Polar Representation Research Articles

Polar transform network for prostate ultrasound segmentation with uncertainty estimation.

Automatic and accurate prostate ultrasound segmentation is a long-standing and challenging problem due to the severe noise and ambiguous/missing prostate boundaries. In this work, we propose a novel polar transform network (PTN) to handle this problem from a fundamentally new perspective, where the prostate is represented and segmented in the polar coordinate space rather than the original image grid space. This new representation gives a prostate volume, especially the most challenging apex and base sub-areas, much denser samples than the background and thus facilitate the learning of discriminative features for accurate prostate segmentation. Moreover, in the polar representation, the prostate surface can be efficiently parameterized using a 2D surface radius map with respect to a centroid coordinate, which allows the proposed PTN to obtain superior accuracy compared with its counterparts using convolutional neural networks while having significantly fewer (18%∼41%) trainable parameters. We also equip our PTN with a novel strategy of centroid perturbed test-time augmentation (CPTTA), which is designed to further improve the segmentation accuracy and quantitatively assess the model uncertainty at the same time. The uncertainty estimation function provides valuable feedback to clinicians when manual modifications or approvals are required for the segmentation, substantially improving the clinical significance of our work. We conduct a three-fold cross validation on a clinical dataset consisting of 315 transrectal ultrasound (TRUS) images to comprehensively evaluate the performance of the proposed method. The experimental results show that our proposed PTN with CPTTA outperforms the state-of-the-art methods with statistical significance on most of the metrics while exhibiting a much smaller model size. Source code of the proposed PTN is released at https://github.com/DIAL-RPI/PTN.

Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupiedspaces in radar images by leveraging lidar supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images. These sensing differences cause false positive and degeneration of radar penetrating capability, respectively. In this letter, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region.

Temporal dynamics of the neural representation of hue and luminance polarity

Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.

Wavefield Analysis Tools for Wavenumber and Velocities Extraction in Polar Coordinates.

Experimental characterization of Lamb waves in plate-like structures overcomes the intrinsic limits of a priori semianalytical finite element simulations, where material inaccuracies and nonidealities cannot be easily considered. Unfortunately, the experimental extraction of guided wave dispersion curves, and especially their polar representation along different directions of propagation at a given frequency, is not trivial. In nonisotropic materials, such analysis is a key aspect for a reliable and robust characterization of the behavior of waves. In this work, by exploiting scanning laser Doppler vibrometer measurements with narrowband excitation, two different signal processing methods for the extraction of the wavenumber polar representation at the excitation frequency are investigated and characterized. The first method is based on a distance regularized level set (DRLSE) algorithm, widely used in image processing and computer vision but, to the best of the author's knowledge, never used in the Lamb waves' field. The second method is based on the 2-D sparse wavenumber analysis which exploits the wavefield sparse representation in the wavenumber domain. With a precise and reliable extraction of the wavenumber characteristic in the k -space, the polar representations at the excitation frequency of phase and group velocities can be estimated. The former, by exploiting the well-known wavenumber-frequency relation, the latter, instead, by computing numerical derivative among wavenumbers at multiple frequencies. The methodology has been validated on three different composite plates with different degrees of nonisotropy properties. The results show the effectiveness of the two methods, highlighting the advantages and disadvantages of both.

Classification of targets detected by mmWave radar using YOLOv5

Frequency Modulated Continuous Wave (FMCW) radar is gradually used in industrial applications or advanced driver assistance systems (ADAS) for obstacle detection, this is due to its low cost and extreme operating conditions, unlike camera and Lidar, which is more accurate than ultrasonic or infrared sensors. In this work, we used the dataset of a cascaded radar called ColoRadar, we transformed the dataset from the polar representation (range, angle) to a Cartesian representation (X, Y), then we normalized the dataset so that all the Doppler peaks are between 0 and 1, to obtain uniform heatmap images, in which to train a YOLOv5 model that can classify and detect the presence of a human on heatmap images, we propose a YOLOv5 model and then compare it with several YOLOv5 models, the average accuracy (mAP_O.5) reaches 0.89, and the computational power consumed by this model is 12 Billion Floating Point Operations Per Second (BFLOPS).

Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency

This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness of this work is established by determining the overall sentiment of a politician’s tweets based on TF-IDF values of terms used in their published tweets. By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity. The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.

Excessive acceleration simplified Operational Guidance

Excessive acceleration criterion, developed by the International Maritime Organization within the Second Generation Intact Stability Criteria, deals with lateral accelerations experienced by people onboard. Level 2 criterion considers the ship at zero speed in beam waves, neglects diffraction and gives the expressions for the Froude-Krylov roll moment. In this paper, the Level 2 procedure is generalized for any ship speed and heading by developing the expressions for the Froude-Krylov exciting roll moment as a function of the heading angle and introducing a variance preserving transformation from encounter to wave frequency. The proposed procedure can be easily implemented in a user's code to assess the simplified Operational Guidance without the use of commercial software. The proposed expression is validated comparing the Froude-Krylov roll moment with the one obtained by the 3-D potential code HydroStar®, referring to a barge and a bulk carrier. The bulk carrier is selected as test case to develop the simplified Operational Guidance according to the proposed procedure. A polar diagram representation is chosen to identify safe combinations of ship speeds and heading while a tabular representation is proposed for a given heading to identify the minimum ship speed to avoid large lateral accelerations, for the sea states reported in standard wave scatter table.

Beyond Policy: The Use of Social Group Appeals in Party Communication

ABSTRACT Extant research points to the importance of social identity and group attitudes for political behavior. Even though this should have important consequences for political communication, few scholars have investigated how parties use appeals to social groups to capitalize on these predispositions. This study introduces the concept of social group yield as a new theoretical framework to explain how parties strategically emphasize groups to mobilize their core voters and broaden their support base among the general electorate. Empirically, I examine the case of Austria based on a content analysis of electoral manifestos for the national elections in 2013, 2017, and 2019 to measure parties’ group emphasis, combined with cross-sectional survey data measuring voters’ group attitudes. The results confirm that group attitudes of party supporters and the wider electorate direct parties’ emphasis of different groups. These findings have important implications for the representation of social groups, political polarization, and party competition.

Going to Extremes: Post-9/11 Discrimination in Fiction

The aim of the article is to discuss the representation of discrimination and polarization of the American society after the events of 9/11 in Laila Halaby’s novel Once in a Promised Land (2007). The novel presents the point of view of “the Other” and focuses on the analysis of the antagonistic processes in the American society and their outcomes in the lives of ordinary citizens, accused of being “the Other.” The article examines the deterioration of beliefs and values and the “death” of the American Dream. Based on the fundamental theory of Trauma Studies, the article discusses the issues of personal and collective trauma and their representation in Laila Halaby’s novel. Collective traumas may unify or polarize the society–both aspects have had negative outcomes in the USA. Increased patriotism and solidarity were particularly prominent during the immediate aftermath of 9/11 and resulted in the discrimination and polarization of the society, the anger being directed at Muslim communities. The first days of the aftermath marked the start of antagonism on different levels: despite being US citizens, representatives of the Muslim communities experienced harsh reactions in their neighborhoods, jobs, social spheres, etc. For many of those “on the other side” these processes meant the end of their normal lives and dreams. The article examines both the informational and empathic approach used by the author of the novel to disclose irreparable processes that may happen in any society.

Powers of Elliptic Scator Numbers

Elliptic scator algebra is possible in 1+n dimensions, n∈N. It is isomorphic to complex algebra in 1 + 1 dimensions, when the real part and any one hypercomplex component are considered. It is endowed with two representations: an additive one, where the scator components are represented as a sum; and a polar representation, where the scator components are represented as products of exponentials. Within the scator framework, De Moivre’s formula is generalized to 1+n dimensions in the so called Victoria equation. This novel formula is then used to obtain compact expressions for the integer powers of scator elements. A scator in S1+n can be factored into a product of n scators that are geometrically represented as its projections onto n two dimensional planes. A geometric interpretation of scator multiplication in terms of rotations with respect to the scalar axis is expounded. The powers of scators, when the ratio of their director components is a rational number, lie on closed curves. For 1 + 2 dimensional scators, twisted curves in a three dimensional space are obtained. Collecting previous results, it is possible to evaluate the exponential of a scator element in 1 + 2 dimensions.

SiamPolar: Semi-supervised realtime video object segmentation with polar representation

Video object segmentation (VOS) is an essential part of autonomous vehicle navigation. Besides the accuracy metric, the real-time speed is very important for the algorithms of autonomous vehicles. In this paper, we propose a semi-supervised real-time method based on the Siamese network using a new polar representation. The input of bounding boxes are initialized rather than the object masks, which are applied to the video object detection tasks. The polar representation could reduce the parameters for encoding masks with subtle accuracy loss, so that the algorithm speed can be improved significantly. An asymmetric siamese network is also developed to extract the features from different spatial scales. Moreover, the peeling convolution is proposed to reduce the antagonism among the branches of the polar head. The repeated cross-correlation and semi-FPN are designed based on this idea. The experimental results on the DAVIS-2016 dataset and other public datasets demonstrate the effectiveness of the proposed method.

MB-Fit: Software infrastructure for data-driven many-body potential energy functions.

Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg (Thole-type model energy) and MB-nrg (many-body energy) theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy and force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, particularly within the MB-nrg framework, we believe that MB-Fit will enable routine predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of quantum isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem − 1) and a regression-based network is utilized to identify the material properties (inverse problem − 2).

Torque Analysis of a Gyratory Crusher with the Discrete Element Method

Comminution by gyratory crusher is the first stage in the size reduction operation in mineral processing. In the copper industry, these machines are widely utilized, and their reliability has become a relevant aspect. To optimize the design and to improve the availability of gyratory crushers, it is necessary to calculate their power and torque accurately. The discrete element method (DEM) has been commonly used in several mining applications and is a powerful tool to predict the necessary power required in the operation of mining machines. In this paper, a DEM model was applied to a copper mining gyratory crusher to perform a comprehensive analysis of the loads in the mantle, the crushing torque, and crushing power. A novel polar representation of the radial forces is proposed that may help designers, engineers, and operators to recognize the distribution of force loads on the mantle in an easier and intuitive way. Simulations with different operational conditions are presented and validated through a comparison with nominal data. A calculation procedure for the crushing power of crushers is presented, and recommendations for the selection of the minimum resolved particle size are given.

Polarizing Šolc filters: a pedagogical derivation in the complex plane

For the theoretical study of Šolc filters, used in particular in solar observatories or coronagraphs, we use the complex plane representation of polarization, which had apparently never been done before in this context. This avoids the cumbersome matrix calculus, for the two models of basic Šolc filters. That technique seems promising for further studies on the important question of the apodisation of these filters.

Stitching interferometric characterization of geometric errors of hemispherical shells

Geometric errors including the surface error and thickness variation of a hemispherical shell are determinant of the system performance, e.g. the frequency splitting of a hemispherical resonator gyroscope. Therefore, complete characterization at 0.1 µm level of accuracy is required which is difficult with current roundness measuring instruments. This paper presents an interferometric method for axially eccentric hemispherical shells. The outer surface and inner surface are measured individually by simply translating the shell along its anchor axis. The two measurements are registered after correction of pixel misalignment and mapping distortion through ray tracing. Thickness variation is then calculated from their difference. A multi-axis platform is designed to scan multiple subapertures covering the hemispherical shell. All subapertures are stitched together with misalignment corrected, according to the minimized inconsistency in the overlapping region of neighboring subapertures. The full aperture map is thus obtained for both the surface error and thickness variation. Polar harmonic representation is suggested by applying annular Zernike polynomial fitting. The method is experimentally demonstrated and validated with roundness cross test.

Negative voting and party polarization: A classic tragedy

Elections around the globe attest to the persistence of polarization in democratic politics. Popular support for antagonistic elite strategies defies standard predictions of ideological convergence. This paper develops a new solution to the theoretical puzzle: The centrifugal drive in representative democracy is a byproduct of voters’ disposition to evaluate policy platforms on the basis of issue positions that they dislike—to wit, negative voting. While reasonable individually, this behavior backfires collectively as elections dominated by negativity produce more polarized legislatures. Quite tragically, party polarization ultimately reflects an uncoordinated struggle of the electorate to avoid the worst rather than to pursue the best. Support for these claims comes from a theoretical model and a large comparative analysis of vote choice and party platforms. The evidence also favors the negativity model over earlier spatial models of elections. In light of negativity, normative concerns about polarization and democratic representation stand to be reconsidered.

Study on the Localization Method of Multi-Aperture Acoustic Array Based on TDOA

When the background noise is large or the sound source is far away from the microphone array, the localization accuracy of target sound sources will decrease dramatically. In order to solve this problem, a localization method of multi-aperture acoustic array based on time difference of arrival(TDOA) is proposed. The multi-aperture acoustic array is constructed by two small sub microphone arrays, which is supposed to surpass the performance of the equivalent large array composed of all microphones. The positions of the target sound source in two local coordinates are separately estimated by the two sub microphone arrays based on the generalized trust-region sub-problem in the modified polar representation (GTRS-MPR) method. Through the space coordinate transformation, the estimated two positions of the target sound source in the two local coordinates can be transferred into one global coordinate. To optimize the final estimated position of the target sound source by the multi-aperture acoustic array, two spatial lines are formed between the reference microphones of the two sub-arrays and the estimated two sources' positions in the global coordinate, and a search algorithm of shortest distance point based on the least-square method is proposed to fuse the localization results of the two sub-arrays. A simulation and an experimental platform are set up to verify the proposed method. The results show that the proposed method has higher localization accuracy compared with the existing methods. Especially, the accuracy of range estimation is greatly improved, which will be more suitable for the far-field sound source localization.

Identifying and determining crowdsourcing service strategies: An empirical study on a crowdsourcing platform in China

<p style='text-indent:20px;'>The crowdsourcing platforms, as mediators and service providers, play a critical role in crowdsourcing initiatives. The service quality of a platform has a direct impact on solver satisfaction, and ultimately affects the platform's continuous operation. Service quality can be measured by service quality attributes (SQAs). Thus, identifying and quantifying SQAs are crucial to enhance solver satisfaction. Besides, choosing pertinent strategies and determining priorities for the SQAs are another core issue. To address these issues, this study proposes a novel decision framework that combines the Fuzzy Analytical Kano (FAK) and the Importance-performance analysis (IPA) models. Firstly, 24 related SQAs are identified from five dimensions of service quality. Secondly, we quantify these SQAs into a polar form representation scheme in accordance with the FAK model. In addition, the pertinent service strategies and priorities of the SQAs are confirmed by using the IPA model and Kano categories. Finally, decision priority rules for corresponding strategies and priorities of SQAs are constructed. An empirical study is presented to demonstrate our proposed decision framework on ZBJ platform, which is one of the most widely used online crowdsourcing platform in China.</p>

A Multiple Sources Localization Method Based on TDOA Without Association Ambiguity for Near and Far Mixed Field Sources

A new method for multiple sources localization is proposed to eliminate association ambiguity for near and far mixed field sources. A spatial source localization model in the modified polar representation was constructed without the prior knowledge needed if the source is near-field or far-field. The localization model for multiple sources was deduced by using all the possible permutation of the TDOA sequences obtained from the original array by GCC-PHAT and the generalized trust region optimization processing method. In order to eliminate the phantom sources in the multiple sources localization, a set of calibration sub-arrays was constructed by switching the reference microphone in the array. The TDOA sequences of the estimated possible sources and all actual sources to the calibration sub-arrays were calculated separately. A reliability evaluation function was constructed based on the two sets of TDOA sequences, as well as a reliability evaluation between the sources identified by all the calibration sub-arrays. According to the principle of minimization of the reliability evaluation function, the real sources were screened out to solve the association ambiguity. Comparison analyses through simulation and experiment on real speech datasets were carried out under different localization scenarios. The results of simulation are consistent with the experimental results, which show that the proposed method effectively eliminates the phantom sources, and has higher positioning accuracy and robustness than the comparison methods, no matter sources are in the near-field or far-field.