Binary Codes Research Articles

The Orbit and Companion of PSR J1622-0315: Variable Asymmetry and a Massive Neutron Star

The companion to PSR J1622-0315, one of the most compact known redback millisecond pulsars, shows extremely low irradiation despite its short orbital period. We model this system to determine the binary parameters, combining optical observations from the New Technology Telescope in 2017 and the Nordic Optical Telescope in 2022 with the binary modeling code ICARUS. We find a best-fit neutron star mass of 2.3 ± 0.4 M ⊙, and a companion mass of 0.15 ± 0.02 M ⊙. We detect for the first time low-level irradiation from asymmetry in the minima as well as a change in the asymmetry of the maxima of its light curves over five years. Using starspot models, we find better fits than those from symmetric direct heating models, with consistent orbital parameters. We discuss an alternative scenario where the changing asymmetry is produced by a variable intrabinary shock. In summary, we find that PSR J1622-0315 combines low irradiation with variable light-curve asymmetry and a relatively high neutron star mass.

Joint contrastive self-supervised learning and weak-orthogonal product quantization for fast image retrieval

Hashing and quantization methods based on deep learning are being widely used in large-scale image retrieval. Most methods use traditional supervised models to train models. Although these methods achieve excellent performance, they rely too much on expensive label information and do not fully utilize data resources. These methods overly encourage binary codes to retain original information, which may lead to models building too much useless information at the expense of discriminative information that is more important for retrieval. To address these issues, we propose a self-supervised method that does not require labeled information, called Contrastive Self-Supervised Weak-Orthogonal Product Quantization (CSWPQ). The method is trained in a label-free self-supervised manner to reduce the need for labels by maximizing the similarity of different views of the same image and minimizing the similarity of different image views, which not only improves the generalization ability of the model but also enhances the robustness. We introduce a quantified contrastive learning method that is different from traditional contrastive learning to learn more discriminative image representations by constructing quantified image features of different views of the image. In addition, in order to reduce the quantization error caused by product quantization, we impose a weak-orthogonal constraint to increase the diversity of representations, reduce the information loss and computational overhead in the quantization process, and improve the computational speed. Extensive experiments on CIFAR-10, NUS-WIDE and FLICKR25K datasets show that our proposed method achieves better performance.

On the largest minimum distances of [n,6] LCD codes

Linear complementary dual (LCD) codes can be used to resist side-channel attacks and fault noninvasive attacks. Let da(n,6) and dl(n,6) be the minimum weights of all binary optimal linear codes and LCD codes with length n and dimension 6, respectively. In this article, we aim to obtain the values of dl(n,6) for n≥51 by investigating the nonexistence and constructions of LCD codes with given parameters. Suppose that s≥0 and 0≤t≤62 are two integers and n=63s+t. Using the theories of defining vectors, generalized anti-codes, reduced codes and nested codes, we exactly determine dl(n,6) for t∉{21,22,25,26,33,34,37,38,45,46}, while we show that dl(n,6)∈{da(n,6)−1,da(n,6)} for t∈{21,22,26,34,37,38,46} and dl(n,6)∈{da(n,6)−2, da(n,6)−1} for t∈{25,33,45}.

Societalized Politics in the German Civil Sphere: The 2015 “Refugee Crisis” and Post-Potsdam Civil Backlash

AbstractThis article introduces a theory of societalized politics to investigate crisis events in the German civil sphere between 2015 and 2024, and to proffer an answer to the disputed question of the preconditions that facilitated the rapid rise of right-wing populism in the German context. Drawing on civil sphere and societalization theory, the article specifies the foundational cultural elements, or binary cultural codes (BCCs), upon which German political elites crafted meso-level narratives to contest and manage strains in the civil sphere. Through an analysis of communicative and regulative institutions’ responses to the arrival of refugees in 2015, and the publication of the Correctiv.org report and the backlash protests it inspired in 2024, the article charts the rise of the far-right party Alternative für Deutschland (AfD), and explains its success as due in part to its leaders’ capacities to represent themselves and their supporters as embodying the BCC’s civil democratic signifiers. Introducing a theory of societalized politics, the analysis demonstrates a processual approach to the construction and contesting of crisis events that emerge within the civil sphere itself. The article also introduces a civil sphere theory of right-wing populism, which frames the phenomenon as the elevation of nativist, primordial signifiers born foremost of the noncivil spheres of ethnicity and religion. It concludes by arguing that while the German civil sphere is in flux, the post-Potsdam civil protests indicate a significant portion of the nation’s publics remain committed to universalistic, civil democratic principles.

Mitigating Disparate Elevation Differences between Adjacent Topobathymetric Data Models Using Binary Code

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.

True unicorns and false positives: Simulated probabilities of dark massive companions to bright stars

Abstract Many compact objects (black holes and neutron stars) exist in binaries. These binaries are normally discovered through their interactions, either from accretion as an X-ray binary or collisions as a gravitational wave source. However, the majority of compact objects in binaries should be non-interacting. Recently proposed discoveries have used radial velocities of a bright star (main sequence or evolved) that are indicative of a massive but dark companion, which is inferred to be a compact object. Unfortunately, this burgeoning new field has been hindered by false positives, including the “Unicorn” (V723 Mon) which was initially believed to be a red giant/black hole binary before being refuted. In this work, we investigate the evolution of stellar binary populations over time, using the binary evolution code COSMIC to simulate binary populations and determine the probability of a candidate object being either a “true Unicorn” (actual compact objects in binaries) or a false positive. We find that main sequence stars have a higher true Unicorn probability than red giants or naked helium stars (an exposed core of an evolved star), particularly if the companion is more massive and is ≥3 times less luminous than the MS star. We also find that a top-heavy initial mass function raises the true Unicorn probability further, that super-solar metallicity reduces the probability, and that most true Unicorns are found at periods ≤100 days. Finally, we find that a significant fraction of true Unicorns do not evolve into x-ray binaries during the age of the universe.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

SIGACT News Complexity Theory Column

This month's guest author is Dean Doron, who provides a wonderful survey on construction, encoding and decoding of binary codes with distance close to half. Thank you Dean for your hard work! Coming up next, we'll have columns by Stacey Jeffrey (on Quantum Las Vegas Algorithms and Composition), Igor Carboni Oliveira (on the Meta-Mathematics of Computational Complexity Theory), Noam Mazor (topic TBA) and Elena Grigorescu (on Locally Decodable Codes for Insertions and Deletions). Stay tuned for more!

Biochemical approaches for decoding the information stored with metabolites

Molecular data storage is an emerging supplement to conventional information storage methods. In addition to the information storage with DNA or synthetic polymers, a recently proposed strategy that uses small molecule mixtures as information carriers has sparked interest since it is a synthesis-free method. Here, we demonstrate two methods for decoding digital information stored in two sets of small-molecule metabolites. The information is stored as binary code, 0 or 1, based on the absence or presence of each particular metabolite in the mixture. In the first approach, the information was encoded by five metabolites (sarcosine, cholesterol, xanthine, glucose, and galactose) and stored as mixture solutions in a 384-well microplate. The information was read out by cascade enzymatic reactions that can specifically convert the metabolites to a colorimetric product, which was then assayed by a microplate reader. We stored a 60×32-pixel picture (1920 bits) with 384 mixtures of small molecules with a reading accuracy of 96.68 %, and also demonstrated the encoding of a 220-bit text message. Alternatively, another set of six metabolites (bilirubin, uric acid, urea, creatinine, glucose, and triglyceride) was deployed to encode a 78×102-pixel picture (7956 bits) with 1326 mixtures, which was decoded automatically by a biochemistry analyzer with a 98.96 % accuracy. This work showcases that common metabolites can be used as digital molecules for information storage, and the information decoding can be done by accessible instruments in an ordinary biochemical laboratory.

On Recursive Tiling of some Mathematical Objects.

This essay, in a novel way, uses various tiles, such as dominoes, square-, interlocking-, and flow-tiles, to visualize approximations to recursively defined, potentially infinite mathematical objects, such as binary code tables, and variants of the space-filling Hilbert curve. The power of L-systems is shown by defining these tiled objects as L-systems.

Bit Reduction for Locality-Sensitive Hashing.

Locality-sensitive hashing (LSH) has gained ever-increasing popularity in similarity search for large-scale data. It has competitive search performance when the number of generated hash bits is large, reversely bringing adverse dilemmas for its wide applications. The first purpose of this work is to introduce a novel hash bit reduction schema for hashing techniques to derive shorter binary codes, which has not yet received sufficient concerns. To briefly show how the reduction schema works, the second purpose is to present an effective bit reduction method for LSH under the reduction schema. Specifically, after the hash bits are generated by LSH, they will be put into bit pool as candidates. Then mutual information and data labels are exploited to measure the correlation and structural properties between the hash bits, respectively. Eventually, highly correlated and redundant hash bits can be distinguished and then removed accordingly, without deteriorating the performance greatly. The advantages of our reduction method include that it can not only reduce the number of hash bits effectively but also boost retrieval performance of LSH, making it more appealing and practical in real-world applications. Comprehensive experiments were conducted on three public real-world datasets. The experimental results with representative bit selection methods and the state-of-the-art hashing algorithms demonstrate that the proposed method has encouraging and competitive performance.

Improved implementation of serial pseudorandom/natural code converters used in absolute position encoders

Abstract Pseudorandom encoders represent a significant type of absolute position encoders that can be implemented using only a single code track on a code disk and a single reading head, making them especially suitable for high-resolution position measurements. Since pseudorandom code words used for encoding positions are not compatible with other electronics, pseudorandom encoders include a converter that translates the pseudorandom code into the natural binary code. This paper proposes an improved implementation of the serial Fibonacci pseudorandom/natural code converter used in pseudorandom position encoders. The improvement is based on modifying the way bits are written into the converter’s shift register, which reduces the propagation delay inside the code converter, thus enabling an increase in the clock frequency. Simulations of the proposed solution, performed in Multisim software, provide a realistic picture of its behaviour and performance. Based on the simulation results, the proposed solution enables a significant increase in the clock frequency of the code converter by 180.54% or 57.56%, depending on the encoder resolution. This significantly boosts the speed of code conversion and enhances the overall performance of the position encoder. The proposed solution is highly valuable for numerous position measurement applications, especially in the increasingly relevant high-resolution applications.

Doctor simulator: Delta-Age-Sex-AdaIn enhancing bone age assessment through AdaIn style transfer.

Bone age assessment assists physicians in evaluating the growth and development of children. However, deep learning methods for bone age estimation do not currently incorporate differential features obtained through comparisons with other bone atlases. To propose a more accurate method, Delta-Age-Sex-AdaIn (DASA-net), for bone age assessment, this paper combines age and sex distribution through adaptive instance normalization (AdaIN) and style transfer, simulating the process of visually comparing hand images with a standard bone atlas to determine bone age. The proposed Delta-Age-Sex-AdaIn (DASA-net) consists of four modules: BoneEncoder, Binary code distribution, Delta-Age-Sex-AdaIn, and AgeDecoder. It is compared with state-of-the-art methods on both a public Radiological Society of North America (RSNA) pediatric bone age prediction dataset (14,236 hand radiographs, ranging from 1 to 228 months) and a private bone age prediction dataset from Zigong Fourth People's Hospital (474 hand radiographs, ranging from 12 to 218 months, 268 male). Ablation experiments were designed to demonstrate the necessity of incorporating age distribution and sex distribution. The DASA-net model achieved a lower mean absolute deviation (MAD) of 3.52 months on the RSNA dataset, outperforming other methods such as BoneXpert, Deeplasia, BoNet, and other deep learning based methods. On the private dataset, the DASA-net model obtained a MAD of 3.82 months, which is also superior to other methods. The proposed DASA-net model aided the model's learning of the distinctive characteristics of hand bones of various ages and bothsexes by integrating age and sex distribution into style transfer.

An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional Antennas.

Neighbor discovery and tracking with directional antennas in flying ad hoc networks (FANETs) is a challenging issue because of dispersed node distribution and irregular maneuvers in three-dimensional (3D) space. In this paper, we propose an adaptive 3D neighbor discovery and tracking algorithm in battlefield FANETs with directional antennas. With time synchronization, a flying node transmits/receives the neighbor discovery packets sequentially in each beam around it to execute a two-way handshake for neighbor discovery. The transmitting or receiving status of each discovery slot depends on the binary code corresponding to the identification of the node. Discovered neighbor nodes exchange their 3D positions in tracking slots periodically for node tracking, and the maximum tracking period is determined by node velocity, beamwidth, and the minimum distance between nodes. By configuring the relevant parameters, the proposed algorithm can also apply to two-dimensional planar ad hoc networks. The simulation results suggest that the proposed algorithm can achieve shorter neighbor discovery time and longer link survival time in comparison with the random scanning algorithm in scenarios with narrow beamwidth and wide moving area. When the frame length increases, the protocol overhead decreases but the average neighbor discovery time increases. The suitable frame length should be determined based on the network range, node count, beamwidth, and node mobility characteristics.

Towards better governance: creating a corporate governance index for South African firms

PurposeThe purpose of this study is to construct a robust index and subindices to measure the quality of corporate governance for 266 firms listed in South Africa from 2004 to 2021.Design/methodology/approachPublic information on the compliance of King Code of Good Corporate Governance is used to construct a main index predicated on provisions relating to board characteristics, accounting and auditing and risk management. These categories are transformed into three subindices. All constructs are scored with binary coding and equally weighted.FindingsCronbach’s alpha test reveals that the index and subindices are highly reliable measures of corporate governance. The principal component analysis supports the construct validity of all measures.Research limitations/implicationsThe index is limited to only three corporate governance subcategories and only focuses on South Africa.Practical implicationsThese corporate governance indices provide governing authorities, policymakers, investors and other market participants direct information on the quality of corporate governance in South African firms.Originality/valueAs South Africa lacks a formal corporate governance indicator, the development of an appropriate corporate governance index and subindices contributes towards understanding the quality of corporate governance in South African firms. To the best of the authors’ knowledge, this is the first paper to conduct robustness tests on corporate governance indices designed for South African companies.

SPGMVC: Multiview Clustering via Partitioning the Signed Prototype Graph.

Multiview clustering (MVC) has been widely studied in machine learning and data mining for its capability of improving clustering performance by fusing the information from multiview data. In the past decade, a large number of MVC methods have made impressive progress, but most of them suffer from computational burdens, especially in large-scale tasks. Binary MVC (BMVC) is proposed to address this issue by representing the large-scale high-dimensional dataset as a group of consensus and low-dimensional binary codes. However, current BMVC-based approaches generate the clustering by executing binary k -means on the obtained binary codes, which fail to capture the embedded geometric information, leading to poor clustering performance. In addition, parameter selection is another "mission impossible" in unsupervised learning tasks including MVC. To tackle these challenges, a framework of multiview clustering via partitioning the signed prototype graph (SPGMVC) is proposed in this work. The SPGMVC framework offers several contributions. First, SPGMVC is designed as a unified framework for MVC. It combines effective technologies, such as consensus binary coding, code compression (CC), signed prototype graph (SPG) partitioning, and prototype-based cluster assignment. Second, SPGMVC partitions the signed graph (SG) based on the relationships between positive and negative edges. By capturing the underlying structure of the data, this partitioning strategy improves clustering accuracy (ACC). CC techniques are applied to reduce the graph's scale, enabling further partitioning and enhancing computational efficiency. Third, SPGMVC employs an alternate minimizing strategy to efficiently handle the optimization problem. This strategy has nearly linear time and space complexity with respect to the data volume, making it suitable for large-scale tasks. Fourth, SPGMVC proposes an automatic parameter selection strategy, eliminating the need for extensive parameter exploration. Comprehensive experiments illustrate the superiority of our model. The implementation of SPGMVC is available at: https://github.com/gepingyang/PSGMVC.

Multi-color Na3GaF6:Tm3+,Yb3+@SiO2 for dual-mode security and information encryption

The development of advanced luminescent materials is important for anti-counterfeiting applications, but still faces many challenges. In this study, Na3GaF6:Tm3+,Yb3+@SiO2 core@shell type nanocrystals with cryolite structure were synthesized for the first time by co-precipitation method. By modulating the excitation mode and Tm3+/Yb3+ doping ratio, the Na3GaF6:Tm3+,Yb3+@SiO2 nanocrystals exhibits multicolor up-conversion emission. Multicolor anti-counterfeiting ink was configured with up-conversion nanocrystals, and a variety of multi-colour anti-counterfeiting patterns were printed using screen printing technology. Additionally, multi-colour fluorescent barcodes are combined with binary coding technology to achieve encryption of anti-counterfeiting information. This work not only discovered an excellent up-conversion nanocrystals (Na3GaF6:Tm3+,Yb3+@SiO2) suitable for anti-counterfeiting applications, but also provided an effective encryption strategy for anti-counterfeiting information.

An empirical study on divergence of differently-sourced LLVM IRs

In solving binary code similarity detection, many approaches choose to operate on certain unified intermediate representations (IRs), such as Low Level Virtual Machine (LLVM) IR, to overcome the cross-architecture analysis challenge induced by the significant morphological and syntactic gaps across the diverse instruction set architectures (ISAs). However, the LLVM IRs of the same program can be affected by diverse factors, such as the acquisition source, i.e., compiled from source code or disassembled and lifted from binary code. While the impact of compilation settings on binary code has been explored, the specific differences between LLVM IRs from varied sources remain underexamined. To this end, we pioneer an in-depth empirical study to assess the discrepancies in LLVM IRs derived from different sources. Correspondingly, an extensive dataset containing nearly 98 million LLVM IR instructions distributed in 808,431 functions is curated with respect to these potential IR-influential factors. On this basis, three types of code metrics detailing the syntactic, structural, and semantic aspects of the IR samples are devised and leveraged to assess the divergence of the IRs across different origins. The findings offer insights into how and to what extent the various factors affect the IRs, providing valuable guidance for assembling a training corpus aimed at developing robust LLVM IR-oriented pre-training models, as well as facilitating relevant program analysis studies that operate on the LLVM IRs.

Do Not Let the Beginning Trap you! On Inhibition, Associative Creative Chains, and Hopfield Neural Networks

ABSTRACTCreative thinking stems from the cognitive process that fosters the creation of new ideas and problem‐solving solutions. Artificial intelligence systems and neural network models can reduce the intricacy of understanding creative cognition. For instance, the generation of ideas could be symbolized as patterns of binary code in which clusters of neurons synchronize their firing and store information inside a neural network, forming connections based on correlation. The Hopfield neural network (HNN) is a simple model known for its biological plausibility in storing and retrieving neuron patterns. We implemented certain modifications to HNN as a step toward the larger framework of creative thinking‐based association. These modifications included introducing pattern weights control, which provides a robust representation for content addressable memory and conceptual links in stored data. We identified two mechanisms controlling the transition from analytical to associative‐based thinking. The first mechanism refers to the activation threshold of neurons, which acts as an on/off switch for the network. The second was the inhibition of stored concepts, similar to an on/off switch that guides the network to search for associative links and when to stop. Our findings suggest that neurons step back from the contextual focus and find alternatives when analytical thinking is insufficient. These alternatives are linked to seemingly unrelated ideas, using inhibition as an analogy to the hyperparameters. Using hyperparameters to inhibit the stored patterns, we could control the creation of associative links.

An Investigation on LP Decoding of Short Binary Linear Codes With the Subgradient Method

An Investigation on LP Decoding of Short Binary Linear Codes With the Subgradient Method