and Dolph-Chebyshev patterns.

We revisit the single-machine preemptive scheduling problem to minimize total completion time with release times and deadlines. Du and Leung (1993) showed that the problem is binary NP-hard. But the exact complexity (unary NP-hardness or pseudo-polynomial-time solvability) of the problem is a long standing open problem. We show in this paper that the problem is unary NP-hard.

This paper studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices. This poses challenges in designing a structure of a neural network and its training strategies for generating feasible assignment solutions. To this end, this paper develop a new Sinkhorn neural network which learns a non-convex projection task onto a set of permutation matrices. An unsupervised training algorithm is proposed where the Sinkhorn neural network can be applied to network assignment problems. Numerical results demonstrate the effectiveness of the proposed method in various network scenarios., to appear in IEEE Communications Letters

Gas fluidized beds with bi-dispersed particles of different sizes, densities, and shapes are encountered in many industrial processes spanning the manufacture of energy, material and resources to greenhouse emissions mitigation and renewable resources utilization. The design and operation of such systems heavily rely on the prediction of minimum fluidization velocity of the mixture (Umf(mixture)) to avoid particle segregation and maximize mixing. Many correlations exist for predicting Umf(mixture); however, they exhibit limitations in accuracy and/or applicability. The limitations of these correlations are often expressed in terms of particles properties (e.g. effective size and density), flow conditions, and solids composition. This study has critically assessed highly cited correlations, delivered a comparative analysis of their applicability and predictability against a large pool of experimental data, and deepened the understanding of parameters that affect the prediction of Umf(mixture) using different correlations.

Discrete element method (DEM) simulations of size-driven segregation of binary particles in a rotary drum were conducted to investigate the influence of filling level, size ratio, and rotation speed on the segregation performance. Segregation experiments with different filling levels were used to verify the DEM model and analyze the influence of filling levels on segregation. The granular bed was divided into five layers to study the axial segregation in the rotary drum. The total velocity fluctuation was used to discuss the granular behavior from a mesoscopic perspective. A segregation index was adopted to quantify the segregation performance with different parameters. It was found that binary particles in the drum with different filling levels have different segregation patterns. A core of small particles was formed in the middle of granular bed for the case with a higher filling level, while there is no core formed for the case with a lower filling level. Results obtained indicate that the size-driven segregation in the drum with lower filling level increases with the increase of size ratio. Within the rolling regime, the rotation speed has little influence on the final segregation index of particles.

The one-versus-one (OVO) binarization decomposition scheme is considered as one of the most effective techniques to deal with multi-class classification problems. Its inherent mechanism is to use the “divide-and-conquer” strategy to decompose the multi-class classification problem into as many pairs of easier-to-solve binary sub-problems as possible. One common issue in the OVO scheme is that of non-competent classifiers. In this study, we proposed a novel OVO scheme strategy, named DRCW-FRkNN-OVO, to reduce the negative effect of non-competent classifiers. Specifically, we focused on the definition of region of competence, which plays a crucial role in managing the non-competent classifiers. To overcome the issue of skew and sparse distribution during the management of non-competent classifiers, we developed a relative competence weighting combination method via the fixed radius nearest neighbour search to find the local region within each class for the query sample. Our proposed DRCW-FRkNN-OVO is tested on 30 real-world multi-class datasets compared with several well-known related works. Experimental results supported by thorough statistical analysis confirmed the effectiveness and robustness of our proposed method.

In this paper, error performance of a communication link subjected to a non-Gaussian noise model known as symmetric alpha-stable (S α S) noise and α − μ fading environment is analyzed with selection combining at the receiver. To this end, using binary phase-shift keying (BPSK), closed-form expressions of exact and asymptotic bit-error-rate (BER) are obtained. Furthermore, closed-form expressions of outage probability and n -th moment are obtained. From the n -th moment, the average signal-to-noise ratio (SNR) and channel quality estimation index (CQEI) are calculated. The effect of the number of antennas (L) and the fading parameters on the BER is studied for different impulsive settings. Monte-Carlo simulations are performed to validate the derived results.

Binary sensing matrices can offer rapid multiplier-less data acquisition, owing to their binarization structure and competitive sampling efficiency, which promise to promote compressive sensing from theory to application. However, the size of existing binary constructions is often limited, and the generating strategies require extensive computational complexity. In this work, we propose a trivial strategy to deterministically construct non-Cartesian spiral binary sensing matrices (SbMs) with arbitrary size for compressive sensing. The mutual coherence of SbMs is proven to satisfy the Welch bound, i.e., optimal theoretical guarantee. Moreover, the simulation results show that the proposed SbMs can not only outperform their counterparts in sampling performance but also facilitate reconstruction speed.

In the field of fuzzy set theory, whether from the perspective of theory or practical application, as an important property, the migrativity has an extraordinary significance for the research of binary aggregation functions. There have been a lot of contributions on migrativity properties for some binary aggregation functions, for instance, t-norms, t-operators, uninorms, mayor's operators, copulas and so on, but few for 2-uninorms. Therefore, we make a deep study on the migrativity of uninorms over 2-uninorms in this article. According to whether the absorbing element of a 2-uninorm is consistent with the neutral element of a uninorm or not, we study the migrativity properties of uninorms in the usual classes over the three different subclasses of 2-uninorms. In addition, all solutions of the migrative equations are respectively analyzed and characterized for all possible combinations of uninorms in the usual classes and the three different subclasses of 2-uninorms. The usual classes of uninorms consist of U min e 1 , U max e 1 , U r e p e 1 , U i d e e 1 , U c o s , min e 1 and U c o s , max e 1 , while the three different subclasses of 2-uninorms are made up of C 0 , C 1 and C k .

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called “full binary pursuit” or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses. NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.

Stochastic Computing (SC) is a re-emerging computing paradigm with a great potential to surpass conventional binary implementations in term of hardware cost. The inaccuracy of computations is an important challenge with conventional SC designs. Recently, some deterministic approaches to SC were proposed. These methods are able to produce completely accurate results. However, they take a long processing time to produce exact results which directly translates to very high energy consumption. This work proposes a design methodology based on the Residue Number System (RNS) to mitigate the long processing time of the deterministic methods of SC. Leveraging RNS, the length of bit-streams decreases exponentially as high bit-width operands are replaced with low bit-width residues. Compared to the state-of-the-art deterministic methods, the proposed approach delivers more than 2400× and 3200× reduction in processing time and energy consumption, respectively, for 8-bit multiplication operation. Synthesis results further show that for a processing element (PE) in DNN accelerators, the proposed design occupies 84%, 52% and 30% less area compared to the conventional binary, RNS, and deterministic designs of SC, respectively.

Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. We develop PrISM (Precision for Integrative Structural Models), using autoencoders, a type of unsupervised deep neural network, to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with localization density maps, while providing more fine-grained information. Finally, the generated networks are also interpreted by gradient-based attention analysis. Significance Statement Validation of integrative models and data is an open research challenge. This is timely due to the new worldwide Protein Data Bank archive for integrative structures (http://pdb-dev.wwpdb.org). Currently, a single precision value is reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of information available for different regions. We develop a method using unsupervised deep learning to efficiently and accurately annotate precision for regions of an integrative model. This will ultimately improve the quality and utility of deposited structures.