Contaminated Confessions Revisited

A second wave of false confessions is cresting. In the first twenty-one years of post-conviction DNA testing, 250 innocent people were exonerated, forty of which had falsely confessed. Those false confessions attracted sustained public attention from judges, law enforcement, policymakers, and the media. Those exonerations not only showed that false confessions can happen, but did more by shedding light on the problem of confession contamination, in which details of the crime are disclosed to suspects during the interrogation process. As a result, false confessions can appear deceptively rich, detailed, and accurate. In just the last five years, there has been a new surge in false confessions — a set of twenty-six more false confessions among DNA exonerations. All but two of these most recent confessions included crime scene details corroborated by crime scene information. Illustrating the power of contaminated false confessions, in nine of the cases, defendants were convicted despite DNA tests that excluded them at the time. As a result, this second wave of false confessions should cause even more alarm than the first. In the vast majority of cases there is no evidence to test using DNA. Unless a scientific framework is adopted to regulate interrogations, including by requiring recording of entire interrogations, overhauling interrogation methods, providing for judicial review of reliability at trial, and informing jurors with expert testimony, the insidious problems of confession contamination will persist

Efficient Estimation of Copula-based Semiparametric Markov Models

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all geometrically ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functionals are root-$n$ consistent, asymptotically normal and efficient; and that their sieve likelihood ratio statistics are asymptotically chi-square distributed. We present Monte Carlo studies to compare the finite sample performance of the sieve MLE, the two-step estimator of Chen and Fan (2006), the correctly specified parametric MLE and the incorrectly specified parametric MLE. The simulation results indicate that our sieve MLEs perform very well; having much smaller biases and smaller variances than the two-step estimator for Markov models generated via Clayton, Gumbel and other tail dependent copulas.Copula, Tail dependence, Nonlinear Markov models, Geometric ergodicity, Sieve MLE, Semiparametric efficiency, Sieve likelihood ratio statistics, Value-at-Risk

Research on the Complexity Characteristics of Urban Metro Network Based on Complex Network Theory

It is to provide decision support for later planning of metro network. Firstly, the space-L method is used to model the metro network topology. Secondly, four different indicators are used to analyze the complexity of metro network. The results show that the degree of metro network nodes in Xuzhou is generally low, and the degree distribution and power distribution are quite different. The network has no scale network properties. In Xuzhou metro network, the path between random station pairs is long, and the degree of node aggregation is low. There is a positive correlation between degree and betweenness, which can make more accurate importance assessment of the site

Gallai's path decomposition conjecture for cartesian product of graphs (\uppercase\expandafter{\romannumeral 2})

Let $G$ be a graph of order $n$. A path decomposition $\mathcal{P}$ of $G$ is a collection of edge-disjoint paths that covers all the edges of $G$. Let $p(G)$ denote the minimum number of paths needed in a path decomposition of $G$. Gallai conjectured that if $G$ is connected, then $p(G)\leq \lceil\frac{n}{2}\rceil$. In this paper, we prove that Gallai's path decomposition conjecture holds for the cartesian product $G\Box H$, where $H$ is any graph and $G$ is a unicyclic graph or a bicyclic graph.Comment: 26 pages, 2 figures. arXiv admin note: text overlap with arXiv:2310.1118

Multiple male and female reproductive strategies and the presence of a polyandric mating system in the termite Reticulitermes labralis (Isoptera:Rhinotermitidae)

Reproductive systems of termite colonies may involve the number of individuals in the reproductive caste and the copulatory selectivity of reproductive individuals (i.e., polyandry or polygamy), both of which directly impact the fertility and genetic diversity of the colony. Polygamy is widespread in the lower termites, whereas polyandry appears to be mostly absent in termites. In this paper, the differentiation of male and female neotenics were observed in orphaned experimental colonies of the subterranean termite Reticulitermes labralis. The artificial orphaned colonies began to produce neotenics a week after colony establishing, with more neotenics appearing in the same group over time. Finally, each experimental group reserved multi-neotenics that consisted of male and female neotenic individuals. Our results demonstrated that these neotenic individuals retained in the colony participated in reproduction. A genetic analysis at four microsatellite loci showed that in addition to the conspicuous morphologically male reproductives, there were inconspicuous males or workers that had copulated with the females in the orphaned colony. Multiple male and female reproductive individuals existed together in a single colony, and one female neotenic could mate with several male reproductives in a short time. Thus, multiple male and female reproductive systems and a polyandric mating system are present in R. labralis

Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup

The Decomposition Theorems of AG-Neutrosophic Extended Triplet Loops and Strong AG-(l, l)-Loops

In this paper, some new properties of Abel Grassmann‘s Neutrosophic Extended Triplet Loop (AG-NET-Loop) were further studied. The following important results were proved: (1) an AG-NET-Loop is weakly commutative if, and only if, it is a commutative neutrosophic extended triplet (NETG); (2) every AG-NET-Loop is the disjoint union of its maximal subgroups

A New Image Segmentation Algorithm and Its Application in Lettuce Object Segmentation

Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity