Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of syntactic and acoustic cues on the perception of prosodic boundaries

This study investigates how the perception of prosodic boundaries is shaped by syntactic phrasing and acoustic cues for English and Mandarin listeners. Syntactically-parsed speech corpora were used as the stimuli for the perception experiment. The relative strength of the syntactic boundary of both the left and right sides of the constituents was extracted from the syntactic parsing annotations. A wide range of acoustic cues of both prosodic domain-final and domain-initial positions were examined. Linear-mixed-effects modeling of the likelihood of boundary perception suggests that, for both languages, prosodic boundary perception was influenced by both the strength of syntactic boundary and acoustic cues: boundary perception was heavily driven by the presence of pause; pause also modulated the contribution of other acoustic cues; and larger syntactic boundaries were generally more likely to be perceived as prosodic boundaries. However, there is also cross-linguistic variation: the effect of syntactic phrasing cues was generally stronger for English; acoustically, the effect of final lengthening and pitch reset was stronger in English, while pause was the dominant cue in Mandarin. We discuss the important implica-tions of these findings related to the nature of prosodic hierar-chy, and the nature of the prosody-syntax interface.
Perceptual Identification of Speech Acts in Gallo-Romance Dialects: A Study Based on Prosody Re-synthesis

In earlier works we examined four types of speech acts in several Gallo-Romance dialects: statements, polar questions, incredulous questions (expecting a negative response), and queries for confirmation (expecting a positive response). In a perception experiment we found that the first two types are generally reliably identified whereas for the latter two, only a limited number of utterances yielded recognition rates far above chance. We identified confusions mostly between polar and incredulous questions, as well as between the other two. We also established that differences that facilitated discrimination mostly concerned the fundamental frequency ( F0 ) contours. In the current work we conduct a perceptual experiment employing synthetic stimuli, involving pairs of reliably recognized, but by type confusable utterances and attempt to morph one type of speech act into the confusable other. Our results indeed show, that manipulating the utterance-final F0 contour appropriately actually increases the probability of one type of speech act being confused with the other, and what the categorical perception thresholds are. as low vs. high onsets. In conclusion our study shows that the resynthesis paradigm is very well suited to investigate and confirm our earlier categorical observations on natural speech data and a useful tool in subsequent works.
Rules governing swarm robot in continuum mechanics

To determine material body’s deformation is usual to solve Newton’s equation, under assigned boundary conditions; but this is not the only possibility to obtain a plausible description of the phenomenon. In recent years, position-based dynamic (PBD) concept has consolidated its success. The idea has its roots in computer graphic graphics aimed at saving processing time in computing object’s deformation and breakage in video games applications; PBD tries to give a plausible description of the deformation taking into account only the relative point’s positions to describe the action of internal forces. The aim is to obtain an algebraical equation capable of calculating the position of a point using as input the position of some other points. This kinematic approach has the advantage to work with simple algebraical equations; the complexity of the system increases linearly, rather than quadratically, with increasing the point’s number. Working on swarm robotics we have addressed the problem concerning flocking rules, to determine the behaviour of a single element to achieve an assigned configuration of the group. One possibility to perform this task, for each single robot, is to see what some of its neighbours are doing. We then use these rules to reproduce some behaviour of bi-dimensional deformable bodies both consistent with the standard Cauchy model and according to the second-gradient theory. All constitutive properties of the material are hidden within the rules, determining the displacements of the particles each relative to the others. The tool has an advantage in terms of computational cost and is very flexible to be adapted to objects of complex geometry and problems of different nature. The results are encouraging and fracture can be easily managed. However, a connection between the parameters of the tool and the constitutive parameters of the materials is not yet provided and our efforts are addressed in this direction, to validate the tool by a solid classical theory.
A Simple Approach to Achieving Ultrasmall III-Nitride Microlight-Emitting Diodes with Red Emission

The microdisplays for augmented reality and virtual reality require ultrasmall micro light-emitting-diodes (μLEDs) with a dimension of ≤5 μm. Furthermore, the microdisplays also need three kinds of such μLEDs each emitting red, green, and blue emission. Currently, in addition to a great challenge for achieving ultrasmall μLEDs mainly based on III-nitride semiconductors, another fundamental barrier is due to an extreme difficulty in growing III-nitride-based red LEDs. So far, there has not been any effective approach to obtain high indium content InGaN as an active region required for a red LED while maintaining high optical performance. In this paper, we have demonstrated a selective epitaxy growth approach using a template featuring microhole arrays. This allows us to not only obtain the natural formation of ultrasmall μLEDs but also achieve InGaN with enhanced indium content at an elevated growth temperature, at which it is impossible to obtain InGaN-based red LEDs on a standard planar surface. By means of this approach, we have demonstrated red μLEDs (at an emission wavelength of 642 nm) with a dimension of 2 μm, exhibiting a high luminance of 3.5 × 107 cd/m2 and a peak external quantum efficiency of 1.75% measured in a wafer form (i.e., without any packaging to enhance an extraction efficiency). In contrast, an LED grown under identical growth conditions but on a standard planar surface shows green emission at 538 nm. This highlights that our approach provides a simple solution that can address the two major challenges mentioned above.
AUTOMATIC EXTRACTION OF SOLAR AND SENSOR IMAGING GEOMETRY FROM UAV-BORNE PUSH-BROOM HYPERSPECTRAL CAMERA

Abstract. Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra. The results show that the solar zenith angle (SZA) and solar azimuth angle (SAA) calculated by our method provided higher precision angular values compared to other available tools. The viewing zenith angle (VZA) was lower near the flight path and higher near the edge of the images. The viewing azimuth angle (VAA) pattern showed higher values to the left and lower values to the right side of the flight line. The methods described in this study is easily reproducible to other study areas and applications.
A New Methodology for Wheat Attenuation Correction at C-Band VV-Polarized Backscatter Time Series

Wheat is one of the most important crops worldwide, and thus, the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic aperture radar (SAR) observations show that at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture (SM) retrieval is attempted. The objective of this article is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by SM dynamics. The method relies on time-series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and SM content in six wheat fields with available SM data. The water cloud model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series and achieved the highest correlation with SM, thus improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur.
A PRE-TRAINING METHOD FOR 3D BUILDING POINT CLOUD SEMANTIC SEGMENTATION

Abstract. As a result of the success of Deep Learning (DL) techniques, DL-based approaches for extracting information from 3D building point clouds have evolved in recent years. Despite noteworthy progress in existing methods for interpreting point clouds, the excessive cost of annotating 3D data has resulted in DL-based 3D point cloud understanding tasks still lagging those for 2D images. The notion that pre-training a network on a large source dataset may help enhance performance after it is fine-tuned on the target task and dataset has proved vital in numerous tasks in the Natural Language Processing (NLP) domain. This paper proposes a straightforward but effective pre-training method for 3D building point clouds that learns from a large source dataset. Specifically, it first learns the ability of semantic segmentation by pre-training on a cross-domain source Stanford 3D Indoor Scene Dataset. It then initialises the downstream networks with the pre-trained weights. Finally, the models are fine-tuned with the target building scenes obtained from the ArCH benchmarking dataset. Our paper evaluates the proposed method by employing four fully supervised networks as backbones. The results of two pipelines are compared between training from scratch and pre-training. The results illustrate that pre-training on the source dataset can consistently improve the performance of the target dataset with an average gain of 3.9%.
RAILWAY LIDAR SEMANTIC SEGMENTATION WITH AXIALLY SYMMETRICAL CONVOLUTIONAL LEARNING

Abstract. This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy.
Gender Prototypes Shape Perceptions of and Responses to Sexual Harassment

We provide a model describing how the narrow prototype of women as having conventionally feminine attributes and identities serves as a barrier to perceiving sexual harassment and appropriately responding to sexual-harassment claims when the victims of harassment do not resemble this prototype. We review research documenting that this narrow prototype of women overlaps with mental representations of sexual-harassment targets. The prototype of women harms women who diverge from this prototype: Their experiences with sexual harassment are less likely to be perceived as such, and they experience more negative interpersonal, organizational, and legal consequences when they experience harassment. Perceptions of sexual harassment are the catalyst by which sexual harassment is recognized and remedied. Thus, narrow gender prototypes may impede the promise and potential of civil rights laws and antiharassment policy.
Effects of Drugs Formerly Suggested for COVID-19 Repurposing on Pannexin1 Channels

Although many efforts have been made to elucidate the pathogenesis of COVID-19, the underlying mechanisms are yet to be fully uncovered. However, it is known that a dysfunctional immune response and the accompanying uncontrollable inflammation lead to troublesome outcomes in COVID-19 patients. Pannexin1 channels are put forward as interesting drug targets for the treatment of COVID-19 due to their key role in inflammation and their link to other viral infections. In the present study, we selected a panel of drugs previously tested in clinical trials as potential candidates for the treatment of COVID-19 early on in the pandemic, including hydroxychloroquine, chloroquine, azithromycin, dexamethasone, ribavirin, remdesivir, favipiravir, lopinavir, and ritonavir. The effect of the drugs on pannexin1 channels was assessed at a functional level by means of measurement of extracellular ATP release. Immunoblot analysis and real-time quantitative reversetranscription polymerase chain reaction analysis were used to study the potential of the drugs to alter pannexin1 protein and mRNA expression levels, respectively. Favipiravir, hydroxychloroquine, lopinavir, and the combination of lopinavir with ritonavir were found to inhibit pannexin1 channel activity without affecting pannexin1 protein or mRNA levels. Thusthree new inhibitors of pannexin1 channels were identified that, though currently not being used anymore for the treatment of COVID-19 patients, could be potential drug candidates for other pannexin1-related diseases.
The effect of musicality on the development of Mandarin prosody

Past work has shown a link between children’s musicality and language learning. But research is still sparse on the effect of musicality on the development of prosody, which uses tonal and temporal cues also relevant for processing music. In particular, the questions of when and how musicality affects the development of various aspects of the prosodic grammar remain largely unknown. In this study, we investigate the effect of musicality on the development of focus-marking in Mandarin-speaking 4-to 6-year-olds using speech data elicited in a controlled but interactive setting. We have found that the development of focus-marking in Mandarin is only weakly affected by the learner’s musicality. Specifically, children produce adult-like distinctions between on-focus and pre-focus positions, regardless of musicality. A musicality effect is observed in the contrast between on-focus and post-focus positions only in the 4-year-olds. The limited musicality effect on focus-marking is in contrast with our previous work, in which we found that musicality has a salient effect on the lexical tone production by children younger than 6 years. Together, the current results suggest that musicality advantage in the development of prosody depends on aspects of the prosodic grammar and the stage of development.
Fast decay of classification error in variational quantum circuits

Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not understood. To unleash the genuine discriminative power of a VQC, we propose a VQC system with the optimal classical post-processing—maximum-likelihood estimation on measuring all VQC output qubits. Via extensive numerical simulations, we find that the error of VQC quantum data classification typically decays exponentially with the circuit depth, when the VQC architecture is extensive—the number of gates does not shrink with the circuit depth. This fast error suppression ends at the saturation towards the ultimate Helstrom limit of quantum state discrimination. On the other hand, non-extensive VQCs such as quantum convolutional neural networks are sub-optimal and fail to achieve the Helstrom limit, demonstrating a trade-off between ansatz complexity and classification performance in general. To achieve the best performance for a given VQC, the optimal classical post-processing is crucial even for a binary classification problem. To simplify VQCs for near-term implementations, we find that utilizing the symmetry of the input properly can improve the performance, while oversimplification can lead to degradation.
Simulation and Measurement Methods for RCS Estimations of a Scale Model Airplane

In a military context, the radar cross section (RCS) of an airplane is a very important subject. For an RCS estimation object, a scaled-down model is often employed for ease of measurements. Recently, electromagnetic simulations of the RCS has become very convenient because of developments in high-speed calculation methods such as MLFMM and HOBF in electromagnetic simulators employing the MoM algorithm. As for the measurements, high-resolution measurements via the time domain analysis are promising. In this paper, important estimation expressions for use with a scale model are briefly explained. In terms of calculations, comparisons are drawn between the actual calculation abilities of MoM, MLFMM, and HOBF at 18 GHz for a 1/48-scale model. In terms of measurement, a high-resolution measurement system employing a compact range and time domain analysis that is used in this study is explained. Next, the calculated and measured results are compared in order to show the accuracy of the obtained results. The results in vertical and horizontal planes of a scale model are shown at vertical and horizontal polarizations. Moreover, using the calculation method to determine estimates at higher frequencies is also investigated in order to obtain the practical RCS value of a real-sized airplane
Top-Down and Bottom-up Processing of Familiar and Unfamiliar Mandarin Dialect Tone Systems

Speech processing involves active integration of bottom-up and top-down information types. In the present study, we investigated the relative weighting of top-down expectedness and bottom-up lexical tone in the perception of familiar and unfamiliar lexical tone systems. Standard Mandarin and Chengdu Mandarin are mutually intelligible language varieties with comparable segmental and highly distinct tonal realizations. In a spoken semantic-plausibility judgment task, we manipulated whether a word was high-surprisal or low-surprisal given the preceding context and dialect-specific tone. All participants were native Standard Mandarin speakers with minimal Chengdu Mandarin experience. Lower judgment accuracy was observed when the stimulus was Chengdu Mandarin, and suggested that expectedness (i.e., top-down) information overrides tonal (i.e., bottom-up) information in sentence plausibility judgments. However, judgment response times to sentence surprisal were uniform across stimuli from both dialects, suggesting that speakers are aware of the surprisal conveyed by a non-standard tone, even if not used in their final decision. These findings reveal listener sensitivity to both top-down expectedness and bottom-up tone regardless of the initial tone reliability. For unfamiliar tone systems, top-down influence overrides bottom-up processing to access utterance meaning, but bottom-up processing is indeed present and may reflect rapid learning of the unfamiliar tone system.
Multi-branch Feature Difference Learning Network for Cross-Spectral Image Patch Matching

Cross-spectral image patch matching is still challenging due to significant nonlinear differences between image patches. Recently, image patch matching methods based on feature relation learning have attracted increasing attention and achieved good performance. However, we find that the metric learning methods based on feature difference cannot comprehensively and effectively extract useful discriminative information between image patch pairs by only adopting two branches network structure. Therefore, we propose a novel multi-branch feature difference learning network (MFD-Net). Specifically, we build a multi-branch parallel feature difference extraction network, which can capture richer and more discriminative feature difference information and achieve significant improvements on matching tasks. Furthermore, we propose a combined metric network composed of a master metric network module and multiple branch metric network modules, which promotes the forward update of network weights and reduces the similarity of features extracted by each feature difference extraction module with negligible increase in inference time. Extensive experimental results show that the proposed MFD-Net achieves superior performances on cross-spectral image patch matching and single spectral image patch matching.
ADVANCES IN OPTICAL POLARIZATION REMOTE SENSING FOR MARINE OBSERVATION: A CASE STUDY IN NANCHANG RIVER PARK

Abstract. Marine observation is a worldwide challenge, which implicates for a large number of social, economic and scientific problems. Satellite remote sensing provides incredible convenience for marine observation, and remote sensing techniques with different wavelength range have been developed for scientific use related to oceanography, among of which optical polarization remote sensing is a rapidly growing field in the recent decade. Although some attempts have been made about utilizing optical polarization technique for marine observation, the potential of optical polarization remote sensing is far from being fully released and the current skills of optical polarization image processing are too coarse to extract deep information from raw images. In our experiment at Nanchang river park, three application scenarios are selected to illustrate advances in optical polarization remote sensing for marine observation, specifically including sun-glint observation, phytoplankton monitoring and coastal topography mapping. A baseline for optical polarization image processing is established for marine observation and the advantages of optical polarization technique are assessed qualitatively and quantitatively, proving that: For marine observation, optical polarization remote sensing can reduce overexposure rate, enhance dynamic range, depict subsurface phytoplankton and map coastal topography.