Binary Approach Research Articles

The Effects of Intent, Outcome, and Causality on Moral Judgments and Decision Processes.

Over the past decade, moral judgments and their underlying decision processes have more frequently been considered from a dynamic and multi-factorial perspective rather than a binary approach (e.g., dual-system processes). The agent’s intent and his or her causal role in the outcome–as well as the outcome importance–are key psychological factors that influence moral decisions, especially judgments of punishment. The current research aimed to study the influence of intent, outcome, and causality variations on moral decisions, and to identify their interaction during the decision process by embedding the moral scenarios within an adapted mouse-tracking paradigm. Findings of the preregistered study (final n = 80) revealed main effects for intent, outcome, and causality on judgments of punishment, and an interaction between the effects of intent and causality. We furthermore explored the dynamics of these effects during the decision process via the analysis of mouse trajectories in the course of time. It allowed detecting when these factors intervened during the trial time course. The present findings thus both replicate and extend previous research on moral judgment, and evidence that, despite some ongoing challenges, mouse-tracking represents a promising tool to investigate moral decision-making.

How Gender Dynamics Shape Off‐farm Work in Upland Southwest China☆

AbstractGendered demands for productive and reproductive labor differently affect men's and women's decisions about not only whether to undertake off‐farm work, but where. With data from a survey of households in southwestern China, we examine the effects of individual, household, and community attributes on decisions to take local or distant off‐farm work. Men's and women's off‐farm work decisions respond differently to care and farmwork needs. For women, the primary trade‐off is between distant off‐farm work and none. For men, the primary trade‐off is between distant and local off‐farm work. Women in households with more working‐age members are more likely to take distant off‐farm work rather than stay on the farm. Increased education and household labor make men more likely to take distant relative to local off‐farm work. Household land holdings also have differing effects: for women, increased cropland area reduces odds of migration, while for men it increases odds of local relative to distant off‐farm work. By disaggregating off‐farm work location, we uncover gendered patterns that binary approaches to off‐farm work or migration obscure.

Learning to increase matching efficiency in identifying additional b-jets in the text {t}bar{text {t}}text {b}bar{text {b}} process

The text {t}bar{text {t}}text {H}(text {b}bar{text {b}}) process is an essential channel in revealing the Higgs boson properties; however, its final state has an irreducible background from the text {t}bar{text {t}}text {b}bar{text {b}} process, which produces a top quark pair in association with a b quark pair. Therefore, understanding the text {t}bar{text {t}}text {b}bar{text {b}} process is crucial for improving the sensitivity of a search for the text {t}bar{text {t}}text {H}(text {b}bar{text {b}}) process. To this end, when measuring the differential cross section of the text {t}bar{text {t}}text {b}bar{text {b}} process, we need to distinguish the b-jets originating from top quark decays and additional b-jets originating from gluon splitting. In this paper, we train deep neural networks that identify the additional b-jets in the {text {t}}{bar{text {t}}}{text {b}}{bar{text {b}}} events under the supervision of a simulated text{t}bar{text{t}}text{b}bar{text{b}} event data set in which true additional b-jets are indicated. By exploiting the special structure of the text {t}bar{text {t}}text {b}bar{text {b}} event data, several loss functions are proposed and minimized to directly increase matching efficiency, i.e., the accuracy of identifying additional b-jets. We show that, via a proof-of-concept experiment using synthetic data, our method can be more advantageous for improving matching efficiency than the deep learning-based binary classification approach presented in [1]. Based on simulated text {t}bar{text {t}}text {b}bar{text {b}} event data in the lepton+jets channel from pp collision at sqrt{s} = 13 TeV, we then verify that our method can identify additional b-jets more accurately: compared with the approach in [1], the matching efficiency improves from 62.1% to 64.5% and from 59.9% to 61.7% for the leading order and the next-to-leading order simulations, respectively.

"Comparison of Color Classification Using Computer Vision and Deep Neural Network"

Research on artificial intelligence and machine learning is currently ongoing and is focused on real-world problems. Machine learning is used by computers to make predictions based on the provided data set or existing knowledge. The main goal of our project is to use machine learning to categorize different colors while separating CNN from computer vision. In this work, we used supervised learning to categorize different hues using a binary classification approach. Color detection is the technique of identifying a color. In this scenario, humans can recognize the hue and choose with ease. A computer, however, cannot quickly recognize color. It is challenging to get a computer to quickly detect the color. Given that, we decide to pursue this initiative. Pandas, OpenCV, and the Naive Bayes algorithm are all used in Python. Naive Bayes classifiers are models that assign category labels to issue occurrences that are represented as vectors of feature values, where the category labels are selected from a finite set. There isn't a single method for training these classifiers; rather, there is a family of algorithms built on the premise that, given a category variable, the value of one feature is independent of the value of the other feature. Open-Source Computer Vision Library OpenCV was designed to be computationally effective and with a major emphasis on real-time applications. specialized video encoding for the cloud. Panda may be a platform that runs in the cloud and provides infrastructure for encoding audio and video.

Repurposing open‐source data from weather radars to reduce the costs of aerial waterbird surveys

Abstract Aerial counts are the primary means of monitoring waterbird populations. A valid population assessment requires a significant proportion of the population to be surveyed. For broad‐ranging species, this requires costly reconnaissance flights and surveys over large areas of potential habitat. Here, we assess whether free, autonomously collected weather radar reflectivity data can identify waterbird aggregations over broad areas of potential habitat, thereby providing a low‐cost means of directing the timing and location of aerial survey counts. Our study was undertaken over a large flood plain known to be a key seasonal nesting area of the Magpie Goose (Anseranas semipalmata). A standardized aerial survey technique was employed, firstly using reconnaissance flights to determine when the birds were nesting and then 400‐m wide transects 3 km apart over approximately 1100 km, visually counting the geese. Radar reflectivity data from an operational weather radar in Darwin, Australia, 2 weeks prior to and during the aerial survey, were processed to link averaged radar reflectivity to terrestrial habitat at a spatial polar resolution of 250 m × 0.5°. A binary classification approach evaluated the accuracy, precision, sensitivity and specificity of reflectivity data in identifying areas where the aerial surveys recorded geese. Aerial surveys of Magpie Geese are most effective when the geese are nesting, which is usually identified by reconnaissance flights. Here, we show nesting correlated with a decline in radar reflectivity and a concentration of hotspots. By achieving reasonable accuracy and precision (77% and 67%, respectively), the weather radar reflectivity identified high aggregations of Magpie Geese and demonstrated high efficacy when identifying areas where Magpie Geese did not occur (specificity, 94%). We conclude that using weather radar data to guide aerial survey timing would reduce reconnaissance flight needs, and enable aerial surveys to focus on areas with a large proportion of the target waterbird population.

Discrete Dynamical System Approaches for Boolean Polynomial Optimization

In this article, we discuss the numerical solution of Boolean polynomial programs by algorithms borrowing from numerical methods for differential equations, namely the Houbolt scheme, the Lie scheme, and a Runge-Kutta scheme. We first introduce a quartic penalty functional (of Ginzburg-Landau type) to approximate the Boolean program by a continuous one and prove some convergence results as the penalty parameter $$\varepsilon $$ converges to 0. We prove also that, under reasonable assumptions, the distance between local minimizers of the penalized problem and the set $$\{\pm 1\}^n$$ is of order $$O(\sqrt{n}\varepsilon )$$ . Next, we introduce algorithms for the numerical solution of the penalized problem, these algorithms relying on the Houbolt, Lie and Runge-Kutta schemes, classical methods for the numerical solution of ordinary or partial differential equations. We performed numerical experiments to investigate the impact of various parameters on the convergence of the algorithms. We have tested our ODE approaches and compared with the classical nonlinear optimization solver IPOPT and a quadratic binary formulation approach (QB-G) as well as an exhaustive method using parallel computing techniques. The numerical results on various datasets (including small and large-scale randomly generated synthetic datasets of general Boolean polynomial optimization problems, and a large-scale heterogeneous MQLib benchmark dataset of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) problems) show good performances for our ODE approaches. As a result, our ODE algorithms often converge faster than the other compared methods to better integer solutions of the Boolean program.

Automatic Hate Speech Detection Using CNN Model and Word Embedding

Hatred spreading through the use of language on social media platforms and in online groups is becoming a well-known phenomenon. By comparing two text representations: bag of words (BoW) and pre-trained word embedding using GloVe, we used a binary classification approach to automatically process user contents to detect hate speech. The Naive Bayes Algorithm (NBA), Logistic Regression Model (LRM), Support Vector Machines (SVM), Random Forest Classifier (RFC) and the one-dimensional Convolutional Neural Networks (1D-CNN) are the models proposed. With a weighted macro-F1 score of 0.66 and a 0.90 accuracy, the performance of the 1D-CNN and GloVe embeddings was best among all the models.

Has the Adaptation‐Mitigation Binary Outlived Its Value? Indigenous Ways of Knowing Present an Alternative

AbstractCommunity‐level actions addressing anthropogenic climate change are paramount to survival. However, there are limitations to the current binary approach which considers adaptation and mitigation as mutually exclusive actions. Drawing from research in the Pamir Mountains of Tajikistan, this commentary demonstrates: (a) Indigenous knowledge, emerging from a deep connectivity to habitat, cumulative over generations, and empirically‐based, is the foundation of ecological calendars; (b) ecological calendars build anticipatory capacity, the ability to envision possible and sustainable futures, for anthropogenic climate change; and (c) this anticipatory approach engages adaptive and mitigative actions to climate change working in tandem to ensure wellbeing and food security. This paper maintains that the adaptation‐mitigation continuum involves foresight and action today in preparation for future change. Furthermore, context‐specific ecological calendars represent an effective mechanism for communities to build and retain knowledge across generations and deep connections to their habitat. Finally, further modeling needs to be undertaken with participation and leadership from Indigenous and rural communities to understand how they use the adaptation‐mitigation continuum for anticipatory action to develop multiple optimal solutions to address environmental change.

Hybrid Breast Reconstruction Following Mastectomy

The challenge following mastectomy is to safely reconstruct breasts of adequate size, shape, symmetry, softness, and sensation ("6 S"). Historically, patients have been offered two reconstructive modalities, namely either implant-based or autologous reconstruction. While this binary approach is appropriate for most patients, there remain a subset of patients for whom this simplistic approach is not suitable. For these, hybrid reconstruction, i. e., the combination of an implant with microsurgical tissue transfer, offers the possibility of individualised reconstruction, which avoids some of the limitations of traditional approaches. Hybrid breast reconstruction, thus expands the indications for microsurgical reconstruction and offers the advantages of this reconstructive modality to a larger patient population. In this article, the surgical technique of hybrid breast reconstruction is described, along with a discussion of important parameters related to this reconstructive modality, including plane and timing of implant placement, and the issue of radiotherapy.

Abstract 472: PACpAInt: A deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Abstract Introduction: Pancreatic adenocarcinoma (PAC) is highly heterogeneous, resulting in overall ineffectiveness of most anti-tumor treatments. Two tumor subtypes (Classical and Basal) and two stromal subtypes (“active” and “inactive”) have been described. The Basal and the active stroma have worse prognosis. These subtypes could also be predictive of the response to different chemotherapies. To date, molecular subtype classification or phenotype quantification can only be defined by RNAseq, a complex technique sensitive to the quantity and quality of samples, requiring a timescale limiting its routine application. We propose a deep learning approach (PACpAInt) to predict molecular subtypes in PAC on routine histological slides. Patients and Methods: 424 digitalized HES slides of 202 resected PAC from 3 centers with clinical and transcriptomic data were used as training cohort. 3 validation cohorts were used (i) 250 resected PAC from a 4th center including 97 cases with an exact HES/RNAseq spatial match and all tumor slides digitalized (n = 891); (ii) 126 resected PAC from the TCGA (HES + RNAseq); (iii) 25 liver biopsies from metastatic PAC (HES + RNAseq). A multi-step deep learning model was developed to recognize tumor tissue, tumor from stroma cells, and then predicts their transcriptomic molecular subtypes, either at the level of an entire slide, or at the tile level (squares of 112 μm) allowing to study intratumor heterogeneity. Results: PACpAInt correctly predicted the tumor subtype at the whole slide level (AUC = 0.86 and 0.81 in 2 validation cohorts) and improved for samples with unambiguous molecular subtype (AUC = 0.91 and 0.88) confirming the limit of a binary approach. Similar results were obtained on liver biopsies (AUC = 0.85 and 0.92 on unambiguous cases). PACpAInt independently predicted progression-free and overall survival (PFS HR=1.37 [1.16 - 1.62] and OS HR=1.27 [1.08 - 1.49]). Analysis of all tumor slides from 77 Classical cases showed that 39% were heterogeneous with a Basal contingent. These cases had shorter PFS (15 vs. 47 months, p= 0.001) and OS (31 vs. 64 months, p= 5e-5).The analysis of intratumoral heterogeneity using PACpAInt predicted the molecular subtype of tumor and stroma cells of each tile within a slide (> 6 million tiles analyzed). 61% of cases had a main subtype either Classical (42%) or Basal (19%). 39% of the cases were ambiguous could be considered either hybrid (10%) with coexistence of Basal and Classical cells, or intermediate (29%) corresponding to homogeneous tumors but of intermediate differentiation. This classification had a strong prognostic impact (OS: 45.1 vs 33.0 vs 23.4 vs 13.6 months, resp. Classical, intermediate, hybrid, Basal; p <e-12). Conclusion: This study provides the first PAC subtyping tool widely usable in clinical practice, opening the possibility of molecular classification useful for routine care and clinical trials. Citation Format: Charlie Saillard, Flore Delecourt, Benoit Schmauch, Olivier Moindrot, Magali Svrcek, Armelle Bardier-Dupas, Jean Francois Emile, Mira Ayadi, Vinciane Rebours, Louis De Mestier, Pascal Hammel, Cindy Neuzillet, Jean Baptiste Bachet, Juan Iovanna, Nelson Dusetti, Yuna Blum, Magali Richard, Yasmina Kermezli, Valerie Paradis, Mikhail Zaslavskiy, Pierre Courtiol, Aurelie Kamoun, Remy Nicolle, Jerome Cros. PACpAInt: A deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 472.

Prediction of Water Stress Episodes in Fruit Trees Based on Soil and Weather Time Series Data

Water is a limited resource in arid and semi-arid regions, as is the case in the Mediterranean Basin, where demographic and climatic conditions make it ideal for growing fruits and vegetables, but a greater volume of water is required. Deficit irrigation strategies have proven to be successful in optimizing available water without pernicious impact on yield and harvest quality, but it is essential to control the water stress of the crop. The direct measurement of crop water status is currently performed using midday stem water potential, which is costly in terms of time and labor; therefore, indirect methods are needed for automatic monitoring of crop water stress. In this study, we present a novel approach to indirectly estimate the water stress of 15-year-old mature sweet cherry trees from a time series of soil water status and meteorological variables by using Machine Learning methods (Random Forest and Support Vector Machine). Time information was accounted for by integrating soil and meteorological measurements within arbitrary periods of 3, 6 and 10 days. Supervised binary classification and regression approaches were applied. The binary classification approach allowed for the definition of a model that alerts the farmer when a dangerous crop water stress episode is about to happen a day in advance. Performance metrics F2 and recall of up to 0.735 and 0.769, respectively, were obtained. With the regression approach a R2 of up to 0.817 was achieved.

Vectorial Decoding Algorithm for Fast Correlation Attack and Its Applications to Stream Cipher Grain-128a

Fast correlation attack, pioneered by Meier and Staffelbach, is an important cryptanalysis tool for LFSR-based stream cipher, which exploits the correlation between the LFSR state and key stream and targets at recovering the initial state of LFSR via a decoding algorithm. In this paper, we develop a vectorial decoding algorithm for fast correlation attack, which is a natural generalization of the original binary approach. Our approach benefits from the contributions of all correlations in a subspace. We propose two novel criteria to improve the iterative decoding algorithm. We also give some cryptographic properties of the new FCA which allows us to estimate the efficiency and complexity bounds. Furthermore, we apply this technique to the well-analyzed stream cipher Grain-128a. Based on a hypothesis, an interesting result for its security bound is deduced from the perspective of iterative decoding. Our analysis reveals the potential vulnerability for LFSRs over matrix ring and also for nonlinear functions with biased multidimensional linear approximations such as Grain-128a.

Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach

Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach

Retraction Note to: A multi objective binary bat approach for testcase selection in object oriented testing

Retraction Note to: A multi objective binary bat approach for testcase selection in object oriented testing

Challenging anti-carceral feminism: Criminalisation, justice and continuum thinking

While anti-carceral feminism – which challenges the use of the criminal law and criminal justice system to tackle violence against women – is increasingly dominant, this article builds on an emerging body of work contesting its central premises. In particular, this article emphasises that some sexual violence survivors seek criminal justice redress and examines the work of feminist organisations both supporting survivors and demanding radical change. It argues that some anti-carceral feminism risks reifying existing criminal laws and reproducing sexual violence myths and stereotypes. In doing so, it defends criminalisation of ‘new’ and emerging forms of abuse and offers ‘continuum thinking’ (Boyle, 2019) as a way of moving beyond the polarised and binary approaches of current debates and activism. The aim is to encourage a nuanced, complex approach to the criminal law and criminalisation which recognises both a role for criminal justice and alternatives; which listens to the voices of all survivors, including those whose understanding of justice includes criminal justice; and which is fully alive to the risks and challenges that all justice approaches entail whether state or community based.

Discerning subsets of breast cancer with very low and absent HER2 protein expression

Breast cancers are currently eligible for treatment with anti-HER2 therapies if they exhibit amplification of the gene ERBB2 and overexpression of its protein product HER2. Recently, breast cancers with low HER2 expression have shown response to novel anti-HER2 antibody-drug conjugates, and the lower end of "low-HER2" tumors has not yet been clinically delineated. The historically binary approach to HER2 scoring will need to evolve and reporting of HER2 status may require refinement to better stratify low-HER2 statuses. We performed a quality review of HER2 immunohistochemical (IHC) scoring of breast carcinomas with low HER2 expression (71 core biopsies and 51 excisions). We also investigated the feasibility of discerning cases with total lack of HER2 expression from those cases with "very low" HER2 expression that did not meet current criteria for a HER2(1+) score. Rescoring HER2 achieved substantial agreement when performed at 200×, and near-perfect agreement at 400× magnification. Examination under 400× magnification led to recognition of more cases with HER2 expression. Less than 10% of cases showed complete lack of HER2 protein expression by IHC. Cases with "very low" expression were readily identified, and such a category would be feasible to implement in pathologist workflow.

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation

Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events. The ability to accurately predict how long accidents will last can provide significant benefits to both end-users in their route choice and traffic operation managers in handling of non-recurrent traffic congestion. This paper presents a novel bi-level machine learning framework enhanced with outlier removal and intra–extra joint optimisation for predicting the incident duration on three heterogeneous data sets collected for both arterial roads and motorways from Sydney, Australia and San-Francisco, U.S.A. Firstly, we use incident data logs to develop a binary classification prediction approach, which allows us to classify traffic incidents as short-term or long-term. We find the optimal threshold between short-term versus long-term traffic incident duration, targeting both class balance and prediction performance while also comparing the binary versus multi-class classification approaches using quantiled duration groups and varying threshold split. Secondly, for more granularity of the incident duration prediction to the minute level, we propose a new intra–extra Joint Optimisation algorithm (IEO-ML) which extends multiple baseline ML models tested against several regression scenarios across the data sets. Final results indicate that: (a) 40–45 min is the best split threshold for identifying short versus long-term incidents and that these incidents should be modelled separately, (b) our proposed IEO-ML approach significantly outperforms baseline ML models in 66% of all cases showcasing its great potential for accurate incident duration prediction. Lastly, we evaluate the feature importance and show that time, location, incident type, incident reporting source and weather at among the top 10 critical factors which influence how long incidents will last.

Integrability, conservation laws and exact solutions for a model equation under non-canonical perturbation expansions

In this paper, the non-linear for the small long amplitude waves in two dimensional (2D) shallow water waves propagation with free surface are considered. The shallow water wave problem leads to the non-linear Hamiltonian model equation. Based on the binary Bell-polynomials approach, the bilinear form, bilinear Bäcklund transformation and multiple wave solutions are obtained. The conservation laws are constructed using two different techniques, namely, the Ibragimov's theorem and the multiplier method. The Noether's approach was applied to the non-linear Hamiltonian model equation to obtain the conservation laws. Also, we show that the non-linear Hamiltonian model equation is nonlinearly self-adjoint. Conserved quantities of Hamiltonian model equation are illustrated. Finally, with the help of the extended homogeneous balance method, and an exponential method, a set of new exact solutions for the non-linear Hamiltonian model equation are obtained.

Binary Classification with Minimum Observations

Binary classification with minimum observations is an important task in applications where enough training data are not available. In this paper, we propose a binary classification approach that is based on an unsupervised ranking algorithm for objects with numerical attributes. The ranking algorithm takes normalized attributes of numerical objects as input and returns the weights of attributes. These weights are used to rank the objects. We propose a class labelling algorithm that labels each side of the ranked objects as a class using less than or equal to 15 labeled data objects or observations. Evaluation on six different data sets shows that the proposed approach is comparable to the state-of-the-art binary classification algorithms that use 70 percent observations compared to less than one percent observations (on average) used by the proposed approach.

Preparation and Characterization of Hydrophilic Polymer Based Sustained-Release Matrix Tablets of a High Dose Hydrophobic Drug.

The objective of this study was the preparation and characterization of a sustained-release matrix tablet containing a high-dose hydrophobic drug and its comparison with marketed products. In the present study, HPMC was applied as the matrix-forming polymer for the sustained release of clarithromycin (500 mg). The compatibility of clarithromycin and excipients was studied using a binary mixture approach and compatible excipients were selected. Matrix tablets were prepared using the high-shear wet granulation technique. Tablets were compressed using oblong (19 mm), shallow concave punches, under a compression weight of 900 mg/tablet. The flow of granules was evaluated by determining their bulk density, tapped density, angle of repose, Hausner ratio, and Car’s index. Compressed tablets were tested for their physical parameters, mechanical characteristics, drug content, and in vitro drug release, as per United States Pharmacopeia (USP). Clarithromycin is a drug having poor water solubility and showed compatibility with all the excipients used in the formulation of polymeric matrix tablets. FTIR spectra of clarithromycin, before and after being subjected to the stress conditions, confirmed the compatibility of clarithromycin and other ingredients of the matrix tablets. All the formulations exhibited good rheological characteristics and all the parameters related to flow showed results in the acceptable range. Physically, matrix tablets were smooth and shiny, without any surface defects. Weight variation (±5%) and drug content of the tablets (95–102%) were within the pharmacopeial limits. HPMC successfully sustained the drug release for 24 h. It is concluded from the study that dissolution rate of clarithromycin can be sustained using hydrophilic polymer (HPMC) as a release-controlling agent.