Hunting for significance: Bayesian classifiers under a mixture loss function

This paper presents a theoretical analysis in discrete time for a multi-tier weak radiofrequency (RF) signal estimation process with N simultaneous signals. Discrete time dynamic sampling is introduced and is shown to provide the capability to extract signal parameter values with increased accuracy compared with accuracy of estimates obtained in prior work. This paper advances phase measurement approaches by proposing discrete time dynamic sampling which our paper shows offers the desirable capability for more accurate weak signal parameter estimates. For N=2 simultaneous signals with a strong signal at 850 MHz and a weak signal at 855 MHz, the results show that dynamically sampling the instantaneous frequency at 24 times the Nyquist rate provides weak signal frequency estimates that are within 1.7 times 10^{-5} of the actual weak signal frequency and weak signal amplitude estimates that are within 428 PPM of the actual weak signal amplitude. Results are also presented for situations with N=2 simultaneous 5G signals. In one case, the strong signal is 3950 MHz, and the weak signal is 3955 MHz; in the other case the strong case is 5950 MHz, and the weak signal is 5955 MHz. The results for these cases show that estimates obtained with dynamic sampling are more accurate than estimates provided using a single sample rate of 65 MSPS. This work has promising applications for weak signal parameters estimation using instantaneous frequency measurements.

Sparse regularization discriminant analysis for face recognition

This study aims to propose methods for identifying entrepreneurial discovery processes with weak/strong signals of technological changes and incorporating technology foresight in the design and planning of the Smart Specialization Strategy (S3). For this purpose, we first analyse patent abstracts from 2000 to 2009, obtained from the European Patent Office and use a keyword-based text mining approach to collect weak and strong technology signals; the word2vec algorithm is also employed to group weak signal keywords. We then utilize Correlation Explanation (CorEx) topic modelling to link technology weak/strong signals to invention activities for the period 2010-2018 and use the ANOVA statistical method to examine the relationship between technology weak/strong signals and patent values. The results suggest that patents related to weak rather than strong signals are more likely to be high-impact innovations and to serve as a basis for future technological developments. Furthermore, we use latent Dirichlet allocation (LDA) topic modelling to analyse patent activities related to weak/strong technology signals and compute regional topic weights. Finally, we present implications of the research.

Estimation of empirical null using a mixture of normals and its use in local false discovery rate

Symmetric low-rank representation with adaptive distance penalty for semi-supervised learning

Over the years, many dimensionality reduction algorithms have been proposed for learning the structure of high dimensional data by linearly or non-linearly transforming it into a low-dimensional space. Some techniques can keep the local structure of data, while the others try to preserve the global structure. In this paper, we propose a linear dimensionality reduction technique that characterizes the local and global properties of data by firstly applying k-means algorithm on original data, and then finding the projection by simultaneously globally maximizing the between-cluster scatter matrix and locally minimizing the within-cluster scatter matrix, which actually keeps both local and global structure of data. Low complexity and structure preserving are two main advantages of the proposed technique. The experiments on both artificial and real data sets show the effectiveness and novelty of proposed algorithm in visualization and classification tasks.

The strategy for any organization can be set from the outside in, according to the school of positioning; according to this, the supply of products and / or services has to adapt to changes that occur in the environment. According to the movement schools of thought, a strategy can be set from the inside out in which case the organization influences the surroundings with its offer of products and / or services. Given the importance of weak signals in the definition of the strategy, the research goal is to treat the issue of pre-emptive strategy in the context of strategic decision making. An answer is provided for the following questions: (1) What is a weak signal? (2) How to tell if it is anticipative, relevant and useful for the decision makers? and (3) How to turn a weak signal in an effective anticipative clue? Based on investigator's experience in strategic consulting for small, medium and large companies, the weak and strong signals are explained and a set of criteria which are representative of their identification is presented.

BackgroundThere is a growing trend towards the production of "hospital report-cards" in which hospitals with higher than acceptable mortality rates are identified. Several commentators have advocated for the use of Bayesian hierarchical models in provider profiling. Several researchers have shown that some degree of misclassification will result when hospital report cards are produced. The impact of misclassifying hospital performance can be quantified using different loss functions.MethodsWe propose several families of loss functions for hospital report cards and then develop Bayes rules for these families of loss functions. The resultant Bayes rules minimize the expected loss arising from misclassifying hospital performance. We develop Bayes rules for generalized 1-0 loss functions, generalized absolute error loss functions, and for generalized squared error loss functions. We then illustrate the application of these decision rules on a sample of 19,757 patients hospitalized with an acute myocardial infarction at 163 hospitals.ResultsWe found that the number of hospitals classified as having higher than acceptable mortality is affected by the relative penalty assigned to false negatives compared to false positives. However, the choice of loss function family had a lesser impact upon which hospitals were identified as having higher than acceptable mortality.ConclusionThe design of hospital report cards can be placed in a decision-theoretic framework. This allows researchers to minimize costs arising from the misclassification of hospitals. The choice of loss function can affect the classification of a small number of hospitals.

In this article, we consider a stochastic profit model with discounting for a simple delivery scheduling problem of spare units and maximize the expected total discounted profit over an infinite time planning horizon. Then the familiar net present value (NPV) approach is applied to formulate the present value of expected total profit for an infinite time planning span. In a fashion similar to Dohi et al. (2000), we further propose a statistical estimation procedure for the optimal delivery schedule, based on the non-parametric method. More precisely, the concept of total time on test statistics is utilized for the development of a non-parametric estimation method. In a simulation study, we investigate numerically asymptotic properties of the resulting estimator of the optimal delivery schedule. It can be concluded that the developed method here can provide nice convergence properties to the real optimal solution and be useful for actual spare parts management

Explainable AIs (XAIs) often do not provide relevant or understandable explanations for a domain-specific human-in-the-loop (HIL). In addition, internally used metrics have biases that might not match existing structures in the data. The habilitation thesis presents an alternative solution approach by deriving explanations from high dimensional structures in the data rather than from predetermined classifications. Typically, the detection of such density- or distance-based structures in data has so far entailed the challenges of choosing appropriate algorithms and their parameters, which adds a considerable amount of complex decision-making options for the HIL. Central steps of the solution approach are a parameter-free methodology for the estimation and visualization of probability density functions (PDFs); followed by a hypothesis for selecting an appropriate distance metric independent of the data context in combination with projection-based clustering (PBC). PBC allows for subsequent interactive identification of separable structures in the data. Hence, the HIL does not need deep knowledge of the underlying algorithms to identify structures in data. The complete data-driven XAI approach involving the HIL is based on a decision tree guided by distance-based structures in data (DSD). This data-driven XAI shows initial success in the application to multivariate time series and non-sequential high-dimensional data. It generates meaningful and relevant explanations that are evaluated by Grice’s maxims.

High-resolution methods are generally defined to be high-performance methods for estimating and/or detecting the desired and/or undesired signal components present in a given set of data. The term “high-resolution” also implies a good ability to resolve very “similar” signal components. One of the most common problems in signal processing is known as frequency estimation. In frequency estimation, “high-resolution” often refers to a good ability to resolve two or more closely located frequencies in the given data. There are two groups of high-resolution methods. One is parametric methods, and the other non-parametric methods. The parametric high-resolution methods result from ingenious exploitations of known data structures. The non-parametric high-resolution methods maximize the output of some desired information with little knowledge of the data structure. The choice between parametric methods and non-parametric methods largely depends on one’s confidence in the assumed data model. In this chapter, we expose the readers to a range of existing parametric high-resolution methods. In Section 1.2, we present several frequency estimation techniques using algebraic principles. They are linear prediction, matrix pencil, and iterative quadratic maximum likelihood. The linear prediction method and the matrix pencil method can achieve near-optimal accuracy of estimation without the local convergence issues associated with the optimal methods. The computational complexities of the two methods are among the most efficient. The iterative quadratic maximum likelihood method is an approximation of the (exact) maximum likelihood method. Under some condition (e.g., high SNR), this approximation achieves the optimal accuracy. Concepts like forwardand-backward averaging, total least square, and (joint) singular value decomposition of orthonormal matrices will also be discussed. In Section 1.3, we present methods that exploit large sample theorems in statistics. In particular, we focus on data of multiple independent measurements. The key data structure is captured by the dominant (principal) subspace of the data matrix or the dominant eigenvectors of the data covariance matrix. The principal subspace is referred

Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three‐component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug‐ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.

In recent radio-communication applications, receivers may be jammed by high power unwanted signals. In this case, the received signal can be considered as the sum of a strong unwanted signal and a very weak target signal. Even though the two signals don't overlap in the frequency domain, the processing of the weak signal becomes very hard as it can be vanished at the output of the Analog Digital Converter. To avoid this scenario, many nonlinear circuits are proposed in the literature. Our study focuses on the separation of a weak and a very strong signals which are wideband signals and they are very close in the frequency domain. Several circuits have been implemented and simulated. The proposed circuit diagram is also presented. Finally, simulations are presented and discussed.

Thymic selection eliminates T cells expressing TCRs with a high affinity for self-antigen. However, many of the most promising tumor antigens for T cell-based cancer immunotherapies are unmodified self-antigens. Therefore, tumor-specific T cells often have a low avidity for tumor antigens, thus limiting the efficacy of this approach. TCR gene therapy using TCR genes that have been modified for increased affinity can overcome this limitation. The generation of enhanced affinity TCRs generally involves saturation mutagenesis targeting the complementarity determining regions of the TCR genes to be modified. However, this approach requires considerable trial and error, and is still not always successful at generating a higher affinity TCR without promiscuous off-target activity. We have developed a system for selecting high affinity tumor antigen-specific TCRs from a population of T cells selected by agonist signaling during T cell development in vitro. The αβ/γδ lineage choice is in part dictated by the strength of TCR/pre-TCR signaling during early T cell development, such that the pre-TCR provides a weak signal that drives αβ T cell development while the mature γδ TCR provides a stronger signal that drives γδ T cell development. In TCR transgenic mice, early expression of the transgenic TCRα chain prior to β-selection can also provide a stronger γδ-like signal through the mature αβ TCR, leading to the development of lineage redirected TCRαβ+ CD4/CD8 double negative (DN) γδ wanna-be” cells. The development of these DN TCRαβ+ cells is enhanced in the presence of cognate antigen or TCR crosslinking; suggesting that early expression of an antigen-specific TCRα chain before β-selection could promote the generation of DN TCRαβ+ cells in the presence of antigen, through a process in which the introduced TCRα chain pairs with an endogenous TCRβ chain to produce an αβ TCR with high affinity for the target antigen. To test this hypothesis, the TCRα chain from a tumor antigen-specific TCR was transduced into hematopoietic progenitor cells and a library of TCRβ chains was isolated from the lineage redirected DN TCRαβ+ T cells that developed. Several TCRβchains of varying CDR3 lengths were isolated that confer enhanced affinity for the target antigen when paired with the introduced TCRα chain. This abstract is also presented as Poster A13. Citation Format: Thomas M. Schmitt, Philip D. Greenberg, David H. Aggen, David Kranz, Sebastian Ochsenreither. Early TCRα expression during T cell development for the generation of enhanced-affinity TCRs for TCR gene therapy. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology: Multidisciplinary Science Driving Basic and Clinical Advances; Dec 2-5, 2012; Miami, FL. Philadelphia (PA): AACR; Cancer Res 2013;73(1 Suppl):Abstract nr PR12.