Unpredictable Errors Research Articles

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

BackgroundThe global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.PurposeThis review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety.MethodsWe deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.ResultsWe report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.ConclusionsOur findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Experimental identification of longitudinal and vertical lift aerodynamic admittance functions of thin sections

Previous studies of two-dimensional (2D) aerodynamic admittance function (AAF) identification methods have basically ignored the effects of different turbulence components, which may lead to unpredictable errors for practical engineering applications. This paper presents pressure measurement experiments on thin sections with a geometric ratio of 1:50 in various turbulent fields. Based on the three-dimensional (3D) two-wavenumber theoretical analytical framework, the two-wavenumber coherence function is obtained by fitting the measured spanwise root-coherence function at the effective spacing with an empirical model. A new method is proposed, assuming that the ratio relationship between the lift AAFs due to longitudinal and vertical turbulence components of the measured model section satisfies its corresponding theoretical solution ratio relationship. The 2D lift AAFs of the airfoil section induced by different turbulence components are identified following the proposed identification framework. Consistent with the previous research, force correlation is significantly larger than turbulence correlation, and the traditional 3D one-wavenumber AAFs obtained are different in different turbulent fields. The separated 2D lift AAFs demonstrate that the contributions of u- and w- turbulence to the buffeting lift force are significantly different, and are more precise, according to the ratio between the Horlock function and Sears function. Then, the method is successfully extended to estimate the AAFs with respect to the u- and w-turbulence components of a streamlined box girder section, further validating the applicability of the identification method to the streamlined box girder. Furthermore, comparing the separated 2D lift AAFs under different turbulence fields reveals their independence from turbulence characteristics.

Similar assembly state discriminator for reinforcement learning-based robotic connector assembly

In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

PENGGUNAAN MODEL EKONOMETRIKA UNTUK MENENTUKAN INSENTIF PERPAJAKAN BAGI INVESTOR DAN PENGARUHNYA BAGI INVESTASI

This research aims to determine the effect of tax incentives for investors to invest their capital in Indonesia and calculate how many tax incentives can be given to investors. Methodology, The method used is a literature review using multiple linear analysis econometric models. Meanwhile, to test the correlation between incentives and investment in the textile sector in 2020-2021. Results, the of multiple econometric model calculations with existing data, the tax incentive amount is 73.7, with an unpredictable random error (?). Meanwhile, from the correlation test, it was found that tax incentives do not affect investment in the textile industry sector.

Dynamic modulation of the processing of unpredicted technical errors by the posterior cingulate and the default mode network

The pervasive use of information technologies (IT) has tremendously benefited our daily lives. However, unpredicted technical breakdowns and errors can lead to the experience of stress, which has been termed technostress. It remains poorly understood how people dynamically respond to unpredicted system runtime errors occurring while interacting with the IT systems on a behavioral and neuronal level. To elucidate the mechanisms underlying such processes, we conducted a functional magnetic resonance imaging (fMRI) study in which 15 young adults solved arithmetic problems of three difficulty levels (easy, medium and hard) while two types of system runtime errors (problem errors and feedback errors) occurred in an unexpected manner. The problem error condition consisted of apparently defective displays of the arithmetic problem and the feedback error condition involved erroneous feedback. We found that the problem errors positively influenced participants’ problem-solving performance at the high difficulty level (i.e., hard tasks) at the initial stage of the session, while feedback errors disturbed their performance. These dynamic behavioral changes are mainly associated with brain activation changes in the posterior cingulate and the default mode network, including the posterior cingulate cortex, the mPFC, the retrosplenial cortex and the parahippocampal gyrus. Our study illustrates the regulatory role of the posterior cingulate in coping with unpredicted errors as well as with dynamic changes in the environment.

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

Although deep neural networks have made significant progress in tasks related to remote sensing image scene classification, most of these tasks assume that the training and test data are independently and identically distributed. However, when remote sensing scene classification models are deployed in the real world, the model will inevitably encounter situations where the distribution of the test set differs from that of the training set, leading to unpredictable errors during the inference and testing phase. For instance, in the context of large-scale remote sensing scene classification applications, it is difficult to obtain all the feature classes in the training phase. Consequently, during the inference and testing phases, the model will categorize images of unidentified unknown classes into known classes. Therefore, the deployment of out-of-distribution (OOD) detection within the realm of remote sensing scene classification is crucial for ensuring the reliability and safety of model application in real-world scenarios. Despite significant advancements in OOD detection methods in recent years, there remains a lack of a unified benchmark for evaluating various OOD methods specifically in remote sensing scene classification tasks. We designed different benchmarks on three classical remote sensing datasets to simulate scenes with different distributional shift. Ten different types of OOD detection methods were employed, and their performance was evaluated and compared using quantitative metrics. Numerous experiments were conducted to evaluate the overall performance of these state-of-the-art OOD detection methods under different test benchmarks. The comparative results show that the virtual-logit matching methods without additional training outperform the other types of methods on our benchmarks, suggesting that additional training methods are unnecessary for remote sensing image scene classification applications. Furthermore, we provide insights into OOD detection models and performance enhancement in real world. To the best of our knowledge, this study is the first evaluation and analysis of methods for detecting out-of-distribution data in remote sensing. We hope that this research will serve as a fundamental resource for future studies on out-of-distribution detection in remote sensing.

ProMvSD: Towards unsupervised knowledge graph anomaly detection via prior knowledge integration and multi-view semantic-driven estimation

Knowledge graphs (KGs) have found extensive applications within intelligent systems, such as information retrieval. Much of the research has predominantly focused on completing missing knowledge, with little consideration given to examining errors. Unfortunately, during customizing KGs, diverse unpredictable errors are virtually unavoidable to be introduced, and these anomalies significantly impact the performance of applications. Detecting erroneous knowledge presents a formidable challenge due to the costly acquisition of ground-truth labels. In this work, we develop an unsupervised anomaly detection framework named ProMvSD, aiming to adapt KGs of varying scales via serialization components. To overcome the insufficient contextual information provided by the topological structure, we introduce the large language model as a reasoner to extract prior knowledge from extensive pre-trained textual data, thereby enhancing the understanding of KGs. Anomalous triple may result in a larger semantic gap between the head and tail neighborhoods. To uncover latent anomalies effectively, we propose a multi-view semantic-driven model (MvSD) based on the assumptions of self-consistency and information stability. MvSD jointly estimates the suspiciousness of triples from three hyperviews: node-view semantic contradiction, triple-view semantic gap, and pathway-view semantic gap. Extensive experiments on three English benchmark KGs and a Chinese medical KG demonstrate that, for the top 1% of the most suspicious triples, we can detect real anomalies with at most 99.9% accuracy. Furthermore, ProMvSD significantly outperforms state-of-the-art representation learning baselines, achieving a 29.2% improvement in detecting all anomalies.

Optimization of parameters for a fringe projection measurement system by use of an improved differential evolution method.

Fringe projection 3D measurement is widely used for object surface reconstruction. While improving measurement accuracy is a crucial task. Measurement accuracy is profoundly affected by various optical structural parameters. However, the current practice of system construction lacks theoretical guidelines and often relies on the experience of the operator, inevitably leading to unpredictable error. This paper investigates a theoretical optimization model and proposes an automatic optimization method for qualitatively determining the multiple optimal optical structural parameters in fringe projection measurement system. The aim is to enhance measurement accuracy conducting a rational comprehensive optimal structural parameters design prior to the system construction. Firstly, the mathematical model of the measurement system is established based on the principle of optical triangulation, and the phase sensitivity criterion is defined as the optimization norm. Within the full measurement range, the optimization merit function is formulated by combing three positions: the center position, the left and right boundary of the CCD. The imaging effectiveness criteria and sensor geometric dimensions are taken into account as the constraint boundaries. Subsequently, a combined improved differential evolution and Levy flight optimization algorithm is applied to search for the optimal parameters. The optimal structural parameters of the system were designed based on the optimization process. Experimental results validated the improvement in measurement accuracy achieved by the optimized structural parameters.

One-step electrodeposition of MWCNTs-Cu MOF films for the ratiometric electrochemical analysis of glyphosate.

Sensitive and accurate determination of glyphosate (GLYP) is vital for food safety and environmental protection. Herein, a novel electrochemical ratiometric biosensor was designed for the accurate quantification of GLYP through one-step electrodeposition of MWCNTs-Cu MOF films. MWCNTs-Cu MOF nanostructures were directly electro-synthesized in situ on the electrode from the precursor solution. The combination of Cu MOFs with MWCNTs not merely improved the conductivity of MOFs, but also enhanced the sensitivity of the biosensor. Furthermore, Cu sites within Cu MOFs were turned into CuCl to further amplify the current signal and enable the specific recognition of GLYP through competing reactions with the transformation of CuCl into non-electroactive Cu-GLYP. Meanwhile, internal reference molecules of methylene blue (MB) were incorporated to improve the measurement accuracy of GLYP for reducing unpredictable measurement errors aroused by environmental deviations. The ratiometric electrochemical sensor exhibited a high linearity with the logarithmic value of GLYP concentration from 0.5 nM to 400 nM. The detection limit was estimated to be as low as 0.014 nM. Finally, the present sensor with ratiometric signal export was applied for GLYP analysis in real samples with high sensitivity and accuracy. The simplicity and reliability of the ratiometric sensor make it a worthy and powerful tool for food and environmental monitoring. This design strategy also provides an avenue for the development of simple and efficient biosensors for other substances.

Determination of the radii of coated and uncoated silicon AFM sharp tips using a height calibration standard grating and a nonlinear regression function.

AFM sharp tips are used to characterize nanostructures and quantify the mechanical properties of the materials in several areas of research. The analytical results can show unpredicted errors if we do not know the exact values of the AFM tip radius. There are many techniques of in situ measurements for determining the actual AFM tip radius, but they are limited to uncoated tips. This paper presents an alternative and simple method to determine the radii of coated tips and an uncoated tip. Pt-coated, Cr/Au-coated, and uncoated Si tips were used to scan a calibration standard grating in AFM contact mode with sub-nanonewton load to obtain the curved scan profile of the edge corner of the grating structure. The data points of the curved profile of each tip were fitted with a nonlinear regression function to estimate the curvature radius of the tip. The results show that the estimated radius of the coated tips is in the range of nominal values provided by the tip manufacturer, while the estimated radius of the uncoated Si tip is bigger than the nominal radius because of tip blunting during the scan. However, this method yields an accurate estimate of the tip radius with a low root mean squared error of the curve fitting results.

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

Veterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives pertaining to companion animals. BCS values were extracted from 4415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT, prompting the model to return the BCS information. Data were manually reviewed for comparison. The precision of RegexT was higher (100%, 95% confidence interval [CI] 94.81%-100%) than that of ChatGPT (89.3%, 95% CI 82.75%-93.64%). However, the recall of ChatGPT (100%, 95% CI 96.18%-100%) was considerably higher than that of RegexT (72.6%, 95% CI 63.92%-79.94%). Prior anonymisation and subtle prompt engineering are needed to improve ChatGPT output. Large language models create diverse opportunities and, while complex, present an intuitive interface to information. However, they require careful implementation to avoid unpredictable errors.

Automated, Consistent, and Even-Handed Selection of Active Orbital Spaces for Quantum Embedding.

A widely used strategy to reduce the computational cost of quantum-chemical calculations is to partition the system into an active subsystem, which is the focus of the computational efforts, and an environment that is treated at a lower computational level. The system partitioning is mostly based on localized molecular orbitals. When reaction paths or energy differences are to be calculated, it is crucial to keep the orbital space consistent for all structures. Inconsistencies in orbital space can lead to unpredictable errors on the potential energy surface. While successful strategies to ensure this consistency have been established for organic and even metal-organic systems, these methods often fail for metal clusters or nanoparticles with a high density of near-degenerate and delocalized molecular orbitals. However, such systems are highly relevant for catalysis. Accurate yet feasible quantum-mechanical ab initio calculations are therefore highly desired. In this work, we present an approach based on the subsystem projected atomic orbital decomposition algorithm that allows us to ensure automated and consistent partitioning even for systems with delocalized and near-degenerate molecular orbitals and demonstrate the validity of this method for the binding energies of small molecules on transition-metal clusters.

Deep Reinforcement Learning Assisted Beam Tracking and Data Transmission for 5G V2X Networks.

Beam tracking is a core issue in 5G vehicle-to-everything (V2X) networks. Specifically, higher beamforming gain is required to compensate for the path loss at higher frequencies, e.g., 5G FR2, to realize high data rate vehicle-toinfrastructure (V2I) communications. However, shorter time slots at higher frequencies, high velocity of vehicles, and unpredictable localization errors make this problem more challenging. Under these circumstances, wider beams can lead to higher beam tracking accuracy. Bear in mind that wider beams mean lower beamforming gain, which cannot compensate for high path loss at high frequencies and would further influence the data rate of V2I communications. Thus, there exists a trade-off between tracking accuracy and data rate in V2I communications. Furthermore, this problem needs to be solved within an extremely short time slot according to the high transmission frequency. To solve this problem, we propose a reinforcement learning (RL) assisted, high-resolution codebook-based beam tracking method. By comparing several different RL frameworks, we found that the twin delayed deep deterministic policy gradient (TD3) framework can help the roadside infrastructure (RSI) determine a proper beam pattern within a short duration. Moreover, according to the Hurst exponent analysis, recurrent neural networks (RNNs) are selected to improve the performance of the RL framework. The simulation results show that the proposed method performs well in tracking accuracy, data rate, and temporal efficiency.

A ku-band common-leg transceiver with built-in configurable register in 130-nm CMOS technology for phased-array systems

To solve the bottleneck of amplitude and phase accuracy deterioration caused by process and unpredictable errors, this paper proposes a Ku-band common-leg transceiver with built-in configurable register to achieve 64-state amplitude and phase accuracy correction, respectively, by redefining control codes of each state after taping out. Two-stage cascaded variable gain amplifiers (VGAs) and the vector-summing phase shifter (VSPS) with tail current source controlled by DAC are adopted to achieve large amplitude and phase coverage of 31.5 dB with the LSB of 0.5 dB and 360° with the LSB of 5.625°, respectively. According to the measurement results, in the frequency range of 14–18 GHz, the chip has amplitude accuracy with the root mean square (rms) error of ＜ 0.4 dB, while the rms phase error is ＜ 5.3°. The phase shifting accuracy is measured to be ＜ 2.2°, while the rms amplitude error is ＜ 0.67 dB. The receiving and transmitting channels have reference gain of 3.6–6 dB and 8–10.4 dB, respectively. The whole transceiver occupies 3 × 2.3 mm2 chip area including pads.

High accuracy detection technique for information data under the influence of multiple factors based on fractional partial differential equations

In engineering practice, several factors affect the various types of information during the collection process. For example, information data measurement errors are caused by equipment performance and the working environment. During the transmission of detection information, signal distortion caused by energy loss and signal interference causes unpredictable detection errors in collected data. Through the study of fractional calculus theory, it was found that it is suitable for studying nonlinear, noncausal, and nonstationary signals, and has the dual functions of improving detection information and enhancing signal strength. Therefore, under the influence of many factors, we applied the fractional difference algorithm to the field of information-data processing. A multisensor detection data fusion algorithm based on the fractional partial differential equation was adopted to establish online detection data. A multi-sensor detection data fusion algorithm based on a fractional partial differential equation was established, which effectively fuses the information data detection errors caused by various influencing factors and significantly improves the detection accuracy of information data. The effectiveness of this method was experimentally demonstrated by its application.

Outlier bias: AI classification of curb ramps, outliers, and context

Technologies in the smart city, such as autonomous vehicles and delivery robots, promise to increase the mobility and freedom of people with disabilities. These technologies have also failed to “see” or comprehend wheelchair riders, people walking with service animals, and people walking with bicycles—all outliers to machine learning models. Big data and algorithms have been amply critiqued for their biases—harmful and systematic errors—but the harms that arise from AI's inherent inability to handle nuance, context, and exception have been largely overlooked. In this paper, I run two machine learning models across nine cities in the United States to attempt to fill a gap in data about the location of curb ramps. I find that while curb ramp prediction models may achieve up to 88% accuracy, the rate of accuracy varied in context in ways both predictable and unpredictable. I look closely at cases of unpredictable error (outlier bias), by triangulating with aerial and street view imagery. The sampling of cases shows that while it may be possible to conjecture about patterns in these errors, there is nothing clearly systematic. While more data and bigger models might improve the accuracy somewhat, I propose that a bias toward outliers is something fundamental to machine learning models which gravitate to the mean and require unbiased and not missing data. I conclude by arguing that universal design or design for the outliers is imperative for justice in the smart city where algorithms and data are increasingly embedded as infrastructure.

A Numerical-Experimental Framework to Separate the Effects of Different Turbulence Components on the Buffeting Forces of Bluff-body Structures

In conventional buffeting analyses of flexible civil engineering structures, the differences between the effects of longitudinal (u-) and vertical (w-) turbulence components were usually ignored. This assumption could cause unpredictable errors and needs to be refined. To improve the accuracy of buffeting analyses, this paper proposes a framework combining Computational Fluid Dynamics simulation and wind tunnel test to analyze the buffeting forces considering the differences between turbulence components. First, the Aerodynamic Admittance Functions (AAFs) with respect to u- and w- turbulence are numerically evaluated. Next, the total buffeting forces are experimentally measured. Finally, the numerical and experimental results are combined following a theoretical scheme to separate the effects of u- and w-turbulence. Results show that u- and w- turbulence have different contributions to the buffeting forces, directly indicating the inaccuracy of the conventional assumption. In the turbulence field investigated, the buffeting lift force at zero angle of attack (AoA) are all contributed from the w-turbulence, while the contribution from u-turbulence increase as AoA increases. This numerical-experimental framework overcomes the limitations of the conventional method by quantitatively describing the different contributions of u- and w- turbulence.

Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network

The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as Intermediate Scale Quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this paper, we propose a Machine Learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using Graph Neural Network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.

Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization.

Detailed analysis and simulation verification for reconstruction of plasma optical boundary with spectrometric technique on HL-2M tokamak

The plasma optical boundary reconstruction technique based on Hommen’s theory is promising for future tokamaks with high parameters. In this work, we conduct detailed analysis and simulation verification to estimate the ‘logic loophole’ of this technique. The finite-width effect and unpredictable errors reduce the technique’s reliability, which leads to this loophole. Based on imaging theory, the photos of a virtual camera are simulated by integrating the assumed luminous intensity of plasma. Based on Hommen’s theory, the plasma optical boundary is reconstructed from the photos. Comparing the reconstructed boundary with the one assumed, the logic loophole and its two effects are quantitatively estimated. The finite-width effect is related to the equivalent thickness of the luminous layer, which is generally about 2‒4 cm but sometimes larger. The level of unpredictable errors is around 0.65 cm. The technique based on Hommen’s theory is generally reliable, but finite-width effect and unpredictable errors have to be taken into consideration in some scenarios. The parameters of HL-2M are applied in this work.