Probability Of Discrimination Research Articles

Comparative assessment of the eighth and seventh AJCC staging edition prognostic performance of patients with p16 positive oropharynx cancer.

6067 Background: The American Joint Committee on Cancer (AJCC) TNM staging system defines the anatomical extent of disease and serves as a guide for treatment and prognosis. The favorable prognosis of p16+ oropharyngeal squamous cell carcinoma (OPSCC) compared to p16 negative counterpart led to major updates in the AJCC 8th edition. Its prognostic performance, however, warrants further validation. Methods: We included patients diagnosed with p16+ OPSCC enrolled in a prospective registry ( Stiefel) at The University of Texas MD Anderson Cancer Center between March 2015 and December 2018. Patients’ stage at diagnosis was classified according to the AJCC 7th (AJCC-7) and 8th (AJCC-8) editions. Overall survival (OS) and progression-free survival (PFS) was defined as time from diagnosis to death or to progression or death, respectively. The Kaplan-Meier method was used to calculate 1- and 3-year survival probabilities. Differences between groups were compared using the log-rank test. Prognostic discriminative performance of each staging system was evaluated using Harrel’s C-statistic. Survival differences between heavy (> 10 pack-years [PY]) vs. light/never smokers (≤ 10 PY) by AJCC-8 staging groups was assessed with the log-rank test. Results: Of 463 patients, the median follow-up was 34.7 months (2.3-169.74). Nearly 90% (N=413) of patients were down-staged from AJCC-7 to AJCC-8 with 69% of patients with IVA disease based on AJCC-7 (N=319) re-staged as stage I (N=196 [42%]), II (N=79 [17%]) or III (44 [10%]) according to AJCC-8. Over 60% (N=279) of patients were staged as I with AJCC-8. Compared to AJCC-7, AJCC-8 had improved prognostic ability (C-statistic, 0.58 for AJCC-7 vs. 0.63 for AJCC-8) and provided better discriminative survival probabilities at 1 and 3-year follow-up (Table). Similar results were observed for PFS. Smoking status did not impact OS when stratified by AJCC-8 staging groups: I, p=0.347; II, p=0.310; and III, p=0.532 for > 10 vs. ≤ 10 PY. Conclusions: Our cohort validates that the AJCC-8 provides better prognostic discriminative performance when compared to AJCC-7, however, a disproportionate number of patients were classified as stage I. Smoking was not associated with survival within each staging group.[Table: see text]

Multiple-shot and unambiguous discrimination of von Neumann measurements

We present an in-depth study of the problem of multiple-shot discrimination of von Neumann measurements in finite-dimensional Hilbert spaces. Specifically, we consider two scenarios: minimum error and unambiguous discrimination. In the case of minimum error discrimination, we focus on discrimination of measurements with the assistance of entanglement. We provide an alternative proof of the fact that all pairs of distinct von Neumann measurements can be distinguished perfectly (i.e. with the unit success probability) using only a finite number of queries. Moreover, we analytically find the minimal number of queries needed for perfect discrimination. We also show that in this scenario querying the measurements in parallel gives the optimal strategy, and hence any possible adaptive methods do not offer any advantage over the parallel scheme. In the unambiguous discrimination scenario, we give the general expressions for the optimal discrimination probabilities with and without the assistance of entanglement. Finally, we show that typical pairs of Haar-random von Neumann measurements can be perfectly distinguished with only two queries.

Simple target acquisition model based on Fλ/d

The US Army Night Vision and Electronic Sensors Directorate’s Night Vision Integrated Performance Model (NVIPM) is a robust, comprehensive, and sophisticated model that calculates the performance of an infrared imager in the task of target acquisition. The inputs are specifics about the target, atmosphere, optics, detector, electronics, and display and the outputs are probabilities of target detection, recognition, and identification. NVIPM is complicated and takes a good bit of time to master obtaining correct answers based on numerous assumptions and modeling experience. For midwave infrared (MWIR) and longwave infrared (LWIR) sensors, past research has shown that imaging system performance is strongly related to Fλ / d, where F is the f-number, λ is the wavelength, and d is the detector pitch. Fλ / d provides a metric that relates how closely to diffraction-limited performance a sensor operates. We use the past Fλ / d work to develop a simple model that provides probability of target discrimination as a function of range that can be performed with a simple hand calculator or spreadsheet. We compare this model to the NVIPM calculations on 10 very disparate MWIR and LWIR sensors to show robust agreement. We also describe the conditions under which the simple model is valid.

Lower bounds on the failure probability of unambiguous discrimination

We present a systematic approach for obtaining lower bounds on the failure probability of unambiguous state discrimination by introducing an ancillary degree of freedom. Specifically, the lower bounds are obtained by introducing a suitable homogeneous function of the off-diagonal entries of the reduced matrix in the ancillary degree of freedom. A series of lower bounds on the failure probability of unambiguous discrimination of both pure states and mixed states are derived using the ${l}_{r}$-norm of the off-diagonal entries. Our method adds insights into the problem of unambiguous state discrimination. In particular, since we have related the lower bound with off-diagonal entries of density matrices, e.g., the ${l}_{1}$-norm coherence, our work suggests that unambiguous state discrimination may provide further applications in quantifying coherence, and vise versa.

LOCC protocols with bounded width per round optimize convex functions

We start with the task of discriminating finitely many multipartite quantum states using LOCC protocols, with the goal to optimize the probability of correctly identifying the state. We provide two different methods to show that finitely many measurement outcomes in every step are sufficient for approaching the optimal probability of discrimination. In the first method, each measurement of an optimal LOCC protocol, applied to a [Formula: see text]-dimensional local system, is replaced by one with at most [Formula: see text] outcomes, without changing the probability of success. In the second method, we decompose any LOCC protocol into a convex combination of a number of “slim protocols” in which each measurement applied to a [Formula: see text]-dimensional local system has at most [Formula: see text] outcomes. To maximize any convex functions in LOCC (including the probability of state discrimination or fidelity of state transformation), an optimal protocol can be replaced by the best slim protocol in the convex decomposition without using shared randomness. For either method, the bound on the number of outcomes per measurement is independent of the global dimension, the number of parties, the depth of the protocol, how deep the measurement is located, and applies to LOCC protocols with infinite rounds, and the “measurement compression” can be done “top-down” — independent of later operations in the LOCC protocol. The second method can be generalized to implement LOCC instruments with finitely many classical outcomes: if the instrument has [Formula: see text] coarse-grained final measurement outcomes, global input dimension [Formula: see text] and global output dimension [Formula: see text] for [Formula: see text] conditioned on the [Formula: see text]th outcome, then one can obtain the instrument as a convex combination of no more than [Formula: see text] slim protocols; that is, [Formula: see text] bits of shared randomness suffice.

Mutual Information and Quantum Discord in Quantum State Discrimination with a Fixed Rate of Inconclusive Outcomes.

We studied the mutual information and quantum discord that Alice and Bob share when Bob implements a discrimination with a fixed rate of inconclusive outcomes (FRIO) onto two pure non-orthogonal quantum states, generated with arbitrary a priori probabilities. FRIO discrimination interpolates between minimum error (ME) and unambiguous state discrimination (UD). ME and UD are well known discrimination protocols with several applications in quantum information theory. FRIO discrimination provides a more general framework where the discrimination process together with its applications can be studied. In this setting, we compared the performance of optimum probability of discrimination, mutual information, and quantum discord. We found that the accessible information is obtained when Bob implements the ME strategy. The most (least) efficient discrimination scheme is ME (UD), from the point of view of correlations that are lost in the initial state and remain in the final state, after Bob’s measurement.

The social credit system in China: a model of constitutionalism for the era of crises

This paper focuses on the probable transformative effects of the application of the Chinese Social Credit System and similar projects in the realm of public administration on constitutional rights and freedoms, balances in citizen-state relations, and the model of statehood. The starting point of the research is the assumption that the Social Credit System, despite its specifically national Chinese peculiarities, can be seen as a reflection of a broader tendency towards the use of reputational information, techniques of ranking (grading) and risk management in the process of exercising the state power. To test this hypothesis, the author analyzes the actual experience of the Social Credit System’s introduction in the People’s Republic of China, compares this project with e-government projects, and proposes the umbrella-term of “social-credit mechanisms” to describe procedures and means of social control, based on the permanent collection and analysis of reputation data relating to persons. It is argued in this paper that the introduction of social-credit mechanisms to the practice of public administration ultimately leads to the emergence of a gap between formally enshrined rights and the actual ability to exercise them, between the legal capacity of a person and the ability to realise this capacity in certain legal relations. Examining the prospects of introduction of reputation-based social-credit mechanisms to the public administration, the author notices the probability of discrimination against persons who took a false step. As is demonstrated in the paper, the use of reputation data and social ratings by state authorities may result in the gradual differentiation in quality and scope of public services depending on social ratings (grades) of their addressees. This state of affairs may signify the birth of a new caste society and the end of the principle of equality before the law. According to the conclusions made in the paper, projects akin the Chinese Social Credit System reflect the global tendency towards the formation of a new type of constitutionalism. In the framework of this new constitutionalism the main emphasis will be shifted from citizens’ democratic participation in the execution of state power and the citizenry’s political subjectivity to ensure public safety and social stability. The issue of social-credit mechanisms’ introduction to the process of public administration is de facto an issue between the values of freedom and the values of security — the issue of choosing between political subjectivity and guaranteed biological existence.

Generalized Discrimination Between Symmetric Coherent States for Eavesdropping in Quantum Cryptography

Symmetric coherent states are of interest in quantum cryptography, since for such states there is an upper bound for unambiguous state discrimination (USD) probability, which is used to resist USD attack. But it is not completely clear what an eavesdropper can do for shorter channel length, when USD attack in not available. We consider the task of generalized discrimination between symmetric coherent states and construct an operation which enlarges the information content of the states with fixed failure probability. We apply this transformation to develop a zero-error eavesdropping strategy for quantum cryptography on symmetric coherent states.

The Difficulty of Defining Sensitive Data – the Concept of Sensitive Data in the EU Data Protection Framework

The concept of sensitive data has been a mainstay of data protection for a number of decades. The concept itself is used to denote several categories of data for which processing is deemed to pose a higher risk for data subjects than other forms of data. Suck risks are often perceived in terms of an elevated probability of discrimination (or related harms) to vulnerable groups in society. As a result, data protection frameworks have traditionally foreseen a higher burden for the processing of sensitive data than other forms of data. The sui generis protection of sensitive data (stronger than the protection of non-sensitive personal data) can also seemingly be a necessity from a fundamental rights-based perspective (as indicated by human rights jurisprudence). This paper seeks to analyse the continued relevance of sensitive data in both contemporary and potential future contexts. Such an exercise is important for two main reasons. First, the legal regime responsible for the regulation of the use of personal data has evolved considerably since the concept of sensitive data was first used. This has been exemplified by the creation of the EU's General Data Protection Regulation in Europe. It has introduced a number of requirements relating to sensitive data that are likely to represent added burdens for controllers who want to process personal data. Second, the very nature of personal data is changing. Increases in computing power, more complex algorithms and the availability of ever more potentially complimentary data online mean that more and more data can be considered of a sensitive nature. This creates various risks going forward, including an inflation effect whereby the concept loses its value and also the possibility that data controllers may increasingly seek to circumvent complying with the requirements placed upon the use of sensitive data. This paper analyses how such developments are likely to influence the concept of sensitive data and in particular its ability to protect vulnerable groups form harms. The authors propose a possible interpretative solution: a hybrid approach where a purpose-based definition acquires a bigger role in deciding whether data is sensitive combined with a context-based ‘backstop’ based on reasonable foreseeability.

Allelic diversity and forensic estimations of the Beijing Hans: Comparative data on sequence-based and length-based STRs

Short tandem repeat (STR) profiling is routinely used in forensic genetics. At present, STR analysis is mainly performed by capillary electrophoresis (CE). However, due to limitations associated with the CE method, STR genotyping has been limited to length polymorphisms only. Because next generation sequencing (NGS) is capable of providing full resolution STR data at the sequence variation level, the individual identification capability of forensic STR loci could be significantly improved. Here we present sequence-based STR data for the Beijing Han population in which 291 individuals were screened for 23 commonly used forensic STRs using the SeqTypeR24 CASE kit on an Ion PGM platform. In total, 234 length-based alleles and 356 sequence-based alleles, which included 22 novel core repeat sequences, were observed. The sequence-based matching probability and power of discrimination were superior to the length-based numbers for 16 loci bearing micro-variant alleles. Combined matching probability reached 8.2 × 10−29 for 23 STR loci at the sequence level. This was two orders of magnitude higher than the parameters at length level and provides a data base for sequence-based STR casework applications.

Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets

In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.

Quantum state discrimination on reconfigurable noise-robust quantum networks

A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Stochastic Walk. In particular, the structure of the graph mimics those of neural networks, with the quantum states to discriminate encoded on input nodes and with the discrimination obtained on the output nodes. We optimize the parameters of the network to obtain the highest probability of correct discrimination. Numerical simulations show that after a transient time the probability of correct decision approaches the theoretical optimal quantum limit. These results are confirmed analytically for small graphs. Finally, we analyze the robustness and reconfigurability of the network for different set of quantum states, and show that this architecture can pave the way to experimental realizations of our protocol as well as novel quantum generalizations of deep learning.

Genetic diversity and validation of a microsatellite panel for parentage testing for alpacas (Vicugna pacos) on three Peruvian farms

• There is a high genetic diversity within and among the three alpaca farms. • The inbreeding levels of the three alpaca farms are moderate. • A panel of 15 microsatellite markers have been validated for parentage control in Peruvian alpaca farms. The alpaca is of greatest economic importance in the Peruvian High Andes. This study aimed to determine the genetic diversity of three Peruvian alpaca farms, as well as, to validate a microsatellite markers panel for paternity testing. In this study, 247 samples of Huacaya alpacas were taken from three different localities (Sanjo, San Pedro de Raco and Cachipampa) from Pasco Region in Peru. DNA was obtained from hair follicles and genotyped for 15 microsatellites markers in multiplex electrophoresis runs. A total of 225 alleles were detected across the 15 loci investigated. The polymorphism information content considering all loci was 0.82, which indicated that the microsatellite panel was very polymorphic and highly informative. The estimated diversity parameter showed that farms have high levels of genetic diversity (H E = 0.826), and revealed the existence of genetic differentiation among the farms ( F ST = 2.8 %). The highest inbreeding coefficient was in the Sanjo farm ( F IS = 0.303). The results of the parentage testing indicated that all loci showed values greater than 70 % probability of discrimination. However, the highest values found were 94 % (YWLL08) and 90 % (YWLL36). The average of the probability of exclusion obtained was 0.999994 if the genotype for one alleged parents is known, and 0.99999 if the genotypes for both alleged parents are known. The results obtained show that there is a high genetic diversity and validate the panel of microsatellite markers, that would help to improve the identification system and genealogical data collection.

Abstract 2103: Detecting pancreatic cancer through neural network analysis of human plasma MR spectra

Abstract The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly due to late-stage diagnosis [1]. Similarities in the clinical behavior and imaging features of PDAC and chronic pancreatitis further complicate the detection of PDAC [2]. There is an urgent need for noninvasive clinically translatable biomarkers of PDAC. Plasma based tests provide an attractive option for routine screening. Here we have applied neural network analysis to 1H magnetic resonance (MR) spectra of human plasma samples to differentiate between healthy subjects (control), subjects with benign lesions, and subjects with pancreatic ductal adenocarcinoma (PDAC). Our data support developing a neural-network approach to identify PDAC from 1H MRS of plasma samples. Plasma samples from healthy subjects (control, n=28), from subjects with benign pancreatic lesions (benign, n=32), and from subjects with PDAC (PDAC/malignant, n=34) were analyzed with 1H MRS. 1H MR spectra were acquired on a Bruker Avance III 750 MHz (17.6 T) MR spectrometer equipped with a 5 mm probe. After processing the spectral data for normalization against a reference peak signal and plasma volume, spectral features were extracted to develop an artificial neural-network technique to discriminate between the three classes of spectra. Three spectral features showed high probability for successful discrimination: (a) signal intensity weighted-mean of the spectra, (b) standard deviation of the spectra about the weighted-mean from (a), and (c) weighted mean of the difference spectra. This led to the design of our three-layer artificial neural network model to solve the discrimination problem. The artificial neural-network, developed in MATLAB 2019b (MathWorks, Inc), was limited to a stack of two auto-encoder layers and a ‘softmax' layer. Due to the limited size of the data all of the data were used to train the network while keeping low L2-regularization to avoid over-training. Cross-validation results were captured as confusion matrices and receiver operating characteristic (ROC) curves. With a combination of spectral features extraction and neural network processing of MRS data of plasma samples we could discriminate between control, benign and PDAC. The sensitivity and specificity for detecting PDAC was 84% and 95%, respectively. Our data suggest that the approach can be extended to encompass a large sample size to improve accuracy and provide a rigorous and robust solution for plasma based detection of PDAC. Acknowledgement: This work was supported by NIH R35CA209960 and R01CA193365.

Unambiguous Discrimination between Coherent States of the Two-Mode Optical Field

The procedure of quantum measurement—unambiguous discrimination—of four two-mode coherent states of the optical field which are of interest for information transmission by the optical communication channel is studied. It is shown that complex conjugation of the amplitude of one of the modes leads to better discriminability between the states. An interferometric scheme for unambiguous discrimination between such states is proposed and the probability of successful discrimination is found. Applications of the considered set of states in quantum cryptography, quantum teleportation, and optical communication with a high level of losses are discussed.

Research on Intelligent Identification of Ore Minerals Based on CART Algorithm

The identification of ores and minerals is essential in geology, and the use of data mining to identify and classify ores in the era of widespread use of big data will help its development more effectively. This article takes the pyrite simulation data as an example and based on the ore mineral color, hardness, relative density, and electrical conductivity as the characteristic values. It is built in the CHDC model to use the CART algorithm to classify and identify the pyrite ore. After a lot of training on the data, the recognition model tends to be stable. The probability of correct discrimination in the CHDC model based on the CART algorithm’s confusion matrix is 93.8%. However, due to the complex composition of ore minerals, the component analysis cannot be performed in a short time, so the component characteristic value is not taken. The ore returned by the sensor is different in color under different light sources, which affects the accuracy of judgment. Using the CART algorithm in the CHDC model can judge the identification of the ore, thereby completing the intelligent identification of the ore mineral.

Unambiguous discrimination between linearly dependent symmetric states with multiple copies

We investigate the unambiguous discrimination (UD) among three linearly dependent (LD) symmetric states with [Formula: see text] copies. The optimal discrimination probability is derived. Our result shows that a set of linearly dependent symmetric states can be perfectly discriminated as the number of copies goes to infinity.

Minimum-error state discrimination and quantitative wave particle duality

We establish the link between minimum-error discrimination of two pure states and wave particle duality in two-path interferometers. In particular, the upper bound of the probability of success discrimination is derived directly from the corresponding duality relation. It is already known that quantum state discrimination can produce wave particle duality relations, here we show the converse is also true: Wave particle duality can be used to obtain information about state discrimination.

Population genetic analysis of Shaanxi male Han Chinese population reveals genetic differentiation and homogenization of East Asians.

BackgroundShaanxi province, located in the upper Yellow River, has been evidenced as the geographic origin of Chinese civilization, Sino‐Tibetan‐speaking language, and foxtail or broomcorn millet farmers via the linguistic phylogenetic spectrum, archeological documents, and genetic evidence. Nowadays, Han Chinese is the dominant population in this area. The formation process of modern Shaanxi Han population reconstructed via the ancient DNA is on the way, however, the patterns of genetic relationships of modern Shaanxi Han, allele frequency distributions of high mutated short tandem repeats (STRs) and corresponding forensic parameters are remained to be explored.MethodsHere, we successfully genotyped 23 autosomal STRs in 630 unrelated Shaanxi male Han individuals using the recently updated Huaxia Platinum PCR amplification system. Forensic allele frequency and parameters of all autosomal STRs were assessed. And comprehensive population genetic structure was explored via various typical statistical technologies.ResultsPopulation genetic analysis based on the raw‐genotype dataset among 15,803 Eurasian individuals and frequency datasets among 56 populations generally illustrated that linguistic stratification is significantly associated with the genetic substructure of the East Asian population. Principal component analysis, multidimensional scaling plots and phylogenetic tree further demonstrated that Shaanxi Han has a close genetic relationship with geographically close Shanxi Han, and showed that Han Chinese is a homogeneous population during the historic and recent admixture from the STR variations. Except for Sinitic‐speaking populations, Shaanxi Han harbored more alleles sharing with Tibeto‐Burman‐speaking populations than with other reference populations. Focused on the allele frequency correlation and forensic parameters, all loci are in accordance with the minimum requirements of HWE and LD. The observed combined probability of discrimination of 8.2201E‐28 and the cumulative power of exclusion of 0.9999999995 in Shaanxi Han demonstrated that the studied STR loci are informative and polymorphic, and this system can be used as a powerful routine forensic tool in personal identification and parentage testing.ConclusionBoth the geographical and linguistic divisions have shaped the genetic structure of modern East Asian. And more forensic reference data should be obtained for ethnically, culturally, geographically and linguistically different populations for better routine forensic practice and population genetic studies.

Forensic features and genetic background exploration of a new 47-autosomal InDel panel in five representative Han populations residing in Northern China.

BackgroundInsertion/deletion (InDel) analysis plays an indispensable role in human identification, population genetics, and biogeographic research. Profiles of individuals in forensic applications worldwide based on a set of autosomal InDel loci (A‐InDels) in human genomes have been widely used over the past few years.MethodsThe new AGCU InDel 50 Kit contains 47 well‐chosen A‐InDels, ensuring high discriminatory power, and the 2 Y chromosome InDel loci (Y‐InDels) are used for sex determination in case of allele dropout at Amelogenin. In this study, five Northern Han populations residing in different geographic areas of China were recruited and genotyped using the assay.ResultsAfter Bonferroni correction, all 47 A‐InDels were in accordance with the lack of significant departures of Hardy–Weinberg equilibrium in all loci and investigated groups. The combined probability of discrimination and the probability of exclusion in the Han population range from 1–3.2240 × 10–19 to 1–1.3030 × 10–19 and 0.9997, respectively. A comprehensive population genetic relationship investigation between Han Chinese and 26 worldwide populations based on allele frequency correlation was carried out. Our results revealed no significant genetic differentiation in Chinese Han groups. Hierarchical clustering, phylogenetic relationship reconstructions, multidimensional scaling, principal component analysis, and structure analysis were performed, and the results indicated that, genetically, Han populations are closely related to East Asians.ConclusionOverall, this novel 47 A‐InDel assay is a valuable tool that could potentially be used for forensic identification and parentage tests.