Coordination Sequences Research Articles

Cut-and-project graphs and other complexes

The cut-and-project method may be applied to graphs and complexes, although there are technical difficulties, most notably “collisions,” i.e. when the images (under the projection) of two disjoint edges or cells intersect. Given a particular periodic structure, a particular projection space, an appropriate “window” in the orthogonal complement of that space, the induced substructure within the cartesian product of the window and the projection space is projected to the projection space to produce a “model structure.” We may use an index space of vectors from the orthogonal complement to move the window around and obtain a “model system” of model structures. We adapt the notion of “general position” to higher dimensions: the projection space is in “doubly general position” with respect to a graph or complex when the projection of that structure maps vertices of that structure injectively into the projection space and the edges or polytopes of that structure injectively so that their dimension is not reduced and disjoint edges and polytopes remain disjoint. We find that if the initial structure was a periodic graph, if the projection space and its complement are in general position with respect to the vertices and edges, and the window is also in general position, then the model system has uncountably many isomorphism classes - with distinct coordination sequences.

An Enhanced Ubiquitous-Joint Model for a Rock Mass With Conjugate Joints and Its Application on Excavation Simulation of Large Underground Caverns

A conjugate jointed rock mass (CJRM) is a rock mass with two sets of intersecting joints formed from intact rock under shear. Its mechanical properties and excavation-induced hazards of large underground caverns are different from those of common rock masses because of the unique geological origin thereof. To demonstrate numerically the excavation responses of CJRM, the ubiquitous-joint model is enhanced by consideration of the specific mechanical behaviors of the rock mass. In the enhanced model, CJRM is considered as the composite of columns of rock and two sets of weak planes of joints. The local coordinates, failure modes, and failure sequences of the rock columns and joints are redefined based on the composite characteristics of CJRM, and the failure criteria and plastic potential functions are accordingly modified. The enhanced model is verified numerically by triaxial compression tests and then employed to simulate the excavation of large underground caverns of a pumped storage power station in China. Results show that the modification of the local coordinate system, failure modes, and failure sequences made in the enhanced model is suited to the simulation of the mechanical behaviors of CJRM. Compared with the original ubiquitous-joint model, the enhanced model allows better predictions of the distribution of plastic zones and magnitudes of deformations in simulating underground excavations in CJRM and helps to assess the excavation-triggered hazards more accurately.

An Order-Reduction Method of Interharmonic Analysis Model Based on the Principle of Interharmonic Interaction

This paper presents a method on order-reduction of interharmonic analysis based on the principle of interharmonic interaction. First, we obtain the transformation between dynamic phasors in dq coordinate and dynamic phasor sequence components (DPSCs). Then, a full-order interharmonic analysis model is developed taking the voltage and current double closed-loop controller into account via the transformation mentioned before. The principle of interharmonic interaction is studied based on the full-order DPSCs model. On the basis of the principle, the dominant frequencies for all kinds of variables are selected and a reduced-order interharmonic analysis model is derived. Finally, two examples are presented for illustration of the proposed method. It is shown that the reduced-order interharmonic analysis model based on the principle of interharmonic interaction keeps high accuracy with substantial computational savings.

Hospital preparedness for mass gathering events and mass casualty incidents in Matera, Italy, European Capital of Culture 2019.

Mass Casualty Incidents (MCI) may occur during Mass Gathering Events (MGE). A failure to prepare and train the health care system for potential MCI, can cause chaos and delays in the response, leading to an increased morbidity and mortality. Education and training of staff are crucial for preparedness. In Italy, hospital Emergency Plans for Massive Influx of Injured (in Italian designated with the acronym PEMAF) are mandatory since the '90's. However, when available, they are usually poorly known by the staff, rarely reviewed and validated. In 2014, Matera, a city in Southern Italy, was designated as the European Capital of Culturefor 2019. As a result, we took this opportunity to revise the "Madonna delle Grazie" PEMAF and to start a program for increasing the awareness of the plan among the medical staff and provide specific training for MCI management. The PEMAF was reviewed through simulations that involved the entire staff. A partnership with the International Association for Medical Response to Major Incidents & Disasters (MRMI) led to the support of experts and to the organization of residential courses based on the MAss Casualty SIMulation tool (MACSIM®). In total, six residential educational events of MACSIM-PEMAF were organized. Individual capacity was tested before and after the education through self-administered semi-quantitative questionnaires. All the available resources were mapped and the functional areas identified. Alert, coordination and command sequences were defined. The communication network was improved. Documentation and registration systems were developed. Standard operational procedures (action cards) were created for the key positions. The knowledge and capacity to function in active roles during a MCI was improved among the participants in the educational program. MGE are great opportunities for the development of the hosting community but also represent an increased risk of MCI. Preparedness is mandatory for health care systems. The educational format MACSIM-PEMAF seems to be adequate to review and improve the existing plans and transfer specific skills to attendants.

The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.

Slicing Algorithm and Partition Scanning Strategy for 3D Printing Based on GPU Parallel Computing.

Aiming at the problems of over stacking, warping deformation and rapid adjustment of layer thickness in electron beam additive manufacturing, the 3D printing slicing algorithm and partition scanning strategy for numerical control systems are studied. The GPU (graphics processing unit) is used to slice the 3D model, and the STL (stereolithography) file is calculated in parallel according to the normal vector and the vertex coordinates. The voxel information of the specified layer is dynamically obtained by adjusting the projection matrix to the slice height. The MS (marching squares) algorithm is used to extract the coordinate sequence of the binary image, and the ordered contour coordinates are output. In order to avoid shaking of the electron gun when the numerical control system is forming the microsegment straight line, and reduce metal overcrowding in the continuous curve C0, the NURBS (non-uniform rational b-splines) basis function is used to perform curve interpolation on the contour data. Aiming at the deformation problem of large block components in the forming process, a hexagonal partition and parallel line variable angle scanning technology is adopted, and an effective temperature and deformation control strategy is formed according to the European-distance planning scan order of each partition. The results show that the NURBS segmentation fits closer to the original polysurface cut line, and the error is reduced by 34.2% compared with the STL file slice data. As the number of triangular patches increases, the algorithm exhibits higher efficiency, STL files with 1,483,132 facets can be cut into 4488 layers in 89 s. The slicing algorithm involved in this research can be used as a general data processing algorithm for additive manufacturing technology to reduce the waiting time of the contour extraction process. Combined with the partition strategy, it can provide new ideas for the dynamic adjustment of layer thickness and deformation control in the forming process of large parts.

Forecasting the concentration of sulfur dioxide in Beijing using a novel grey interval model with oscillation sequence

Beijing is one of the cities with serious sulfur dioxide pollution in China. The frequent occurrence of severe fog and haze brings great challenges to it's economic and social development. It is of great significance to forecast reasonably the trend of sulfur dioxide concentration in Beijing to improve the air quality management and residents' living environment. Traditional grey models use prediction of time points, and it is difficult to reflect the randomness of data in a complex system. When the modeling object is an oscillation sequence with a small sample, they usually perform poorly. The complex atmospheric conditions in Beijing lead to the oscillation of sulfur dioxide concentration, for which the interval prediction method is more appropriate. Based on the idea of linear envelopes, a novel grey interval forecasting model is proposed to predict sulfur dioxide concentration in Beijing. The original modeling data is extended to the area sequence and coordinate sequence with equal information, which is used to deduce the boundary equation of the original sequence. Compared with three other different forecasting models, the proposed model is the best in both simulation and prediction. Based on the novel model, the future sulfur dioxide concentration in Beijing is obtained. The findings can assist the local environmental protection agencies in improving the urban air quality.

Field-road trajectory segmentation for agricultural machinery based on direction distribution

Field-road segmentation that automatically divides a trajectory into a sequence of field/road segments is an important component in segmentation process for the trajectories of agricultural machinery. A trajectory is a sequence of geospatial coordinates recorded by GNSS receivers during the driving of the machine. The objective of this paper is to develop a field-road segmentation method in the case of the unavailability of field boundary information. The developed method consists of two stages. The first stage uses DBSCAN, a typical clustering algorithm, to do field-road segmentation, and the second stage uses a rule-based inference to correct two types of false segmentation cases from output of DBSCAN-based clustering. Based on the parallel direction distribution that strips in the same field are almost parallel, two inference rules, Field2Road-Cluster and Road2Field-Segment are performed sequentially. Field2Road-Cluster uses the direction distribution difference (parallel in fields vs. not parallel on roads) to correct false field segmentation cases and Road2Field-Segment uses the parallel relationship among strips in the same field to correct false road segmentation cases. The developed method was validated by 60 selected trajectories. The results demonstrated that the rule-based inference achieved an increase of 7.95% in F1 scores, where Field2Road-Cluster and Road2Field-Segment contributed 6.40% and 1.55% increase, respectively.

Binary Sequences Derived from Dickson Permutation Polynomials over Binary Extension Field

In this paper, based on a result of Lidl and Mullen (Mathematical Journal of Okayama University, 1991), the maximum length and the second maximum length that can be attained by cycles of Dickson permutation polynomial (of the first kind) with parameter 1 are studied. Necessary and sufficient conditions for these two lengths to be attained are given, which are connected with Fermat primes and Mersenne primes, respectively. Furthermore, a class of coordinate sequences that maintains a large period is obtained, which is shown to be the coordinate sequences derived from cycles of the second maximum length. Explicit formulas for their periodicity and shift-equivalences are also presented.

Moving Fast and Slow: Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation

ABSTRACT This paper presents a novel framework for speech-driven gesture production, applicable to virtual agents to enhance human-computer interaction. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. We provide an analysis of different representations for the input (speech) and the output (motion) of the network by both objective and subjective evaluations. We also analyze the importance of smoothing of the produced motion. Our results indicated that the proposed method improved on our baseline in terms of objective measures. For example, it better captured the motion dynamics and better matched the motion-speed distribution. Moreover, we performed user studies on two different datasets. The studies confirmed that our proposed method is perceived as more natural than the baseline, although the difference in the studies was eliminated by appropriate post-processing: hip-centering and smoothing. We conclude that it is important to take both motion representation and post-processing into account when designing an automatic gesture-production method.

Identity and compatibility of reference genome resources.

Genome analysis relies on reference data like sequences, feature annotations, and aligner indexes. These data can be found in many versions from many sources, making it challenging to identify and assess compatibility among them. For example, how can you determine which indexes are derived from identical raw sequence files, or which annotations share a compatible coordinate system? Here, we describe a novel approach to establish identity and compatibility of reference genome resources. We approach this with three advances: first, we derive unique identifiers for each resource; second, we record parent–child relationships among resources; and third, we describe recursive identifiers that determine identity as well as compatibility of coordinate systems and sequence names. These advances facilitate portability, reproducibility, and re-use of genome reference data. Available athttps://refgenie.databio.org.

Coordination sequences of crystals are of quasi-polynomial type.

The coordination sequence of a graph measures how many vertices the graph has at each distance from a fixed vertex and is a generalization of the coordination number. Here it is proved that the coordination sequence of the graph obtained from a crystal is of quasi-polynomial type, as had been postulated by Grosse-Kunstleve et al. [Acta Cryst. (1996), A52, 879-889].

Analysis of distance measures in spatial trajectory data clustering

Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.

The Method of Dance Movement Segmentation and Labanotation Generation Based on Rhythm

Labanotation is a widely used dance recording system, which plays an important role in inheriting and protecting folk dances. However, manual drawing of dance notation is time-consuming and labor-intensive. Therefore, research on the automatic generation of Labanotation has received extensive attention. In this article, a method to automatically generate Labanotation based on motion capture data is proposed. Firstly, in order to facilitate the calculation of the bending angle and motion feature sequences, the BVH-formatted Euler angle data is converted into a sequence of the 3D Cartesian world coordinates according to the analysis of human bones. Secondly, a motion segmentation method based on kinematics features and rhythms is proposed, which can segment different styles of motion into fragments for further identification. Finally, we generate the Labanotation with the platform we developed. Experiments show that our segmentation method has good accuracy in segmenting action sequences, which makes the generated Labanotation more reliable.

Суперпозиционная измеримость многозначной функции при обобщенных условиях Каратеодори

For a multivalued mapping F:[a; b] × R^m → comp(R^m), the problem of superpositional measurability and superpositional selectivity is considered. As it is known, for superpositional measurability it is sufficient that the mapping F satisfies the Caratheodory conditions, for superpositional selectivity it is sufficient that F(•,x) has a measurable section and F(t; •) is upper semicontinuous. In this paper, we propose generalizations of these conditions based on the replacement, in the definitions of continuity and semicontinuity, of the limit of the sequence of coordinates of points in the images of multivalued mappings to a one-sided limit. It is shown that under such weakened conditions the multivalued mapping F possesses the required properties of superpositional measurability / superpositional selectivity. Illustrative examples are given, as well as examples of the significance of the proposed conditions. For single-valued mappings, the proposed conditions coincide with the generalized Caratheodory conditions proposed by I.V. Shragin (see [Bulletin of the Tambov University. Series: natural and technical sciences, 2014, 19:2, 476–478]).

Characteristic analysis of coordinate time series of tide gauge station

In this paper, 298 tide gauge stations are used as the research object, and three models of generalized gauss markov (GGM) noise model, autoregressive moving average (ARMA) noise model and Autoregressive fractionally integrated moving average (ARFIMA) noise model are used to estimate the noise model characteristics of the coordinate time series of tide gauge stations and the trend of sea level changes. The influence of time span on the velocity estimation of tide gauge stations analyzed and discussed. The experimental results show that the noise characteristics of coordinate time series of tide gauge stations are mainly ARFIMA(1,0), ARFIMA(2,2), ARMA (1,0); the velocity estimation results of tide gauge stations show that 64.77% of the station velocity values are in the interval of 0 mm/a to 4 mm/a, and the average sea level velocity is 1.25 mm/a, which is on an upward trend. As the time span increases, the velocity uncertainty of the coordinate sequence, of the tide gauge stations, gradually tends to converge from divergence, and a time span more than 110 a helps to obtain a robust estimate of the tide gauge station velocity.

A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network

This paper reports a study into the methods for recognizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of categorizing multidimensional complex vectors of objects’ features based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordinates of its two-dimensional contour. A technique has been proposed to train a neural network to recognize the type of an air object based on three image classes corresponding to three projections. This makes it easier to solve the classification problem owing to a more compact arrangement of the multidimensional feature vectors. The architecture of an air situation video monitoring system has been suggested, which includes an image preprocessing module and a module of a complex-valued neural network. Pre-processing makes it possible to identify an object’s contour and build a sequence of normalized descriptors, which are partially independent of the spatial position of the object and the contour processing technique. Existing methods of air object recognition require significant computational resources and do not take into consideration the specificity of recognizing objects with three degrees of freedom or do not account for the complex nature of the numerical representation of a contour. This study has shown that the reported results make it easier to train a neural network and reduce the hardware requirements in order to solve the task of air situation video monitoring. The proposed solution leads to increased mobility and extends the scope of application of such systems, including individual devices

Bottom Detection Method of Side-Scan Sonar Image for AUV Missions

In order to obtain the measurement parameters of the sea bottom geomorphology or underwater objects, the first step in side-scan sonar (SSS) image processing is bottom detection. Due to the complexity of the marine environment, the acoustic signals received by SSS are usually polluted by noises, which affect its image quality and make the extraction of image features difficult. To address this problem, this study proposes an automatic detection method for the sea bottom line based on the actual experimental acquisition of SSS images, which is supposed to support the autonomous underwater vehicle (AUV) for intelligent target detection and classification. The proposed method comprises four main steps. First, the raw SSS data is analyzed to obtain a grayscale image, and the blind zone boundary of the image is obtained using the threshold method. Then, the noise characteristics of the image are analyzed and the denoising algorithm is optimized to effectively remove high-frequency noise. Next, spatial-temporal matching calculations are performed on each ping port and starboard data, and the accurate coordinates of first bottom returns are obtained through extreme value detection. Finally, automatic and accurate detection of the bottom line is realized according to the smooth processing of the coordinate sequence of first bottom returns. The experiments have demonstrated the effectiveness of the proposed method. As the method does not require human intervention in adjusting parameters during operation, the proposed method with a certain time window imposed during image acquisition will be suitable for AUV missions when the SSS is determined.

GNSS Multipath Errors Mitigation Algorithm Based on Observation Domain for Landslide Deformation Monitoring

The Global Navigation Satellite System (GNSS) real-time kinematic (RTK) technology is widely used for landslide deformation monitoring due to its precision and simplicity of its algorithm. However, GNSS signals are susceptible to multipath effects under complex circumstances such as terrain and surrounding vegetation. Reducing the impact of multipath errors and improving the RTK positioning accuracy is vital in ensuring accurate landslide monitoring in mountainous regions. The traditional method based on sidereal filtering in the coordinate domain only considers comprehensive multipath errors of all satellites, and the calculated time shift of coordinate sequence is not very accurate. In this paper, a sidereal filtering algorithm based on the double-difference residual in the GNSS observation domain is used for mitigating the multipath errors of landslide deformation monitoring in mountainous regions. First, we calculate the double-difference residuals of the observation in the previous cycle. Next, the empirical mode decomposition (EMD) filtering is used for denoising double-difference residual sequences. Based on the calculated period of the respective satellite, we use the residuals to correct the double-difference observation equation. Subsequently, we obtain the clean double-difference observations after mitigating multipath errors. Finally, the baseline coordinates of all monitoring stations can be calculated. The experimental results show that this method effectively improves the accuracy of GNSS landslide deformation monitoring in mountainous regions and the improved effect exceeds the traditional sidereal filtering algorithm in the coordinate domain.

Convergence acceleration of Monte Carlo many-body perturbation methods by direct sampling.

In the Monte Carlo many-body perturbation (MC-MP) method, the conventional correlation-correction formula, which is a long sum of products of low-dimensional integrals, is first recast into a short sum of high-dimensional integrals over electron-pair and imaginary-time coordinates. These high-dimensional integrals are then evaluated by the Monte Carlo method with random coordinates generated by the Metropolis-Hasting algorithm according to a suitable distribution. The latter algorithm, while advantageous in its ability to sample nearly any distribution, introduces autocorrelation in sampled coordinates, which, in turn, increases the statistical uncertainty of the integrals and thus the computational cost. It also involves wasteful rejected moves and an initial "burn-in" step as well as displays hysteresis. Here, an algorithm is proposed that directly produces a random sequence of electron-pair coordinates for the same distribution used in the MC-MP method, which is free from autocorrelation, rejected moves, a burn-in step, or hysteresis. This direct-sampling algorithm is shown to accelerate second- and third-order Monte Carlo many-body perturbation calculations by up to 222% and 38%, respectively.