Scale Representation Research Articles

Towards improved speed and accuracy of laser powder bed fusion simulations via representation of multiple time scales

The current work reports the result of investigations into alternative representations of the temporal evolution of the laser powder bed fusion (LPBF) process. In order to provide the reader with sufficient context for the following discussion, the modeling challenge and its underlying cause, as well as current attempts to provide a satisfactory solution, will be discussed. Next, the author will describe two numerical methods (multirate time integration and parallel-in-time) to better represent the temporal scales of the problem. Finally, the results of applying these methods to a two-dimensional version of the heat transfer portion of the LPBF problem will be presented, reporting on both convergence and performance behavior, which indicate the possibility of significant speedup of solution calculations (40–100 times), along with well-characterized effects on solution accuracy.

The Importance of the Coordinate Transformation Process in Using Heterogeneous Data in Coastal and Marine Geographic Information System

Coastal and Marine Geographic Information Systems (CMGISs) permit to collect, manage, and analyze a great amount of heterogeneous data concerning coastal, sea, and ocean environments, e.g., nautical charts, topographic maps, remotely sensed images. To integrate those heterogeneous layers in CMGIS, particular attention is necessary to ensure the perfect geo-localization of data, which is a basic requirement for the correct spatial analysis. In fact, the above-mentioned types of information sources are usually available in different cartographic projections, geodetic datum, and scale of representation. Therefore, automatic conversions supplied by Geographic Information System (GIS) software for layer overlay do not produce results with adequate positional accuracy. This paper aims to describe methodological aspects concerning different data integration in CMGIS in order to enhance its capability to handle topics of coastal and marine applications. Experiments are carried out to build a CMGIS of the Campania Region (Italy) harmonizing different data (maps and satellite images), which are heterogeneous for datum (World Geodetic System 1984 and European Datum 1950), projection (Mercator and Universal Transverse of Mercator), and scale of representation (large and medium scale). Results demonstrate that automatic conversion carried out by GIS software are insufficient to ensure levels of positional accuracy adequate for large scale representation. Therefore, additional operations such as those proposed in this work are necessary.

Singular Measures and Information Capacity of Turbulent Cascades.

How weak is the weak turbulence? Here, we analyze turbulence of weakly interacting waves using the tools of information theory. It offers a unique perspective for comparing thermal equilibrium and turbulence. The mutual information between modes is stationary and small in thermal equilibrium, yet it is shown here to grow with time for weak turbulence in a finite box. We trace this growth to the concentration of probability on the resonance surfaces, which can go all the way to a singular measure. The surprising conclusion is that no matter how small is the nonlinearity and how close to Gaussian is the statistics of any single amplitude, a stationary phase-space measure is far from Gaussian, as manifested by a large relative entropy. This is a rare piece of good news for turbulence modeling: the resolved scales carry significant information about the unresolved scales. The mutual information between large and small scales is the information capacity of turbulent cascade, setting the limit on the representation of subgrid scales in turbulence modeling.

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings

BackgroundDespite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models.MethodsTo this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzy-set based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task.ResultsWe found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case.ConclusionsIn perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.

Bayesian Inference for Multidimensional Scaling Representations with Psychologically Interpretable Metrics

Multidimensional scaling (MDS) models represent stimuli as points in a space consisting of a number of psychological dimensions, such that the distance between pairs of points corresponds to the dissimilarity between the stimuli. Two fundamental challenges in inferring MDS representations from data involve inferring the appropriate number of dimensions and the metric structure of the space used to measure distance. We approach both challenges as Bayesian model-selection problems. Treating MDS as a generative model, we define priors needed for model identifiability under metrics corresponding to psychologically separable and psychologically integral stimulus domains. We then apply a differential evolution Markov-chain Monte Carlo (DE-MCMC) method for parameter inference, and a Warp-III method for model selection. We apply these methods to five previous data sets, which collectively test the ability of the methods to infer an appropriate dimensionality and to infer whether stimuli are psychologically separable or integral. We demonstrate that our methods produce sensible results, but note a number of remaining technical challenges that need to be solved before the method can easily and generally be applied. We also note the theoretical promise of the generative modeling perspective, discussing new and extended models of MDS representation that could be developed.

RETRACTED ARTICLE: A feature based change detection approach using multi-scale orientation for multi-temporal SAR images

ABSTRACT Excellent operation regardless of weather conditions and superior resolution independent of sensor light are the most attractive and desired features of synthetic aperture radar (SAR) imagery. This paper proposes an exclusive multi-scale with multiple orientation approach for multi-temporal SAR images. This approach integrates pre-processing and change detection. Pre-processing is performed on the SAR imagery through speckle reducing anisotropic diffusion and discrete wavelet transform. The processed speckle-free images are designed by Log-Gabor filter bank in terms of multi-scale with multiple orientations. The maximum magnitude of multiple orientations is concatenated to obtain feature-based scale representation. Each scale is dealt with multiple orientations and is compared by band-wise subtraction to retrieve difference image (DI) coefficient. The series of the difference coefficients from each scale are add-on together to estimate a DI. Thus, the resultant image of multi-scale orientation gives perception of detailed information with specific contour. Constrained k-means clustering algorithm is preferred to achieve change and un-change map. Performance of the proposed approach is validated on three real SAR image datasets. The effective change detection is examined by using confusion matrix parameters. Experimental results are described to show the efficacy of the proposed approach.

Division of TIN_DDM topographic forms and continuous scale representation based on rolling ball transformation

Division of TIN_DDM topographic forms and continuous scale representation based on rolling ball transformation

SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching.

Social media advertising efficiency on higher education programs

PurposeThe purpose of this study is to examine the different results and the level of success obtained with advertising campaigns developed on Facebook to promote postgraduate programs to create awareness and engagement.Design/methodology/approachThis study combined the data envelopment analysis technique to measure advertising efficiency with multidimensional scaling (MDS) representation, thus offering alternatives for practitioners and organizations on how to evaluate social advertising performance.FindingsInvestments on social paid advertising are an affordable and effective way both to promote postgraduate programs and create engagement with prospective students. Facebook advertisements maximize visibility, which improves social and online positioning and encourages student recruitment.Practical implicationsHigher education institutions can efficiently promote their programs with a minimal social investment contributing to dissemination and engagement. Compared to other forms of traditional or digital advertising, social media ads can be efficient and affordable with wider segmentation and targeting options. Moreover, results are immediate and measurable and campaigns can be instantly modified to better suit the audience’s requirements.Originality/valueThis study is unique as it offers a new, alternative way of measuring efficiency, in addition to the classic ratios of payment models in digital advertising that combine clicks and impressions, on a sector where there are few empirical studies. Moreover, it can be easily applied to many other sectors in public and private organizations.

Six weeks into the 2019 coronavirus disease outbreak: it is time to consider strategies to impede the emergence of new zoonotic infections.

Six weeks into the 2019 coronavirus disease outbreak: it is time to consider strategies to impede the emergence of new zoonotic infections.

Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1

Abstract. The added value of global simulations on the convection-permitting (CP) scale is a subject of extensive research in the earth system science community. An increase in predictive skill can be expected due to advanced representations of feedbacks and teleconnections in the ocean–land–atmosphere system. However, the proof of this hypothesis by corresponding simulations is computationally and scientifically extremely demanding. We present a novel latitude-belt simulation from 57∘ S to 65∘ N using the Weather Research and Forecasting (WRF)-Noah-MP model system with a grid increment of 0.03∘ over a period of 5 months forced by sea surface temperature observations. In comparison to a latitude-belt simulation with 45 km resolution, at CP resolution the representation of the spatial-temporal scales and the organization of tropical convection are improved considerably. The teleconnection pattern is very close to that of the operational European Centre for Medium Range Weather Forecasting (ECMWF) analyses. The CP simulation is associated with an improvement of the precipitation forecast over South America, Africa, and the Indian Ocean and considerably improves the representation of cloud coverage along the tropics. Our results demonstrate a significant added value of future simulations on the CP scale up to the seasonal forecast range.

Lightweight and Robust Representation of Economic Scales from Satellite Imagery

Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown.

Systematic circuit design and analysis using generalised g m / I D functions of MOS devices

The conventional approach to implementing analogue integrated circuits in nano-scale complementary metal oxide semiconductor (CMOS) technologies relies basically on circuit simulations using the SPICE models provided by the foundries. Depending on the circuit complexity, the designer should, however, spend a significant amount of time sizing the metal oxide semiconductor field effect transistors such that maximum efficiency is achieved for minimum power consumption and silicon area. Analytical-based design procedures can assist the designer in confronting the sizing challenge of the metal oxide semiconductor (MOS) devices. The procedures are, however, dependent on circuit topology, model parameters, and device physics. This study aims at presenting a systematic approach for analysis and design of analogue circuits in scaled CMOS. For this purpose, the behaviour of short-channel MOS devices is characterised in various process and temperature corners using an updated matrix representation of different device scales, bias conditions, and small-signal parameters. The details to effectively extract the matrix derivation of the technology model files are presented, enabling to devise generalised functions for the design and analysis of the circuits. The design examples include a 0.39 V – 28 µA reference circuit, and a 7.50 µA/V operational-transconductance amplifier with 1.0 V voltage supply in 90-nm CMOS.

The Adjoint Petrov–Galerkin method for non-linear model reduction

We formulate a new projection-based reduced-order modeling technique for non-linear dynamical systems. The proposed technique, which we refer to as the Adjoint Petrov–Galerkin (APG) method, is derived by decomposing the generalized coordinates of a dynamical system into a resolved coarse-scale set and an unresolved fine-scale set. A Markovian finite memory assumption within the Mori–Zwanzig formalism is then used to develop a reduced-order representation of the coarse scales. This procedure leads to a closed reduced-order model that displays commonalities with the adjoint stabilization method used in finite elements. The formulation is shown to be equivalent to a Petrov–Galerkin method with a non-linear, time-varying test basis, thus sharing some similarities with the Least-Squares Petrov–Galerkin method. Theoretical analysis examining a priori error bounds and computational cost is presented. Numerical experiments on the compressible Navier–Stokes equations demonstrate that the proposed method can lead to improvements in numerical accuracy, robustness, and computational efficiency over the Galerkin method on problems of practical interest. Improvements in numerical accuracy and computational efficiency over the Least-Squares Petrov–Galerkin method are observed in most cases.

Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification

Time series classification is an essential task in many real-world application domains. As a popular deep learning network, convolutional neural networks have achieved excellent performance in time series classification tasks. The filters of the convolutional neural networks are fixed length and shared by each sample. However, each time series usually has different time scale features. Therefore, convolutional neural networks are not capable of extracting multi-scale features for each sample flexibly. In this paper, we propose dynamic multi-scale convolutional neural network to extract multi-scale feature representations existing in each time series dynamically. Specifically, we design a variable-length filters generator to produce a set of variable-length filters conditioned on the input time series. To make model differentiable, we use the learnable soft masks to control the lengths of variable-length filters. Therefore, the feature representations of different time scales can be captured through the variable-length filters. Then, the max-over-time pooling is used to select the most discriminative local patterns. Finally, the fully connected layer with softmax output is employed to calculate the final probability distribution for each class. Experiments conducted on extensive time series datasets show that our approach can improve the performance of time series classification through the learning of variable-length filters. Furthermore, we demonstrate the effectiveness of dynamically learning variable-length filters for each sample through the visualization analysis.

The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling

A combined snow modelling approach integrating remote sensing data, in-situ data, and an improved hydrological model is presented. Complementary information sources are evaluated in terms of its value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation. The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site. The available remote sensing datasets include Terra MODIS based medium resolution and Landsat-7/8 and Sentinel-2A based high resolution fractional snow covered area maps. Recently, Sentinel-1 based wet snow covered area maps have become increasingly available. To the knowledge of the authors the first evaluation of their value for snow-hydrological modelling is presented. Besides conventional small footprint station data, in-situ time-series of snow water equivalent (SWE) of a Cosmic-Ray Neutron Sensor (CRNS) having a footprint of several hectares is additionally used. For including these data the model now provides respective outputs such as fractional snow cover, wet/dry snow surface and SWE areal means equivalent to the CRNS sensor footprint. By means of 40,000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS-SWE data. Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel-1 backscatter. The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes.

Through scale material flow investigation in novel incremental bulk forming process

Abstract Innovative incremental bulk forming (IBF) process dedicated to form integral elements for the aerospace industry is presented in the paper. Particular attention is put on analysis of advantages and disadvantages of this process, as well as on detailed evaluation of material flow characteristics across different length scales. Thus, a multi scale numerical model of the IBF process was established based on the isotropic hardening law at the macro scale and digital material representation concept connected with the crystal plasticity theory at the micro scale. Numerical results are also supported by a series of experimental investigations realized at the prototype of the IBF press.

Reconstructing Lost Instruments: Praetorius’s <em>Syntagma musicum</em> and the Violin Family <em>c.</em> 1619

Michael Praetorius’s De organographia and Theatrum instrumentorum provide valuable clues that contribute to a new understanding of the violin family c. 1619, many surviving examples of which are reduced in size from their sixteenth- and seventeenth-century dimensions. The record of surviving instruments – especially those of the Amati family – alongside metrologic, documentary and iconographic evidence shows that Michael Praetorius describes a large instrument conforming remarkably well to the original dimensions of the basso da braccio (violoncello), as well as furnishing an excellent scale representation of the violin family as it was at the time of these works’ publication and an accurate tuning scheme.

DSPNet: Deep scale purifier network for dense crowd counting

Crowd counting has produced considerable concern in recent years. However, crowd counting in highly congested scenes is a challenging problem owing to scale variation. To remedy this issue, we propose a novel deep scale purifier network (DSPNet) that can encode multiscale features and reduce the loss of contextual information for dense crowd counting. Our proposed method has two strong points. First, the DSPNet model consists of a frontend and a backend. The frontend is a conventional deep convolutional neural network, while the unified deep neural network backend adopts a “maximal ratio combining” strategy to learn complementary scale information at different levels. The scale purifier module, which improves scale representations, can effectively fuse multiscale features. Second, DSPNet performs the whole RGB image-based inference to facilitate model learning and decrease contextual information loss. Our customized network is end-to-end and has a fully convolutional architecture. We demonstrate the generalization ability of our approach by cross-scene evaluation. Extensive experiments on three publicly available crowd counting benchmarks (i.e., UCF-QNRF, ShanghaiTech, and UCF_CC_50 datasets) show that our DSPNet delivers superior performance against state-of-the-art methods.

Columnar scale representation of faces in the human inferotemporal cortex

Columnar scale representation of faces in the human inferotemporal cortex