Declining Worker Turnover: The Role of Short-Duration Employment Spells

Using the Quarterly Workforce Indicators database, we document that a significant amount of the decline in labor market turnover during the last two decades is accounted for by the decline in employment spells that last just one or two quarters. This phenomenon is pervasive: short-term employment spells have declined across industries, firm size categories, demographic groups, and geographic regions. Using a search-and-matching model in the Diamond-Mortensen-Pissarides tradition that incorporates noisy signals about the quality of a worker-firm match, we argue that improved screening by workers and firms can account for much of the decline in short-lived employment spells. (JEL E24, J23, J41, J63, M51)

Automatic Speech Recognition in Noise for Parkinson's Disease: A Pilot Study

The sophistication of artificial intelligence (AI) technologies has significantly advanced in the past decade. However, the observed unpredictability and variability of AI behavior in noisy signals is still underexplored and represents a challenge when trying to generalize AI behavior to real-life environments, especially for people with a speech disorder, who already experience reduced speech intelligibility. In the context of developing assistive technology for people with Parkinson's disease using automatic speech recognition (ASR), this pilot study reports on the performance of Google Cloud speech-to-text technology with dysarthric and healthy speech in the presence of multi-talker babble noise at different intensity levels. Despite sensitivities and shortcomings, it is possible to control the performance of these systems with current tools in order to measure speech intelligibility in real-life conditions.

Imaginary Finger Movements Decoding Using Empirical Mode Decomposition and a Stacked BiLSTM Architecture

Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent researches because they deliver relevant classification rates for BCI systems. However, smaller and noisy signals corresponding to hand-finger imagined movements are less frequently used because they are difficult to classify. This study proposes a method for decoding finger imagined movements of the right hand. For this purpose, MI-EEG signals from C3, Cz, P3, and Pz sensors were carefully selected to be processed in the proposed framework. Therefore, a method based on Empirical Mode Decomposition (EMD) is used to tackle the problem of noisy signals. At the same time, the sequence classification is performed by a stacked Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed method was evaluated using k-fold cross-validation on a public dataset, obtaining an accuracy of 82.26%.

Математический алгоритм для обнаружения утечек малой интенсивности

The operating principle of leak detection systems, based on registration of transported medium hydroacoustic fluctuations, appearing due to pipeline loss of containment, consists of identification of hydraulic impulse, originating in case of leakage, using acoustic dynamic pressure measuring sensors - hydrophones. However, during pumping at pipeline stationary operating mode hydrophones also register background noises, which can mask the leakage signal. To separate the useful leakage signal it is important to construct an algorithm that allows lowering the noise component of the signals. Within the scope of experimental research, two pairs of hydrophones were used, which were installed at the functioning main oil pipeline at a distance of 20 km of each other. The distance between the adjacent paired hydrophones was no more than 1 km. Leaks were imitated by draining the product (diesel fuel) in the middle of control section. Authors considered the methods of noisy signals filtration and possible methods of cleared signals processing to determine the leak parameters. Mathematical algorithm that allows minimizing the influence of signal noise by filtration and mutual hydrophone readings compensation was proposed. It is established, that the developed algorithm allows detecting the leakages of low intensity (up to 0.1 % of actual flow) in cases of stationary pipeline operating mode and pumping stop mode. Принцип работы систем обнаружения утечек, основанных на регистрации гидроакустических колебаний транспортируемой среды, возникающих из-за разгерметизации трубопровода, состоит в идентификации гидравлического импульса, возникающего при образовании утечки, с помощью акустических датчиков измерения динамического давления – гидрофонов. Однако гидрофоны в процессе перекачки при стационарном режиме работы трубопровода регистрируют в том числе фоновые шумы, которые могут маскировать сигнал от утечки. Для выделения полезного сигнала утечки актуально построение алгоритма, позволяющего понизить шумовые составляющие сигналов. В рамках экспериментальных исследований использовались две пары гидрофонов, которые устанавливались на действующем магистральном нефтепродуктопроводе на расстоянии 20 км друг от друга. Расстояние между соседними гидрофонами в паре составляло не более 1 км. Утечки имитировались путем выполнения натурных сливов продукта (дизельного топлива) в середине контрольного участка. Авторами рассмотрены методы фильтрации зашумленных сигналов и возможные способы обработки очищенных сигналов с целью определения параметров утечки. Предложен математический алгоритм, позволяющий минимизировать влияние шумовых составляющих сигналов путем фильтрации и взаимной компенсации показаний пар гидрофонов. Установлено, что разработанный алгоритм позволяет обнаруживать утечки малой интенсивности (до 0,1 % от фактического расхода) в условиях стационарного режима работы трубопровода и режима остановленной перекачки.

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

Optimal Passive Source Localization for Acoustic Emissions

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Fragmentation inside PTR-based mass spectrometers limits the detection of ROOR and ROOH peroxides

Proton-transfer-reaction (PTR) is a commonly applied ionization technique for mass spectrometers, where hydronium ions (H3O+) transfer a proton to analytes with higher proton affinities than the water molecule. This method has most commonly been used to quantify volatile hydrocarbons, but later generation PTR-instruments have been designed for better throughput of less volatile species, allowing detection of more functionalized molecules as well. For example, the recently developed Vocus PTR time-of-flight mass spectrometer (PTR-TOF) has been shown to agree well with an iodide adduct based chemical ionization mass spectrometer (CIMS) for products with 3-5 O-atoms from oxidation of monoterpenes (C10H16). However, while several different types of CIMS instruments (including those using iodide) detect abundant signals also at “dimeric” species, believed to be primarily ROOR peroxides, no such signals have been observed in the Vocus PTR, even though these compounds fulfil the condition of having higher proton affinity than water. More traditional PTR instruments have been limited to volatile molecules as the inlets have not been designed for transmission of easily condensable species. Some newer instruments, like the Vocus PTR, have overcome this limitation, but are still not able to detect the full range of functionalized products, suggesting that other limitations need to be considered. One such limitation, well-documented in PTR literature, is the tendency of protonation to lead to fragmentation of some analytes. In this work, we evaluate the potential for PTR to detect dimers and the most oxygenated compounds, as these have been shown to be crucial for forming atmospheric aerosol particles. We studied the detection of dimers using a Vocus PTR-TOF in laboratory experiments as well as through quantum chemical calculations. Only noisy signals of potential dimers were observed during experiments on the ozonolysis of the monoterpene α-pinene, while a few small signals of dimeric compounds were detected during the ozonolysis of cyclohexene. During the latter experiments, we also tested varying the pressures and electric fields in the ionization region of the Vocus PTR-TOF, finding that only small improvements were possible in the relative dimer contributions. Calculations for model ROOR and ROOH systems showed that most of these peroxides should fragment partially following protonation. With inclusion of additional energy from the ion-molecule collisions driven by the electric fields in the ionization source, computational results suggest substantial or nearly complete fragmentation of dimers. Our study thus suggests that while the improved versions of PTR-based mass spectrometers are very powerful tools for measuring hydrocarbons and their moderately oxidized products, other types of CIMS are likely more suitable for the detection of ROOR and ROOH species.

TwinCons: Conservation score for uncovering deep sequence similarity and divergence

We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a ‘cost’ of transforming one group to the other at each position of the alignment. The output distinguishes conserved, variable and signature positions. A signature is conserved within groups but differs between groups. The method automatically detects continuous characteristic stretches (segments) within alignments. TwinCons provides a convenient representation of conserved, variable and signature positions as a single score, enabling the structural mapping and visualization of these characteristics. Structure is more conserved than sequence. TwinCons highlights alternative sequences of conserved structures. Using TwinCons, we detected highly similar segments between proteins from the translation and transcription systems. TwinCons detects conserved residues within regions of high functional importance for the ribosomal RNA (rRNA) and demonstrates that signatures are not confined to specific regions but are distributed across the rRNA structure. The ability to evaluate both nucleic acid and protein alignments allows TwinCons to be used in combined sequence and structural analysis of signatures and conservation in rRNA and in ribosomal proteins (rProteins). TwinCons detects a strong sequence conservation signal between bacterial and archaeal rProteins related by circular permutation. This conserved sequence is structurally colocalized with conserved rRNA, indicated by TwinCons scores of rRNA alignments of bacterial and archaeal groups. This combined analysis revealed deep co-evolution of rRNA and rProtein buried within the deepest branching points in the tree of life.

DCT Domain Detail Image Enhancement for More Resolved Images

This paper develops a detail image signal enhancement that makes images perceived as being clearer and more resolved and so more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengthens the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.