Binary Sets Research Articles

Binary Level Set Method for Variational Implicit Solvation Model

Binary Level Set Method for Variational Implicit Solvation Model

String Matching Algorithm Using Multi-Characters Inverted Lists

This research article introduces a new string-matching algorithm that utilizes the multi-character inverted lists structure, implemented using a perfect hashing method. The new solution processes the pattern input string in a single pass. In the searching phase, the algorithm takes a linear time complexity. This approach demonstrates efficient and rapid performance in practical experiments when a binary character set is used. It also handles fewer characters and shorter lengths, even when the pattern length is increased. In some cases, this algorithm exhibits superior text search performance compared to other algorithms. It has been observed that this superiority is particularly evident when searching for smaller amounts of text. This algorithm can be explored more quickly, especially when the search factor is between 0.25 to 0.75 times the length of the pattern, approaching the performance of the traditional algorithm.

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.

A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and Relevance

We present a simple and versatile framework for evaluating ranked lists in terms of Group Fairness and Relevance, in which the groups (i.e., possible attribute values) can be either nominal or ordinal in nature. First, we demonstrate that when our framework is applied to a binary hard group membership setting, our Group Fairness and Relevance (GFR) measures can easily quantify the overall polarity of each ranked list. Second, by utilising an existing diversified search test collection and treating each intent as an attribute value, we demonstrate that our framework can also handle soft group membership and that the GFR measures are highly correlated with a diversified information retrieval (IR) measure in this context as well. Third, using real data from a Japanese local search service, we demonstrate how our framework enables researchers to study intersectional group fairness based on multiple attribute sets. We also show that the similarity function for comparing the achieved and target distributions over the attribute values should be chosen carefully when the attribute values are ordinal. For such situations, our recommendation is to use multiple similarity functions with our framework: for example, one based on Jensen-Shannon Divergence (which disregards the ordinal nature of the groups) and another based on Root Normalised Order-aware Divergence (which has been designed specifically for handling ordinal groups). In addition, we highlight the fundamental differences between our framework and Attention-Weighted Rank Fairness (AWRF), a group fairness measure used at the TREC Fair Ranking Track.

Efficient binary sequence set designs for MIMO PMCW radar

Multiple-input multiple-output (MIMO) phase-modulated continuous wave (PMCW) radar systems have major advantages for mutual-interference mitigation over their linear frequency-modulated continuous wave (LFMCW) counterparts. To attain a virtual array length of M×M̃ with M transmit and M̃ receive antennas, a probing sequence set with both low auto-correlation sidelobes and low cross-correlations is needed for MIMO PMCW radar. To reduce the hardware cost of sequence generation, binary probing sequence (BPS) sets are desirable. We present herein four efficient computational algorithms to generate BPS sets with desirable properties, and arbitrary sequence lengths and arbitrary number of sequences in the set are allowed. When initialized by random BPS sets, they can be used to obtain many different BPS sets with similar properties, i.e., with large diversity. Moreover, fast Fourier transform (FFT) are exploited, making the algorithms computationally efficient. They can be used to design long BPS sets that are hardly possible using existing methods in the literature. The convergence properties and the relationships among the proposed algorithms are also analyzed under a unified framework. Numerical examples are included to illustrate the effectiveness of our designs.

VitaDNet: A Deep Learning-Based Approach for Vitamin-D Deficiency Prediction

Vitamin D (VD) deficiency is a very common disease among elderly people. The lack of VD causes various diseases related to skin, eyes and throat. The previous epidemiological studies tried to predict the vitamin B6 and VD levels from the blood samples. Since this is laborious and time-taking, it is very difficult for the homely people to work on it. There is a strong requirement for the noninvasive method as there is a necessity to detect the deficiency at the early stage. Certain crucial parameters that could be used for analysis are based on the intake of anthropogenic parameters along with the commonly known body vitals. These parameters include the body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR) and body roundness index (BRI). The dataset used for the prediction of VD has been collected from 501 patients in the age of 40–75 years old. The prediction of VD levels in the body has various complications, like the sex, previous health records, inherent health conditions and body pathology. To consolidate all those parameters and to analyse, a robust model is required to associate the parameters which are used to predict the deficit of VD. A binary set of gated recurrent units (GRUs) are used along with the auto-encoders. The feature extraction and selection module in the network are composed of two different patch-based networks which makes the three-stage network robust. Despite these difficulties, the model is robust enough to predict the levels of VD in the body based on the anthropogenic parameters. To support this network, a sub-VitaDNet module is proposed based on the food taken. Through this network, the food taken is continuously observed and the levels of VD are predicted. Hence, the authors believe that the model is robust enough to predict the VD levels in the body.

Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications

A Unifying Framework for the Convexification of Mixed-Integer Conic Binary Sets The paper “Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications,” by Fatma Kilinc-Karzan, Simge Kucukyavuz, Dabeen Lee, and Soroosh Shafieezadeh-Abadeh, develops a unifying framework for convexifying mixed-integer conic binary sets. Many applications in machine-learning and operations research give rise to integer programming models with nonlinear structures and binary variables. The paper develops general methods for generating strong valid inequalities that take into account multiple conic constraints at the same time. The authors demonstrate that their framework applies to conic quadratic programming with binary variables, fractional programming, best subset selection, distributionally robust optimization, and sparse approximation of positive semidefinite matrices.

Classifying Suicide-Related Content and Emotions on Twitter Using Graph Convolutional Neural Networks

Recent work in Natural Language Processing has increasingly focused on detecting suicidal intent in textual data, where the main aim is to detect expressions in a binary setting. However, previous research has shown that search results and other mentions of suicide online are not only limited to expressions of suicidal intent. Therefore, previously proposed algorithms and datasets might for example struggle to distinguish between suicidal intent of a user and suicide mentioned in a humorous context. In this paper, we introduce a new dataset called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TWISCO</i> , which proposes an alternative approach to classifying expressions of suicidality online. For this, we use a coding framework developed in Psychology to distinguish between different mentions of suicide. Next, we present a variety of machine and deep learning baselines in three different classification settings ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">text only</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">features only</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">text and features</i> ). Furthermore, we introduce a Feature GCN that improves performance over the GCN baseline. Finally, we investigate the hypothesis that feelings of dominance are correlated with users expressing their own suicidality. We provide an in-depth discussion of the trade-offs in classifying suicidal intent online.

Toward Deeper Understanding of Camouflaged Object Detection

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localize, segment, and rank</i> camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: https://github.com/JingZhang617/ COD-Rank-Localize-and-Segment.

Theory of operators and Unitary operators on the power set and the way below relation on transitive binary relational sets

Theory of operators and Unitary operators on the power set and the way below relation on transitive binary relational sets

Theory of operators and Unitary operators on the power set and the way below relation on transitive binary relational sets

Theory of operators and Unitary operators on the power set and the way below relation on transitive binary relational sets

TG‐compound optimal design for bivariate binary responses modelled via Copulas

AbstractUnivariate data has been the focus of optimal design for generalized linear models. It is common practice to conduct experiments with multiple dependent responses that are modelled using regression techniques. To design the experiment for all these responses, one needs a multivariate distribution supporting a previously chosen data model. The T‐optimal criterion is used to discriminate between two competing models. The designs that are optimal for discrimination between two models may be unsuitable for describe the quality of predictions for test data. G‐optimal criterion overcomes this restriction by minimizing the maximum variance of the predicted values. The design of experiments with dependent bivariate binary data is a topic we discuss in this paper. For dependent bivariate binary data, a technique for deriving TG‐optimal experimental designs using Copula is shown. The new compound TG‐criterion satisfy the dual aim to selecting the true model and minimizing the maximum prediction variance. The equivalence theorem is demonstrated and proved for the new compound criterion. A numerical example is presented to show the properties of the new compound criterion for bivariate binary setting and its value in meeting the dual aims.

Dimension‐independent Markov chain Monte Carlo on the sphere

AbstractWe consider Bayesian analysis on high‐dimensional spheres with angular central Gaussian priors. These priors model antipodally symmetric directional data, are easily defined in Hilbert spaces and occur, for instance, in Bayesian density estimation and binary level set inversion. In this paper we derive efficient Markov chain Monte Carlo methods for approximate sampling of posteriors with respect to these priors. Our approaches rely on lifting the sampling problem to the ambient Hilbert space and exploit existing dimension‐independent samplers in linear spaces. By a push‐forward Markov kernel construction we then obtain Markov chains on the sphere which inherit reversibility and spectral gap properties from samplers in linear spaces. Moreover, our proposed algorithms show dimension‐independent efficiency in numerical experiments.

Gender rules in the community corrections context: Examining how case managers navigate trans client supervision in a binary setting

This article explores one US residential community corrections facility and the ways that case managers navigate working with transgender women under supervision in a facility that relies on the sex binary for housing placement. Similar to other research on this population in prisons that has been conducted both within and outside of the US context, we find that case managers contend with significant uncertainty in their work with these clients because of a lack of specific and formal training. To contend with that uncertainty, case managers reported relying on Gender Responsive Training and the Prison Rape Elimination Act to guide their understanding of and work with trans clients. However, we also found a great deal of discretion in the ways case managers managed programming for their trans clients that disrupted the gendered organization of the facility. We conclude with specific programmatic recommendations based on these findings.

Demand and Welfare Analysis in Discrete Choice Models with Social Interactions

Abstract Many real-life settings of individual choice involve social interactions, causing targeted policies to have spillover effects. This article develops novel empirical tools for analysing demand and welfare effects of policy interventions in binary choice settings with social interactions. Examples include subsidies for health-product adoption and vouchers for attending a high-achieving school. We show that even with fully parametric specifications and unique equilibrium, choice data, that are sufficient for counterfactual demand prediction under interactions, are insufficient for welfare calculations. This is because distinct underlying mechanisms producing the same interaction coefficient can imply different welfare effects and deadweight-loss from a policy intervention. Standard index restrictions imply distribution-free bounds on welfare. We propose ways to identify and consistently estimate the structural parameters and welfare bounds allowing for unobserved group effects that are potentially correlated with observables and are possibly unbounded. We illustrate our results using experimental data on mosquito-net adoption in rural Kenya.

Nature as a symbiotic edge of humanity: eco-critical reading of Armah’s The Healers

In his historical novel, The Healers, Ayi Kwei Armah correlates elements of the fictional settings of Esuano and the Eastern Forest, with different phases of social communication. The binary settings and their elements mirror the symbiotic character of the relationship that ought to exist between humans and nature. Armah employs symbolic, temporal and cultural codes reflective of African tradition and indigenous values through natural representation in the text as communicative properties. Eco-critical theory guides this study to explore the relationship that exists between humans and the natural world. Principles from eco-criticism are explored with a focus on Armah’s creation of groups in the Eastern Forest and rivers as emblems of the divine with particular powers, in contrast to what Esuano represents to these groups. In this novel, Esuano is associated with manipulation, while the Eastern Forest is the site for inspiration and healing.

Bayesian Analysis of Neutron-star Properties with Parameterized Equations of State: The Role of the Likelihood Functions

We have investigated the systematic differences introduced when performing a Bayesian-inference analysis of the equation of state (EOS) of neutron stars employing either variable- or constant-likelihood functions. The former has the advantage of retaining the full information on the distributions of the measurements, making exhaustive usage of the data. The latter, on the other hand, has the advantage of a much simpler implementation and reduced computational costs. In both approaches, the EOSs have identical priors and have been built using the sound speed parameterization method so as to satisfy the constraints from X-ray and gravitational waves observations, as well as those from chiral effective theory and perturbative quantum chromodynamics. In all cases, the two approaches lead to very similar results and the 90% confidence levels essentially overlap. Some differences do appear, but in regions where the probability density is extremely small and are mostly due to the sharp cutoff on the binary tidal deformability set in the constant-likelihood approach. Our analysis has also produced two additional results. First, an inverse correlation between the normalized central number density, n c,TOV/n s , and the radius of a maximally massive star, R TOV. Second, and most importantly, it has confirmed the relation between the chirp mass and the binary tidal deformability. The importance of this result is that it relates , which is measured very accurately, and , which contains important information on the EOS. Hence, when is measured in future detections, our relation can be used to set tight constraints on .

Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample

Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.

Decreasing Proof Size of BLS Scheme

Abstract Bootle et al. in CRYPTO 2019 proposed a zero knowledge proof for an $\mathrm{ISIS}_{m,n,q,\beta }$ instance $A\vec{s} = \vec{u} \bmod q$ with $\|\vec{s}\|_{\infty }\leq \beta $ (BLS scheme). It was implemented by transforming the instance into the form $A^{\prime }\vec{s}^{\prime } =\vec{u}\bmod q$, where the coefficients of $\vec{s}^{\prime}$ are in $\{0,1,2\}$, and proved the latter in an exact way. With the concrete parameters $m=1024,n=2048,\beta =1,q\approx 2^{32}$, their proof is of length 384.03KB. In this paper, we decrease the proof size of BLS scheme by two techniques. The first one takes effect on some special parameters. For these parameters, using the binary basic set instead of the ternary one results in a shorter proof. The second one deals with the repetition of the lower half in BLS scheme. Observing that what the lower half proves is of form $\mathbf{B}\vec{\mathbf{r}}=\vec{\mathbf{t}}$ with a short vector $\vec{\mathbf{r}}$ of polynomials, a variant of parallel repetition can be used to shorten the proof size. Combining these two techniques together, the proof size of the above-mentioned instance can be reduced to 220.01KB, only 57.3$\%$ of BLS scheme.

Sensory Adaptation in Biomolecular Memristors Improves Reservoir Computing Performance

Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks.