Two-dimensional Representation Research Articles

Predicting drug–protein interaction using quasi-visual question answering system

Identifying novel drug–protein interactions is crucial for drug discovery. For this purpose, many machine learning-based methods have been developed based on drug descriptors and one-dimensional protein sequences. However, protein sequences cannot accurately reflect the interactions in three-dimensional space. However, direct input of three-dimensional structure is of low efficiency due to the sparse three-dimensional matrix, and is also prevented by the limited number of co-crystal structures available for training. Here we propose an end-to-end deep learning framework to predict the interactions by representing proteins with a two-dimensional distance map from monomer structures (Image) and drugs with molecular linear notation (String), following the visual question answering mode. For efficient training of the system, we introduce a dynamic attentive convolutional neural network to learn fixed-size representations from the variable-length distance maps and a self-attentional sequential model to automatically extract semantic features from the linear notations. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on the directory of useful decoys, enhanced (DUD-E), human and BindingDB benchmark datasets. Further attention visualization provides biological interpretation to depict highlighted regions of both protein and drug molecules. When predicting the interaction of proteins with potential drugs, the protein can be encoded as its one-dimensional sequence or a three-dimensional structure, which can capture more relevant features of the protein, but also makes the task to predict the interactions harder. A new method predicts these interactions using a two-dimensional distance matrix representation of a protein, which can be processed like a two-dimensional image, striking a balance between the data being simple to process and rich in relevant structures.

이중 주파수 대역 검출기반의 강인한 Audio Fingerprinting 시스템

In this paper, we present a new audio identification system which is robust to various attacks. The types of attacks employed are modification such as changes of tempo, pitch and speed and noise addition also low and high pass filtering. We proposed a two-dimensional representation for the audio signal called FFMAP. This consists of pitch components and frame components. We also employ Pearson’s correlation score to calculate similarity between original audio data and query. Experimental results show that the proposed algorithm has a high performance.

Three-Dimensional Representation of Electrical Circuit Quantities

This letter proposes a new representation of electrical circuit quantities based on three-phase dimensional space, in contrast to the conventional two-dimensional representation. It contributes to an alternative mathematical approach to display the voltage and current waveforms in three-dimensional frame, in which an unbalance displacement angle becomes evident, and can be used as an unbalance metric. It is outside the scope of this letter to propose a new power theory, or state new definitions for the power terms. In fact, such electric power representation can be applied to different power theories. Finally, this letter exemplifies the three-dimensional representation through numerical and illustrative case studies.

Fast Quasi-Conformal Regional Flattening of the Left Atrium.

Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities. While cardiac ventricles, especially the left ventricle, have an established standard representation (bull's eye plot), the 2D depiction of the left atrium (LA) remains challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA). Quasi-conformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases. Some registration-based methods have been proposed but surface registration is time-consuming and prone to errors when the geometries are very different. We propose a novel atrial flattening methodology where a 2D standardised map of the LA is obtained quickly and without errors related to registration. The LA is divided into five regions which are then mapped to their analogue two-dimensional regions. 67 human left atria from magnetic resonance images (MRI) were studied to derive a population-based template representing the averaged relative locations of the PVs and LAA. The clinical application of our methodology is illustrated on different use cases including the integration of MRI and electroanatomical data.

Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners

In the crowded cell, a strong selective pressure operates on the proteome to limit the competition between functional and non-functional protein-protein interactions. We developed an original theoretical framework in order to interrogate how this competition constrains the behavior of proteins with respect to their partners or random encounters. Our theoretical framework relies on a two-dimensional (2D) representation of interaction energy landscapes, with 2D energy maps, which reflect in a synthetic way the spatial distribution of the interaction propensity of a protein surface for another protein. We realized the interaction propensity mapping of proteins' surfaces in interaction with functional and arbitrary partners and asked whether the distribution of their interaction propensity is conserved during evolution. Therefore, we performed several thousands of cross-docking simulations to systematically characterize the energy landscapes of 103 proteins interacting with different sets of homologs, corresponding to their functional partner’s family or arbitrary protein families. Then, we systematically compared the energy maps resulting from the docking of each protein with the different protein families of the dataset. Strikingly, we show that the interaction propensity not only of the binding sites but also of the rest of the surface is conserved for docking partners belonging to the same protein family. Interestingly, this observation holds for docked proteins corresponding to true but also arbitrary partners. Our theoretical framework enables the characterization of the energy behavior of a protein in interaction with hundreds of proteins and opens the way for the characterization of the behavior of proteins in a specific environment.

Development of the unmanned aerial vehicle safe flight trajectorycalculation and adjustment process immitation model

The article is devoted to the development and research the unmanned aerial vehicle safe flight trajectory calculation and adjustment process imitation model. The main distinguishing feature of the presented model is taking into account the signals of radar stations during the determination and adjustment the location of the aerial vehicle in the case of an autonomous flight and adapting the route based on possible obstacles and deviations. This will improve the safety of the unmanned aerial vehicle flight trajectory. Main studies of path planning methods approaches are analyzed. It was concluded that two-dimensional representation and visualization has disadvantages. Based on disadvantages, the three-dimensional calculated trajectory visualization of a flight mission by an unmanned aerial vehicle simulation model is developed. The simulation model combines the solution of following subtasks: a) modeling a three-dimensional environment with obstacles; b) building an unmanned aerial vehicle trajectory in such environment with avoidance the obstacles. Missions simulation is performed based on developed software system, sequentially, step by step. The following results of the simulation are achieved: 1) a special graphical interface for entering input data and displaying the results is developed; 2) the subsystem for simulation the space for performing a flight task taking into account the location and signals of external influences (for example, radar stations) was developed; 3) the subsystem for flight trajectory simulation calculation and visualization is developed. The software system is built using Delphi based on modular basis. The functional software system structure and the order of each functional module operation are described in article. The system’s graphical interface structure is discussed separately. An example of calculating and adjusting the safe flight trajectory of an unmanned aerial vehicle is illustrated.

Human Behavior Recognition Using Range-Velocity-Time Points

Radar-based sensors do not require optimal lighting and atmospheric conditions and nonocclusion, making them a promising solution for human behavior analysis in complex environments. Existing radar-based models generally retrieve features from either the time-velocity domain or the time-range domain. Such two-dimensional representations cannot fully depict dynamic human motion features. In this paper, we propose a temporal range-Doppler PointNet-based method to analyze human behavior. We transform human echoes to 3D point sets and then feed them into the hierarchical PointNet model for classification. The proposed point network can learn structural features from the micromotion trajectory more effectively than directly processing the raw point cloud. To further improve our model’s robustness in practical applications, we design an outlier detection module for detecting anomalies such as in multitarget scenarios. The results of experiments on motion capture databases and range-Doppler radar measurements demonstrate that our method realizes outstanding performance in terms of the classification accuracy, noise robustness, and anomaly detection accuracy.

Interactive design and architecture by using virtual reality, augmented reality, and 3D printing

New methods of teaching architectural design consider the elements of religion, spirituality, modernism, history and other cultural factors to address the physical, spatial and emotional needs of society. This article presents an educational training that emphasises on these critical elements. This paper describes a basic design course taught to second-semester students in the design and architecture course using virtual reality (VR), augmented reality (AR), and 3D printing to incorporate inner sensations and feelings of a human being in the designs. Applied techniques have transformed the classrooms into interactive spaces where the professors use strategies requiring the participants to work together to achieve the objectives of the course. Acquiring skills to create two-dimensional (2D) and three-dimensional (3D) representations facilitated the final products that meet the needs of the users in multitude of manners. The results of this project demonstrated an increase in spatial visualisation, analysis, and critical thinking among the students. Additionally, 100% of the students showed high level of satisfaction with the course and the experience of using these technological tools in the design and architecture course.

Using Pixel-Based Microscope Images to Generate 3D Reconstructions of Frozen and Thawed Plant Tissue.

Histological analysis of frozen and thawed plants has been conducted for many years but the observation of individual sections only provides a two-dimensional representation of a three-dimensional phenomenon. Currently available optical sectioning techniques for viewing internal structures in three dimensions are either low in resolution or the instrument cannot penetrate deep enough into the tissue to visualize the whole plant. Methods using higher resolution equipment are expensive and often require time-consuming training. In addition, conventional stains cannot be used for optical sectioning techniques. We present a relatively simple and less expensive technique using pixel-based (JPEG) images of conventionally stained histological sections of an Arabidopsis thaliana plant. The technique uses commercially available software to generate a 3D representation of internal structures.

To switch or not to switch – a machine learning approach for ferroelectricity

With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.

Integrating Manipulatives and Animations to Visualize Holliday Junctions

The complex molecular architecture of Holliday junctions is often challenging for undergraduate biochemistry students to conceptualize. In particular, they have difficulty translating from two-dimensional, linear representations shown in textbooks to the atomic-resolution DNA structures. This challenge impedes their comprehension of the mechanisms by which proteins are able to bind, hydrolyze, and re-ligate the component DNA strands. We have developed an affordable, portable Holliday junction model using colored chenille stems (a.k.a. pipe cleaners) that students use to first visualize the DNA structures within Holliday junctions, and relate those structures to the static two-dimensional representations in textbooks. To fully integrate the linear depictions with the three-dimensional models, students then watch an online animated movie that models the dynamics of Holliday junction resolution. Students then use those models to work problem sets that include questions about double-stranded break repair, mating-type switching, and genetic crossover. The implementation of these personal manipulative models for each student has notably improved student comprehension of the molecular mechanism of homologous recombination. <em>Primary image:&nbsp;</em>Representations of Holliday Junctions. Top row: a conventional line schematic of a Holliday junction, in which there are four heteroduplexes of double-stranded DNA, shown beside a cartoon representation of a Holliday junction solved by x-ray crystallography (PDB: 1DCW). Bottom row: a chenille-stem manipulative, which can be interconverted between the conventional line schematic and the open-cruciform structure that are depicted in the top row.

Roby-Gould bond indices as a tool for understanding chemical bonding from a mathematical and quantum mechanical perspective

In this paper we elaborate and evaluate the Roby-Gould bond index method (RGBI) for classifying and interpreting the nature of chemical bonds [Gould et al., Theor. Chem. Acc., 2008, 119, 275]. We emphasize that the RGBI method defines atoms as subspaces of a Hilbert space, represented as projection operators, and in accord with basic mathematical requirements concerning the assignment of probabilities to such subspaces. The definition of the bond indices between such atoms follows from simple set theoretical ideas concerning the sharing of, and differences between, atomic electron populations; and from the geometric properties associated with these subspaces—properties which are closely aligned with older chemical notions, which are reviewed. We also introduce the Araki angle percentage ionicity of a bond, which is easily read off from a compact two-dimensional representation of the chemical bond, which we call the bond dial diagram. Additionally we explore the theory with defining atoms by the occupied natural atomic spinorbitals (NAOs) of Reed and coworkers, instead of the atomic natural orbitals (ANOs) previously used. Bond indices from these ANO- and NAO-based atoms are applied to a dataset of 353 molecules, including 27 first row molecules, 280 organic molecules, and 46 semiochemical molecules, comprising 33 A–H bonds, where A are first and second row elements, 1779 C–X, 3415 H–X bonds (X = C, N, O, S), and a few more other types of bonds. The bond indices from the ANO-based atoms are much more in accord with chemists' expectations as compared to those obtained from NAO-based atoms provided that the excited triplet state defines the ANOs for group 2 atoms.

Finding the Largest Successful Coalition under the Strict Goal Preferences of Agents

Coalition formation has been a fundamental form of resource cooperation for achieving joint goals in multiagent systems. Most existing studies still focus on the traditional assumption that an agent has to contribute its resources to all the goals, even if the agent is not interested in the goal at all. In this article, a natural extension of the traditional coalitional resource games (CRGs) is studied from both theoretical and empirical perspectives, in which each agent has uncompromising, personalized preferences over goals. Specifically, a new CRGs model with agents’ strict preferences for goals is presented, in which an agent is willing to contribute its resources only to the goals that are in its own interest set. The computational complexity of the basic decision problems surrounding the successful coalition is reinvestigated. The results suggest that these problems in such a strict preference way are complex and intractable. To find the largest successful coalition for possible computation reduction or potential parallel processing, a flow-network–based exhaust algorithm, called FNetEA, is proposed to achieve the optimal solution. Then, to solve the problem more efficiently, a hybrid algorithm, named 2D-HA, is developed to find the approximately optimal solution on the basis of genetic algorithm, two-dimensional (2D) solution representation, and a heuristic for solution repairs. Through extensive experiments, the 2D-HA algorithm exhibits the prominent ability to provide reassurances that the optimal solution could be found within a reasonable period of time, even in a super-large-scale space.

Reality-based algebras with a two-dimensional representation

It is shown that a two-dimensional irreducible representation of a reality-based algebra with a standard degree map is determined by the other irreducible representations of the algebra and the action of its given anti-automorphism on the distinguished basis. This generalizes known results for algebras of dimensions 5, 6, 7, and 8. Explicit conclusions equivalent to those for dimensions 5 and 6 are recovered here by specializing the procedure of proof of the main theorem.

A Correspondence Analysis of Twelve Japanese Historical Fifth-Year English-as-a-Foreign-Language Textbooks

This paper aims to quantitatively analyze the features of twelve Japanese historical fifth-year English-as-a-Foreign-Language (EFL) textbooks (Book-5) and their current counterpart by using correspondence analysis, and to compare the results with those of the correspondence analyses of the first- and third-year EFL textbooks (Book-1 and Book-3). The following were the obtained results. First, the correspondence analysis results proved capable of differentiating quantitatively the features of the textbooks dealt with. Second, the explaining categories were explained 84.4 percent by four dimensions: speech-oriented (+) vs. exposition-oriented (-) discourse (Dim 1), easy (+) vs. difficult (-) texts (Dim 2), variety-rich (+) vs. variety-poor (-) (Dim 3), and teacher-assistance (+) vs. non-teacher-assistance (Dim 4). Third, the similarities/differences of the thirteen Book-5 textbooks were explained by the categories similar to but slightly different from the Book-1 and Book-3 results up to the fourth dimension. Finally, the results of the present correspondence analysis were graphically represented by (1) two-dimensional coordinate representation covering only Dim 1 and Dim 2 and (2) cluster-analysis-based dendrogram covering all the features of twelve dimensions specified. On the former, only Sunshine-5 and Seisoku-5 were distinctively different from the other textbooks, which proved the correspondence analysis map comprised of Dim 1 and Dim 2 to contribute to differentiating their inter-relationships, while on the latter, the same two textbooks were also distinctively different from the others. This could suggest the two textbooks should be closely examined from the qualitative perspective as well as the quantitative one. https://doi.org/10.26803/ijlter.18.12.6 Â

3D Cadastres in India: Examining the status and potential for land administration and management in Delhi

Urbanization and the trend towards complex infrastructure development challenges the traditional two-dimensional (2D) cadastral representations used in conventional land administration. Three-dimensional (3D) representations are argued to serve as the future basis upon which to spatially define rights, restrictions and responsibilities (RRRs) in these environments. In Delhi, the capital city of India, where horizontal expansion via new settlements in peri-urban areas, and vertical expansion by densification of city infrastructure, are both prevalent, the need to manage this increasingly complex 3D infrastructure environment presents a great challenge for land administration and management agencies. This paper explores both the current and potential future application of 3D representation in Delhi, with respect to land use planning, development and management. A cross-cutting thematic case study analysis is undertaken: policy drivers, legal frameworks, organizational aspects, and technical standards are considered. For each aspect a separate analytical approach is used. Based on the results, it is concluded that current practices related to 3D representation are somewhat immature and not adequate for capturing the future vertical growth of Delhi. This could lead to legal and physical boundary inconsistencies and irregularities, and dispute cases. However, by learning from international developments and standards-based approaches, agencies have the potential to enhance and support processes in their respective land administration systems. To realize this potential, internal and external policies, institutional settings and, technical and financial arrangements need to be reformed. In the short term it is recommended to focus on raising awareness for the widespread adoption of 3D representations in relevant agencies in Delhi.

Application of 2D temperature measurement to a coal-fired furnace using CT-TDLAS

The measurement of temperature and species concentration in combustion fields is very significant for the development of highly efficient combustion technologies for energy conservation and emission reduction. There are various measurement technologies, including contact and non-contact measurement. Tunable diode laser absorption spectroscopy (TDLAS) technology is a proven non-contact method for detection of the temperature and species concentration by absorption measurement. To enable two-dimensional (2D) representation of temperature and species concentration in combustion fields, the TDLAS technology is usually combined with computed tomography (CT). The latter is, however, considerably new in combustion research, especially in the solid fuel reaction environment. In this paper, a 32-path 2D CT-TDLAS system for temperature measurement in a pilot-scale coal-fired furnace was developed. The accuracy of the CT algorithm in the reconstruction of 2D temperature distributions in different laser-path arrangements was first analyzed using the sum of squared difference (SSD) and zero-mean normalized cross-correlation (ZNCC) by comparing it to the 2D temperature distribution of a full-scale coal-fired furnace simulated using computational fluid dynamics (CFD). The accuracy was improved by 32-path reconstruction. The study was then progressed to investigate its measurement accuracy in a simple CH4–air burner configuration with rounded and rectangular cells as well as sensitivity for flame shift detection, whereby the reconstructed temperature distribution was compared to the temperature measured using a thermocouple. It is verified that this CT reconstruction was feasible for various measurement areas, even if the center of the flame was shifted. Finally, a 32-path 2D CT-TDLAS system with a rectangular-structured cell was developed and applied for temperature measurement in a Tenaga Nasional Berhad (TNB) Research’s pilot-scale coal-fired furnace. The 2D temperature distribution in the coal-fired furnace was reconstructed according to the experimental results. The potential of the CT-TDLAS for online 2D temperature measurement for actual applications is demonstrated.

A two-dimensional topological representation theorem for matroid polytopes of rank 4

The Folkman–Lawrence topological representation theorem, which states that every (loop-free) oriented matroid of rank r can be represented as a pseudosphere arrangement on the (r−1)-dimensional sphere Sr−1, is one of the most outstanding results in oriented matroid theory. In this paper, we provide a lower-dimensional version of the topological representation theorem for uniform matroid polytopes of rank 4. We introduce 2-weak configurations of points and pseudocircles (2-weak PPC configurations) on S2 and prove that every uniform matroid polytope of rank 4 can be represented by a 2-weak PPC configuration. As an application, we provide a proof of Las Vergnas conjecture on simplicial topes for the case of uniform matroid polytopes of rank 4.

Changes in Body Esteem After Viewing a 3D Full-Body Representation of Oneself

Three-dimensional (3D) body scanning is becoming an increasingly popular tool for research in the field of family and consumer sciences (FCS) because it quickly and accurately measures constructs that describe the human form. This study compared viewing a 3D body representation to viewing a two-dimensional (2D) representation and whether the effects of these assessments varied by gender. College-student participants (N = 164, 63 women, 101 men) were randomly assigned to one of two different body image groups, a 3D image and a somatomorphic (2D) assessment of body shape. Participants in both conditions completed a body-esteem pre-assessment, had their bodies scanned by a KX-16 body scanner, assessed the copy of their 3D or 2D body image, and then completed the body-esteem post-assessment. The 3D body scan group reported larger drops in body esteem than did the 2D group from pretest to posttest. More specifically, women in the 3D condition reported a larger drop in body esteem from pretest to posttest compared to what the men reported. The results suggest that using a 3D body scanner could have negative short-term effects on body esteem.

Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Gesture recognition has been applied in many fields as it is a natural human–computer communication method. However, recognition of dynamic gesture is still a challenging topic because of complex disturbance information and motion information. In this paper, we propose an effective dynamic gesture recognition method by fusing the prediction results of a two-dimensional (2D) motion representation convolution neural network (CNN) model and three-dimensional (3D) dense convolutional network (DenseNet) model. Firstly, to obtain a compact and discriminative gesture motion representation, the motion history image (MHI) and pseudo-coloring technique were employed to integrate the spatiotemporal motion sequences into a frame image, before being fed into a 2D CNN model for gesture classification. Next, the proposed 3D DenseNet model was used to extract spatiotemporal features directly from Red, Green, Blue (RGB) gesture videos. Finally, the prediction results of the proposed 2D and 3D deep models were blended together to boost recognition performance. The experimental results on two public datasets demonstrate the effectiveness of our proposed method.