Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naïve Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds >0.95 and >0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp >0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp >0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.     

三联体亲权指数的统一算法及其扩展应用

目的介绍一种在生母参与情形下,孩子与被鉴定人间常用亲缘关系指数的统一算法。方法以计算三联体亲权指数（paternity index in trios,PIT）的公式为基础,分析不同亲缘关系指数计算公式中的公因子,并以此公因子对不同情形下PIT计算公式进行统一描述,采用叔伯指数定律和亲缘系数（r）将上述PIT统一计算公式扩展至祖孙、半同胞、叔侄及第一代堂兄弟等不同情形下的亲缘关系指数的运算。结果以状态一致性等位基因的频率倒数加1为公因子,在引入亲缘系数（r）的情形下,获得了可计算在生母参与时,孩子与被鉴定人间任意亲缘关系指数的两个公式。结论在生母参与情形下,孩子与被鉴定人间亲缘关系指数的统一算法简化了运算过程,有利于编程实现批量计算。     

How to use Lire le Capital

Abstract

This article contributes to a pragmatist analysis of pricing and valuation through an account of the production of closing prices at the Paris Bourse. The Paris Bourse is an electronic stock exchange and the actors in charge of its technological configuration often need to face concerns about the quality of the prices that the configuration produces. Closing prices are particularly important because they constitute references that circulate widely. The author analyses how a problem of representativeness of closing prices was raised in the late 1990s and how several techniques aimed at solving it. In order to deal with this problem of representativeness, the author proposes the consideration of prices as signs in a pragmatist manner. Adapting Charles S. Peirce's theory of the sign to the study of prices, the author concentrates attention on the material display of prices, on their capacity to stand as traces of some event, and on the way they may suit a set of calculative conventions.     

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*,†

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.     

What is an algorithm? Financial regulation in the era of high-frequency trading

Abstract

In response to the flash crashes and market manipulations blamed on high-frequency trading (HFT), algorithms have been brought inside the regulatory perimeter. This paper focuses on the most ambitious regulation directed at the practice: the algorithm-tagging rule in the German High-Frequency Trading Act. Fifteen interviews with stakeholders in the Act’s implementation serve to reconstruct how regulators defined an algorithm and help pose the question of to what extent regulatory definitions and data need accurately to represent financial practices to be useful. Although tentative in its findings, the research suggests that the algorithm-tagging rule may be providing valuable signals in the noise to trade surveillance officers and having virtuous effects on the cultures of trading firms. The conclusion argues that sociologists of finance should adopt a more balanced approach when evaluating regulatory technologies and heed MacKenzie’s 2005 call to open up their black boxes.     

Introduction to “charting,tracking, and mapping: new technologies,labor, and surveillance”

Advances in information technologies over the last two decades have offered the promise of revolutionizing workplaces and other settings by expanding the capabilities of individuals working in various professions. Enabled by high-speed computing systems and marked by an intensification of command and control properties, new technologies have expanded, and in some cases intentionally surpassed, the scope of the senses. Technologies such as drones, surveillance cameras, and airport and border security systems promise the seemingly infinite catchment and infinitesimal account of human actions, while computer processing software provides real-time data on large-scale transactional processes occurring in monetary systems and security markets. Charting, tracking, and mapping functions open up new vistas for quantifying social behaviors and detecting misbehaviors, creating new frontiers for criminal justice, the military, and economic activity. The papers in this special issue reveal the processes guiding the rapid transformation and flexible adaptation of institutional structures, economic and financial systems, and threat surveillance and military/police mobilization in the near future.     

Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance,

Soil DNA profiling has potential as a forensic tool to establish a link between soil collected at a crime scene and soil recovered from a suspect. However, a quantitative measure is needed to investigate the spatial/temporal variability across multiple scales prior to their application in forensic science. In this study, soil DNA profiles across Miami‐Dade, FL, were generated using length heterogeneity PCR to target four taxa. The objectives of this study were to (i) assess the biogeographical patterns of soils to determine whether soil biota is spatially correlated with geographic location and (ii) evaluate five machine learning algorithms for their predictive ability to recognize biotic patterns which could accurately classify soils at different spatial scales regardless of seasonal collection. Results demonstrate that soil communities have unique patterns and are spatially autocorrelated. Bioinformatic algorithms could accurately classify soils across all scales with Random Forest significantly outperforming all other algorithms regardless of spatial level.     

Predictive Policing: Review of Benefits and Drawbacks

This literature review illuminates the conceptualization of predictive policing, and also its potential and realized benefits and drawbacks. The review shows a discrepancy between the considerable attention for potential benefits and drawbacks of predictive policing in the literature, and the empirical evidence that is available. The empirical evidence provides little support for the claimed benefits of predictive policing. Whereas some empirical studies conclude that predictive policing strategies lead to a decrease in crime, others find no effect. At the same time, there is no empirical evidence at all for the claimed drawbacks. We conclude that the current thrust of predictive policing initiatives is based on convincing arguments and anecdotal evidence rather than on systematic empirical research. We urge the research community to do independent tests of both positive and negative expectations to generate an evidence base for predictive policing.     

Politics of on-demand food delivery: Policy design and the power of algorithms

In this article, we examine the politics of on-demand food delivery using insights from the theory of social construction and policy design. On-demand food delivery is a service built on algorithm-based technology known for its precarity and physical risks for couriers. We compare how the on-demand food delivery sector is regulated and its observable effects in two Asia-Pacific cities with contrasting food courier profiles: Melbourne, Australia (international students), and Singapore (citizens, permanent residents). We show how the social construction of food couriers in other policy subsystems (migration, higher education, citizenship) affects debates of their fair treatment in the on-demand food delivery sector. By interrogating the politics of digitally enabled versions of reality, we argue for embracing a design perspective to identify how reforms could be introduced in change-resistant sectors.