Single View Research Articles

Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification

Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson's correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN "features" that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality.

Transformer-based comparative multi-view illegal transaction detection.

In recent years, as the Ether platform has grown by leaps and bounds. Numerous unscrupulous individuals have used illegal transaction to defraud large sums of money, causing billions of dollars of losses to investors worldwide. Facing the endless stream of the illegal transaction based on Ether smart contracts problems, such as illegal transaction, money laundering, financial fraud, phishing. Currently, illegal transaction are only detected by a single view of the smart contract's contract code view feature and account transaction view feature, which is not only incomplete, but also not fully representative of the smart contract's features. More importantly, the single view detection model cannot accurately capture the global structure and semantic features between the Tokens of the view features. In this case, it is particularly important that all view features are shared among themselves. In this paper, we investigate a Transformer-based model for contrasting illegal transaction detection networks under multiple views (TranMulti-View Net). The model in this paper is based on Transformer to learn a multi-view fusion representation, which aims to maximise the fusion of the interaction information of different view features under the same condition. In this model we first use the Transformer model to learn global structure and semantic features from a sequence of Tokens tokenised by a view, capturing the remote dependencies of Tokens in the view features, and then we share the contract code view features and the account transaction view features across all views to learn important semantic information between views from each other. In addition, we find that the approach of semi-supervised training of multi-view features using contrast learning outperforms the scheme of prediction based on direct fusion of different view features, resulting in stronger correlation between view features. As a result, the underlying semantic information can be captured more accurately, leading to more accurate predictions of illegal transaction. The experimental results show that our proposed TranMulti-View Net obtains good detection results with a Precision score of 98%.

Ulnar nerve thickness at the elbow on longitudinal ultrasound view in control subjects

IntroductionUlnar mononeuropathy at the elbow is the second most frequent neuropathy in humans. Diagnosis is based on clinical and electrophysiological criteria and, more recently, also on ultrasound. Cross-sectional ultrasound is currently the most valued, although longitudinal ultrasound allows assessment of the entire affected trajectory of the nerve in a single view, but always in a straight line with no changes in direction, as in the extended elbow. The main aim of this work is to propose normative values ​​for longitudinal ultrasound of the ulnar nerve at the elbow.MethodsThe neurological exploration of upper extremity, and electrophysiological and ultrasound parameters at the elbow of ulnar nerve were evaluated in 76 limbs from 38 asymptomatic subjects.ResultsThe diameters of the nerve as well as the distal and proximal areas were larger at the proximal region of the ulnar groove, and even more so in older individuals. In most of these elderly subjects, we found a small, non-significant slowdown in motor conduction velocity at the elbow with respect to the forearm (less than 5 m/s).ConclusionsWe observed a good correlation between the longitudinal and cross-sectional ultrasounds of the ulnar nerve at the elbow. Longitudinal ultrasound proved to be sensitive, reliable, simple and rapid, but its greatest contribution was allowing the visualization of the entire nerve trajectory in an integrated way, providing an image with good definition of the outline, proportions and intraneural characteristics of the nerve.

About the phenomenon of «post-pandemic» reality and the social purpose of the state

Until now, there is no single view on the concept of state. Leading domestic and foreign legal institutions recognize this problem as highly controversial and not fully resolved. The concept of state came to us from ancient times and it will constantly change depending on the socio-political characteristics of the current historical period. The COVID -19 epidemic has made significant changes in the socio-economic way of life of a huge number of states, including Russia. Because of these processes, prerequisites have appeared for the formation of a still little-studied phenomenon of post-pandemic social reality, which needs a comprehensive study. The purpose of the article is to consider the concept, features, essence and social purpose of the state and the phenomenon of post-pandemic social reality. The objectives of the scientific article are to reveal the concept and identify the signs of the state, to analyze the essence and social purpose of the state on the example of the Russian Federation, consider the phenomenon of post-pandemic social reality and its impact on our country. The methodological basis of this research is the universal dialectical method of cognition, as well as general scientific and particular scientific methods: historical, systemic, formal-logical, methods of analysis and synthesis, induction and deduction, statistical, formal-legal, comparative-legal methods, methods of interpretation of law. Conclusions: each state is unique in its own way and has a number of specific features. At the same time, each state has generalizing features, such as territorial integrity, taxation, the presence of public authority, etc. The state will always remain in the focus of attention of many scientists due to their increased interest in the fundamental features of the state and its essence. The essence of the state is stable. However, it is impossible to neglect external and internal factors that can modify the essence of the state, make some adjustments to it, change the content. The Russian Federation will continue to strive by all available means to become a socially oriented power with highly developed social institutions, strong public support from state structures and a developed network of non-profit foundations. For this status, it has all the necessary tools at its disposal rich cultural heritage, vast expanses with a huge amount of resources, and scientific and legal base. The phenomenon of post-pandemic social reality in the scientific space has appeared quite recently, but it quite clearly characterizes the fundamental changes that have occurred in the life of Russian society against the background of the protracted coronavirus pandemic. These changes affected not only the healthcare system, but also the development and integration of new technologies, the labor market, demographic indicators and many other indicators. The timely use of state reserves aimed at strengthening funding for industries in need, the ongoing training of highly qualified personnel, the development and implementation of proven innovations, and support for the socially vulnerable population will help to avoid such incidents in the future.

Learning invariant and uniformly distributed feature space for multi-view generation

Multi-view generation from a given single view is a significant, yet challenging problem with broad applications in the field of virtual reality and robotics. Existing methods mainly utilize the basic GAN-based structure to help directly learn a mapping between two different views. Although they can produce plausible results, they still struggle to recover faithful details and fail to generalize to unseen data. In this paper, we propose to learn invariant and uniformly distributed representations for multi-view generation with an “Alignment” and a “Uniformity” constraint (AU-GAN). Our method is inspired by the idea of contrastive learning to learn a well-regulated feature space for multi-view generation. Specifically, our feature extractor is supposed to extract view-invariant representation that captures intrinsic and essential knowledge of the input, and distribute all representations evenly throughout the space to enable the network to “explore” the entire feature space, thus avoiding poor generative ability on unseen data. Extensive experiments on multi-view generation for both faces and objects demonstrate the generative capability of our proposed method on generating realistic and high-quality views, especially for unseen data in wild conditions.

Multi-view Shape Generation for a 3D Human-like Body

Three-dimensional (3D) human-like body reconstruction via a single RGB image has attracted significant research attention recently. Most of the existing methods rely on the Skinned Multi-Person Linear model and thus can only predict unified human bodies. Moreover, meshes reconstructed by current methods sometimes perform well from a canonical view but not from other views, as the reconstruction process is commonly supervised by only a single view. To address these limitations, this article proposes a multi-view shape generation network for a 3D human-like body. Particularly, we propose a coarse-to-fine learning model that gradually deforms a template body toward the ground truth body. Our model utilizes the information of multi-view renderings and corresponding 3D vertex transformation as supervision. Such supervision will help to generate 3D bodies well aligned to all views. To accurately operate mesh deformation, a graph convolutional network structure is introduced to support the shape generation from 3D vertex representation. Additionally, a graph up-pooling operation is designed over the intermediate representations of the graph convolutional network, and thus our model can generate 3D shapes with higher resolution. Novel loss functions are employed to help optimize the whole multi-view generation model, resulting in smoother surfaces. In addition, two multi-view human body datasets are produced and contributed to the community. Extensive experiments conducted on the benchmark datasets demonstrate the efficacy of our model over the competitors.

3D Reconstruction for Multi-view Objects

Deep learning-based 3D reconstruction neural networks have achieved good performance on generating 3D features from 2D features. However, they often lead to feature loss in reconstruction. In this paper we propose a multi-view object 3D reconstruction neural network, named P2VNet. The depth estimation module of the front and back layers of P2VNet realizes the smooth transformation from 2D features to 3D features, which improves the performance of single view reconstruction. A multi-scale fusion sensing module in multi-view fusion is also proposed, where more receptive fields are added to generate richer context-aware features. We also introduce 3DFocal Loss to replace binary cross-entropy to address the problems of unbalanced space occupation of the voxel grid and complex division of partial grid occupation. Our experimental results have demonstrated that P2VNet has achieved higher accuracy than existing works.

DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning.

Drug-drug interaction (DDI) prediction identifies interactions of drug combinations in which the adverse side effects caused by the physicochemical incompatibility have attracted much attention. Previous studies usually model drug information from single or dual views of the whole drug molecules but ignore the detailed interactions among atoms, which leads to incomplete and noisy information and limits the accuracy of DDI prediction. In this work, we propose a novel dual-view drug representation learning network for DDI prediction ('DSN-DDI'), which employs local and global representation learning modules iteratively and learns drug substructures from the single drug ('intra-view') and the drug pair ('inter-view') simultaneously. Comprehensive evaluations demonstrate that DSN-DDI significantly improved performance on DDI prediction for the existing drugs by achieving a relatively improved accuracy of 13.01% and an over 99% accuracy under the transductive setting. More importantly, DSN-DDI achieves a relatively improved accuracy of 7.07% to unseen drugs and shows the usefulness for real-world DDI applications. Finally, DSN-DDI exhibits good transferability on synergistic drug combination prediction and thus can serve as a generalized framework in the drug discovery field.

IFKD: Implicit field knowledge distillation for single view reconstruction.

In 3D reconstruction tasks, camera parameter matrix estimation is usually used to present the single view of an object, which is not necessary when mapping the 3D point to 2D image. The single view reconstruction task should care more about the quality of reconstruction instead of the alignment. So in this paper, we propose an implicit field knowledge distillation model (IFKD) to reconstruct 3D objects from the single view. Transformations are performed on 3D points instead of the camera and keep the camera coordinate identified with the world coordinate, so that the extrinsic matrix can be omitted. Besides, a knowledge distillation structure from 3D voxel to the feature vector is established to further refine the feature description of 3D objects. Thus, the details of a 3D model can be better captured by the proposed model. This paper adopts ShapeNet Core dataset to verify the effectiveness of the IFKD model. Experiments show that IFKD has strong advantages in IOU and other core indicators compared with the camera matrix estimation methods, which verifies the feasibility of the new proposed mapping method.

An Algorithmic Approach to Understanding Osteoarthritic Knee Pain.

Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a single radiograph view. We used data from the Osteoarthritis Initiative, a 10-year observational study of patients with knee osteoarthritis. We paired >50,000 weight-bearing, posteroanterior knee radiographs with corresponding Knee Injury and Osteoarthritis Outcome Score (KOOS) pain, symptoms, and activities of daily living subscores and used them to train a series of deep learning models to predict those scores solely from raw radiographic input. We created regression models for specific score predictions and classification models to predict whether the modeled KOOS subscore exceeded a range of thresholds. The root-mean-square errors were 15.7 for KOOS pain, 13.1 for KOOS symptoms, and 14.2 for KOOS activities of daily living. Modeling was performed to predict whether pain was above or below given pain thresholds, and was able to predict extreme pain (KOOS pain < 40) with an area under the curve (AUC) of 0.78. Notably, the system was also able to correctly predict numerous cases where the Kellgren-Lawrence (KL) grade assigned by the radiologist was 0 but patient pain was high, and cases where the KL grade was 4 but patient pain was low. A deep neural network can be trained to predict the osteoarthritic knee pain that a patient experienced and other symptoms with reasonable accuracy from a single posteroanterior view of the knee, even using low-resolution images. The system can predict pain and dysfunction that the traditional KL grade does not capture. Deep learning applied to raw imaging inputs holds promise for disentangling sources of pain within the knee from aggravating factors external to the knee. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices, and the training time of the reconstructed network is too long. In this paper, we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network (GCN) and topology modification. We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology. Meanwhile, a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically. We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process. Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces, but also has better qualitative and quantitative results.

DEVELOPING TOLERANCE IN PRESCHOOL CHILDREN: PROBLEMS AND APPROACHES

Tensions in interethnic relations complicate a person’s adaptation to a multi-ethnic world. The high preschool age is a sensitive stage for tolerance development. As a quality of personality, it determines value attitude to the world, to others, and to oneself. The purpose of our research is to examine the problems and approaches in the formation of tolerance in preschool age. Methods. Theoretical analysis and synthesis of scientifi c literature were used as methods of research. Main results. Theoretical analysis demonstrated that there is no single view on the defi nition of tolerance. Foreign and Russian scientists consider this concept as a quality of personality, as an ability, as a position or attitude. It should also be noted that foreign studies most often consider educational environment as a condition in the formation of tolerance in older preschool children. In Russian studies the primary role of the family in the formation of tolerance is considered. After analyzing the psychological and pedagogical literature, it should be noted that the methods of tolerance diagnostics are presented to a greater extent for primary schoolchildren, adolescents, and adults. For the study of tolerance at preschool age, most often methods that examine the emotional sphere of the preschooler and the level of well-being in the children’s group are used. Conclusion. Despite the actuality of this phenomenon and its wide dissemination, there is no clear explanation of tolerance in psychological and pedagogical science. Having analyzed the research on this topic, we came to the conclusion that there are certain diffi culties with diagnostic tools, allowing to study the formation of tolerance in the late preschool age. The infl uence of the family on the formation of tolerance as a personality quality is insuffi - ciently studied.

Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

In the multi-view multi-label (MVML) classification problem, multiple views are simultaneously associated with multiple semantic representations. Multi-view multi-label learning inevitably has the problems of consistency, diversity, and non-alignment among views and the correlation among labels. Most of the existing multi-view multi-label methods for non-aligned views assume that each view has a common or shared label set, but because a single view cannot contain the entire label information, they often learn suboptimal results. Based on this, this paper proposes a non-aligned multi-view multi-label classification method that learns view-specific labels (LVSL), aiming to explicitly mine the information of view-specific labels and low-rank label structures in non-aligned views in a unified model framework. Furthermore, to alleviate insufficient available label information, we thoroughly explored the global and local structural information among labels. Specifically, first, we assume that there is structural consistency between the view and the label space and then construct the view-specific label model in turn. Second, to enrich the original label space information, we mine the consistent information of multiple views and the low-rank correlation information hidden among multiple labels. Finally, the contribution weight of each view is combined with learning the complementary information among the views in the decision-making stage, and extend the model to handle nonlinear data. The results of the proposed method compared with existing state-of-the-art algorithms on several datasets validate its effectiveness.

Dual Fusion-Propagation Graph Neural Network for Multi-View Clustering

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or modalities. With the rapidly growing attention of graph neural networks, more and more researchers have introduced various graph models into multi-view learning. Although considerable achievements have been made, most existing methods usually propagate information in a single view and fuse multiple information only from the perspective of attributes or relationships. To solve the aforementioned problems, we propose an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and apply it to deep multi-view clustering tasks. The proposed method is designed with three submodules and has the following merits: a) The proposed view-specific and cross-view propagation modules can capture the consistency and complementarity information among multiple views; b) The designed fusion module performs multi-view information fusion with the attributes of nodes and the relationships among them simultaneously. Experiments on popular databases show that DFP-GNN achieves significant results compared with several state-of-the-art algorithms.

ViewBirdiformer: Learning to Recover Ground-Plane Crowd Trajectories and Ego-Motion From a Single Ego-Centric View

We introduce a novel learning-based method for view birdification [1], the task of recovering ground-plane trajectories of pedestrians of a crowd and their observer in the same crowd just from the observed ego-centric video. View birdification becomes essential for mobile robot navigation and localization in dense crowds where the static background is hard to see and reliably track. It is challenging mainly for two reasons; i) absolute trajectories of pedestrians are entangled with the movement of the observer which needs to be decoupled from their observed relative movements in the ego-centric video, and ii) a crowd motion model describing the pedestrian movement interactions is specific to the scene yet unknown a priori. For this, we introduce a Transformer-based network referred to as ViewBirdiformer which implicitly models the crowd motion through self-attention and decomposes relative 2D movement observations onto the ground-plane trajectories of the crowd and the camera through cross-attention between views. Most important, ViewBirdiformer achieves view birdification in a single forward pass which opens the door to accurate real-time, always-on situational awareness. Extensive experimental results demonstrate that ViewBirdiformer achieves accuracy similar to or better than state-of-the-art with three orders of magnitude reduction in execution time.

СЕМАНТИЧНЕ ТЛО БІНОМІВ З НАЗВАМИ ЇЖІ ЯК РІЗНОВИДІВ СУРЯДНИХ СЛОВОСПОЛУЧЕНЬ

The article is devoted to the study of the semantic level of English idiomatic expressions, which linguists distinguish into a separate group of binomials. It is noted that there is no single unified view of these phrases, and most linguists distinguish them as a type of phraseological units. However, binomials differ from other constructions in their formal and grammatical organization. A binomial construction combines two words belonging to a specific lexical and grammatical class and is connected through coordinating relation. It is emphasized that these units are “frozen” coordinate word combinations with copulative relations, from which they have adopted the main parameters and, at the same time, acquired new ones due to transformation. In contrast to free coordinative phrases, binomials cannot be rearranged, changed or supplemented. The analysis has shown that, despite the formal and grammatical openness caused by the specificity of the copulative conjunction and, binomials are inherently semantically closed. It is noted that, unlike ordinary coordinate phrases, the components of a binomial can be service words that do not have lexical meaning by their nature. However, the coordinating relation that connects them determines the acquisition of different semantic shades by such a binomial construction. Therefore, these idiomatic expressions are a vivid example of the close interaction between formal grammatical, semantic, and syntactic levels. Another feature of binomials is the interaction of their constituents: in a free coordinate phrase, the components show equality and independence, while in a binomial expression, the interdependence of the left and right components is clearly traced. Considering the semantic, it is advisable to classify binomials into three groups: 1) those in which the meaning of the binomial is fully motivated by the component composition; 2) idiomatic expressions that, having a direct meaning, can simultaneously acquire a figurative meaning; 3) binomials whose meaning is not motivated by the meaning of their components.

Application of three-dimensional reconstruction in stomatology

Three-dimensional reconstruction (3D) in computer vision, is the process of three-dimensional view information from single or multiple views. In medicine, 3D reconstruction techniques are mainly based on MSCT, magnetic resonance imagin and other output medical digital image communication protocol (DICOM) format files. As a new digital medical technology, 3D reconstruction technology has been widely used in the field of stomatology, using 3D software, we can freely rotate, segment and view the CT scan results from many different angles, so that we can get more clear skull contrast and more information, data in CT scan results can also be processed so that reference markers can be clearly displayed for better diagnosis and treatment [1]. It has the unique preoperation design advantage, reduces the operation time, enhances the operation accuracy.

The Queering Accounting Manifesto

The Queering Accounting Manifesto draws on the queer revolution, which began in the aftermath of the 1969 Stonewall Riots, to declare the following statements: We denounce all expressions of ‘norms’ and ‘normal’. We will be alert to and call out all expressions of gender and sexual control within accounting. We reject a single view of accounting. We embrace fluidity and reject false binaries. We encourage Queer unlearning. Queer and non-queer accountants must hold space for one another. Readers should continuously Queer this Queering Accounting Manifesto. We end by calling on all readers, queer and non-queer, to join us and dismantle heteropatriarchy in accounting.

Neurological aspects of connective tissue dysplasia

Disturbances in the formation of connective tissue lead to significant pathological changes in both individual organs and tissues, and at the organismal level. The complexity of diagnostics is also connected with the fact that there is no single terminology, a single view of the diagnostic criteria, a single approach among doctors of different specialties. The prevalence of external phenotypic signs of connective dysplasia is quite high, which can lead to overdiagnosis. On the other hand, insufficient attention to the manifestations of dysplasia can lead to delayed diagnosis, which can cause adverse complications. The most studied are clinical manifestations in dysplastic changes in the cardiovascular system, musculoskeletal system. This article provides an overview of current data on changes in the nervous system. Sufficient attention was paid to the pathology of the nervous system in differentiated forms (Marfan syndrome, Ehlers-Danlos, etc.). Currently, the role of various vascular anomalies, aneurysms associated with undifferentiated forms of connective tissue dysplasia is widely discussed. Much attention is also paid to clinical manifestations of the autonomic nervous system: sympathicotonic manifestations predominate in connective tissue dysplasia. There is evidence of an association of headaches, musculoskeletal pain, and connective tissue dysplasia in both children and adults.

Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate information. To precisely extract accurate inner structural data, a multiple centroids approach is employed and defined in this paper, which defines the cluster configuration by allocating weights to each state in the cluster. The multiple EEG view fuzzy learning approach incorporates data from every single view to enhance the model's clustering performance. Also a fuzzy Density-Based Clustering model with multiple centroids (FDBC) is presented. This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm. The Experimental results validate the medical importance of the proposed clustering model.