Analysis of Serial Search Based Code Acquisition in the Multiple Transmit/Multiple Receive Antenna Aided DS-CDMA Downlink,

Abstract—In this paper, we investigate the serial-search-based initial code-acquisition performance of direct-sequence code division multiple access (DS-CDMA) employing multiple transmit/multiple receive antennas when communicating over uncorrelated Rayleigh channels. We characterize the associated performance trends as a function of the number of antennas. It is demonstrated that, in contrast to our expectation, the achievable correctdetection probability degrades in our typical target operational Ec/I0 range as the number of transmit antennas is increased. When maintaining a given total transmit power, our findings suggest that increasing the number of transmit antennas results in the combination of the low-energy noise-contaminated signals of the transmit antennas, which ultimately increases the mean acquisition time (MAT). However, it is extremely undesirable to increase theMAT when the system is capable of attaining its target bit-error-ratio performance at reduced signal-power levels, as a benefit of employing multiple transmit antennas. Index Terms—Code acquisition, direct-sequence code division multiple access (DS-CDMA), multiple transmit/multiple receive antennas (MTMR), serial search

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Joint modeling of time-to-event data and multiple ratings of a discrete diagnostic test without a gold standard

Histologic tumor grade is a strong predictor of risk of recurrence in breast cancer. Nevertheless, tumor grade readings by pathologists are susceptible to intra- and inter-observer variability due to its subject nature. Because of this limitation, histologic tumor grade is not included in the breast cancer stating system. Latent class models have been considered for analysis of such discrete diagnostic tests with regarding the underlying truth as a latent variable. However, the model parameters in latent class models are only locally identifiable, that is, any permutation on the categories of the underlying truth can lead to the same likelihood value. In many clinical practices, the underlying truth is known associated with the risk of a certain event in a trend. Here, we proposed a joint model with a Cox proportional hazards model for time-to-event data where the underlying truth is a latent predictor and a latent class model for multiple ratings of a discrete diagnostic test without a gold standard. With the known association between the underlying truth and the risk of an event in a trend, the proposed joint model not only fully identifies all model parameters but also provides valid assessment of the association between the diagnostic test result and the risk of an event. The modified EM algorithm was used for estimation with employing the survey-weighted Cox model in the M-step. To test whether the known trend imposed on model parameters can be assumed, we applied the Union-Intersection principle for the proposed joint model. The proposed method is illustrated in the analysis of data from the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14 sub-study and through simulation studies. The proposed method is relevant to public health fields, such as chronic diseases and psychiatry, where some components of the initial diagnostics are subjective but have important implications in patient management. Application of our method leads to accurate assessment on the association between the diagnostic tests and the clinical outcomes and subsequently significant improvement in decision-making on treatment or patient management

Optimal designs for asymmetric sigmoidal response curves in bioassays and immunoassays

Recent studies show that asymmetric sigmoidal response curves are not uncommonin biomedical studies. For example, the 5-parameter logistic (5PL) model isfrequently used to model and analyze responses from bioassays and immunoassayswhich can be skewed. Various types of optimal experimental designs for 2, 3 and4-parameter logistic models have been reported but not for the more complicated5-parameter logistic (5PL) model. Designs currently used for the 5PL model seemad-hoc with no formal quantitative assessment of their efficiencies. We constructdifferent types of optimal designs for studying various features of the 5PL modeland use them to evaluate efficiencies of commonly used designs in bioassays andimmunoassays. We also create a user-friendly software package to search for optimaldesigns tailor-made to user-specified problems for the 5PL model and evaluaterobustness properties of the design under a variation of criteria, model formsand mis-specification in the nominal values of the model parameters. Our designstrategies can also account for several objectives with varying degrees of importance.As an application, we generate optimal designs for the 5PL model for real studiesin immunoassays and bioassays, and show currently used designs are generallyinefficient for statistical inference

Recommendation: A Less Explored Killer-App of Uncertainty?

Due to the unprecedented amount of information available, it is becoming more and more important to provide personalized recommendations on data, based on past user feedbacks. However, available user feedbacks or ratings are extremely sparse, which motivates the needs for rating prediction. The most widely adopted solution has been collaborative filtering, which (1) identifies "neighboring" users with similar tastes and (2) aggregates their ratings to predict the ratings of the given user. However, while each of such aggregation involves varying levels of uncertainty, e.g., depending on the distribution of ratings aggregated, which has not been systematically considered in recommendation, though recent study suggests such consideration can boost prediction accuracy. To consider uncertainty in rating prediction, this paper reformulates the collaborative filtering problem as aggregating community ratings into multiple predicted ratings with varying levels of certainty, based on which we identify top-k results with both high confidence and rating. We empirically study the efficiency and accuracy of our proposed framework, over a classical collaborative filtering system