False Events Research Articles

Dynamic normalization for compact binary coalescence searches in non-stationary noise

The output of gravitational-wave interferometers, such as LIGO and Virgo, can be highly non-stationary. Broadband detector noise can affect the detector sensitivity on the order of tens of seconds. Gravitational-wave transient searches, such as those for colliding black holes, estimate this noise in order to identify gravitational-wave events. During times of non-stationarity we see a higher rate of false events being reported. To accurately separate signal from noise, it is imperative to incorporate the changing detector state into gravitational-wave searches. We develop a new statistic which estimates the variation of the interferometric detector noise. We use this statistic to re-rank candidate events identified during LIGO–Virgo’s second observing run by the PyCBC search pipeline. This results in a 5% improvement in the sensitivity volume for low mass binaries, particularly binary neutron stars mergers.

Radar Range-Breathing Separation for the Automatic Detection of Humans in Cluttered Environments

In this article, a new sensing approach for the automatic detection and ranging of human targets in cluttered environments is detailed. The person detection does not require to preliminarily define a target threshold, thus minimizing the likelihood of false alarm events. Moreover, the proposed approach allows to resolve the artifacts due to the presence of multiple objects within the same radiation beam without increasing the system complexity and cost. A powerful graphical representation, denominated range-breathing graph, is exploited for quickly making the reader aware of the exact human location, employing the breathing rate as the key element for discerning between humans and non-human targets. The method is analytically described while exhaustive simulations are carried out for confirming the reliability and suitability of the method with regard to the state of art. The effectiveness of the range-breathing separation approach is demonstrated by using two different radar boards operating at the frequencies of 24 GHz and 122 GHz. The obtained results show the general validity of this approach for identifying humans in complex cluttered scenarios

The Validity of Automated Tackle Detection in Women's Rugby League.

Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951-1955, 2022-This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in elite women's rugby league match-play. Elite female players (n = 17) wore a microtechnology device (OptimEye S5 device; Catapult Group International) during a representative match, which involved a total of 512 tackles of which 365 were defensive and 147 were attacking. Tackles automatically detected by Catapult's tackle detection algorithm and video-coded tackles were time synchronized. True positive, false negative and false positive events were utilized to calculate sensitivity (i.e., when a tackle occurred, did the algorithm correctly detect this event) and precision (i.e., when the algorithm reported a tackle, was this a true event based on video-coding). Of the 512 video-derived attacking and defensive tackle events, the algorithm was able to detect 389 tackles. The algorithm also produced 81 false positives and 123 false negatives. As such when a tackle occurred, the algorithm correctly identified 76.0% of these events. When the algorithm reported that a tackle occurred, this was an actual event in 82.8% of circumstances. Across all players, the algorithm was more sensitive to the detection of an attacking event (sensitivity: 78.2%) as opposed to a defensive event (sensitivity: 75.1%). The sensitivity and precision of the algorithm was higher for forwards (sensitivity: 81.8%; precision: 92.1%) when compared with backs (sensitivity: 64.5%; precision: 66.1%). Given that understanding the tackle demands of rugby league is imperative from both an injury-prevention and physical-conditioning perspective there is an opportunity to develop a specific algorithm for the detection of tackles within women's rugby league.

Evaluation of safety critical event triggers in the UDrive data

The understanding of the root causes of traffic collisions has taken a new turn with naturalistic driving studies. Naturalistic driving Data bring insight into normal behavior on the road and help characterize safety related and safety critical events (SCEs) through kinematic triggers and video analysis. The UDRIVE Database includes two million kilometers of naturalistic driving and was recorded in five European countries between 2015 and 2017. Given the large volume of raw data considered, the performance of these kinematic triggers in detecting SCEs should be carefully evaluated to optimize the human resources required to discard false positive events. The aim of the present article is twofold: (i) to describe the triggers used to detect safety critical events from the UDRIVE Database and (ii) to discuss the relevance of SCE triggers performance evaluation used in the literature.

Measuring Extracellular Vesicles by Conventional Flow Cytometry: Dream or Reality?

Intense research is being conducted using flow cytometers available in clinically oriented laboratories to assess extracellular vesicles (EVs) surface cargo in a variety of diseases. Using EVs of various sizes purified from the HT29 human colorectal adenocarcinoma cell line, we report on the difficulty to assess small and medium sized EVs by conventional flow cytometer that combines light side scatter off a 405 nm laser with the fluorescent signal from the EVs general labels Calcein-green and Calcein-violet, and surface markers. Small sized EVs (~70 nm) immunophenotyping failed, consistent with the scarcity of monoclonal antibody binding sites, and were therefore excluded from further investigation. Medium sized EVs (~250 nm) immunophenotyping was possible but their detection was plagued by an excess of coincident particles (swarm detection) and by a high abort rate; both factors affected the measured EVs concentration. By running samples containing equal amounts of Calcein-green and Calcein-violet stained medium sized EVs, we found that swarm detection produced false double positive events, a phenomenon that was significantly reduced, but not totally eliminated, by sample dilution. Moreover, running highly diluted samples required long periods of cytometer time. Present findings raise questions about the routine applicability of conventional flow cytometers for EV analysis.

A Comparison of Tropical Cyclone Genesis Forecast Verification from Three Global Forecast System (GFS) Operational Configurations

AbstractOperational forecasting of tropical cyclone (TC) genesis has improved in recent years but still can be a challenge. Output from global numerical models continues to serve as a primary source of forecast guidance. Bulk verification statistics (e.g., critical success index) of TC genesis forecasts indicate that, overall, global models are increasingly able to predict TC genesis. However, as global model configurations are updated, TC genesis verification statistics will change. This study compares operational and retrospective forecasts from three configurations of NCEP’s Global Forecast System (GFS) to quantify the impact of model upgrades on TC genesis forecasts. First, bulk verification statistics from a homogeneous sample of model initialization cycles during the period 2013–14 are compared. Then, composites of select output fields are analyzed in an attempt to identify any key differences between hit and false alarm events. Bulk statistics indicate that TC genesis forecast performance decreased with the implementation of the 2015 version of the GFS, but then modestly recovered with the 2016 version of the model. In addition, the composite analysis suggests that false alarm forecasts in the 2015 version of the GFS may have been the result of inaccurately forecasting the location and/or strength of upper-level troughs poleward of the TC. There is also evidence of convective feedbacks occurring, such as ridging above the low-level circulation and upper-level convective outflow that were too strong, in this same set of false alarm forecasts. Overall, analyzing retrospective forecasts can assist forecasters in determining the strengths and weaknesses associated with a new configuration of a global model with respect to TC genesis.

The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data. In desert seismic data, the energy of noise is stronger. Also, the frequency-band overlap between noise and effective signals is more serious. Recently, some methods based on supervised learning can suppress the desert seismic noise to some extent. Generally, supervised learning-based methods use synthetic noisy data and paired pure data as training sets to train model. However, the difference between synthetic noisy data of training and real seismic data of testing leads to the degradation of the model, and the denoising results often have many false seismic events when dealing with field seismic data. To solve the above problem, we introduce Cycle-generative adversarial networks (GANs) into the denoising of desert seismic records. Cycle-GAN is an unsupervised learning-based method. It can learn the domain mapping from noisy data domain to effective signal data domain through unpaired data training. So we use unpaired real desert common-shot-point data and synthetic pure data to train Cycle-GAN, so as to effectively improve the denoising ability of the method for real seismic data. Finally, the denoising of desert seismic data is realized. The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude. Also, the denoising result has less false seismic reflection.

Multi-branch long short-time memory convolution neural network for event identification in fiber-optic distributed disturbance sensor based on φ-OTDR

In this paper, we propose a novel neural network model named by Multi-branch Long Short-Time Memory Convolution Neural Network (MLSTM-CNN) for identifying disturbance signals in distributed optical fiber sensing system based on phase-sensitive optical time domain reflectometry (φ-OTDR). By unifying feature extraction and classification in a framework, MLSTM-CNN automatically extracts features at different time scales leveraging multi-branch layer and learnable LSTM layers, and then the disturbance signals are identified in the learnable CNN layers. Through constructing 25.05 km φ-OTDR experimental system, four kinds of real disturbance events, including watering, climbing, knocking, and pressing, and a false disturbance event can be effectively identified. Experimental results show that the average identification rate can reach 95.7%, and nuisance alarm rate (NAR) is 4.3%. Compared with the LSTM and CNN model, the recognition accuracy of the proposed model can be improved and the signal processing time can be efficiently reduced as well.

Performance and optimization of an ear tag automated activity monitor for estrus prediction in dairy heifers

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (>13.5 m of age). For the first AI, estrus was synchronized using 500 μg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index < 45 and a rumination change < -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r < 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16–46 h), 22 h (10–40 h) and 16 h (0–36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

Computational model for detector timing effects in Compton-camera based prompt-gamma imaging for proton radiotherapy

This paper describes a realistic simulation of a Compton-camera (CC) based prompt-gamma (PG) imaging system for proton range verification for a range of clinical dose rates, and its comparison to PG measured data with a pre-clinical CC. We used a Monte Carlo plus Detector Effects (MCDE) model to simulate the production of prompt gamma-rays (PG) and their energy depositions in the CC. With Monte Carlo, we simulated PG emission resulting from irradiation of a high density polyethylene phantom with a 150 MeV proton pencil beam at dose rates of 5.0 × 108, 2.6 × 109, and 4.6 × 109 p+ s−1. Realistic detector timing effects (e.g. delayed triggering time, event-coincidence, dead time, etc,) were added in post-processing to allow for flexible count rate variations. We acquired PG emission measurements with our pre-clinical CC during irradiation with a clinical 150 MeV proton pencil beam at the same dose rates. For simulations and measurements, three primary changes could be seen in the PG emission data as the dose rate increased: (1) reduction in the total number of detected events due to increased dead-time percentage; (2) increase in false-coincidence events (i.e. multiple PGs interacting, rather than a single PG scatter); and (3) loss of distinct PG emission peaks in the energy spectrum. We used the MCDE model to estimate the quality of our measured PG data, primarily with regards to true and false double-scatters and triple-scatters recorded by the CC. The simulation results showed that of the recorded double-scatter PG interactions 22%, 57%, and 70% were false double-scatters and for triple-scatter interactions 3%, 21%, and 35% were false events at 5.0 × 108, 2.6 × 109, and 4.6 × 109 p+ s−1, respectively. These false scatter events represent noise in the data, and the high percentage of these events in the data represents a major limitation in our ability to produce usable PG images with our prototype CC.

Magnitude Agreement, Occurrence Consistency, and Elevation Dependency of Satellite-Based Precipitation Products over the Tibetan Plateau

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP.

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: An Information Theory Approach for Non-Stationary Single-Molecule Data Analysis

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: An Information Theory Approach for Non-Stationary Single-Molecule Data Analysis

When and how are lies told? And the role of culture and intentions in intelligence‐gathering interviews

PurposeLie‐tellers tend to tell embedded lies within interviews. In the context of intelligence‐gathering interviews, human sources may disclose information about multiple events, some of which may be false. In two studies, we examined when lie‐tellers from low‐ and high‐context cultures start reporting false events in interviews and to what extent they provide a similar amount of detail for the false and truthful events. Study 1 focused on lie‐tellers' intentions, and Study 2 focused on their actual responses.MethodsParticipants were asked to think of one false event and three truthful events. Study 1 (N = 100) was an online study in which participants responded to a questionnaire about where they would position the false event when interviewed and they rated the amount of detail they would provide for the events. Study 2 (N = 126) was an experimental study that involved interviewing participants about the events.ResultsAlthough there was no clear preference for lie position, participants seemed to report the false event at the end rather than at the beginning of the interview. Also, participants provided a similar amount of detail across events. Results on intentions (Study 1) partially overlapped with results on actual responses (Study 2). No differences emerged between low‐ and high‐context cultures.ConclusionsThis research is a first step towards understanding verbal cues that assist investigative practitioners in saving their cognitive and time resources when detecting deception regardless of interviewees' cultural background. More research on similar cues is encouraged.

Improved Strategies for the Maritime Industry to Target Vessels for Inspection and to Select Inspection Priority Areas

Inspection authorities such as the Port State Control Memoranda of Understanding use different policies and targeting methods to select vessels for inspections and rely primarily on past inspection outcomes. One of the main goals of inspections is to improve the safety quality of vessels and to reduce the probability of future incidents. This study shows there is room for improvement in targeting vessels for inspections and in determining vessel-specific inspection priority areas (e.g., bridge management versus machinery related items). For the year 2018, sixty percent of vessels that experienced very serious or serious (VSS) incidents were not selected for inspection up to three months prior to the incident and forty percent of the vessels that were inspected still had incidents of which only four percent were detained. Furthermore, one can observe a very low correlation (−0.04) between the probabilities of detention and incidents (VSS) for the year 2018. The proposed approach treats detention and incident types as separate risk dimensions and evaluates seven targeting methods against random selection of vessels using empirical data for 2018. The analysis is based on three comprehensive data sets that cover the world fleet and shows potential gains (reduction of false negative events) of 14–27 percent compared to random selection. This can be further improved by adding eight inspection priority risk areas that help inspectors to focus inspections by providing insight in the individual risk profile of vessels. Policy makers can further customize the approach by classifying the risk of vessels into categories and by selecting inspection targets and benchmark samples. A small application example is provided to demonstrate feasibility of the proposed approach for policy makers and inspection authorities.

Inherent Uncertainty in the Determination of Multiple Event Cross Sections in Radiation Tests

In radiation tests on SRAMs or field-programmable gate arrays (FPGAs), two or more independent bitflips can be misled with a multiple event if they accidentally occur in neighbor cells. In the past, different tests, such as the “birthday statistics,” have been proposed to estimate the accuracy of the experimental results. In this article, simple formulas are proposed to determine the expected number of false 2- and 3-bit multiple cell upsets (MCUs) from the number of bitflips, memory size, and the method used to search multiple events. These expressions are validated using the Monte Carlo simulations and experimental data, and a technique is proposed to refine the experimental data and thus partially removing possible false events. Finally, it is demonstrated that there is a physical limit to determine the cross section of memories with arbitrary accuracy from a single experiment.

Laboratory testing for activated protein C resistance: rivaroxaban induced interference and a comparative evaluation of andexanet alfa and DOAC Stop to neutralise interference.

Background Investigation of hemostasis is problematic when patients are on anticoagulant therapy. Rivaroxaban especially causes substantial interference, extending many clot-based tests, thereby leading to false positive or negative events. In particular, rivaroxaban affects some assays for activated protein C resistance (APCR). Methods We assessed, in an international setting, cross laboratory (n = 31) testing using four samples to evaluate rivaroxaban induced interference in APCR testing, and whether this interference could be neutralised. The samples comprised: (A) pool of normal plasma (APCR-negative control); (B) this normal pool spiked with rivaroxaban (200 ng/mL) to create rivaroxaban-induced interference (potential 'false' positive APCR event sample); (C) the rivaroxaban sample subsequently treated with a commercial direct oral anticoagulant 'DOAC-neutraliser' (DOAC Stop), or (D) treated with andexanet alfa (200 μg/mL). Testing was performed blind to sample type. Results The rivaroxaban-spiked sample generated false positive APCR results for some, but unexpectedly not most APCR-tests. The sample treated with DOAC Stop evidenced a correction in the rivaroxaban-affected APCR assays, and did not otherwise adversely affect the rivaroxaban 'unaffected' APCR assays. The andexanet alfa-treated sample did not evidence correction of the false positive APCR, and instead unexpectedly exacerbated false positive APCR status with many tests. Conclusions DOAC Stop was able to neutralise any APCR interference induced by rivaroxaban. In contrast, andexanet alfa did not negate such interference, and instead unexpectedly created more false-positive APCR events.

Evaluation of GBAS flight trials based on BDS and GPS

Ground-based augmentation systems (GBASs) are widely used augmentation systems using satellite navigation. GBAS can improve the performance of BeiDou navigation satellite system (BDS) in civil aviation application. Evaluation is necessary before using a GBAS. On the basis of the flight trials conducted at the Dongying airport, evaluation results show that based on both the global positioning system (GPS) L1 signal and BDS B1I signal, ground accuracy designators (GADs) of four reference receivers are consistent with GAD-C levels. The improvement in accuracy obtained with GBAS differential process is ∼60%, and the differential position error based on the BDS B1I signal is 40% greater than that of the GPS L1 signal, because currently incomplete BDS satellites distribution is worse than that of GPS. Airborne protection levels assessments show that the approach trials based on the BDS B1I signal exhibit several false alarm events. Further analysis indicates that BDS vertical protection levels were larger because of larger pseudoinverse S result from low elevation angle and unsatisfactory geometric distribution of BDS satellites. Besides, monitors show that BDS B1I signal is more affected by ionospheric delay than GPS L1 signal, which can also contribute to false alarm events.

A nonparametric method to evaluate significance of events in search for gravitational waves with false discovery rate

During recent observation runs by LIGO and Virgo, tens of gravitational wave events have been observed. The gravitational wave network of LIGO/Virgo/KAGRA is planning longer observation runs with improved sensitivity in the future. In this situation, it is important to evaluate the significance of each event with reliable methods since we expect, as the observation period becomes longer and as the sensitivity of the detector improves, we expect to detect more false positive events with a given threshold for the false alarm rate. In this paper, we introduce q-value which was first introduced by Storey[1], and is interpreted as False Discovery Rate (FDR). We introduce an algorithm to evaluate FDR in a non-parametric way, and apply it to publically available results of the analysis of the advanced LIGO O1 data, and find that we can obtain a better significance for GW151012 which has the lowest significance in the O1 events. For other events, we obtain consistent results already obtained by LIGO and Virgo.

Event Identification by F-ELM Model for $\varphi$ -OTDR Fiber-Optic Distributed Disturbance Sensor

A novel event identification method, which combines the extreme learning machine (ELM) and fisher score feature selection method, is proposed to reduce the nuisance alarm rate (NAR) in fiber-optic distributed disturbance sensors based on phase-sensitive optical time-domain reflectometer (φ-OTDR). Through constructing 25.05 km long φ-OTDR experimental system and analyzing the selected features with the ELM, four kinds of real disturbance events, including watering, climbing, knocking and pressing, and a false disturbance event can be effectively identified. Experimental results show that the average identification rate of five disturbance events exceeds 95%, the identification time is below 0.1 s and the NAR is 4.67% through selecting 25 features. Compared with the ELM model without feature selection, the ELM model with feature selection by fisher method has several distinguished advantages of higher identification rate, shorter identification time, and lower NAR.

The OpenPicoAmp-100K, an Open-source High Performance Amplifier for Single Channel Recording in Planar Lipid Bilayers

We propose an upgraded version of our previously designed open-source lipid bilayer amplifier. This improved amplifier is now suitable both for the use in introductory courses in biophysics and neurosciences at the undergraduate level and for scientific research. Similar to its predecessor, the OpenPicoAmp-100k is designed using the common lithographic printed circuit board fabrication process and off-the-shelf electronic components. It consists of the high-speed headstage, followed by voltage-gain amplifier with built-in 6-order Bessel filter. The amplifier has a bandwidth of 100 kHz in the presence of 100 pF input membrane capacitance and is capable of measuring ion channel current with amplitudes from sub-pA and up to ±4 nA. At the full bandwidth and with a 1 GΩ transimpedance gain, the amplifier shows 12 pArms noise with an open input and 112 pArms noise in the presence of 100 pF input capacitance, while at the 5 kHz bandwidth (typical in single-channel experiments) noise amounts to 0.45 pArms and 2.11 pArms, respectively. Using an optocoupler circuit producing TTL-controlled current impulses and using 50% threshold analysis we show that at full bandwidth the amplifier has deadtimes of 3.5 µs and 5 µs at signal-to-noise ratios(SNR) of 9 and 1.7, respectively. Near 100% of true current impulses longer than 5 µs and 6.6 µs are detected at these two respective SNRs, while false event detection rate remains acceptably low. The wide bandwidth of the amplifier was confirmed in bilayer experiments with alamethicin, for which open ion channel current events shorter that 10 µs could be resolved.