Identification Of Weak Signals Research Articles

Weak Signal Identification and Inference in Penalized Likelihood Models for Categorical Responses

Penalized likelihood models are widely used to simultaneously select variables and estimate model parameters. However, the existence of weak signals can lead to inaccurate variable selection, biased parameter estimation, and invalid inference. Thus, identifying weak signals accurately and making valid inferences are crucial in penalized likelihood models. We develop a unified approach to identify weak signals and make inferences in penalized likelihood models, including the special case when the responses are categorical. To identify weak signals, we use the estimated selection probability of each covariate as a measure of the signal strength and formulate a signal identification criterion. To construct confidence intervals, we propose a two-step inference procedure. Extensive simulation studies show that the proposed procedure outperforms several existing methods. We illustrate the proposed method by applying it to the Practice Fusion diabetes data set.

Impact of Weak Signals on the Digitalization of Risk Analysis in Process Safety Operational Environments

Weak signals in risk analysis digitalization are of great importance for preventing major accidents in risk analysis in the process industry, especially for process operations and production. However, some of the negative impacts are incorrect operational risk identification, significant inventory carrying costs, disruption of risk frequency, and risk consequence analysis, all of which will signal inaccurate information about unforeseen and current dangers in process facilities and operational environments. While the positive impacts are viewed as an early warning system that provides information on operational risk system status, the identification of potential risk weaknesses in process facilities, indicators of a transition or an emerging problem that may become significant in the future, highlighting future assumptions, challenge our views of the future and expand the selection of a processing facility. Lastly, weak signal identification in the digitalization of risk analysis can provide relevant information in supporting, assessing and analyzing the risks associated with the operation, in order to design a technical system and estimate the industry’s level of accident risk, as well as the possible control of a system. The present research will provide valuable information to the process industry on how to protect their operational facilities and increase process safety by providing information on weak safety risk monitoring systems in operations, strengthening the processes of the operational area.

LC\u2013MS peak assignment based on unanimous selection by six machine learning algorithms

Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

New Type of Spectral Nonlinear Resonance Enhances Identification of Weak Signals

Some nonlinear systems possess innate capabilities of enhancing weak signal transmissions through a unique process called Stochastic Resonance (SR). However, existing SR mechanism suffers limited signal enhancement from inappropriate entraining signals. Here we propose a new and effective implementation, resulting in a new type of spectral resonance similar to SR but capable of achieving orders of magnitude higher signal enhancement than previously reported. By employing entraining frequency in the range of the weak signal, strong spectral resonances can be induced to facilitate nonlinear modulations and intermodulations, thereby strengthening the weak signal. The underlying physical mechanism governing the behavior of spectral resonances is examined, revealing the inherent advantages of the proposed spectral resonances over the existing implementation of SR. Wide range of parameters have been found for the optimal enhancement of any given weak signal and an analytical method is established to estimate these required parameters. A reliable algorithm is also developed for the identifications of weak signals using signal processing techniques. The present work can significantly improve existing SR performances and can have profound practical applications where SR is currently employed for its inherent technological advantages.

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.

Weak signal identification and inference in penalized model selection

Weak signal identification and inference are very important in the area of penalized model selection, yet they are underdeveloped and not well studied. Existing inference procedures for penalized estimators are mainly focused on strong signals. In this paper, we propose an identification procedure for weak signals in finite samples, and provide a transition phase in-between noise and strong signal strengths. We also introduce a new two-step inferential method to construct better confidence intervals for the identified weak signals. Our theory development assumes that variables are orthogonally designed. Both theory and numerical studies indicate that the proposed method leads to better confidence coverage for weak signals, compared with those using asymptotic inference. In addition, the proposed method outperforms the perturbation and bootstrap resampling approaches. We illustrate our method for HIV antiretroviral drug susceptibility data to identify genetic mutations associated with HIV drug resistance.

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.

Optimization of micro-Laser Induced Breakdown Spectroscopy analysis and signal processing

As a result of continuing instrumental development (Echelle spectrometer and ICCD detectors), micro-Laser Induced Breakdown Spectroscopy analysis may become an increasingly recognized analytical technique for determining elemental compositions of geologic materials. Best conditions of time resolution conditions (delay and time acquisition window) are estimated with respect to the collection geometry of optical plasma emission of our system. It turns out that the level of the Bremsstrahlung continuum emission is weak in the first tens of nanoseconds after the laser excitation pulse. The enlargement of the emission lines is identified in the first 100 ns but remains comparable to the spectral resolution of our system. Thus, results show that time-resolved conditions are not necessarily required to perform elemental analysis at the micrometric scale using LIBS, contrary to macro-LIBS. This suggests potential improvements of micro-LIBS analysis (sensitivity and spectral resolution) using non-intensified CCD connected with the laser pulse. However, in order to improve the detection of weak signals obtained with an ICCD detector, working at high gain, a new method of signal processing on two-dimensional Echelle images has been developed. This method, based on the comparison of two 2D images, allows the identification of group of pixels (particles) that can be considered as representative of actual signals. This methodology applied to LIBS spectra eliminates the majority of noise peaks, allows the identification of weak signals which were almost impossible to extract from the noise, and does not alter the intensity ratio between emission lines. This method overcomes the poor dynamics of ICCD used at maximum gains and opens new possibilities in micro-LIBS analysis.