Proposed Algorithm Research Articles

Simple Contrastive Graph Clustering.

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming graph convolutional operations undermine the efficiency of these methods. To solve this problem, we propose a simple contrastive graph clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, that is, preprocessing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent preprocessing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter unshared Siamese encoders and perturbing the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average. The code of SCGC is released at SCGC. Besides, we share a collection of deep graph clustering, including papers, codes, and datasets at ADGC.

Evolving Memristive Reservoir.

In light of the dynamic plasticity, nanosize, and energy efficiency of memristors, memristive reservoirs have attracted increasing attention in diverse fields of research recently. However, limited by deterministic hardware implementation, hardware reservoir adaptation is hard to realize. Existing evolutionary algorithms for evolving reservoirs are not designed for hardware implementation. They often ignore the circuit scalability and feasibility of the memristive reservoirs. In this work, based on the reconfigurable memristive units (RMUs), we first propose an evolvable memristive reservoir circuit that is capable of adaptive evolution for varying tasks, where the configuration signals of memristor are evolved directly avoiding the device variance of the memristors. Second, considering the feasibility and scalability of memristive circuits, we propose a scalable algorithm for evolving the proposed reconfigurable memristive reservoir circuit, where the reservoir circuit will not only be valid according to the circuit laws but also has the sparse topology, alleviating the scalability issue and ensuring the circuit feasibility during the evolution. Finally, we apply our proposed scalable algorithm to evolve the reconfigurable memristive reservoir circuits for a wave generation task, six prediction tasks, and one classification task. Through experiments, the feasibility and superiority of our proposed evolvable memristive reservoir circuit are demonstrated.

A Novel Alignment-based Approach for PARSEVAL Measures

Abstract We propose a novel method for calculating PARSEVAL measures to evaluate constituent parsing results. Previous constituent parsing evaluation techniques were constrained by the requirement for consistent sentence boundaries and tokenization results, proving to be stringent and inconvenient. Our new approach handles constituent parsing results obtained from raw text, even when sentence boundaries and tokenization differ from the preprocessed gold sentence. Implementing this measure is our evaluation by alignment approach. The algorithm enables the alignment of tokens and sentences in the gold and system parse trees. Our proposed algorithm draws on the analogy of sentence and word alignment commonly used in machine translation (MT). To demonstrate the intricacy of calculations and clarify any integration of configurations, we explain the implementations in detailed pseudo-code and provide empirical proof for how sentence and word alignment can improve evaluation reliability.

Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios.

We propose a visual Simultaneous Localization and Mapping (SLAM) algorithm that integrates target detection and clustering techniques in dynamic scenarios to address the vulnerability of traditional SLAM algorithms to moving targets. The proposed algorithm integrates the target detection module into the front end of the SLAM and identifies dynamic objects within the visual range by improving the YOLOv5. Feature points associated with the dynamic objects are disregarded, and only those that correspond to static targets are utilized for frame-to-frame matching. This approach effectively addresses the camera pose estimation in dynamic environments, enhances system positioning accuracy, and optimizes the visual SLAM performance. Experiments on the TUM public dataset and comparison with the traditional ORB-SLAM3 algorithm and DS-SLAM algorithm validate that the proposed visual SLAM algorithm demonstrates an average improvement of 85.70 and 30.92% in positioning accuracy in highly dynamic scenarios. In comparison to the DynaSLAM system using MASK-RCNN, our system exhibits superior real-time performance while maintaining a comparable ATE index. These results highlight that our pro-posed SLAM algorithm effectively reduces pose estimation errors, enhances positioning accuracy, and showcases enhanced robustness compared to conventional visual SLAM algorithms.

Diminishing Batch Normalization.

In this article, we propose a generalization of the batch normalization (BN) algorithm, diminishing BN (DBN), where we update the BN parameters in a diminishing moving average way. BN is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. Our proposed DBN algorithm retains the overall structure of the original BN algorithm while introducing a weighted averaging update to some trainable parameters. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to the trainable parameters. Our analysis can be easily generalized to the original BN algorithm by setting some parameters to constant. To the best of our knowledge, this analysis is the first of its kind for convergence with BN. We analyze a two-layer model with arbitrary activation functions. Common activation functions, such as ReLU and any smooth activation functions, meet our assumptions. In the numerical experiments, we test the proposed algorithm on complex modern CNN models with stochastic gradients (SGs) and ReLU activation on regression, classification, and image reconstruction tasks. We observe that DBN outperforms the original BN algorithm and benchmark layer normalization (LN) on the MNIST, NI, CIFAR-10, CIFAR-100, and Caltech-UCSD Birds-200-2011 datasets with modern complex CNN models such as Resnet-18 and typical FNN models.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

O123: External peri-stomal skin granulations in paediatric tracheostomy: incidence, outcomes and a proposed treatment algorithm

Abstract Background External peri-stomal skin granulations after tracheostomy in children are common and may interfere with routine tube changes. This study is the first attempt to describe the incidence and outcomes, along with a proposed treatment algorithm. Methods A retrospective review of all inpatient children with a tracheostomy between January 2020 and May 2022 at the Royal Hospital for Children (RHC) in Glasgow. The presence of external peri-stomal granulation, date of onset and resolution, recurrence and treatment modalities were noted. Results A total of 50 episodes of peri-stomal granulation were identified in 27 children (52%). Median age at the end of the study period was 4.3 years, with younger children experiencing more frequent granulation. 3 episodes interfered with tracheostomy tube changes. Time to resolution of the granulation was significantly longer with topical steroid/antimicrobial ointment monotherapy, but recurrence was less common when this was used a first treatment modality. Conclusion Non-invasive measures such as topical anti-microbials should be used in the first instance when managing external stoma-site granulations. More invasive measures, such as silver nitrate cautery and surgical excision, should be considered if the granulation tissue is not improving or when it poses a risk to safe tube changes.

Growing Pains: A Rare Case of a Temporal Dermoid Cyst in an Adult and Review of the Literature

Dermoid cysts are common cutaneous developmental anomalies that occur due to the entrapment of ectodermal elements during embryonic closure. While the exact origin of dermoid cysts is unknown, they are theorized to originate from multipotent embryonic stem cells or translocated epithelial cells. Dermoid cysts are typically congenital but are sometimes diagnosed later in childhood and rarely in adulthood. They are reported as the most common pediatric skull tumors. Surgical excision is recommended to prevent enlargement, invasion, rupture, or infection. Temporal dermoid cysts, specifically, are extremely rare, with only a few cases having been reported in adulthood. We present a case of a 51-year old female with an enlarging left temporal mass found to be a dermoid cyst. The mass was excised, resulting in complete resolution of symptoms. This case highlights the importance of early excision, radiologic studies, and a tailored management approach based on cyst location. Our proposed treatment algorithm provides guidance for clinicians in identification and management of adult dermoid cysts.

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

Learning competing risks across multiple hospitals: one-shot distributed algorithms.

To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image

Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques

&lt;p&gt;We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.&lt;/p&gt;

Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition.

Micro-expressions, which are spontaneous and difficult to suppress, reveal a person's true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion computing. In recent years, deep learning-based feature extraction and fusion techniques have been widely used for micro-expression recognition, particularly methods based on Vision Transformer that have gained popularity. However, the Vision Transformer-based architecture used in micro-expression recognition involves a significant amount of invalid computation. Additionally, in the traditional two-stream architecture, although separate streams are combined through late fusion, only the output features from the deepest level of the network are utilized for classification, thus limiting the network's ability to capture subtle details due to the lack of fine-grained information. To address these issues, we propose a new two-level spatio-temporal feature fused with a two-stream architecture. This architecture includes a spatial encoder (modified ResNet) for learning texture features of the face, a temporal encoder (Swin Transformer) for learning facial muscle motor features, a feature fusion algorithm for integrating multi-level spatio-temporal features, a classification head, and a weighted average operator for temporal aggregation. The two-stream architecture has the advantage of extracting richer features compared to the single-stream architecture, leading to improved performance. The shifted window scheme of Swin Transformer restricts self-attention computation to non-overlapping local windows and allows cross-window connections, significantly improving the performance and reducing the computation compared to Vision Transformer. Moreover, the modified ResNet is computationally less intensive. Our proposed feature fusion algorithm leverages the similarity in output feature shapes at each stage of the two streams, enabling the effective fusion of multi-level spatio-temporal features. This algorithm results in an improvement of approximately 4% in both the F1 score and the UAR. Comprehensive evaluations conducted on three widely used spontaneous micro-expression datasets (SMIC-HS, CASME II, and SAMM) consistently demonstrate the superiority of our approach over comparative methods. Notably, our approach achieves a UAR exceeding 0.905 on CASME II, making it one of the few frameworks in the published micro-expression recognition literature to achieve such high performance.

A DEIM Tucker tensor cross algorithm and its application to dynamical low-rank approximation

We introduce a Tucker tensor cross approximation method that constructs a low-rank representation of a d-dimensional tensor by sparsely sampling its fibers. These fibers are selected using the discrete empirical interpolation method (DEIM). Our proposed algorithm is referred to as DEIM fiber sampling (DEIM-FS). For a rank-r approximation of an O(Nd) tensor, DEIM-FS requires access to only dNrd−1 tensor entries, a requirement that scales linearly with the tensor size along each mode. We demonstrate that DEIM-FS achieves an approximation accuracy close to the Tucker-tensor approximation obtained via higher-order singular value decomposition at a significantly reduced cost. We also present DEIM-FS (iterative) that does not require access to singular vectors of the target tensor unfolding and can be viewed as a black-box Tucker tensor algorithm. We employ DEIM-FS to reduce the computational cost associated with solving nonlinear tensor differential equations (TDEs) using dynamical low-rank approximation (DLRA). The computational cost of solving DLRA equations can become prohibitive when the exact rank of the right-hand side tensor is large. This issue arises in many TDEs, especially in cases involving non-polynomial nonlinearities, where the right-hand side tensor has full rank. This necessitates the storage and computation of tensors of size O(Nd). We show that DEIM-FS results in significant computational savings for DLRA by constructing a low-rank Tucker approximation of the right-hand side tensor on the fly. Another advantage of using DEIM-FS is to significantly simplify the implementation of DLRA equations, irrespective of the type of TDEs. We demonstrate the efficiency of the algorithm through several examples including solving high-dimensional partial differential equations.

Acromioclavicular joint separation: Controversies and treatment algorithm.

In this article, we present an uptodate outline of acromioclavicular (AC) joint separation. A clear understanding of acromioclavicular joint injury in terms of the mechanism of injury, clinical picture, diagnostic imaging, and most updated surgical techniques used for the treatment can provide the best care for those patients. This article describes updated treatment strategies for AC separation, including type III AC separation which is known most controversial. Finally, we present a proposed treatment algorithm that can aid in the treatment of AC separation from the most updated evidence.

A deep learning-based and adaptive region proposal algorithm for semantic segmentation

This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results show the efficacy of the proposed method.

A duality-preserving adjoint method for segregated Navier–Stokes solvers

Adjoint methods efficiently compute gradients for systems with many inputs and have been widely used for large-scale gradient-based optimization in fluid mechanics. To ensure optimization's numerical robustness, we need to develop an adjoint solution algorithm that has a similar, if not the same, convergence rate as the primal flow solver at each optimization iteration. This consistent primal-adjoint convergence behavior is also called duality-preserving (DP). Existing DP adjoint methods are mostly for fully coupled Navier–Stokes (NS) solvers with compressible flows. However, few DP adjoints have been proposed for segregated NS solvers, e.g., the semi-implicit method for pressure-linked equations (SIMPLE) algorithm solvers widely used for incompressible flow simulations. This study will fill this gap by deriving a fixed-point segregated adjoint formulation that fully preserves the convergence behavior of primal solvers. We first rewrite the steady-state segregated NS primal solution process into a fully coupled left-preconditioned Richardson format. Then, we transpose the preconditioner matrix to obtain a DP iterative adjoint formulation. In addition to algebraic derivation, we create a new graph representation that significantly streamlines the DP adjoint development for new segregated solvers. We prove that the proposed graph representation is equivalent to the above preconditioner transpose approach and guarantees duality. We evaluate our proposed algorithm using three cases with increasing complexity: a miniature-sized problem, an airfoil, and a wing. To quantify duality, we compare the eigenvalues between the primal and adjoint solvers and observe excellent agreements. We also evaluate to what extent various numerical settings (e.g., inner iteration tolerances and non-dual preconditioner) impact the eigenvalue distributions. Our proposed adjoint achieves machine-precision accurate gradient computation with competitive speed. Finally, we incorporate our adjoint solver into a large-scale gradient-based optimization framework and demonstrate its capability for wing aerodynamic shape optimization. The proposed fixed-point adjoint approach has the potential to make the adjoint solution more robust and efficient for any segregated NS solvers.