Correct Errors Research Articles

Enhancement of PIV measurements via physics-informed neural networks

Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component stereo particle-image velocimetry (PIV) datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNs-based model solves the Reynolds-averaged-Navier–Stokes equations for zero-pressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The turbulent boundary layer (TBL) data has different flow conditions upstream of the measurement location due to the effect of an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean-flow quantities by reducing errors in the PIV data. For comparison, a similar analysis has been applied to numerical data obtained from a spatially-developing ZPGTBL and an adverse-pressure-gradient TBL over a NACA4412 airfoil geometry. The PINNs-predicted results have less than 1% error in the streamwise velocity and are in excellent agreement with the reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories, which includes experiments and numerical simulations for predicting high-fidelity data.

Involvement of Pharmacists in the Emergency Department to Correct Errors in the Medication History and the Impact on Adverse Drug Event Detection.

(1) Incomplete or wrong medication histories can lead to missed diagnoses of Adverse Drug Effects (ADEs). We aimed to evaluate pharmacist-identified ED errors in the medication histories obtained by physicians, and their consequences for ADE detection. (2) This prospective monocentric study was carried out in an ED of a university hospital. We included adult patients presenting with an ADE detected in the ED. The best possible medication histories collected by pharmacists were used to identify errors in the medication histories obtained by physicians. We described these errors, and identified those related to medications involved in ADEs. We also identified the ADEs that could not have been detected without the pharmacists' interventions. (3) Of 735 patients presenting with an ADE, 93.1% had at least one error on the medication list obtained by physicians. Of the 1047 medications involved in ADEs, 51.3% were associated with an error in the medication history. In total, 23.1% of the medications involved in ADEs were missing in the physicians' medication histories and were corrected by the pharmacists. (4) Medication histories obtained by ED physicians were often incomplete, and half the medications involved in ADEs were not identified, or were incorrectly characterized in the physicians' medication histories.

Computational thinking in early childhood education: The impact of programming a tangible robot on developing debugging knowledge

The present study investigates the debugging process of 526 children in preschool (4–6 aged) when programming a tangible robot. When a child is involved in programming, it is needed to identify and correct errors, or in other words, debug a program. Debugging is a high-level thinking skill and is an essential component closely related to developing Computational Thinking in early childhood education. Though the debugging process is a cognitive function in programming investigated by many researchers in recent decades, most studies are conducted either in primary, secondary education, or higher education concerning beginners or experts in programming. Very few reviews, and some focus on preschool education. Our study set out the following objectives: 1) How do children identify the existence of error and locate it? 2) What types of errors emerge when coding? 3) What type of error is complex for novice programmers? 4) What strategies are developed by novice programmers to debug effectively? and 5) What types of knowledge do novice programmers construct by debugging the tangible robot Bee-Bot? The study follows an iterative model of a design-based research approach. It uses multiple case studies to collect qualitative and quantitative data. The programming intervention was implemented by 30 educators in their classrooms and was based on a scenario-based teaching design. The debugging process was analysed using Klahr & Craven's framework (1988): 1) test and evaluate program, 2) identify bug, 3) represent program, 4) locate bug and 5) correct bug. The main finding is the construction and development of syntactic and semantic knowledge.

Studying the Structure of the Sound of Russian Speech by Chinese and Ghanaian Students in a Digital Format: a Linguodidactic Aspect

The purpose of the research is to study the peculiarities of the structure of the sound of Russian speech by Chinese and Ghanaian students at different levels of study, based on experimental phonetic programs. The subject of the study is the intonational structure of the main communicative intentions: greeting, address, completion, incompletion, a question without an interrogative word, a question with the Russian conjunction «а» (“and”), a positive evaluation and surprise. The study contributes to the creation of an ethno-oriented methodology for teaching Russian intonation. The scientific novelty is in the analysis of the sound of the Russian speech of foreign students at the syntagma level in comparison with that of a native Russian speaker using the Praat and Lingvodoc programs. The results of the study consist in the visual representation of the peculiarities of the structure of the main communicative intentions in the Russian speech of Chinese and Ghanaians to predict and correct errors at the level of intonation.

Detect and Correct Errors In Chat GPT With Peter Chew Mental Calculation

Detect and Correct Errors In Chat GPT With Peter Chew Mental Calculation

Bell Labs and the ‘neural’ network, 1986–1996

AbstractBetween 1986 and 1996 researchers at the AT&T Bell Laboratories Adaptive Systems Research Department curated thousands of images of handwritten digits assembled by the United States Postal Service to train and evaluate artificial neural networks. In academic papers and conference literature, and in conversations with the press, Bell Labs researchers, executives and company spokespeople deployed the language of neurophysiology to position the systems as capable of codifying and reproducing feats of perception. Interpretations such as these were pivotal to the formation of brain–computer imaginaries that surrounded the development of the systems, which obscured the institutional infrastructures, clerical and cognitive labour, and the manipulation and maintenance of data on which feats of ‘recognition’ depended. Central to building the group's networks was the development of data sets constructed in consort with the US Postal Service, which arbitrated between the practicality of conducting research and the representation of an extensive catalogue of possible forms and permutations of handwritten digits. These imaginaries, which stressed a likeness with the human brain, were compounded by the promotion of ‘successful applications’ that took place under the AT&T corporate umbrella and with the essential support of US Postal Service workers to correct system errors.

Flapless and Conjunctiva-Sparing Technique for Transscleral Fixation of Intraocular Lens to Correct Refractive Errors in Eyes without Adequate Capsular Support.

To evaluate refractive outcomes, intraocular lens (IOL) power calculation, and IOL position following a novel conjunctiva-sparing transscleral fixation technique. Forty-one eyes of 40 patients managed with a flapless transscleral-sutured technique were included. Preoperative and postoperative refractive errors (spherical equivalents, SE) were compared. IOL position was assessed on the Scheimpflug images. IOL power was calculated by SRK/T, Holladay 1, and Hoffer Q formulas. The mean age was 57.39 ± 14.83 years (range: 26 to 79 years), and the mean follow-up was 7.46 ± 6.42 months (range: 1 to 24 months). Surgical indications were aphakia (n = 14), subluxated lenses (n = 3), and IOL dislocation (n = 24). The SE was 4.50 ± 6.38 diopter (D) (range: -3.75 to 13.75 D) preoperatively and -1.68 ± 1.57 D (range: -5.50 to 1.13 D) postoperatively (P < 0.001). The mean tilt angle and decentration were 2.90° ± 1.93° (range: 0.39° to 9.10°) and 0.23 ± 0.19 mm (range: 0.02 to 0.94 mm) vertically, and 1.75° ± 1.41° (range: 0.24° to 7.65°) and 0.18 ± 0.19 mm (range: 0.02 to 1.06 mm) horizontally, which were clinically insignificant. All three IOL formulas produced myopic errors (range: -0.29 to -0.50 D). The SRK/T had the lowest median absolute error (0.55 D), followed by the Holladay 1 (0.70 D) and the Hoffer Q (0.74 D). The three formulas had the same percentage of prediction errors (PEs) within ±0.5 D (43.48%), while the Hoffer Q had the highest percentage of PEs within ±1.0 D (82.61%). The present technique can serve as an alternative approach for transscleral IOL fixation and refractive correction in eyes with compromised capsular support, ensuring the stability of IOLs and reasonable IOL power calculation accuracy.

IDENTIFICATION OF STUDENTS’ DIFFICULTY IN WRITING EXPOSITORY ESSAY

This study aims to identify English Education Study Program students’ writing problems and how they solve them in expository essays. Whether the problems that occurred may the students solve or not. The subjects of this research were 8 students of English Education Study Program. The research was a case study approach by applied qualitative. The instruments used to collect the data were tests and interviews. The result of data analysis showed that students struggle with generating ideas, vocabulary grammatical elements such as tenses, subject-verb agreement, spelling, and sentence structure while writing in English. To overcome those problems, students browsed on the internet to find other references, Grammarly to correct grammatical errors, and an electronic dictionary to translate Indonesian terms into English Terms. This implies that the students need to work on selecting the appropriate language for the subject of their writing.

Efficient Radiation Pattern Estimation of Slot Array Antennas Covered With Frequency Selective Surfaces Using Deterministic Ray Tracing

In this paper, we propose a novel algorithm to compute the radiation pattern of a slot array antenna covered with frequency selective surfaces (FSS) based on ray tracing technique. We observed that the proposed algorithm could greatly reduce computational resources while yielding accurate numerical solution, compared with cases taking standard full-wave and conventional high-frequency approaches (summarized in Table I). The underlying principle is to combine the image method and physical optics technique. More specifically, the proposed algorithm (i) employs a boundary box that encloses the slot array antenna with FSS to correct phase errors and then (ii) calculates equivalent surface currents flowing on the boundary box. The surface of the boundary box is discretized by regular grids (or boundary meshes) and a ray path reaching each square cell in the boundary meshes is determined based on the image method. After collecting all the rays hitting the boundary meshes, equivalent surface current densities can be computed for the resulting radiation pattern. The proposed algorithm is suited best for dealing with electrically large structures much more efficiently and can advance the existing design process.

MONITORING OF THE EDUCATIONAL PROCESS USING ARTIFICIAL INTELLIGENCE METHODS

Considered the problem of increasing the effectiveness of the educational process due to the introduction of automatic control of attendance in the classroom using face recognition and additional information for the collection and further analysis of the received data. Algorithms and methods used in modern Facial Recognition Attendance System are studied. An intelligent system for monitoring the educational process and its analysis is proposed. Structural and functional schemes of the system, databases, software were developed, testing was carried out. During attendance monitoring, the webcam captures an image of the face of the participant of the educational process from the video stream, then the computer automatically creates a vector of facial features, which is compared with the vectors of facial features, pre-entered images and recorded in the relevant database. Vectors with 68 features are used for face recognition. In the development of the software, the tools of the OpenCV library and the Python programming language were used. With several successful comparisons, the person's data is identified: name and status (student or teacher), the current date and time are recorded in an Excel file. Operational data of the system is displayed on the monitor screen, which allows you to correct recognition errors if necessary. The system allows you to automatically keep a log of class attendance, create reports, analyze data to provide recommendations for improving the schedule and order of lessons. The object-relational database with open source code PostgreSQL is used for data storage. The grouping of the system's software code is carried out using the Django web application. The system user has the opportunity to create a personal account and create reports according to his requirements. The system was tested on the example of a group of 15 students and showed satisfactory results.

Granularity-Driven Management for Reliable and Efficient Skyrmion Racetrack Memories

Skyrmion racetrack memory is a rising star of nonvolatile memories due to its outstanding access performance and storage density. However, the working principle of skyrmion racetrack memory introduces unique issues, such as the position error and data representation problem, which considerably impact the data reliability. Although brilliant error correction codes have been proposed to detect and correct bit errors, however, their time-consuming encoding and decoding processes cannot fully match the nanosecond-level access latency of skyrmion racetrack memory. Observing the dilemma among data reliability, access performance, and space utilization, we propose a granularity-driven management scheme for skyrmion racetrack memory. While eliminating the errors incurred due to the position error and data representation problem, the proposed management scheme aims to jointly optimize access performance and space utilization. To achieve this goal, the proposed scheme adaptively selects different combinations of data encoding, layout, and indexing schemes for the data of different granularities. Moreover, we investigate the port selection problem under our proposed data layouts to minimize shift overheads on data accesses. Through analytical and experimental studies, the proposed management scheme is evaluated, and the obtained results are quite encouraging.

Robust Multilayer Vehicle Model-Aided INS Based on Soft and Hard Constraintsz

The navigation accuracy is an important performance indicator for intelligent vehicles. Errors of the inertial navigation system (INS) diverge fast in the electromagnetic signal denial environment, and the vehicle model aid is a common approach to solve it. However, the accuracy of the vehicle model will be influenced by the road condition, vehicle motion state, and other interferences, so precision degradation and gross error are frequent. Against this problem, this article proposes the robust multilayer vehicle kinematics model (VKM)-aided INS. First, the gross error detection and isolation method is proposed based on the singular value decomposition (SVD) and chi-square test, and problems caused by the inadequate measurement redundancy are analyzed and solved by the “model redundancy.” Second, the multi-layer vehicle model-aided INS is proposed, which contains the sensor layer, the system layer I, and the system layer II. In the sensor layer, the soft and hard constraints of the vehicle model are defined, and the vehicle velocities are estimated with the constraints. Then, in the system layer I, the vehicle velocities from the sensor layer are used to estimate and correct INS errors. At last, the position is updated in the system layer II with the corrected attitude and velocity, so that the navigation performance can be improved. The simulation and field experiments proved that the proposed method has higher navigation accuracy and better robustness against gross error and high-dynamics situation and that its position error is less than 20 m in the 7-min field test.

Online inference in high-dimensional generalized linear models with streaming data.

In this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for realtime estimation and inference. We propose an online debiased lasso method that aligns with the data collection scheme of streaming data. Online debiased lasso differs from offline debiased lasso in two important aspects. First, it updates component-wise confidence intervals of regression coefficients with only summary statistics of the historical data. Second, online debiased lasso adds an additional term to correct approximation errors accumulated throughout the online updating procedure. We show that our proposed online debiased estimators in generalized linear models are asymptotically normal. This result provides a theoretical basis for carrying out real-time interim statistical inference with streaming data. Extensive numerical experiments are conducted to evaluate the performance of our proposed online debiased lasso method. These experiments demonstrate the effectiveness of our algorithm and support the theoretical results. Furthermore, we illustrate the application of our method with a high-dimensional text dataset.

Unsupervised Grammatical Correction With Optimized Layer Normalization and Dynamic Embedding Enhancement

Grammatical error correction aims to detect and correct grammatical errors with all types of mistaken, disordered, missing, and redundant characters. However, most existing methods focus more on detecting errors than correcting them. This paper proposes a domain-adaptive model with Interoperable Layer Normalization (ILN) and dynamic word embedding enhancement to optimize the error correction capability. To further improve the Chinese correction capability, we introduce multiple rounds of error correction to refine the sequence tagging model’s ability to fix mistakes. In addition, we propose a data augmentation method based on the complex tag to represent textual error correction traces more completely. We also explore a migration training method based on multiple training datasets. Further, we offer a unique unsupervised domain adaptation technique based on ILN, an innovative channel fusion approach that can significantly improve models’ domain adaptability. Finally, experimental results show that our proposed method substantially outperforms all robust baseline methods and achieves the best results in position-level and correction-level errors on the CGED-2020 dataset.

Influência da transparência e da opacidade na ortografia de fonemas fricativos.

Abstract Purpose (1) to verify to what extent the occurrence of possible errors is influenced by the relationship (opaque/transparent) between fricative phonemes and the graphemes with which they can be spelled; (2) verify the differences (if present or not) of relationship types among the phonemes that present common graphemic relationships. Methods We analyzed 750 textual productions from children in the first year of Elementary School (ES), and conducted a survey of the frequency of correct answers and errors in all fricative phonemes of Brazilian Portuguese (BP). Results The errors occurred in greater numbers in the group of phonemes with opaque spelling when compared with the number of errors in the group of phonemes with transparent spelling. In the first group, the errors showed a non-symmetrical behavior, since they varied according to the possibilities of graphemes for each phoneme. In the second group, the errors showed a symmetrical behavior. Conclusion Given the symmetry in the errors of the phonemes of the first group and the non-symmetry of those of the second group, our results point to a gradation in the occurrence of errors, which varies as a function of the transparency and degree of opacity in the relations between phonemes and graphemes of a same class.

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

ObjectivesMindfulness meditation is associated with better attention function. Performance monitoring and error-processing are important aspects of attention. We investigated whether experienced meditators showed different neural activity related to performance monitoring and error-processing. Previous research has produced inconsistent results. This study used more rigorous analyses and a larger sample to resolve the inconsistencies.MethodWe used electroencephalography (EEG) to measure the error-related negativity (ERN) and error positivity (Pe) following correct and incorrect responses to a Go/Nogo task from 27 experienced meditators and 27 non-meditators.ResultsNo differences were found in the ERN (all p > 0.05). Meditators showed larger global field potentials (GFP) in the Pe after correct responses and errors, indicating stronger neural responses (p = 0.019, FDR-p = 0.152, np2 = 0.095, BFincl = 2.691). This effect did not pass multiple comparison controls. However, single-electrode analysis of the Pe did pass multiple comparison controls (p = 0.002, FDR-p = 0.016, np2 = 0.133, BFincl = 220.659). Meditators also showed a significantly larger Pe GFP for errors, which would have passed multiple comparison controls, but was not a primary analysis (p = 0.003, np2 = 0.149, BF10 = 9.999).ConclusionsMeditation may strengthen neural responses related to performance monitoring. However, these strengthened neural responses were not specific to error monitoring (although the error-related Pe may be more sensitive to group differences than the correct response Pe). These conclusions remain tentative, because the single-electrode analysis passed multiple comparison controls, but the analysis including all electrodes did not.PreregistrationThis study was not preregistered.

Multilayer Model-Aided INS Based on Real-Time Corrected Vehicle Dynamics Model

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

Calibration for Installation Errors of Ultra-short Baseline Based on Direction Cosine

In marine surveying and mapping, Ultra-short baseline (USBL) positioning system can provide accurate location information. So the USBL is the important guarantee of other marine jobs. However, under the influence of a complex marine environment, system errors, and acoustic curves, ultra-short baselines are subject to installation errors when installed, and the positioning accuracy can be seriously disturbed. To this end, this paper uses the direction cosine method to calibrate installation errors. To further improve positioning accuracy, this new method also consider the sound line correction. This method uses an equal gradient soundtracking algorithm to correct sound bending errors, constructs the error equation based on the transformation relationship between different coordinate systems by the direction cosine method, and linearizes the transformation parameters between the coordinate systems. The above two steps complete the calibration of installation errors of the USBL. The simulation results show that the underwater target position distribution calibrated by this method has a large convergence. The deviations from the data in all directions are concentrated around the zero point and approximately conform to a Gaussian distribution. The results indicate that the algorithm greatly improves the results of ultra-short baseline positioning and significantly increases the accuracy of underwater target positioning.

Interfractional Liver Positional Motion Under Exhaled Breath Holding Based on Cone Beam Computed Tomography.

We measured interfractional liver positional motion in liver stereotactic body radiotherapy (SBRT) with exhaled breath holding (BH) based on kilovoltage (kV) cone-beam computed tomography (CBCT) images. We collected 528 pre-treatment kV-CBCT images from 132 patients who underwent liver SBRT under exhaled BH using the Abches system, a non-electronic contact-based respiratory monitoring device, and analyzed them to investigate interfractional liver positional motion. Planning computed tomography (CT) scans were obtained using the Abches system when the patients were under exhaled BH. Translational 3-degree-of-freedom (DOF) soft-tissue-based image registration was performed using the kV-CBCT images under exhaled BH after 6-DOF vertebral bone image registration. Interfractional liver positional motions in the left-right (LR), anteroposterior (AP), and craniocaudal (CC) directions were defined based on the differences in the position of the liver relative to the vertebral bones. For all fractions, the absolute mean±standard deviation for the interfractional liver positional motion in the LR, AP, and CC directions was 0.7±1.0 mm, 1.0±1.5 mm, and 2.8±3.1 mm, respectively. The liver interfractional systematic/random positional motions in the LR, AP, and CC directions were 0.9/1.2 mm, 1.4/1.8 mm, and 2.9/3.9 mm, respectively. For all fractions, 100.0%, 98.3%, and 86.9% of the interfractional liver positional motions in the LR, AP, and CC directions, respectively, were less than 5 mm. CBCT-guided online correction should be used to correct interfractional liver positions errors present in liver SBRT with exhaled BH.

Stochastic Separation Theorems: How Geometry May Help to Correct AI Errors

Stochastic Separation Theorems: How Geometry May Help to Correct AI Errors