Error Rate Research Articles

The Association Between Task Complexity and Cortical Language Mapping Accuracy.

Direct cortical stimulation (DCS) mapping enables the identification of functional language regions within and around gliomas before tumor resection. Intraoperative mapping is required because glioma-infiltrated cortex engages in synchronous activity during task performance in a manner similar to normal-appearing cortex but has decreased ability to encode information for complex tasks. It is unknown whether task complexity influenced DCS mapping results. We aim to understand correlations between audiovisual picture naming (PN) task complexity and DCS error rate. We also asked what functional and oncological factors might be associated with higher rates of erroneous responses. We retrospectively reviewed intraoperative PN and word reading (WR) task performance during awake DCS language mapping for resection of dominant hemisphere World Health Organization grade 2 to 4 gliomas. The complexity of word tested in PN/WR tasks, patient characteristics, and tumor characteristics were compared between correct and incorrect trials. Between 2017 and 2021, 74 patients met inclusion criteria. At median 18.6 months of follow-up, 73.0% were alive and 52.7% remained recurrence-free. A total of 2643 PN and 978 WR trials were analyzed. A greater number of syllables in PN was associated with a higher DCS error rate (P = .001). Multivariate logistic regression found that each additional syllable in PN tasks independently increased odds of error by 2.40 (P < .001). Older age was also an independent correlate of higher error rate (P < .043). World Health Organization grade did not correlate with error rate (P = .866). More severe language impairment before surgery correlated with worse performance on more complex intraoperative tasks (P < .001). A higher error rate on PN testing did not correlate with lower extent of glioma resection (P = .949). Word complexity, quantified by the number of syllables, is associated with higher error rates for intraoperative PN tasks but does not affect extent of resection.

Visuospatial working memory in behavioural variant frontotemporal dementia: a comparative analysis with Alzheimer's disease using the box task.

This study investigated the visuospatial working memory profiles of behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) using a novel computerised test of visuospatial working memory: the Box Task. Twenty-eight bvFTD and 28 AD patients, as well as 32 age-matched control participants were recruited. All participants completed the Box Task and conventional neuropsychological tests of working memory, episodic memory, and visuospatial function. Both the bvFTD and AD groups exhibited significantly more Box Task between-search errors than the control group across all set sizes. Notably, the AD group demonstrated a significantly higher error rate compared to the bvFTD group. Regression analysis revealed that whilst episodic memory impairment significantly predicted Box Task error performance in AD, this was not the case for bvFTD. Additionally, a noticeable trend was observed for attention in predicting Box Task errors in both bvFTD and AD groups. The Box Task demonstrated high utility in differentiating between bvFTD and AD, with a decision tree correctly classifying 82.1% of bvFTD patients and 75% of AD patients. Our findings reveal significant visuospatial working memory impairments in bvFTD, albeit of lesser severity compared to disease-matched AD patients. The Box Task, a novel measure of visuospatial working memory, proved effective in differentiating between bvFTD and AD, outperforming many traditional neuropsychological measures. Overall, our findings highlight the utility of assessing visuospatial memory when differentiating between bvFTD and AD in the clinical setting.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Implementing Mental Health Screening for Adolescent Hematology and Oncology Patients: A Quality Improvement Initiative.

Purpose: Adolescents and young adults (AYA) with chronic illnesses experience an increase in mental health concerns. A mental health screening (MHS) process for hematology and oncology patients was implemented in a single institution. The quality improvement project was conducted to integrate a MHS process, educate providers about the importance of mental health in this patient population, and evaluate the process. Methods: The COM-B (capability, opportunity, motivation-behavior) model for behavior change was used to inform strategic planning and Plan-Do-Study-Act (PDSA) methodologies for process improvement. Retrospective chart reviews and surveys were conducted to determine missed screening rates and providers' perceptions, knowledge, attitudes, and skills of the MHS process. Results: Of 334 eligible patient encounters, the missed screening rate was 15.0%, the overall error rate of completing the screening was 3.8%, the error rate of completing the suicide or self-harm indicator was 2.6%, and the missed medical social worker touch point was 4.8%. Conclusion: The rising rates of mental health concerns in AYA hematology and oncology patients call for streamlined MHS processes to improve the identification of patients who may need intervention and services. Processes should be tailored to workflows and available resources. Future PDSA cycles will include providing dedicated nursing education and determining the cost needed to meet the rising mental health needs of the AYA hematology and oncology population.

Artificial intelligence to automate assessment of ocular and periocular measurements.

To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements. Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient. A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%. The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.

HUMAN STRESS LEVEL IDENTIFICATION USING MACHINE LEARNING ALGORITHMS BASED ON SLEEPING PATTERNS

Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. The obtained dataset includes various sleep habits and stress levels. Six machine learning techniques, including Multilayer Perception (MLP), Random Forest, Support Vector Machine (SVM), Decision Trees, Naïve Bayes and Logistic Regression were utilized in the classification level after the data had been preprocessed in order to compare and obtain the most accurate results. Based on the experiment results, it can be concluded that the Decision tree algorithm, when used to classify the data, can do so with 93.27% accuracy, high precision, recall, and f1measure values, as well as the lowest mean absolute error (MAE) and root mean squared error rates (RMSE). We can estimate human stress levels using the study's findings, and we can address pertinent problems as soon as possible.

Polar code-based secure transmission with higher message rate combining channel entropy and computational entropy

The existing physical layer security schemes, which are based on the key generation model and the wire-tap channel model, achieve security by utilizing channel reciprocity entropy and noise entropy, respectively. In contrast, we propose a novel secure transmission framework that combines noise entropy with reciprocity entropy, achieved by inserting reciprocity entropy into the frozen bits of polar codes. Note that in real-world scenarios, when eavesdroppers employ polynomial-time attacks, the bit error rate (BER) increases due to the introduction of computational entropy. To achieve indistinguishability security, we convert the practical physical layer security metric, BER, into the average min-entropy, a widely accepted concept in cryptography. The simulation results demonstrate that the eavesdropper’s BER can be significantly increased without compromising the communication performance of the legitimate receiver. Under concrete parameters we selected, when compared to the joint scheme of physical layer key generation and one time pad, the modular semantically-secure scheme based on the wire-tap channel model, and the simple channel entropy combination scheme, our scheme achieves a message rate approximately 1.2 times, 3.8 times, and 1.4 times better, respectively. Experimental testing validates the feasibility of our scheme.

A new long-read mitochondrial-genome protocol (PacBio HiFi) for haemosporidian parasites: a tool for population and biodiversity studies

BackgroundStudies on haemosporidian diversity, including origin of human malaria parasites, malaria's zoonotic dynamic, and regional biodiversity patterns, have used target gene approaches. However, current methods have a trade-off between scalability and data quality. Here, a long-read Next-Generation Sequencing protocol using PacBio HiFi is presented. The data processing is supported by a pipeline that uses machine-learning for analysing the reads.MethodsA set of primers was designed to target approximately 6 kb, almost the entire length of the haemosporidian mitochondrial genome. Amplicons from different samples were multiplexed in an SMRTbell® library preparation. A pipeline (HmtG-PacBio Pipeline) to process the reads is also provided; it integrates multiple sequence alignments, a machine-learning algorithm that uses modified variational autoencoders, and a clustering method to identify the mitochondrial haplotypes/species in a sample. Although 192 specimens could be studied simultaneously, a pilot experiment with 15 specimens is presented, including in silico experiments where multiple data combinations were tested.ResultsThe primers amplified various haemosporidian parasite genomes and yielded high-quality mt genome sequences. This new protocol allowed the detection and characterization of mixed infections and co-infections in the samples. The machine-learning approach converged into reproducible haplotypes with a low error rate, averaging 0.2% per read (minimum of 0.03% and maximum of 0.46%). The minimum recommended coverage per haplotype is 30X based on the detected error rates. The pipeline facilitates inspecting the data, including a local blast against a file of provided mitochondrial sequences that the researcher can customize.ConclusionsThis is not a diagnostic approach but a high-throughput method to study haemosporidian sequence assemblages and perform genotyping by targeting the mitochondrial genome. Accordingly, the methodology allowed for examining specimens with multiple infections and co-infections of different haemosporidian parasites. The pipeline enables data quality assessment and comparison of the haplotypes obtained to those from previous studies. Although a single locus approach, whole mitochondrial data provide high-quality information to characterize species pools of haemosporidian parasites.

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

Differential Item Functioning via Robust Scaling.

This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps: first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling and then tackling the latter using methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic type I error rate. Theoretical results describe the efficiency of the estimator in the absence of DIF and its robustness in the presence of DIF. Simulation studies show that the proposed method compares favorably to currently available approaches for DIF detection, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion.

Examining the contact problem of a functionally graded layer supported by an elastic half‐plane with the analytical and numerical methods

This study offers a comparative study of the analytical and numerical methods for investigating a contact problem. The contact problem comprises a functionally graded layer supported by a half‐plane and loaded with a distributed load from the top surface. First, the analytical and numerical solutions to the problem are acquired by utilizing a theory of elasticity and finite element method, respectively. The problem is transformed into a system of integral equations in which the contact stress is an unknown function. The solution of the integral equation was achieved with Gauss–Jacobi integration formulation. The finite element model of the problem is created using ANSYS software, and the two‐dimensional analysis of the problem is performed. Results were obtained from the samples for different material properties and loading conditions. The distributed load width and non‐homogeneity parameters significantly impact on contact mechanics. The results indicate that the contact area and the contact stress obtained from finite element method (FEM) are close to the analytical results. As a result, acceptable error rates were obtained. Finally, this study provides evidence of a good agreement between the two methods.

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

Optical fiber fronthaul segment in open radio access 5G networks: enhanced performance utilizing AFBG

In open Radio access network (oRAN) 5G networks, a fronthaul segment between virtual Distribution unit (vDU) edge cloud and other external Remote radio units (RRUs) sites is very important to transmit high data rate to achieve 5G requirements. Performance parameters such as Quality factor (Q-factor) and Bit error rate (BER) of an optical fiber fronthaul segment are the main important parameters to enhance latency and bit rate. An Apodized fiber bragg grating (AFBG) is utilized with different apodization profiles to optimize linewidth of an optical signal leading to enhance performance. The designed AFBG is used with a data rate of 25 Gb/s optical Ethernet module at the vDU edge cloud to optimize the linewidth of the optical signal between the vDU and the RRU in the fronthaul optical fiber segment. The AFBG is investigated at different connections pre, post, and symmetrical connections, where different apodization functions are used in the AFBG design. It is found that the best results for the maximum Q-factor and minimum BER are 9.2 and 2.72e− 21, respectively, obtained from uniform function in the symmetrical connection. Finally, the proposed model linewidth is 0.408 nm, which is better than 1.56 nm that is evaluated by related work.

A Cooperative Communication System With P-LDPC For Internet of Underwater Things

The acoustic communications are extremely challenging in underwater environments due to harsh conditions of this kind of nature. That medium's harshness affects the transmission reliability which in turn crucially requires an adoption of robust channel codes based on cooperative approaches to attain a more reliable data communication. This study proposes a cooperative communication system based on a protograph low-density parity check (P-LDPC) code. The system is elaborated in an Internet of underwater things (IoUT) scenario in which a relay node is acting as a helper that provides side information to destination. Compression and channel coding are offered by the system to cope with requirements of IoT in terms of reliable communication and compressed data between nodes. Furthermore, an algorithm for joint source channel coding is presented based on a modified version of offset min-sum. Moreover, layered decoding is Implemented in order to minimize number of iterations for the P-LDPC code. The results prove that proposed design can achieve a bit error rate (BER) of 5 × 10−6 at signal to noise ratio SNR = 8 dB for a coding rate of 1/3 and block length of 1500 bits. In addition, the proposed system shows a coding gain of 3.6 dB when compared with a non cooperative (no relay) approach. Besides, the proposed cooperative design can offer reduced computations versus the non cooperative system to achieve the same BER at a fixed SNR.

Single-Step Parity Check Gate Set for Quantum Error Correction

Abstract A key requirement for an effective Quantum Error Correction (QEC)&amp;#xD;scheme is that the physical qubits have error rates below a certain threshold. The&amp;#xD;value of this threshold depends on the details of the specific QEC scheme, and&amp;#xD;its hardware-level implementation. This is especially important with parity-check&amp;#xD;circuits, which are the fundamental building blocks of QEC codes. The standard&amp;#xD;way of constructing the parity check circuit is using a universal set of gates, namely&amp;#xD;sequential CNOT gates, single-qubit rotations and measurements. We exploit&amp;#xD;the insight that a QEC code does not require universal logic gates, but can be&amp;#xD;simplified to perform the sole task of error detection and correction. By building&amp;#xD;gates that are fundamental to QEC, we can boost the threshold and ease the&amp;#xD;experimental demands on the physical hardware. We present a rigorous formalism&amp;#xD;for constructing and verifying the error behavior of these gates, linking the physical&amp;#xD;measurement of a process matrix to the abstract error models commonly used in&amp;#xD;QEC analysis. This allows experimentalists to directly map the gates used in their&amp;#xD;systems to thresholds derived for a broad-class of QEC codes. We give an example&amp;#xD;of these new constructions using the model system of two nuclear spins, coupled&amp;#xD;to an electron spin, showing the potential benefits of redesigning fundamental gate&amp;#xD;sets using QEC primitives, rather than traditional gate sets reliant on simple single&amp;#xD;and two-qubit gates.

Shades of gravity - effects of planetary gravity levels on electrocortical activity and neurocognitive performance.

The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants' performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.  

Dingo optimization algorithm-based random forests model to evaluate the compressive strength of the concrete at elevated temperatures

The qualities of the materials employed to manufacture concrete are significantly impacted by high temperatures, which results in a noticeable decrease in the material’s strength characteristics. Concrete must be worked very hard and allowed to reach the required compressive strength (fc). Nevertheless, a preliminary estimation of the desired outcome may be made with an outstanding degree of reliability by using supervised machine learning algorithms. The study combined the Dingo optimization algorithm (DOA), Coot bird optimization (COA), and Artificial rabbit optimization (ARO) with Random Forests (RF) evaluation to determine the fc of concrete at high temperatures. The abbreviations used for the combined methods are RFD, RFC, and RFA, respectively. Remarkably, removing the temperature (T) parameter from the input set leads to a remarkable 1100% improvement in the effectiveness index (PI) and normalized root mean squared error (NRMSE), while causing a significant fall in the coefficient of determination (R2). The findings suggest that all RFD, RFC, and RFA have substantial promise in properly forecasting the fc of concrete at high temperatures. More precisely, the RFD algorithm demonstrated exceptional precision with R2 values of 0.9885 and 0.9873 throughout the training and testing stages, respectively. Through a comparison of the error percentages for RFD, RFC, and RFA in error-based measurements, it becomes evident that RFD exhibits an error rate that is about 50% smaller compared to that of RFC and RFA. This prediction is crucial for various industries and applications where concrete structures are subjected to elevated temperatures, such as in fire resistance assessments for buildings, tunnels, bridges, and other infrastructure. By accurately forecasting the compressive strength of concrete under these conditions, engineers and designers can make informed decisions regarding the material’s suitability and performance in high-temperature environments, leading to enhanced safety, durability, and cost-effectiveness of structures.

A novel deep learning based CBIR model using Convolutional Siamese Neural Networks

Content-Based Image Retrieval (CBIR) is a technique that involves retrieving similar images from a large database by analysing the content features of the query image. The heavy usage of digital platforms and devices has in a way promoted CBIR and its allied technologies in computer vision and artificial intelligence. The process entails comparing the representative features of the query image with those of the images in the dataset to rank them for retrieval. Past research was centered around handcrafted feature descriptors based on traditional visual features. But with the advent of deep learning the traditional manual method of feature engineering gave way to automatic feature extraction. In this study, a cascaded network is utilised for CBIR. In the first stage, the model employs multi-modal features from variational autoencoders and super-pixelated image characteristics to narrow down the search space. In the subsequent stage, an end-to-end deep learning network known as a Convolutional Siamese Neural Network (CSNN) is used. The concept of pseudo-labeling is incorporated to categorise images according to their affinity and similarity with the query image. Using this pseudo-supervised learning approach, this network evaluates the similarity between a query image and available image samples. The Siamese network assigns a similarity score to each target image, and those that surpass a predefined threshold are ranked and retrieved. The suggested CBIR system undergoes testing on a widely recognized public dataset: the Oxford dataset and its performance is measured against cutting-edge image retrieval methods. The findings reveal substantial enhancements in retrieval performance in terms of several standard benchmarks such as average precision, average error rate, average false positive rate etc., providing strong support for utilising images from interconnected devices.

A (Sub)penny for Your Thoughts: Tracking Retail Investor Activity in TAQ

ABSTRACTWe placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.