Complete Algorithm Research Articles

A Matrix Completion Method for Imputing Missing Values of Process Data

Real-time process data are the foundation for the successful implementation of intelligent manufacturing in the chemical industry. However, in the actual production process, process data may randomly be missing due to various reasons, thus affecting the practical application of intelligent manufacturing technology. Therefore, this paper proposes the application of appropriate matrix completion algorithms to impute the missing values of real-time process data. Considering the characteristics of online missing value imputation problems, this paper proposes an improved method for a matrix completion algorithm that is suitable for real-time missing data imputation. By utilizing real device data, this paper studies the impact of algorithm parameters on the effect of missing value imputing and compares it with several classical missing value imputing methods. The results show that the introduced method achieves higher accuracy in data imputation compared to the baseline method. Furthermore, the proposed enhancement significantly improves the speed performance of algorithms.

DeepBern-Nets: Taming the Complexity of Certifying Neural Networks Using Bernstein Polynomial Activations and Precise Bound Propagation

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness, and robustness. Unfortunately, on the one hand, sound and complete certification algorithms of ReLU-based NNs do not scale to large-scale NNs. On the other hand, incomplete certification algorithms are easier to compute, but they result in loose bounds that deteriorate with the depth of NN, which diminishes their effectiveness. In this paper, we ask the following question; can we replace the ReLU activation function with one that opens the door to incomplete certification algorithms that are easy to compute but can produce tight bounds on the NN's outputs? We introduce DeepBern-Nets, a class of NNs with activation functions based on Bernstein polynomials instead of the commonly used ReLU activation. Bernstein polynomials are smooth and differentiable functions with desirable properties such as the so-called range enclosure and subdivision properties. We design a novel Interval Bound Propagation (IBP) algorithm, called Bern-IBP, to efficiently compute tight bounds on DeepBern-Nets outputs. Our approach leverages the properties of Bernstein polynomials to improve the tractability of neural network certification tasks while maintaining the accuracy of the trained networks. We conduct experiments in adversarial robustness and reachability analysis settings to assess the effectiveness of the approach. Our proposed framework achieves high certified accuracy for adversarially-trained NNs, which is often a challenging task for certifiers of ReLU-based NNs. This work establishes Bernstein polynomial activation as a promising alternative for improving NN certification tasks across various NNs applications.

S-ID: Causal Effect Identification in a Sub-population

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.

Enhancing View Synthesis with Depth-Guided Neural Radiance Fields and Improved Depth Completion.

Neural radiance fields (NeRFs) leverage a neural representation to encode scenes, obtaining photorealistic rendering of novel views. However, NeRF has notable limitations. A significant drawback is that it does not capture surface geometry and only renders the object surface colors. Furthermore, the training of NeRF is exceedingly time-consuming. We propose Depth-NeRF as a solution to these issues. Specifically, our approach employs a fast depth completion algorithm to denoise and complete the depth maps generated by RGB-D cameras. These improved depth maps guide the sampling points of NeRF to be distributed closer to the scene's surface, benefiting from dense depth information. Furthermore, we have optimized the network structure of NeRF and integrated depth information to constrain the optimization process, ensuring that the termination distribution of the ray is consistent with the scene's geometry. Compared to NeRF, our method accelerates the training speed by 18%, and the rendered images achieve a higher PSNR than those obtained by mainstream methods. Additionally, there is a significant reduction in RMSE between the rendered scene depth and the ground truth depth, which indicates that our method can better capture the geometric information of the scene. With these improvements, we can train the NeRF model more efficiently and achieve more accurate rendering results.

Estimating Non-Linear Heat Flux in Continuous Billet Casting Process With Limited Sensors

In a continuous billet caster, the heat transfer from the pool of molten steel to the cooling water via the copper mold depends non-linearly upon several casting parameters. A reliable and accurate methodology based on the principle of inverse heat transfer technique has been proposed in this work to obtain the nonlinear heat flux of a continuous billet casting mold. Differential evolution optimization technique has been used, in conjunction with the 2-D and 3-D steady state conduction heat transfer equations to develop the complete algorithm. It has been observed that both the 2-D and 3-D models are able to accurately predict the heat flux for different numbers and locations of temperature sensors. While the 2-D model estimates the 1-D heat flux profile varying along casting direction within a very short time period, the 3-D model offers the advantage of estimating a more accurate 2-D heat flux profile varying along both the mold length and width. The 2-D model has also been used to estimate the heat flux for an industrial continuous casting mold using measured plant data. It is observed that the present methodology accurately estimates the boundary heat flux in the continuous billet caster mold.

Combined effect of lightning impulse voltage and temperature stress on the propagation of creeping discharge of oil-impregnated paper

This article presents the results of an experiment designed to study the impact of temperature on the characteristic parameters of creeping discharges. The insulating interfaces consist of a thermally enhanced cellulose surface immersed in mineral oil, palm kernel oil methyl ester (PKOME) and castor oil methyl ester (COME). The study was carried out under a standard negative lightning impulse voltage (1.2/50 μs). The article also presents the complete algorithms for calculating the maximum extension of the discharges, the ionisation rate and the charge produced by them. The results of the study show that temperature favors the propagation of discharges and the ionisation rate. It was observed that liquids with a higher dielectric constant and high electrical conductivity were more exposed to the impact of temperature. The results show ionisation increments of 0.973%/°C, 1.093%/°C and 1.076%/°C in mineral oil (MO), COME and PKOME respectively. The maximum extension of the discharges shows a linear evolution with the applied voltage and temperature but a non-linear increment with the temperature. As for the charge produced, it shows a constant increment with temperature and voltage in each liquid. These values are (5.839%/°C, 1.977%/kV), (6.047%/°C, 2.082%/kV) and (6.177%/°C, 2.113%/kV) respectively in MO, COME and PKOME.

Outlier Detection and Prediction in Evolving Communities

Community detection in social networks is of great importance and is used in a variety of applications such as recommendation systems and targeted advertising. While detecting dense groups with high levels of connectivity and similar interests between their members is the main target of traditional network analysis, finding network members with quite different behavior than the majority of nodes is important as well. These nodes are known as outliers, and their accurate detection can be very useful; when outliers are marked as noisy nodes, their early exclusion from analysis can lead to high computational profits. On the other hand, they can represent interesting components that call for further investigation to find the reasons for their outlying behavior and possible ways to include them in a neighboring community. Both community and outlier detection are challenging in temporal environments where changes occur in real time; thus, dynamic methods need to be deployed rather than to static methods. In our work, we take into account the content of the network, in contrast to most of related studies, where only the network’s structure contributes to community formation. We define an adaptive outlier score to be assigned to each node in order to quantify its outlierness, and introduce a complete online community detection algorithm that analyzes both the network’s structure and content while at the same time detecting community outliers. To evaluate our method, we retrieved and processed two real datasets regarding social networks with temporal and content information. Experimental results show that our method is capable of detecting outliers in real-time evolving communities and provides an outlier score which is a better metric of each node’s outlierness compared to widely used metrics. Finally, experimental results indicate that our method is suitable for predicting the status of future nodes based on their current outlier score.

Improved hourly estimate of aerosol optical thickness over Asian land by fusing geostationary satellites Fengyun-4B and Himawari-9

Asian over-land aerosols are complexities due to a mixture of anthropogenic air pollutants and natural dust. The accuracy of the aerosol optical thickness (AOT) retrieved from the satellite is crucial to their application in the aerosol data assimilation system. Fusion of AOTs with high spatiotemporal resolution from next-generation geostationary satellites such as Fengyun-4B (FY-4B) and Himawari-9, provides a new high-quality dataset capturing the aerosol spatiotemporal variability for data assimilation. This study develops a complete fusion algorithm to estimate the optimal AOT over-land in Asia from September 2022 to August 2023 at 10 km × 10 km resolution with high efficiency. The data fusion involves four steps: (1) investigating the spatiotemporal variability of FY-4B AOT within the past 1 h and 12 km radius calculation domain; (2) utilizing the aerosol spatiotemporal variability characteristics to estimate FY-4B pure and hourly merged AOTs; (3) performing bias corrections for FY-4B and Himwari-9 hourly merged AOT for different observation times and seasons considering pixel-level errors for each satellite; (4) fusing the bias-corrected FY-4B and Himawari-9 hourly merged AOT based on maximum-likelihood estimation (MLE) method. Compared to the original FY-4B AOT, validation with AERONET observation confirms that the root mean square error (RMSE) of hourly merged FY-4B AOT decreases by around 40.6 % and the correlation coefficient (CORR) increases by about 27.8 %. Compared to FY-4B and Himawari-9 merged AOT, the fused AOT significantly decreases (increases) RMSE (CORR) by around 24.7 % (7.3 %) and 20.2 % (5.6 %). In addition, fused AOT is double the number of single-sensor merged AOT. Fusion aerosol map accurately describes the spatial and temporal variations in Asian regions controlled by air pollution and dust storms. Further studies are required for other landscapes with different satellite combinations to promote the application in the data assimilation system.

Better-than-classical Grover search via quantum error detection and suppression

We report better-than-classical success probabilities for a complete Grover quantum search algorithm on the largest scale demonstrated to date, of up to five qubits, using two different IBM platforms. This is enabled by error suppression via robust dynamical decoupling. Further improvements arise after the use of measurement error mitigation, but the latter is insufficient by itself for achieving better-than-classical performance. For two qubits, we demonstrate a 99.5% success probability via the use of the [[4, 2, 2]] quantum error-detection (QED) code. This constitutes a demonstration of quantum algorithmic breakeven via QED. Along the way, we introduce algorithmic error tomography (AET), a method that provides a holistic view of the errors accumulated throughout an entire quantum algorithm, filtered via the errors detected by the QED code used to encode the circuit. We demonstrate that AET provides a stringent test of an error model based on a combination of amplitude damping, dephasing, and depolarization.

Comparative study on the results of orthodontic diagnostics by using algorithms generated by Artificial Intelligence and simple algorithms.

Artificial intelligence (AI) is computer-generated intelligence, as opposed to the natural intelligence of humans and some animals. Kaplan and Haenlein define AI as "the ability of a system to correctly interpret external data, to learn from such data and use what it has learned to achieve specific goals and tasks through a flexible adaptation". The term "artificial intelligence" is used colloquially to describe machines that mimic the "cognitive" functions that people associate with other human minds. One of the areas where technological advances have brought significant changes is orthodontics, especially in terms of diagnosis and orthodontic prediction.The aim of this study is to conduct a comparative analysis between the results obtained by using the complete algorithms that define Artificial Intelligence and the simple algorithms of classical medical software, used in the detection of the position and shape of teeth in various orthodontic anomalies. A group of 45 patients with maxillary-dento anomalies Angle Class I (DDM with crowding and deviation of the superior inter-incisive line) was studied. Two types of algorithms were used in the study group: modern type I algorithms and simple algorithms used in classical software to detect the position of the frontal teeth. Through the symmetrical points of the face the facial axes were determined, and after the detection of the contour of each tooth the incisional curve was calculated. The median line was analyzed against the vertical axis of the face, and the incisional curve towards the horizontal axis. The study shows that AI algorithms offer an increased level of tooth position detection, compared to traditional softwares. Complex algorithms, specific to Artificial Intelligence, showed superior detection, and more stability in the analysis. Technological evolution and the development of machine learning capabilities have opened new perspectives in guiding orthodontic treatments through artificial intelligence (AI).

Computing the binomial part of a polynomial ideal

Given an ideal I in a polynomial ring K[x1,…,xn] over a field K, we present a complete algorithm to compute the binomial part of I, i.e., the subideal Bin(I) of I generated by all monomials and binomials in I. This is achieved step-by-step. First we collect and extend several algorithms for computing exponent lattices in different kinds of fields. Then we generalize them to compute exponent lattices of units in 0-dimensional K-algebras, where we have to generalize the computation of the separable part of an algebra to non-perfect fields in characteristic p. Next we examine the computation of unit lattices in finitely generated K-algebras, as well as their associated characters and lattice ideals. This allows us to calculate Bin(I) when I is saturated with respect to the indeterminates by reducing the task to the 0-dimensional case. Finally, we treat the computation of Bin(I) for general ideals by computing their cellular decomposition and dealing with finitely many special ideals called (s,t)-binomial parts. All algorithms have been implemented in SageMath.

Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data.

Tensor completion is the problem of estimating the missing values of high-order data from partially observed entries. Data corruption due to prevailing outliers poses major challenges to traditional tensor completion algorithms, which catalyzed the development of robust algorithms that alleviate the effect of outliers. However, existing robust methods largely presume that the corruption is sparse, which may not hold in practice. In this article, we develop a two-stage robust tensor completion approach to deal with tensor completion of visual data with a large amount of gross corruption. A novel coarse-to-fine framework is proposed which uses a global coarse completion result to guide a local patch refinement process. To efficiently mitigate the effect of a large number of outliers on tensor recovery, we develop a new M-estimator-based robust tensor ring recovery method which can adaptively identify the outliers and alleviate their negative effect in the optimization. The experimental results demonstrate the superior performance of the proposed approach over state-of-the-art robust algorithms for tensor completion.

Research on the Application of Intelligent Sports Tracking System in Improving the Teaching Effect of University Physical Education

Abstract The enhancement of physical education’s stature necessitates imminent reforms in university physical education models. This study examines the deployment of an intelligent motion-tracking system within physical education, exploring facets such as the precise capture and real-time analysis of movement postures, movement evaluation, and optimization, as well as movement recording and tracking. It further investigates the system’s accuracy and convenience, assessing its impact on enhancing university physical education pedagogy. Our methodology involved constructing a framework for the intelligent motion tracking system, followed by developing a motion model. The design of the intelligent tracking model proceeded through initialization, sampling and prediction, observation, and resampling phases, concluding with the implementation of a complete human body tracking motion algorithm utilizing the VHMT algorithm. Our analysis revealed optimized posture and morphology of the upper arm and forearm during exercise, indicative of enhanced physical education teaching at the university level. Prior to implementation, in the volleyball class scenario, no significant difference was observed between the experimental and control groups in terms of motor skills, with a difference ranging from 1.82 to 4.4 (P>0.05). Post-implementation, however, the motor skill level of the experimental group in each class significantly surpassed that of the control group (P<0.05), with improvements ranging from 6.8% to 43.4%. The integration of an intelligent motion-tracking system in university physical education substantially fosters students’ motor skill acquisition and invigorates their enthusiasm for learning.

Attention-Guided Low-Rank Tensor Completion.

Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional structures from a limited set of observed entries. Despite recent significant successes, the original structures of data tensors are still not effectively preserved in LRTC algorithms, yielding less accurate restoration results. Moreover, LRTC algorithms often incur high computational costs, which hinder their applicability. In this work, we propose an attention-guided low-rank tensor completion (AGTC) algorithm, which can faithfully restore the original structures of data tensors using deep unfolding attention-guided tensor factorization. First, we formulate the LRTC task as a robust factorization problem based on low-rank and sparse error assumptions. Low-rank tensor recovery is guided by an attention mechanism to better preserve the structures of the original data. We also develop implicit regularizers to compensate for modeling inaccuracies. Then, we solve the optimization problem by employing an iterative technique. Finally, we design a multistage deep network by unfolding the iterative algorithm, where each stage corresponds to an iteration of the algorithm; at each stage, the optimization variables and regularizers are updated by closed-form solutions and learned deep networks, respectively. Experimental results for high dynamic range imaging and hyperspectral image restoration show that the proposed algorithm outperforms state-of-the-art algorithms.

COMPLETE AND SEMI-COMPLETE EXPLICIT ALGORITHMS OF A UNIFIED CRITICAL STATE MODEL FOR OVER-CONSOLIDATED SOILS

This paper presents a comparison of the performance of explicit algorithm and semi-complete explicit algorithm in the numerical implementations of an unconventional plastic model for soils. The new model, named CASM-S, is developed by incorporating the sub-loading surface theory into the standard unified clay and sand model (i.e., CASM), to enhance the prediction ability for the mechanical behavior of over-consolidated soils. The complete explicit algorithm of CASM-S is based on the sub-stepping method with the technique of automatic error control (SUBM), while the semi-complete explicit algorithm adopts the cutting-plane integration procedure (CPM). The complete implementation process of this model is performed, and the stability, accuracy, and efficiency of these two algorithms are compared through a series of numerical simulations, such as fluid-structure coupling problem, over-consolidation problem, and square-footing problem. These simulations demonstrate that CASM-S implemented by both the SUBM and CPM can obtain a reliable solution under appropriate size of increments. For the sub-loading surface model with highly nonlinear characteristics used in this paper, the CPM has faster local convergence rate, but the SUBM shows a higher efficiency and accuracy at global level.

The normal sonographic picture of the paratonsillar region and the prerequisites for ultrasound diagnosis of purulent pathology of the pharynx

Paratonsillar abscess (PTA) stands out among all otorhinolaryngological diseases, since it can cause life-threatening complications that develop in the shortest possible time due to the close connection of the cellular spaces of the head, neck and mediastinum. In addition to physical examination, computed tomography and magnetic resonance imaging are routinely used to diagnose PTA, sonography is less often used. With the help of the latter, it is possible to reliably distinguish PTA from both paratonsillitis and a number of other diseases, including an aneurysm of the internal carotid artery, using the Doppler mode. However, there are significant gaps in the ultrasound diagnosis of PTA and paratonsillitis: the use of a percutaneous convex sensor is not described, there is no description of clear anatomical landmarks for reliable and rapid recognition of PTA, and diagnostically complete algorithms for verification and visualization of paratonsillitis and PTA using sonographic examination have not been developed. Our work is aimed at filling these gaps.

Determining the coordinates of a ground target from aircraft board using the method of multiple corner notches

The calculating of the stationary ground target coordinates using multiple angular spatial notches obtained using sighting equipment installed on board of a moving aircraft is considered. As intermediate parameters, auxiliary variables were used — slant ranges, which made it possible to reduce the problem to solving a system of linear algebraic equations obtained using the weighted least squares method. A complete algorithm for determining the geodetic coordinates of the target is presented, taking into account the positional and angular position of the aircraft at the time of measurement. Keywords spatial corner notches, multiple notches method, method of the weighted least squares

Detecting isometries and symmetries of implicit algebraic surfaces

<abstract><p>We presented a new and complete algorithm for detecting isometries and symmetries of implicit algebraic surfaces. First, our method reduced the problem to the case of isometries fixing the origin. Second, using tools from elimination theory and polynomial factoring, we determined the desired isometries between the surfaces. We have implemented the algorithm in Maple to provide evidences of the efficiency of the method.</p></abstract>

A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications