Likelihood Ratio Threshold Research Articles

Heating Up Cold Cases: An Interview with Bruce Budowle on Human Identification

Heating Up Cold Cases: An Interview with Bruce Budowle on Human Identification

Adaptive multichannel detection-resolution of stochastic signals in conditions of parametric prior uncertainty

The purpose of the study is to improve the quality indicators of adaptive multichannel detection-resolution-measurement parameters of stochastic signals under parametric a prior uncertainty.Materials and methods. The methodology for the synthesis of signal detectors of various structures, taking into account various factors, is used to achieve the goal. This makes it possible to exclude from the expression for estimating the signal power the terms due to “colored” internal noises and an uncorrelated background. The tools of correlation analysis, adaptive Bayes approach, criterion of generalized likelihood ratio, methods of calculating its logarithm are also used. Results. In this paper, the problems of synthesis of adaptive multichannel detection-resolution algorithms for stochastic signals of various structures under the influence of intense noise interference are considered. An integral element of the detection task is a joint assessment of the intensity of the useful signal and the correlation matrix of interference. This problem is effectively solved for highintensity signals, and the nonstationarity of the internal noise of the receiving uncorrelated background of the interfering signals is not taken into account. A multi-channel receiving system consisting of a number of independent spatially separated elements that form a linear antenna array is considered. The width of the spectrum of the received signals should be considered sufficiently narrow, so that the delay of the signals at the antenna aperture can be neglected. This provision can significantly improve the performance of detection and resolution of stochastic signals in the background of noise interference. Based on the analysis of a finite discrete sample of complex amplitudes of received oscillations, a detection problem was solved, which is formulated as a problem of checking statistical hypotheses regarding distribution parameters. The detection algorithm is reduced to a comparison with the likelihood ratio threshold, and the threshold level value is determined by the selected optimality criterion and for the Neumann-Pearson criterion remains dependent on the power of interfering oscillations.Conclusion. The presented detector possesses higher characteristics of detection and resolution of stochastic signals in comparison with the known ones. It can be shown that an important property of the obtained statistics is the stabilization of the probability of false detection. This is achieved by normalizing the noise power at the output of the adaptation device. In addition, the resulting algorithm is invariant to the form used for its calculation of the correlation matrix of interference. If we take into account that an uncorrelated background will be added to the internal noise power, with a large number of noise jammers, a significant improvement in the detection performance has been achieved.

Complexity‐adjustable SC decoding of polar codes for energy consumption reduction

This study proposes an enhanced list-aided successive cancellation stack (ELSCS) decoding algorithm with adjustable decoding complexity.Also, a logarithmic likelihood ratio-threshold based path extension scheme is designed to further reduce the memory consumption of stack decoding. Numerical simulation results show that without affecting the error correction performance, the proposed ELSCS decoding algorithm provides a flexible trade-off between time complexity and computational complexity, while reducing storage space up to 70%. Based on the fact that most mobile devices operate in environments with stringent energy budget to support diverse applications, the proposed scheme is a promising candidate for meeting requirements of different applications while maintaining a low computational complexity and computing resource utilisation.

A Controllable Success Fix Rate Threshold Determination Method for GNSS Ambiguity Acceptance Tests

Global navigation satellite system (GNSS) integer ambiguity acceptance test is one of the open problems in GNSS data processing. A number of ambiguity acceptance tests have been proposed from different perspectives and then unified into the integer aperture estimation framework. The existing comparative studies indicate that the impact of test statistics form on the test performance is less critical, while how to construct an efficient, practical test threshold is still challenging. Based on the likelihood ratio test theory, a new computationally efficient ambiguity acceptance test with controllable success fix rate, namely the fixed likelihood ratio (FL-) approach is proposed, which does not require Monte Carlo simulation. The study indicates that the fixed failure rate (FF-) approach can only control the overall failure rate of the acceptance region, but the local failure rate is not controllable. The proposed FL-approach only accepts the fixed solution meeting the likelihood ratio requirement. With properly chosen likelihood ratio threshold, the FL-approach achieves comparable success rate as the FF-approach and even lower failure rate than the FF-approach for the strong underlying model cases. The fixed success fix rate of the FL-approach is verified with both simulation data and real GNSS data. The numerical results indicate that the success fix rate of the FL-approach achieves >98% while the failure rate is <1.5%. The real-time kinematic (RTK) positioning with ambiguities tested by the FL-approach achieved 1–2cm horizontal precision and 2–4 cm vertical precision for all tested baselines, which confirms that the FL-approach can serve as a reliable and efficient threshold determination method for the GNSS ambiguity acceptance test problem.

Evaluation of Trauma Triage Criteria Performance in a Regional Trauma System

Objective: We evaluated the performance of individual trauma triage criteria using data from a regional trauma registry. Methods: Los Angeles County (LAC) paramedics use triage criteria adapted from the 2011 Center for Disease Control (CDC) guidelines to triage injured patients to Trauma Centers (TCs). TCs report outcomes to a LAC EMS registry. We abstracted data for patients 15 years or older from 2013 to 2015 and identified all trauma triage criteria that were met for each encounter. Study outcomes were: (1) “clear need” for a TC, defined as receiving a non-orthopedic operative intervention within 6 hours of arrival, injury severity score (ISS) > 15, or surgical ICU admission; or (2) “no need” for a TC, defined as discharge home from the emergency department (ED). We also defined “possible need” as those patients not discharged home from the ED, inclusive of “clear need” and all other admitted patients. For each individual triage criteria, we calculated the positive likelihood ratios and positive predictive values for TC need. Results: There were 71,536 adult patients in the registry transported by EMS to a LAC TC during the study. Median age was 38 years (IQR 25–55) with 73% male. There were 23,628 (33%) who met “no need” criteria for a TC, leaving 47,908 (67%) patients with “possible need” for a TC, of whom 13,343 patients (19% of total) met “clear need” for a TC. No individual trauma criterion met the a priori likelihood ratio threshold of 10 for predicting “clear need” for a TC. Cardiopulmonary arrest with penetrating torso trauma and flail chest met this threshold for “possible need.” Conclusion: In this retrospective analysis, no individual triage criterion definitively identified patients who benefit from transport to a TC. Yet, the majority of patients demonstrated potential benefit for nearly all criteria, supporting CDC recommendations that trauma triage criteria be considered in their entirety, not as individual criterion.

Categorizing click trains to increase taxonomic precision in echolocation click loggers.

Passive acoustic monitoring is an efficient way to study acoustically active animals but species identification remains a major challenge. C-PODs are popular logging devices that automatically detect odontocete echolocation clicks. However, the accompanying analysis software does not distinguish between delphinid species. Click train features logged by C-PODs were compared to frequency spectra from adjacently deployed continuous recorders. A generalized additive model was then used to categorize C-POD click trains into three groups: broadband click trains, produced by bottlenose dolphin (Tursiops truncatus) or common dolphin (Delphinus delphis), frequency-banded click trains, produced by Risso's (Grampus griseus) or white beaked dolphins (Lagenorhynchus albirostris), and unknown click trains. Incorrect categorization rates for broadband and frequency banded clicks were 0.02 (SD 0.01), but only 30% of the click trains met the categorization threshold. To increase the proportion of categorized click trains, model predictions were pooled within acoustic encounters and a likelihood ratio threshold was used to categorize encounters. This increased the proportion of the click trains meeting either the broadband or frequency banded categorization threshold to 98%. Predicted species distribution at the 30 study sites matched well to visual sighting records from the region.

Validation of SmartRank: A likelihood ratio software for searching national DNA databases with complex DNA profiles

Searching a national DNA database with complex and incomplete profiles usually yields very large numbers of possible matches that can present many candidate suspects to be further investigated by the forensic scientist and/or police. Current practice in most forensic laboratories consists of ordering these ‘hits’ based on the number of matching alleles with the searched profile. Thus, candidate profiles that share the same number of matching alleles are not differentiated and due to the lack of other ranking criteria for the candidate list it may be difficult to discern a true match from the false positives or notice that all candidates are in fact false positives. SmartRank was developed to put forward only relevant candidates and rank them accordingly. The SmartRank software computes a likelihood ratio (LR) for the searched profile and each profile in the DNA database and ranks database entries above a defined LR threshold according to the calculated LR. In this study, we examined for mixed DNA profiles of variable complexity whether the true donors are retrieved, what the number of false positives above an LR threshold is and the ranking position of the true donors. Using 343 mixed DNA profiles over 750 SmartRank searches were performed. In addition, the performance of SmartRank and CODIS were compared regarding DNA database searches and SmartRank was found complementary to CODIS. We also describe the applicable domain of SmartRank and provide guidelines. The SmartRank software is open-source and freely available. Using the best practice guidelines, SmartRank enables obtaining investigative leads in criminal cases lacking a suspect.

Opportunistic Detection Rules: Finite and Asymptotic Analysis

Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples. From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules. Several results regarding ODRs are established in this paper. In the finite regime, the maximum sample size is modeled either as a fixed finite number, or a geometric random variable with a fixed finite mean. For both cases, the corresponding Bayesian formulations are investigated. The former case is a slight variation of the well-known finite-length sequential hypothesis testing procedure in the literature, whereas the latter case is new, for which the Bayesian optimal ODR is shown to be a sequence of likelihood ratio threshold tests with two different thresholds. A running threshold, which is determined by solving a stationary state equation, is used when future samples are still available, and a terminal threshold (simply the ratio between the priors scaled by costs) is used when the statistician reaches the final sample and, thus, has to make a decision immediately. In the asymptotic regime, the tradeoff among the exponents of the (false alarm and miss) error probabilities and the normalized expected stopping time under the alternative hypothesis is completely characterized and proved to be tight, via an information-theoretic argument. Within the tradeoff region, one noteworthy fact is that the performance of the Stein-Chernoff lemma is attainable by ODRs.

Low complexity list successive cancellation decoding of polar codes

The authors propose a low complexity list successive cancellation (LCLSC) decoding algorithm, where the advantages of the successive cancellation (SC) decoding and the list successive cancellation (LSC) decoding are both considered. In the proposed decoding, SC decoding instead of LSC decoding is implemented when all information bits from bad subchannels are received reliably. While the reliability of each information bit is estimated by its likelihood ratio (LR), the bit channel quality is measured via its Bhattacharyya parameter. To achieve this goal, the authors introduce two thresholds: LR threshold and Bhattacharyya parameter threshold. Also, the methods to determine them are both elaborated. The numerical results suggest that the complexity of LCLSC decoding is much lower than LSC decoding and can be close to that of SC decoding, while the error performance is almost equal to that of LSC decoding. Especially, when the code rate is in low region, the advantage of our decoding is more obvious.

Analysis of Kinship Index Distributions in Koreans Using Simulated Autosomal STR Profiles

Kinship testing in forensic casework is largely based on a likelihood ratio (LR) approach with short tandem repeat (STR) markers; however, in order to efficiently identify potential relatives in a specific population, the threshold values for kinship prediction should be determined by analyzing the kinship index distributions of the population in question. In this study, 250,000 DNA profiles were simulated using allele frequencies at 20 autosomal STR loci in Koreans, then the LRs were calculated for true close relatives and unrelated pairs. The LR distributions in related and unrelated pairs under a given relationship were compared in 2 sets of 13 Combined DNA Index System (CODIS) and 20 STR profiles. Using 13 CODIS STRs, true relatives in parent/child and full-sibling relationships were sufficiently discriminated from unrelated pairs with LR thresholds of 1,000 and 100, respectively. However, the CODIS STRs lacked the discriminatory power to differentiate between related and unrelated pairs in uncle/nephew and first cousin relationships due to high false-positive and false-negative rates with a LR threshold of 10. Increasing the number of STR loci to 20 increased discrimination of close relatives, but high false results remained in uncle/nephew and first cousin relationships. The kinship index data from this study will help make decisions on various kinship testing and familial searching in Koreans.

Rich results from poor resources

We report on Korean monolingual, Chinese-Korean English-as-pivot bilingual, and Chinese-English bilingual CLIR experiments using MT software augmented with Web-based entity-oriented translation as resources in the NTCIR-4 environment. Simple stemming is helpful in improving bigram indexing for Korean retrieval. For word indexing, keeping nouns only is preferable. Web-based translation reduces untranslated terms left over after MT and substantially improves CLIR results. Translation concatenation is found to consistently improve CLIR effectiveness, while combining a retrieval list from bigram and word indexing is also helpful. A method to disambiguate multiple MT outputs using a log likelihood ratio threshold was tested. Depending on the nature of the title or description queries, bigram only or a retrieval combination, or relaxed or rigid evaluations, direct bilingual CLIR returned an average precision of 71--79% (English-Korean) and 76--84% (Chinese-English) of the corresponding Korean-Korean and English-English monolingual results. Using English as a pivot in Chinese-Korean CLIR provides about 55--65% the effectiveness that Korean alone does. Entity/terminology translation at the pivot language stage accounts for a large portion of this deficiency. A topic with comparatively worse Chinese-English bilingual result does not necessarily mean that it will continue to under-perform (after further transitive Korean translation) at the Korean retrieval level.

Asymptotic optimality of likelihood ratio threshold tests in decentralized detection

Two distributed systems are considered for discriminating between two finite-alphabet bivariate memoryless sources and for detecting a known signal in stationary bivariate additive Gaussian noise. Each system comprises two sensors, M-ary local quantizers and a fusion center which makes decisions based on quantized source observations. The problem of asymptotically optimal quantization is considered in detail for the binary (M=2) case. It is shown that optimality is achieved by quantizing a locally computed likelihood ratio wherein one distribution is in general different from the appropriate source marginal. For the problem of detection in Gaussian noise, it is further demonstrated that the optimal distributed system attains the same asymptotic performance as the optimal centralized system for appropriate choice of M.