High-level Ones Research Articles

Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation.

Thanks to the advantages of the friendly annotations and the satisfactory performance, weakly-supervised semantic segmentation (WSSS) approaches have been extensively studied. Recently, the single-stage WSSS (SS-WSSS) was awakened to alleviate problems of the expensive computational costs and the complicated training procedures in multistage WSSS. However, the results of such an immature model suffer from problems of background incompleteness and object incompleteness. We empirically find that they are caused by the insufficiency of the global object context and the lack of local regional contents, respectively. Under these observations, we propose an SS-WSSS model with only the image-level class label supervisions, termed weakly supervised feature coupling network (WS-FCN), which can capture the multiscale context formed from the adjacent feature grids, and encode the fine-grained spatial information from the low-level features into the high-level ones. Specifically, a flexible context aggregation (FCA) module is proposed to capture the global object context in different granular spaces. Besides, a semantically consistent feature fusion (SF2) module is proposed in a bottom-up parameter-learnable fashion to aggregate the fine-grained local contents. Based on these two modules, WS-FCN lies in a self-supervised end-to-end training fashion. Extensive experimental results on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the effectiveness and efficiency of WS-FCN, which can achieve state-of-the-art results by 65.02% and 64.22% mIoU on PASCAL VOC 2012 val set and test set, 34.12% mIoU on MS COCO 2014 val set, respectively. The code and weight have been released at:WS-FCN.

Safety Nudged or Empowerment Fueled? Leadership Mechanisms and Boundary Condition for Follower’s Adaptive Communication Behaviors During the COVID-19 Pandemic Crisis in China

ABSTRACT Incorporating social information processing theory and literature on organizational learning, this study explores the process approach to internal communication by theorizing and empirically testing a framework of employees’ adaptive communication amidst the COVID-19 pandemic crisis. A cross-sectional survey on full-time employees (n = 983) in Mainland China demonstrated how followers’ perception of leadership inclusiveness was related to the two dimensions of adaptive communication behaviors (i.e. internal voicing and external scouting). Results also showed the two mediating appraisals of inclusive leadership, i.e. psychological empowerment and psychological safety, functioned differently for followers with lower-level positions versus with higher-level ones. Theoretical and practical implications on internal communication, crisis communication, and public relations were elaborated in detail.

Code-switching in EFL Classroom Settings: A Review from Teachers’ and Students’ Outlook

<p>The use of code-switching in English-as-a-foreign-language (EFL) classrooms has considerably occupied serious discussions. Most language researchers consider whether to use only the target language exclusively or to use it alongside the first language in foreign language classrooms. This paper reviews 15 empirical studies investigating code-switching in different EFL classroom settings and cultures regarding English as a second and foreign languages. There are three main things that this paper wants to seek for the answer from those studies viewed from teachers’ and students’ outlooks; those are the perception, the function, and the strategy to generate learning success from code-switching. The results show that code-switching provides the chance for the lower proficiency level of English learners to feel secure in expressing what they want to speak, but not for those who belong to high proficiency level ones. Besides, code-switching tends to function as classroom management, but the over-functioning of code-switching in the EFL classroom can only hinder learners from receiving comprehensible input of English. Of all, the most important highlight from this paper is that the more the mother tongue is used the worse the success of learning English.</p>

MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging

Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.

An Asynchronous Compaction Acceleration Scheme for Near-Data Processing-enabled LSM-Tree-based KV Stores

LSM-tree-based key-value stores (KV stores) convert random-write requests to sequence-write ones to achieve high I/O performance. Meanwhile, compaction operations in KV stores update SSTables in forms of reorganizing low-level data components to high-level ones, thereby guaranteeing an orderly data layout in each component. Repeated writes caused by compaction ( a.k.a, write amplification) impacts I/O bandwidth and overall system performance. Near-data processing (NDP) is one of effective approaches to addressing this write-amplification issue. Most NDP-based techniques adopt synchronous parallel schemes to perform a compaction task on both the host and its NDP-enabled device. In synchronous parallel compaction schemes, the execution time of compaction is determined by a subsystem that has lower compaction performance coupled by under-utilized computing resources in a NDP framework. To solve this problem, we propose an asynchronous parallel scheme named PStore to improve the compaction performance in KV stores. In PStore, we designed a multi-tasks queue and three priority-based scheduling methods. PStore elects proper compaction tasks to be offloaded in host- and device-side compaction modules. Our proposed cross-leveled compaction mechanism mitigates write amplification induced by asynchronous compaction. PStore featured with the asynchronous compaction mechanism fully utilizes computing resources in both host and device-side subsystems. Compared with the two popular synchronous compaction modes based on KV stores (TStore and LevelDB), our PStore immensely improves the throughput by up to a factor of 14 and 10.52 with an average of a factor of 2.09 and 1.73, respectively.

MEDICAL IMAGING ENHANCEMENT WITH AI MODELS FOR AUTOMATIC DISEASE DETECTION AND CLASSIFICATION BASED ON MEDICAL IMAGES

The development of artificial intelligence (AI) in the healthcare industry has recently been of utmost importance. Early discovery, diagnosis, categorization, analysis, and treatment options are always positive medical breakthroughs. In making diagnoses and strategic decisions for healthcare, precise and consistent picture classification is essential. The semantic gap has emerged as the main problem with picture sorting. To bridge the gap, traditional machine learning techniques for classification mainly depend on low-level features rather than high-level ones, use some hand-crafted features, but compel intensive feature extraction and classification procedures. Deep convolution neural networks (CNNs), a potent technique that has significantly advanced in recent years, are successful in classifying images. A thorough assessment of pertinent studies is required to further assist readers in understanding the research and its main concepts captured. This study highlights the various AI models for automatic disease detection and classification based on medical images. This can include identifying tumors, lesions, fractures, or other abnormalities. The models discussed in this article include Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), One-Class Learning Models, Recurrent Neural Networks (RNNs), and 3D and Multimodal Models. Since these models work with various modalities, there are numerous instances where computed tomography (CT), positron emission computed tomography (PET), X-ray, magnetic resonance imaging (MRI), and ultrasound (US). Are mentioned to help complement the research.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks, and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower- to higher-level nodes, such as lagging birds’ howl in a flock or the opinions of lower-level individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of “bunching” effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks, which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

A precautionary approach to autonomous vehicles.

In this article, we defend an approach to autonomous vehicle ethics and policy based on the precautionary principle. We argue that a precautionary approach is warranted, given the significant scientific and moral uncertainties related to autonomous vehicles, especially higher-level ones. While higher-level autonomous vehicles may offer many important benefits to society, they also pose significant risks, which are not fully understood at this juncture. Risk management strategies traditionally used by government officials to make decisions about new technologies cannot be applied to higher-level autonomous vehicles because these strategies require accurate and reliable probability estimates concerning the outcomes of different policy options and extensive agreement about values, which are not currently available for autonomous vehicles. Although we describe our approach as precautionary, that does not mean that we are opposed to autonomous vehicle development and deployment, because autonomous vehicles offer benefits that should be pursued. The optimal approach to managing the risks of autonomous vehicles is to take reasonable precautions; that is, to adopt policies that attempt to deal with serious risks in a responsible way without depriving society of important benefits.

Contrastive structure and texture fusion for image inpainting

Most recent U-Net based models have shown promising results for the challenging tasks in image inpainting field. However, they often generate content with blurred textures and distorted structures due to the lack of semantic consistency and texture continuity in the missing regions. In this paper, we propose to restore the missing areas at both structural and textural levels. Our method is built upon a U-Net structure, which repairs images by extracting semantic information from high to low resolution and then decoding it back to the original image. Specifically, we utilize the high-level semantic features learned in encoder to guide the inpainting of structure-aware features of its adjacent low-level feature map. Meanwhile, low-level feature maps have clearer texture compared with high-level ones, which can be used as a prior for textural repair of high-level feature maps. subsequently, a module is used to fuse the two repaired feature maps (i.e., structure-aware and texture-aware features) reasonably and obtain a feature map with reasonable semantics. Moreover, in order to learn more representative high-level semantics feature, we design the model as a siamese network for contrastive learning. Experiments on practical data show that our method outperforms other state-of-the-art methods.

Human Factors in Leveraging Systems Science to Shape Public Policy for Obesity: A Usability Study

Background: despite a broad consensus on their importance, applications of systems thinking in policymaking and practice have been limited. This is partly caused by the longstanding practice of developing systems maps and software in the intention of supporting policymakers, but without knowing their needs and practices. Objective: we aim to ensure the effective use of a systems mapping software by policymakers seeking to understand and manage the complex system around obesity, physical, and mental well-being. Methods: we performed a usability study with eight policymakers in British Columbia based on a software tool (ActionableSystems) that supports interactions with a map of obesity. Our tasks examine different aspects of systems thinking (e.g., unintended consequences, loops) at several levels of mastery and cover common policymaking needs (identification, evaluation, understanding). Video recordings provided quantitative usability metrics (correctness, time to completion) individually and for the group, while pre- and post-usability interviews yielded qualitative data for thematic analysis. Results: users knew the many different factors that contribute to mental and physical well-being in obesity; however, most were only familiar with lower-level systems thinking concepts (e.g., interconnectedness) rather than higher-level ones (e.g., feedback loops). Most struggles happened at the lowest level of the mastery taxonomy, and predominantly on network representation. Although participants completed tasks on loops and multiple pathways mostly correctly, this was at the detriment of spending significant time on these aspects. Results did not depend on the participant, as their experiences with the software were similar. The thematic analysis revealed that policymakers did not have a typical workflow and did not use any special software or tools in their policy work; hence, the integration of a new tool would heavily depend on individual practices. Conclusions: there is an important discrepancy between what constitutes systems thinking to policymakers and what parts of systems thinking are supported by software. Tools may be more successfully integrated when they include tutorials (e.g., case studies), facilitate access to evidence, and can be linked to a policymaker’s portfolio.

A vectorized spherical convolutional network for recognizing 3D mesh models with unknown rotation

目的3维目标分类是视觉领域的一个基本问题，3维目标的旋转变化给分类带来极大挑战。同时不规则3维网格模型难以运用传统2维卷积网络提取特征。针对这两个问题，提出一种基于矢量型球面卷积网络的分类方法，用于识别未知旋转的3维网格模型。方法 使用矢量型神经元作为网络的基础神经元，并提出一种新型矢量层间的卷积方式。首先，将3维模型规范化并映射到单位球上，获取球面的信号表示；然后，使用矢量型分类网络和重建网络学习等变的3维模型特征；最后，使用分类网络完成3维模型分类。结果 经过消融实验对比，使用本文提出的球面卷积模块和矢量卷积层，并在训练时加入重建模块。对原本未旋转（no rotation，NR）数据集进行任意旋转（arbitrary rotation，AR），并设定NR/AR，AR/AR，NR/NR共3种训练/测试策略的分类任务，其中NR/AR任务衡量模型识别未知旋转的能力。在刚性数据集ModelNet40上，相比基于球面卷积网络（spherical convolutional neural network，SCNN）的分类方法，在3种任务上分别提高了7.7%，1.8%，3.1%。为验证本文方法在识别非刚性3维网格目标的优越性，在非刚性数据集SHREC15（shape retrieval contest 2015）上，相比SCNN，本文方法在3种任务上分别提高了8.8%，4.5%，5.0%。结论 本文提出一种将矢量型网络运用在3维目标分类的思路，使用光线投射法获得分布在球面空间的特征，便于使用统一的球面卷积算子进行处理；设计一种球面残差模块避免梯度消失；使用矢量型神经元并设计矢量层之间的卷积方式以保证网络的等变性，使得识别任意旋转的3维模型时更加准确。;Objective The 3D meshes are concerned of spatial information-demonstrated surface triangles，which can optimize surface information than other related representations like voxel or point cloud. The 3D shape analysis is still to be resolved in relevant to mesh representation on two aspects：1）the irregular data structure of the mesh model is challenged for feature extraction using traditional 2D convolutional networks，and 2）the 3D rotation transformation is challenged for object recognition as well. The emerging convolutional neural networks（CNNs）have been developing dramatically in the context of 2D vision like classification，segmentation，detection，as well as 3D objects-oriented applications. Current CNNbased 3D mesh classification is developed from two aspects：1）the 3D object is transferred to 2D images and the following 2D-based CNN methods are used，and 2）convolution methods are designed on 3D mesh data. However，it is still challenged to recognize rotated objects due to traditional CNN-equivariant-lacked pooling operation. The lack of rotation equivariance can be improved on the basis of two networks which are vectorized and equivariant networks. The vectorized network，known as capsule network，has shown its potentials in learning spatial transformation on 2D images，but convolutionconstrained method is required to be applied on 3D mesh further. To apply the vectorized neural network to 3D mesh data and preserve rotation equivariance，we develop a vectorized spherical neural network-derived method for 3D mesh classification. Method Our method can be segmented into three categories as mentioned below：First，the 3D mesh model is preprocessed to signals on the sphere. We normalize the 3D mesh into a unit sphere and get the spherical signals on the unit sphere using the ray casting scheme. The obtained spherical signals are nearly equivalent 3D shape representations and can be further processed by spherical convolution methods. The aims of processing 3D mesh to spherical signals are 1）to utilize the spherical signals-defined equivariant spherical convolution operators，and 2）to design vectorized neurons in a coordinated manner. Second，the autoencoder-structured model is used to learn the feature of the spherical signal. The model is composed of two sub-networks：i）a vectorized spherical convolutional neural network（VSCNN）to encode the equivariant feature and classify the 3D object，and ii）a multilayer perceptron decoder to decode the extracted feature back to sphere signal. The VSCNN is based on two kinds of spherical residual convolution block and the vector convolution layer. To train deeper networks and resist overfitting，we develop two spherical convolution modules which are S2 convolution block and SO(3) convolution block，and the primary vectorized neurons are obtained after that. The vector convolution layer is used to learn high-level vectorized features derived from the lower layer. The vector convolutional layer can be used to transfer the primary vectorized neurons to get high-level ones. A deep vectorized network can be constructed through the vector convolutional layer-based stacking. To guarantee the rotation-equivariant spherical vector neurons can be learned well during convolution，we use the SO(3) convolution operator to predict the high-level neurons. The VSCNN and the network-reconstructed are trained simultaneously. Third，the VSCNN-based 3D object classification is demonstrated. For validation，we use VSCNN to clarify the category information of the 3D model only. Result The ModelNet40 and SHREC15 of two 3D datasets are verified for the effectiveness of the proposed method. Our model is trained on the non-rotated（NR） and arbitrarily rotated（AR）training set，and it is tested on the non-rotated and rotated test set as well. The robustness of the model is demonstrated to rotation. For the rigid data set ModelNet40，the accuracy of rotation-unidentified targets can be reached to 85. 2%，surpassing the baseline method by 7. 7%. The comparative analysis shows that our method proposed can surpass most of multi-view and point cloud methods compared to other related 3D data representations. The NR/NR result can show its optimization ability in comparison with the benchmarks. At the same time，to identify non-rigid threedimensional grid targets，we carry out a rotation classification experiment on the non-rigid data set SHREC15，and the accuracy rate can be reached to 90. 4%，surpassing the baseline method by 8. 8%. Conclusion We develop a 3D object classification method for rotated mesh. The robustness to 3D rotation can be optimized in terms of vectorized neurons and the equivariant vector convolution layer. The 3D models-rotated recognition is facilitated excluding rotation augmentation， and it shows the learning ability for vectorized networks-based transformation.

The Self-Synchronisation of AI Ethical Principles

AI has become a hot topic both among the fervent integrators and the terrified apocalyptics; the formers see AI as the ultimate panacea, while the latter look at it as a great danger. In between, there are several organisations and individuals who consider AI to be good for humanity provided it respects certain limits. Collaboration and contributing to international associations and private firms, these people address the problem by trying to mitigate some low-level errors, such as negative biases, or high-level ones, such as problems of accountability and governance. Each of these bodies works towards a goal: reducing, through regulations, standards, advice, assurances, and independent audits. A particular phenomenon has been observed: internationally, ethical principles and approaches to risk reduction are becoming increasingly similar. Based on this assumption, the authors introduce an analogy to understand the ongoing synchronisation effect. Aware that an ultimate alignment will be impossible (due to the tendency to incompleteness of ethical reasoning) and strongly discouraged (due to the tendency of universal systems to flatten the peripheral voices), the authors suggest a theoretical investigation of the phenomenon of synchronisation and invite to feed it practically through the available tools of data governance. They show how ethics-based audits can be a suitable tool for the task through the presentation of a case study.

Dynamic feature learning for COVID-19 segmentation and classification

Since December 2019, coronavirus SARS-CoV-2 (COVID-19) has rapidly developed into a global epidemic, with millions of patients affected worldwide. As part of the diagnostic pathway, computed tomography (CT) scans are used to help patient management. However, parenchymal imaging findings in COVID-19 are non-specific and can be seen in other diseases. In this work, we propose to first segment lesions from CT images, and further, classify COVID-19 patients from healthy persons and common pneumonia patients. In detail, a novel Dynamic Fusion Segmentation Network (DFSN) that automatically segments infection-related pixels is first proposed. Within this network, low-level features are aggregated to high-level ones to effectively capture context characteristics of infection regions, and high-level features are dynamically fused to model multi-scale semantic information of lesions. Based on DFSN, Dynamic Transfer-learning Classification Network (DTCN) is proposed to distinguish COVID-19 patients. Within DTCN, a pre-trained DFSN is transferred and used as the backbone to extract pixel-level information. Then the pixel-level information is dynamically selected and used to make a diagnosis. In this way, the pre-trained DFSN is utilized through transfer learning, and clinical significance of segmentation results is comprehensively considered. Thus DTCN becomes more sensitive to typical signs of COVID-19. Extensive experiments are conducted to demonstrate effectiveness of the proposed DFSN and DTCN frameworks. The corresponding results indicate that these two models achieve state-of-the-art performance in terms of segmentation and classification.

Decoupled pyramid correlation network for liver tumor segmentation from CT images.

Automated liver tumor segmentation from computed tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on fully convolutional network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a decoupled pyramid correlation network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment livertumor. We first design a powerful pyramid feature encoder (PFE) to extract multilevel features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, spatial correlation (SpaCor) and semantic correlation (SemCor) modules, to recursively measure the correlation of multilevel features. The former selectively emphasizes global semantic information in low-level features with the guidance of high-level ones. The latter adaptively enhance spatial details in high-level features with the guidance of low-levelones. We evaluate the DPC-Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge data set. Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state-of-the-art methods. It also achieves a competitive result with a DSC of 96.0% and an ASSD of 1.636 mm for liversegmentation. The experimental results show promising performance of DPC-Net for liver and tumor segmentation from CT images. Furthermore, the proposed SemCor and SpaCor can effectively model the multilevel correlation from both semantic and spatial dimensions. The proposed attention modules are lightweight and can be easily extended to other multilevel methods in an end-to-endmanner.

Developing a feature-centric and affordance-based conceptualization of social media interactions

PurposeDespite the significance of online communication and interactions, previous research has not systematically compared all features on a single platform from the users' perspective. This study aims to fill this gap by extensively reviewing the current literature on social media affordances and proposes and tests a feature-centric and affordance-based conceptualization of social media platforms (SMPs) between users, features, the audience and content.Design/methodology/approachThis research surveys users on Facebook, one of the largest SMPs, and asks them to assess 20 features of Facebook on six relational affordances between users, features, audience and content. The data in this study were collected on Amazon Mechanical Turk (MTurk) with participants from the US Correspondence analysis was employed to examine the relationship between affordances and the ties among affordances, features and outcomes.FindingsResults of the study indicate that users perceive features differently, and employing features as the unit of analysis captures users' interactions effectively. The findings support the presence of user-oriented affordances, such as presentation flexibility, association and content association. These three affordances can be summarized in two higher-level ones: self-expression and connection (SEC) and persona-linked content (PLC). Our findings of the two dimensions, SEC and PLC, highlight the importance of targets and their connections in understanding social media interactions' dynamic nature.Practical implicationsBy proposing to shift the focus from platforms to features, this study suggests that companies should focus on understanding the features they use for their users to interact with their brand, rather than merely ensuring that their company is omnipresent on all platforms. This study underlines the need to focus on features that will help managers influence interpersonal and user-brand communications and interactions on social media.Originality/valueThis research is the first to put features at the center of its investigation and quantitatively examine the relationship between social media features and affordances in a social media context. In all, this research provides a new unit of analysis that is more suitable for researchers to build a robust conceptual foundation for affordances. We believe that conceptualizing audience and content as outcomes, distinguishing it from features and creating connections between them as affordances is the unique aspect of our conceptualization.

EndoBeams.jl: A Julia finite element package for beam-to-surface contact problems in cardiovascular mechanics

The increasing use of mini-invasive and endovascular surgical techniques is at the origin of the pressing need for computational models to support planning and training. Several implantable devices have a wire-like structure, which can be modelled using beam elements. Our objective is to create an efficient Finite Element (FE) modelling framework for such devices. For that, we developed the EndoBeams.jl package, written exclusively in Julia, for the numerical simulation of contact interactions between wire-like structures and rigid surfaces. The package is based on a 3D FE corotational formulation for frictional contact dynamics of beams. The rigid target surface is described implicitly using a signed distance field, predefined in a volumetric grid. Since the main objective behind this package is to find the best compromise between computational speed and code readability, the algorithm, originally in Matlab, was translated and optimised in Julia, a programming language designed to combine the performance of low-level languages with the productivity of high-level ones. To evaluate the robustness, a set of tests were conducted to compare the simulation results and computational time of EndoBeams.jl against literature data, the original Matlab code and the commercial software Abaqus. The tests proved the accuracy of the underlying beam-to-surface formulation and showed the drastic performance improvement of the Julia code with respect to the original one. EndoBeams.jl is also slightly faster than Abaqus. Finally, as a proof of concept in cardiovascular medicine, a further example is shown where the deployment of a braided stent is simulated within an idealised artery.

Kształtowanie się lokalnej administracji terenowej w Polsce w latach 1945-1952

The shaping of the local administration system in Poland in the years 1945-1952 is one of the most interesting issues in the post-war history. On the one hand, there is a formally existing local government system, but on the other hand, there appears a Soviet administrative model of national councils, previously unknown in Poland. The combination of the systems of local government and national councils authorities confused the administrative situation in Poland. The hierarchical arrangement of councils, i.e. the subordination of lower-level councils to higher-level ones, meant a departure from one of the basic features of local government, i.e. the independence of a given level of the administrative structure in relation to the level located above it. The composition of national councils was not determined by way of elections, but only by a process of delegation by so-called democratic organisations closely linked to the communist movement.

SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising

This article presents a novel end-to-end model based on encoder–decoder architecture for hyperspectral image (HSI) denoising, named spatial-spectral quasi-attention recurrent network, denoted as SQAD. The central goal of this work is to incorporate the intrinsic properties of HSI noise to construct a practical feature extraction module while maintaining high-quality spatial and spectral information. Accordingly, we first design a spatial-spectral quasi-recurrent attention unit (QARU) to address that issue. QARU is the basic building block in our model, consisting of spatial component and spectral component, and each of them involves a two-step calculation. Remarkably, the quasi-recurrent pooling function in the spectral component could explore the relevance of spatial features in the spectral domain. The spectral attention calculation could strengthen the correlation between adjacent spectra and provide the intrinsic properties of HSI noise distribution in the spectral dimension. Apart from this, we also design a unique skip connection consisting of channelwise concatenation and transition block in our model to convey the detailed information and promote the fusion of the low-level features with the high-level ones. Such a design helps maintain better structural characteristics, and spatial and spectral fidelities when reconstructing the clean HSI. Qualitative and quantitative experiments are performed on publicly available datasets. The results demonstrate that SQAD outperforms the state-of-the-art methods of visual effect and objective evaluation metrics.

ESL Students’ Readiness for Self-Directed Learning in Improving English Writing Skills

Self-directed learning among students, particularly at the upper secondary level, is still underexplored in Malaysia. Further understanding of ESL students’ readiness for self-directed learning in improving English writing skills is crucial to exploit the advantages of this learning method for their benefit. Thus, this study aimed to examine the levels of self-directedness among secondary school students and their readiness to apply self-directed learning in improving English writing skills through a survey design. A total of 50 ESL students in a secondary school responded to two sets of questionnaires that measured different variables in this research. The first questionnaire included a self-rating scale of self-directed learning, which consisted of awareness, learning strategies, learning activities, evaluation, and interpersonal skills. The second questionnaire contained questions about students’ readiness for self-directed learning in improving English writing skills. Sampling was done randomly without considering students’ academic level of English. The findings found that most students possessed a medium level of self-directedness with not much difference with the high-level ones. The study then revealed a positive relationship between students’ level of self-directedness and students’ readiness to incorporate self-directed learning in English writing skills. For instance, students with a high level of self-directedness scored higher in students’ readiness for self-directed learning in improving English writing skills than those with a low level of self-directedness. Future studies should consider self-directed learning strategies to promote lifelong effects of positive attributes towards learning experiences, such as discipline, attention, responsible, and creativity in planning learning objectives.

Perception of soft materials relies on physics-based object representations: Behavioral and computational evidence

Author(s): Bi, Wenyan; Shah, Aalap D; Wong, Kimberly W; Scholl, Brian; Yildirim, Ilker | Abstract: When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate intuitive physics. We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.