Showing papers on "Binary number published in 2022"

Multiview Learning With Robust Double-Sided Twin SVM

Abstract: Multiview learning (MVL), which enhances the learners’ performance by coordinating complementarity and consistency among different views, has attracted much attention. The multiview generalized eigenvalue proximal support vector machine (MvGSVM) is a recently proposed effective binary classification method, which introduces the concept of MVL into the classical generalized eigenvalue proximal support vector machine (GEPSVM). However, this approach cannot guarantee good classification performance and robustness yet. In this article, we develop multiview robust double-sided twin SVM (MvRDTSVM) with SVM-type problems, which introduces a set of double-sided constraints into the proposed model to promote classification performance. To improve the robustness of MvRDTSVM against outliers, we take L1-norm as the distance metric. Also, a fast version of MvRDTSVM (called MvFRDTSVM) is further presented. The reformulated problems are complex, and solving them are very challenging. As one of the main contributions of this article, we design two effective iterative algorithms to optimize the proposed nonconvex problems and then conduct theoretical analysis on the algorithms. The experimental results verify the effectiveness of our proposed methods.

Non-fullerene acceptors with hetero-dihalogenated terminals induce significant difference in single crystallography and enable binary organic solar cells with 17.5% efficiency

Abstract: A synergistic hetero-dihalogenated terminals strategy was systematically employed for the first time to enhance single-crystal packing, boosting the device performance of a Y-BO-FCl:PM6 device with a remarkable PCE of 17.52%.

Compromising Charge Generation and Recombination with Asymmetric Molecule for High‐Performance Binary Organic Photovoltaics with Over 18% Certified Efficiency

Abstract: Balancing the charge generation and recombination constitutes a major challenge to break the current limit of organic photovoltaics (OPVs). To address this issue, an asymmetric non‐fullerene acceptor, namely the AC9, is developed and high‐performance OPV with a champion efficiency of 18.43% (18.1% certified) is demonstrated. This represents the record value among binary OPVs. Comprehensive analysis on exciton dissociation, charge collection, carrier transport, and recombination has been carried out, unveiling that the improved device performance of asymmetric AC9‐based OPVs is originated from a better compromise between charge generation and non‐radiative charge recombination, compared with the corresponding symmetric ones. This work provides a high‐performing molecule and paves the way for high‐performance OPVs through asymmetric molecular design.

TESS Eclipsing Binary Stars. I. Short-cadence Observations of 4584 Eclipsing Binaries in Sectors 1–26

Abstract: In this paper we present a catalog of 4584 eclipsing binaries observed during the first two years (26 sectors) of the TESS survey. We discuss selection criteria for eclipsing binary candidates, detection of hither-to unknown eclipsing systems, determination of the ephemerides, the validation and triage process, and the derivation of heuristic estimates for the ephemerides. Instead of keeping to the widely used discrete classes, we propose a binary star morphology classification based on a dimensionality reduction algorithm. Finally, we present statistical properties of the sample, we qualitatively estimate completeness, and discuss the results. The work presented here is organized and performed within the TESS Eclipsing Binary Working Group, an open group of professional and citizen scientists; we conclude by describing ongoing work and future goals for the group. The catalog is available from http://tessEBs.villanova.edu and from MAST.

Rapid Stellar and Binary Population Synthesis with COMPAS

Abstract: Compact Object Mergers: Population Astrophysics and Statistics (COMPAS; https://compas.science) is a public rapid binary population synthesis code. COMPAS generates populations of isolated stellar binaries under a set of parametrized assumptions in order to allow comparisons against observational data sets, such as those coming from gravitational-wave observations of merging compact remnants. It includes a number of tools for population processing in addition to the core binary evolution components. COMPAS is publicly available via the github repository https://github.com/TeamCOMPAS/COMPAS/, and is designed to allow for flexible modifications as evolutionary models improve. This paper describes the methodology and implementation of COMPAS. It is a living document which will be updated as new features are added to COMPAS; the current document describes COMPAS v02.21.00.

High-contrast, speckle-free, true 3D holography via binary CGH optimization

Abstract: Holography is a promising approach to implement the three-dimensional (3D) projection beyond the present two-dimensional technology. True 3D holography requires abilities of arbitrary 3D volume projection with high-axial resolution and independent control of all 3D voxels. However, it has been challenging to implement the true 3D holography with high-reconstruction quality due to the speckle. Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization. We adopt the random phase for the true 3D implementation to achieve the maximum axial resolution with fully independent control of the 3D voxels. We develop the high-performance binary hologram optimization framework to minimize the binary quantization noise, which provides accurate and high-contrast reconstructions for 2D as well as 3D cases. Utilizing the fast operation of binary modulation, the full-color high-framerate holographic video projection is realized while the speckle noise of random phase is overcome by temporal multiplexing. Our high-quality true 3D holography is experimentally verified by projecting multiple arbitrary dense images simultaneously. The proposed method can be adopted in various applications of holography, where we show additional demonstration that realistic true 3D hologram in VR and AR near-eye displays. The realization will open a new path towards the next generation of holography.

New binary black hole mergers in the LIGO-Virgo O3a data

Abstract: The present paper applies a refined detection pipeline to publicly available LIGO-Virgo data from the first half of the third observing run (O3a) to identify new binary black hole (BBH) mergers. The study adds ten new BBH mergers to existing catalogs and provides further evidence for the significance level of previously identified events. The new events display interesting new features that include unexplored ranges of the effective spin and mass ratio, they challenge aspects of stellar collapse models, and have implications for BBH formation channels and black hole mass gaps.

Chaotic binary Group Search Optimizer for feature selection

Abstract: Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm’s main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods’ performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing.

Impact of massive binary star and cosmic evolution on gravitational wave observations – II. Double compact object rates and properties

Abstract: ABSTRACT Making the most of the rapidly increasing population of gravitational-wave detections of black hole (BH) and neutron star (NS) mergers requires comparing observations with population synthesis predictions. In this work, we investigate the combined impact from the key uncertainties in population synthesis modelling of the isolated binary evolution channel: the physical processes in massive binary-star evolution and the star formation history as a function of metallicity, Z, and redshift z, $\mathcal {S}(Z,z)$. Considering these uncertainties, we create 560 different publicly available model realizations and calculate the rate and distribution characteristics of detectable BHBH, BHNS, and NSNS mergers. We find that our stellar evolution and $\mathcal {S}(Z,z)$ variations can combined impact the predicted intrinsic and detectable merger rates by factors in the range 102–104. We find that BHBH rates are dominantly impacted by $\mathcal {S}(Z,z)$ variations, NSNS rates by stellar evolution variations and BHNS rates by both. We then consider the combined impact from all uncertainties considered in this work on the detectable mass distribution shapes (chirp mass, individual masses, and mass ratio). We find that the BHNS mass distributions are predominantly impacted by massive binary-star evolution changes. For BHBH and NSNS, we find that both uncertainties are important. We also find that the shape of the delay time and birth metallicity distributions are typically dominated by the choice of $\mathcal {S}(Z,z)$ for BHBH, BHNS, and NSNS. We identify several examples of robust features in the mass distributions predicted by all 560 models, such that we expect more than 95 per cent of BHBH detections to contain a BH $\gtrsim 8\, \rm {M}_{\odot }$ and have mass ratios ≲ 4. Our work demonstrates that it is essential to consider a wide range of allowed models to study double compact object merger rates and properties. Conversely, larger observed samples could allow us to decipher currently unconstrained stages of stellar and binary evolution.

The Graph Structure of the Generalized Discrete Arnold's Cat Map

Abstract: Chaotic dynamics is an important source for generating pseudorandom binary sequences (PRBS). Much efforts have been devoted to obtaining period distribution of the generalized discrete Arnold's Cat map in various domains using all kinds of theoretical methods, including Hensel's lifting approach. Diagonalizing the transform matrix of the map, this article gives the explicit formulation of any iteration of the generalized Cat map. Then, its real graph (cycle) structure in any binary arithmetic domain is disclosed. The subtle rules on how the cycles (itself and its distribution) change with the arithmetic precision $e$e are elaborately investigated and proved. The regular and beautiful patterns of Cat map demonstrated in a computer adopting fixed-point arithmetics are rigorously proved and experimentally verified. The results can serve as a benchmark for studying the dynamics of the variants of the Cat map in any domain. In addition, the used methodology can be used to evaluate randomness of PRBS generated by iterating any other maps.

jTrans: jump-aware transformer for binary code similarity detection

Abstract: Binary code similarity detection (BCSD) has important applications in various fields such as vulnerabilities detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.

Distributed recursive fault estimation with binary encoding schemes over sensor networks

Abstract: In this paper, we investigate the distributed recursive fault estimation problem for a class of discrete time-varying systems with binary encoding schemes over a sensor network. The fault signal with zero second-order difference is taken into account to reflect the sensor failures. Since the communication bandwidth in practice is constrained, the binary encoding schemes are exploited to regulate the signal transmission from the neighbouring sensors to the local fault estimator. In addition, due to the influence of channel noises, each bit might change with a small crossover probability. In the presence of sensor faults and bit errors, an upper bound for the estimation error covariance matrix is ensured and minimized at each time step via designing the gain matrices of the estimator. Finally, the effectiveness of the method is verified by a simulation.

Distributed Support Vector Machines Over Dynamic Balanced Directed Networks

Abstract: In this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database. Agents only share processed information regarding the classifier parameters and the gradient of the local loss functions instead of their raw data. In contrast to the existing work, we propose a continuous-time algorithm that incorporates network topology changes in discrete jumps. This hybrid nature allows us to remove chattering that arises because of the discretization of the underlying CT process. We show that the proposed algorithm converges to the SVM classifier over time-varying weight balanced directed graphs by using arguments from the matrix perturbation theory.

OUP accepted manuscript

Abstract: We examine the capacity to identify binary systems from astrometric errors and deviations alone. Until the release of the fourth Gaia data release we lack the full astrometric time series that the satellite records, but as we show can still infer the presence of binaries from the best fit models, and their error, already available. We generate a broad catalog of simulated binary systems within 100 pc, and examine synthetic observations matching the Gaia survey's scanning law and astrometric data processing routine. We show how the Unit Weight Error (UWE) and Proper Motion Anomaly (PMA) vary as a function of period, and the properties of the binary. Both UWE and PMA peak for systems with a binary period close to the time baseline of the survey. Thus UWE can be expected to increase or remain roughly constant as we observe the same system over a longer baseline, and we suggest $UWE_{eDR3}>1.25$ and $\Delta UWE/UWE_{eDR3}>-0.25$ as criteria to select astrometric binaries. For stellar binaries we find detectable significant astrometric deviations for 80-90\% of our simulated systems in a period range from months to decades. We confirm that for systems with periods less than the survey's baseline the observed $UWE$ scales $\propto \ \varpi$ (parallax), $a$ (semi-major axis) and $\Delta =\frac{|q-l|}{(1+q)(1+l)}$ where $q$ and $l$ are the mass and light ratio respectively, with a modest dependence on viewing angle. For longer periods the signal is suppressed by a factor of roughly $\propto P^{-2}$ (period). PMA is largest in orbits with slightly longer periods but obeys the same approximate scaling relationships.

Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Abstract: Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.

Measuring the propagation speed of gravitational waves with LISA

Abstract: The propagation speed of gravitational waves, cT , has been tightly constrained by the binary neutron star merger GW170817 and its electromagnetic counterpart, under the assumption of a frequency-independent cT . Drawing upon arguments from Effective Field Theory and quantum gravity, we discuss the possibility that modifications of General Relativity allow for transient deviations of cT from the speed of light at frequencies well below the band of current ground-based detectors. We motivate two representative Ansätze for cT (f), and study their impact upon the gravitational waveforms of massive black hole binary mergers detectable by the LISA mission. We forecast the constraints on cT (f) obtainable from individual systems and a population of sources, from both inspiral and a full inspiral-merger-ringdown waveform. We show that LISA will enable us to place stringent independent bounds on departures from General Relativity in unexplored low-frequency regimes, even in the absence of an electromagnetic counterpart.

Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST

Abstract: We introduce GWFAST, a novel Fisher-matrix code for gravitational-wave studies, tuned toward third-generation gravitational-wave detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE). We use it to perform a comprehensive study of the capabilities of ET alone, and of a network made by ET and two CE detectors, as well as to provide forecasts for the forthcoming O4 run of the LIGO-Virgo-KAGRA (LVK) collaboration. We consider binary neutron stars, binary black holes, and neutron star–black hole binaries, and compute basic metrics such as the distribution of signal-to-noise ratio (S/N), the accuracy in the reconstruction of various parameters (including distance, sky localization, masses, spins, and, for neutron stars, tidal deformabilities), and the redshift distribution of the detections for different thresholds in S/N and different levels of accuracy in localization and distance measurement. We examine the expected distribution and properties of golden events, with especially large values of the S/N. We also pay special attention to the dependence of the results on astrophysical uncertainties and on various technical details (such as choice of waveforms, or the threshold in S/N), and we compare with other Fisher codes in the literature. In the companion paper Iacovelli et al., we discuss the technical aspects of the code. Together with this paper, we publicly release the code GWFAST, (https://github.com/CosmoStatGW/gwfast) and the library WF4Py (https://github.com/CosmoStatGW/WF4Py) implementing state-of-the-art gravitational-wave waveforms in pure Python.

Stellar triples on the edge

Abstract: Hierarchical triple stars are ideal laboratories for studying the interplay between orbital dynamics and stellar evolution. Both stellar wind mass loss and three-body dynamics cooperate to destabilise triples, which can lead to a variety of astrophysical exotica. So far our understanding of their evolution was mainly built upon results from extensive binary-single scattering experiments. Starting from generic initial conditions, we evolve an extensive set of hierarchical triples using a combination of the triple evolution code TRES and an N-body code. We find that the majority of triples preserve their hierarchy throughout their evolution, which is in contradiction with the commonly adopted picture that unstable triples always experience a chaotic, democratic resonant interaction. The duration of the unstable phase is much longer than expected, so that stellar evolution cannot be neglected. Typically an unstable triple dissolve into a single star and a binary; sometimes democratically (the initial hierarchy is lost and the lightest body usually escapes), but also in a hierarchical way (the tertiary is ejected in a slingshot, independent of its mass). Collisions are common, and mostly involve the two original inner binary components still on the main-sequence. This contradicts the idea that collisions with a giant during democratic encounters dominate. Together with collisions in stable triples, we find that triple evolution is the dominant mechanism for stellar collisions in the Milky Way. Furthermore, our simulations produce runaway and walk-away stars with speeds up to several tens km/s, with a maximum of a few 100km/s. We suggest that destabilised triples can alleviate the tension behind the origin of the observed run-away stars. Lastly, we present a promising indicator to make general predictions for the fate of a specific triple, based on the initial inclination of the system.

Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems

Abstract: In order to efficiently solve the binary optimization problems by using differential evolution (DE), a class of new transfer functions, Taper-shaped transfer function, is firstly proposed by using power functions. Then, the novel binary differential evolution algorithm based on Taper-shaped transfer functions (T-NBDE) is proposed. T-NBDE transforms a real vector representing the individual encoding into a binary vector by using the Taper-shaped transfer function, which is suitable for solving binary optimization problems. For verifying the practicability of Taper-shaped transfer functions and the excellent performance of T-NBDE, T-NBDE is firstly compared with binary DE based on S-shaped, U-shaped and V-shaped transfer functions, respectively. Subsequently, it is compared with the state-of-the-art algorithms for solving the knapsack problem with a single continuous variable (KPC) and the uncapacitated facility location problem (UFLP). The comparison results show that Taper-shaped transfer functions are competitive than existing transfer functions, and T-NBDE is more effective than existing algorithms for solving KPC problem and UFLP problem.

Full reconstruction of simplicial complexes from binary contagion and Ising data

Abstract: Abstract Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework.