Implementation Of Code Research Articles

Robust two-dimensional PCANet with F-norm distance metric

The principal component analysis network (PCANet) is a simplified convolutional neural network (CNN) implemented using a deep subspace learning model. Since its typical PCA filters utilize the squared Frobenius-norm (F-norm) as the distance metric, it exhibits extreme sensitivity to outliers hidden within the data. Furthermore, PCANet faces challenges in extracting structural information in the row and column directions of images as the filters need to be pre-vectorized. To address these issues, we propose a robust bi-directional two-dimensional PCANet (RBDPCANet), wherein the filters utilize the F-norm instead of the squared F-norm to mitigate the impact of outliers. Additionally, the relationship between reconstruction errors and projection distances is simultaneously considered in the objective function. After a concise analytical description of the PCANet concept and related work, we present the methodology for generating three convolutional kernels in the form of RBDPCANet-1, RBDPCANet-2, and RBDPCANet-3 algorithms. These convolution kernels are iteratively obtained and are suitable for numerical implementation. Each of the three algorithms uses another optimization objective function. It has been shown that in image recognition tasks, the developed methods are characterized by better resistance to noise compared to the classic PCANet approach. The rotation invariance and convergence of RBDPCANet are demonstrated according to theoretical principles. The efficacy of the proposed method is validated using images from publicly available datasets. Extensive experimental results on these datasets demonstrate that the proposed RBDPCANet not only significantly boosts the efficiency of image classification but also has strong resistance to occlusions, changes in lighting, changes in orientation, etc. Our implementation code is available at https://github.com/Wesley-li1/RBDPCANet-.

CoC Dharma (Role of Committee of Creditors-IBC, 2016)

The Committee of Creditors (CoC) has been authorized under the IBC, similar to the Board of Directors, to make decisions concerning the corporate debtor, throughout the Corporate Insolvency Resolution Process (CIRP). As an integral part of this authorizing Code, the Adjudicating Authority, when initiating the CIRP for a company, designates a Resolution Professional, who oversees and implements all decision-making processes during the CIRP and thus administers the CIRP of the company. Within this authority, a significant aspect is the power of the CoC to review and subsequently approve a resolution plan pertaining to a company (As per sections 30 and 31 of the Code). This authorization is contingent upon the ultimate approval of the resolution plan by the respective Adjudicating Authority (Section 31 of the Code). During the Code's implementation, the Adjudicating Authority, followed by the National Company Law Appellate Tribunal and the Supreme Court of India, have consistently emphasized the “commercial wisdom” of the CoC in determining the subsequent steps for the corporate debtor thereby facilitating the resolution of the corporate debtor.

A Frailty Model for Semi-competing Risk Data with Applications to Colon Cancer

In semi-competing risks (which generalizes the competing risks scenario), a subject may experience both terminal and non-terminal events, usually dependent, where the event time to the intermediate non-terminal event (say, tumor recurrence in cancer studies) is subject to censoring by the terminal event (say, death), but not vice-versa. As an alternative to the latent failure time formulation of semi-competing risks with joint survival functions, here, we consider an illness-death (multistate) shared frailty framework, where the dependency between the terminal and non-terminal failure times is incorporated via the power variance frailty between the conditional transition rates that are assumed Markov. Inference is conducted via maximum likelihood. A simulation study is conducted to evaluate the finite sample performance of the model parameters. Finally, we compare and contrast our power variance frailty proposal to known alternatives via application to a colon cancer dataset. Relevant R code for implementation of our model is available in GitHub.

Implementation of a VoF solver with phase change for the simulation of internal cavitation and droplet breakup in injectors

This study presents the implementation and derivation of a Volume of Fluid (VoF) solver capable of taking into account pressure driven phase change and handle three fluids. The implemented code is designed to investigate internal nozzle cavitation and subsequent injection and jet breakup. An extended cavitation model is also implemented that can handle phase change in the presence of a third phase. Detailed description and derivation of the mathematical system is presented. The solver is validated on a one-dimensional benchmark case from the literature and verified on an experimentally well-documented geometry. The verification shows the developing cavitation regime is well captured by comparing velocity profiles and flow images with experimental measurements and visualisation.

The Influence of Islamic Values on the Implementation of the Librarian Code of Ethics at the Library of Raden Intan State Islamic University Lampung

This study discusses the influence of Islamic values on the implementation of the librarian code of ethics at the Raden Intan State Islamic University Library in Lampung. This study aims to determine whether there is a positive and significant influence between Islamic values on the code of ethics of librarians at the Raden Intan State Islamic University Library in Lampung. This is a type of descriptive quantitative research with data collection techniques through questionnaires and documentation. The data analysis in this study used several tests including normality tests, linearity tests, regression tests, correlation tests, and t hypothesis tests. Data analysis using Pearson’s Product Moment test statistics with the help of the SPSS V.22 statistical program with two research variables, namely the variable of Islamic values (X) and the variable of the librarian code of ethics (Y). The results showed a positive and significant influence between Islamic values on the code of ethics of librarians in the library of Raden Intan State Islamic University Lampung, with the results of a regression coefficient value of 0.0440 from a simple linear regression test. This means that there is a positive and significant influence between variable X and variable Y. The level of influence between variable X and variable Y is 37.7%. In other words, the amount of influence exerted by the variable of Islamic values on the librarian code of ethics is 37.7%, the rest is influenced by other factors.
 Keywords: Islamic Values, Librarian Code of Ethics, Library

Efficient truncated randomized SVD for mesh-free kernel methods

This paper explores the utilization of randomized SVD (rSVD) in the context of kernel matrices arising from radial basis functions (RBFs) for the purpose of solving interpolation and Poisson problems. We propose a truncated version of rSVD, called trSVD, which yields a stable solution with a reduced condition number in comparison to the non-truncated variant, particularly when manipulating the scale or shape parameter of RBFs. Notably, trSVD exhibits exceptional proficiency in capturing the most significant singular values, enabling the extraction of critical information from the data. When compared to the conventional truncated SVD (tSVD), trSVD achieves comparable accuracy while demonstrating improved efficiency. Furthermore, we explore the potential of trSVD by employing scale parameter strategies, such as leave-one-out cross-validation and effective condition number. Then, we apply trSVD to solve a 2D Poisson equation, thereby showcasing its efficacy in handling partial differential equations. In summary, this study offers an efficient and accurate solver for RBF problems, demonstrating its practical applicability. The code implementation is provided to the scientific community for their access and reference.

H hybrid control and MRD in a steel frame building subjected to excessive vibrations caused by the dynamic action of wind and earthquake

The dynamic loads from earthquakes and winds can destroy lives, cause collapse in civil structures, and interrupt basic services provided to the population. In this scenario, structural designs must be developed to decrease the damage induced by these actions. The objective of this work is to design a hybrid controller based on the H¥ optimization via state feedback and the magneto-rheological damper (MRD) to mitigate the excessive vibrations of a three-story steel frame building, represented through the shear building model, subjected to the simultaneous dynamic action of wind and earthquake. All research is based on computational simulation, experimental research and results will not be addressed. In the numerical analysis, digital computer and MATLAB® software are used, and implemented codes generate the expected results based on the mathematical modeling. With the application of the H¥ control technique via state feedback, the displacements were reduced by 77%. With MRD this reduction was 79%. With the hybrid controller, this reduction was 100%. Thus, the verifications in relation to maximum displacements were met for NBR 15421:2006, NBR 8800:2008 and NBR 6118:2014. From the results, it is concluded that the hybrid controller proved to be more efficient and achieved the proposed objective. The exogenous inputs had zero influence on the behavior of the system output.

Overview Pluralism Law: Application Ammatoa Customary Crime Kajang, Bulukumba, South Sulawesi After the New Criminal Code Law

Purpose: The purpose of this study is to examine the implementation of the Ammatoa Kajang customary penal code in Bulukumba, South Sulawesi, and its recognition under Regional Regulation Number 9 of 2015, and to explore the interaction and adjustment between customary law, state criminal law, and religious law within the Ammatoa Kajang Customary Law Community. Method: The research employs empirical legal research methods and qualitative data analysis techniques. Inductive reasoning is utilized to draw conclusions from the gathered data. Result and Conclusion: The study finds that the Ammatoa Kajang Customary Law Community in Bulukumba, South Sulawesi, has criminal law instruments, including criminal articles and customary justice systems. The implementation of customary punishment is dominant within the Kajang customary territory, compared to state crimes and religious law. The coexistence of multiple legal systems leads to intersections and adjustments between them. Implication of the Research: The research highlights the complexity of legal pluralism and the interaction between different legal systems within Indigenous communities like the Ammatoa Kajang Customary Law Community. It underscores the importance of understanding and respecting customary laws alongside state and religious laws. Originality/Value: This study contributes to the literature by providing insights into the implementation of customary penal codes in Indigenous communities, particularly in Bulukumba, South Sulawesi. It sheds light on the dynamics of legal pluralism and the adaptation of legal systems within these communities, offering valuable information for policymakers and legal practitioners.

Local environment interaction-based machine learning framework for predicting molecular adsorption energy

Machine learning (ML) models in materials science are mainly developed for predicting global properties, such as formation energy, band gap, and elastic modulus. Thus, these models usually fall short in describing local characteristics, such as molecular adsorption on surfaces. Here, we introduce a local environment interaction-based ML framework that contains a modified graph-based Voronoi tessellation geometrical representation, improved fingerprint feature engineering, and traditional ML and advanced deep learning (DL) algorithms. The precise characterization can be extracted using this framework for representing local information of adsorption of molecules on a surface. Using both traditional ML and advanced DL algorithms, we demonstrate remarkable prediction accuracy and robustness on 0D, two-dimensional (2D), and three-dimensional (3D) catalysts. Furthermore, it is found that the employment of this approach reduces data requirements and augments computational speed, specifically for DL algorithms. This work provides an effective and universal ML framework for various applications of molecular adsorption from catalysis, sensors, carbon capture, and energy storage to drug delivery, signifying a novel and promising avenue in the field of materials informatics. The implementation code in this work is available at https://github.com/mpeshel/LEI-framework LERN .

Penerapan Kode Etik Jurnalistik Pada Peliputan Berita Kriminal di Panturapost.com

This study discusses the application of a journalistic code of ethics in the context of reporting on the online media panturapost.com. In the digital era, online media has become the main source of information for the community. However, with technological developments and intense competition, the journalistic code of ethics is often neglected. This study aims to analyze how the journalistic code of ethics articles 3, 4 and 5 are applied in online media reporting at panturapost.com. This study uses a qualitative research method with a case study approach. This study focuses on examining the application of a code of ethics in several aspects such as editorial policy, news production practices from planning to news loading, journalists' understanding of the code of ethics, to analyzing criminal news content published in panturpost. .com. The results of the study show that the implementation of the journalistic code of ethics in the online media panturapost.com has been implemented. Other results show that panturapost.com in the news production process has quite strict stages to make news worthy of broadcast according to the principles of the journalistic code of ethics.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood

Abstract Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance imaging (MRI) (“brain age”) may have important diagnostic and therapeutic applications as a non-invasive biomarker of brain health, aging, and neurocognition. While previous deep learning tools for predicting brain age have shown promising capabilities using single-institution, cross-sectional datasets, our work aims to advance the field by leveraging multi-site, longitudinal data with externally validated and independently implementable code to facilitate clinical translation and utility. This builds on prior foundational efforts in brain age modeling to enable broader generalization and individual’s longitudinal brain development. Here, we leveraged 32,851 T1-weighted MRI scans from healthy children and adolescents aged 3 to 30 from 16 multisite datasets to develop and evaluate several DL brain age frameworks, including a novel regression diffusion DL network (AgeDiffuse). In a multisite external validation (5 datasets), we found that AgeDiffuse outperformed conventional DL frameworks, with a mean absolute error (MAE) of 2.78 years (interquartile range [IQR]: [1.2-3.9]). In a second, separate external validation (3 datasets), AgeDiffuse yielded an MAE of 1.97 years (IQR: [0.8-2.8]). We found that AgeDiffuse brain age predictions reflected age-related brain structure volume changes better than biological age (R2 = 0.48 vs. R2 = 0.37). Finally, we found that longitudinal predicted brain age tracked closely with chronological age at the individual level. To enable independent validation and application, we made AgeDiffuse publicly available and usable for the research community.

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Orthogonal Dictionary Guided Shape Completion Network for Point Cloud

Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications. Prior approaches achieve this goal by employing a two-stage completion framework, generating a coarse yet complete seed point cloud through an encoder-decoder network, followed by refinement and upsampling. However, the encoded features suffer from information loss of the missing portion, leading to an inability of the decoder to reconstruct seed points with detailed geometric clues. To tackle this issue, we propose a novel Orthogonal Dictionary Guided Shape Completion Network (ODGNet). The proposed ODGNet consists of a Seed Generation U-Net, which leverages multi-level feature extraction and concatenation to significantly enhance the representation capability of seed points, and Orthogonal Dictionaries that can learn shape priors from training samples and thus compensate for the information loss of the missing portions during inference. Our design is simple but to the point, extensive experiment results indicate that the proposed method can reconstruct point clouds with more details and outperform previous state-of-the-art counterparts. The implementation code is available at https://github.com/corecai163/ODGNet.

Game-Theoretic Unlearnable Example Generator

Unlearnable example attacks are data poisoning attacks aiming to degrade the clean test accuracy of deep learning by adding imperceptible perturbations to the training samples, which can be formulated as a bi-level optimization problem. However, directly solving this optimization problem is intractable for deep neural networks. In this paper, we investigate unlearnable example attacks from a game-theoretic perspective, by formulating the attack as a nonzero sum Stackelberg game. First, the existence of game equilibria is proved under the normal setting and the adversarial training setting. It is shown that the game equilibrium gives the most powerful poison attack in that the victim has the lowest test accuracy among all networks within the same hypothesis space when certain loss functions are used. Second, we propose a novel attack method, called the Game Unlearnable Example (GUE), which has three main gradients. (1) The poisons are obtained by directly solving the equilibrium of the Stackelberg game with a first-order algorithm. (2) We employ an autoencoder-like generative network model as the poison attacker. (3) A novel payoff function is introduced to evaluate the performance of the poison. Comprehensive experiments demonstrate that GUE can effectively poison the model in various scenarios. Furthermore, the GUE still works by using a relatively small percentage of the training data to train the generator, and the poison generator can generalize to unseen data well. Our implementation code can be found at https://github.com/hong-xian/gue.

Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective

Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the interpretability of graphs, and thus the learned noisy and task-agnostic information interferes with the prediction of graphs. To this end, with the purpose of exploring the intrinsic rationale of graphs, we accordingly propose to capture the dimensional rationale from graphs, which has not received sufficient attention in the literature. The conducted exploratory experiments attest to the feasibility of the aforementioned roadmap. To elucidate the innate mechanism behind the performance improvement arising from the dimensional rationale, we rethink the dimensional rationale in graph contrastive learning from a causal perspective and further formalize the causality among the variables in the pre-training stage to build the corresponding structural causal model. On the basis of the understanding of the structural causal model, we propose the dimensional rationale-aware graph contrastive learning approach, which introduces a learnable dimensional rationale acquiring network and a redundancy reduction constraint. The learnable dimensional rationale acquiring network is updated by leveraging a bi-level meta-learning technique, and the redundancy reduction constraint disentangles the redundant features through a decorrelation process during learning. Empirically, compared with state-of-the-art methods, our method can yield significant performance boosts on various benchmarks with respect to discriminability and transferability. The code implementation of our method is available at https://github.com/ByronJi/DRGCL.

PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations

Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view with dynamic objects, environmental noise, or other perturbations. To address this challenge, we propose a model called PosDiffNet. Our approach performs hierarchical registration based on window-level, patch-level, and point-level correspondence. We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds. We incorporate position embeddings into a Transformer module based on a neural ordinary differential equation (ODE) to efficiently represent patches within points. We employ the multi-level correspondence derived from the high feature similarity scores to facilitate alignment between point clouds. Subsequently, we use registration methods such as SVD-based algorithms to predict the transformation using corresponding point pairs. We evaluate PosDiffNet on several 3D point cloud datasets, verifying that it achieves state-of-the-art (SOTA) performance for point cloud registration in large fields of view with perturbations. The implementation code of experiments is available at https://github.com/AI-IT-AVs/PosDiffNet.

Geometry-aware 3D pose transfer using transformer autoencoder

3D pose transfer over unorganized point clouds is a challenging generation task, which transfers a source’s pose to a target shape and keeps the target’s identity. Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape. However, all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes. This disadvantage severely limits the generation and generalization capabilities. In this study, we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem. An efficient self-attention mechanism, that is, cross-covariance attention, was utilized across our framework to perceive the correlations between points at different distances. Specifically, the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information. Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner, which does not require corresponding information or modulation steps. The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task. The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transformer-Autoencoder.

International Treaties as Sources of Criminal Law Prescriptions in Chapter 34 of the Criminal Code of the Russian Federation

The authors examine how Russian legislation can be brought in accord with Russia’s international obligations that are used in practice by the lawmakers in the Criminal Code of the Russian Federation: implementation and overt or covert reference. They analyze the examples of implementation and references in Champer 34 of the Criminal Code of the Russian Federation, the specifics of adding articles and the used legislative formulations. The authors present examples of a different scope of criminalizing an action at the domestic and the international levels. Besides, they highlight the problem of sometimes a considerable time gap between the ratification of an international treaty and its implementation in the Criminal Code of the Russian Federation and note that ratified but unimplemented international treaties are only a part of the Russian legal system, however, they are not sources of Russian criminal law. The research revealed that implementation is sometimes non-linear: several international treaties can be implemented into one Article of the Criminal Code of the Russian Federation or, vice versa, one Article of the Code can determine liability for violating several international treaties. A special part of the work is devoted to studying the list of international agreements referred to in Art. 353–356, 360 of the Criminal Code of the Russian Federation. The analysis allowed the authors to conclude in which cases non-self-executing international treaties are the source of criminal law prescriptions. They also mention a new type of a blanket disposition introduced in 2014, which refers not to a normative act from a different sector, but to a court act (Art. 354.1 of the Criminal Code of the Russian Federation). The authors conclude that in this case the Nuremberg Tribunal's decision will be a source of Russian criminal law. In conclusion the authors outline the most urgent problems at the legislative level that require scientific and practical solutions.

GTMFuse: Group-attention transformer-driven multiscale dense feature-enhanced network for infrared and visible image fusion

Infrared and visible images captured by different devices can be seamlessly integrated into a single composite image through the application of image fusion techniques. However, many existing convolutional neural network-based methods for infrared and visible image fusion have exhibited limited capability for effectively amalgamating information from the source images. Consequently, we propose a group-attention transformer into the multiscale feature enhanced network for infrared and visible image fusion, which we abbreviate as GTMFuse. Specifically, GTMFuse employs multiscale dual-channel encoders to independently process the source image and extract multiscale features. Among the encoders, the group-attention transformer module is utilized to facilitate more comprehensive long-range feature dependency modeling at each scale. This innovative module seamlessly combines a fixed-direction stripe attention mechanism with channel attention and window attention, enabling comprehensive global long-range information capture and interaction with feature information across the source images. The multiscale features obtained from the group-attention transformer module are integrated into the fused image through a meticulously designed dense fusion block. Furthermore, this study introduces a novel dataset named HBUT-IV, encompassing surveillance images captured from multiple viewpoints. The HBUT-IV dataset serves as a valuable benchmark for assessing the efficacy of fusion methods. Extensive experiments are conducted on four datasets employing nine comparative methods, revealing the superior performance of the GTMFuse approach. The implementation code is accessible at https://github.com/XingLongH/GTMFuse.