Miracle-dz/UniFAR-FTUs huggingface dataset:
Pacific and Atlantic Multidecadal Variability Relations with the Choco and Caribbean Low-Level Jets during the 1900–2015 Period

This study analyzes the variability of the Choco jet (CJ) and Caribbean low-level jet (CLLJ) with consideration of the simultaneous Pacific interdecadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) low-frequency mean states and their effects on the atmospheric circulation and rainfall in northwestern South America and Central America for the 1900–2015 period, during the seasons with the highest intensities of the CJ (September–November (SON)) and the CLLJ (June–August). Variations in the sea surface temperature (SST) anomaly positioning in the eastern Pacific, tropical North Atlantic (TNA)/Caribbean Sea during different mean states restrict the anomalous circulation, and, consequently, the intensity of the CJ and CLLJ. During the warm AMO (WAMO)/cold PDO (CPDO), the SST gradient from the tropical Pacific into the TNA, accompanied by a cyclonic circulation near the east coast of the Americas, intensifies the west–east circulation in the region, strengthening the CJ and weakening the CLLJ during SON such that rainfall increases over Colombia, Central America and in adjacent oceans. During the cold AMO (CAMO)/warm PDO (WPDO) phase, a relative east/west SST gradient occurs in TNA, consistent with a cyclonic circulation in western TNA, establishing an anomalous southwest–northwestward circulation from the eastern Pacific into the Caribbean basin, forming a well-configured CJ, increasing precipitation over Central America and its adjacent oceans. For the CLLJ, during CAMO phases, the anticyclonic circulations extended over most of the TNA favor its intensification from 30° W to the Caribbean Sea. In contrast, during WAMO, the cyclonic circulation near the east coast of the United States restricts its intensification to the Caribbean Sea region. To the best of our knowledge, the results presented here are new and might be useful in atmospheric modeling and extreme event studies.
A Meta-Learning Approach for User-Defined Spoken Term Classification with Varying Classes and Examples

Recently we formulated a user-deﬁned spoken term classiﬁcation task as a few-shot learning task and tackled the task using Model-Agnostic Meta-Learning (MAML) algorithm. Our re-sults show that the meta-learning approach performs much better than conventional supervised learning and transfer learning in the task, especially with limited training data. In this paper, we extend our work by addressing a more practical problem in the user-deﬁned scenario where users can deﬁne any number of spoken terms and provide any number of enrollment audio examples for each spoken term. From the perspective of few-shot learning, this is an N -way, K -shot problem with varying N and K . In our work, we relax the values of N and K of each meta-task during training instead of assigning ﬁxed values to them, which differs from what most meta-learning algorithms do. We adopt a metric-based meta-learning algorithm named Prototypical Networks (ProtoNet) as it avoids exhaustive ﬁne-tuning when N varies. Furthermore, we use the Max-Mahalanobis Center (MMC) loss as an effective regularizer to address the problem of ProtoNet under the condition of varying K. Experiments on the Google Speech Commands dataset demonstrate that our proposed method outperforms the conventional N -way, K -shot setting in most testing tasks.
Traffic Light and Arrow Signal Recognition Based on a Unified Network

We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition.
Phone-Level Pronunciation Scoring for Spanish Speakers Learning English Using a GOP-DNN System

In today’s globalized world being able to communicate in English is crucial to many people. Computer assisted pronunciation training (CAPT) systems can help students achieve English proﬁciency by providing an accessible way to practice, offering personalized feedback. However, phone-level pronunciation scoring is still a very challenging task, with performance far from that of human annotators. In this paper we compare and present results on the Spanish subset of the L2-ARCTIC corpus and the new Epa-DB database, both containing non-native English speech by native Spanish speakers and intended for the development of pronunciation scoring systems. We show the most frequent errors in each database and compare performance of a state-of-the-art goodness of pronunciation (GOP) system. Results show that both databases have similar error patterns and that performance is similar for most phones, despite differences in recording conditions. For the EpaDB database we also present an analysis of the errors per target phone. This study validates the EpaDB collection and annotations, providing initial results and contributing to the advancement of a challenging low-resource task.
The Influence of Parallel Processing on Illusory Vowels

Research has shown that listeners perceive illusory vowels in-side consonant clusters that are not allowed in their L1. This phenomenon has been examined using several psycholinguistic and computational models, including hidden Markov models (HMMs), applied to human phoneme perception. However, the inference algorithm of HMMs assumes that parallel processing, which has not been proven to have psychological reality, is a valid cognitive process. This study tested the psychological reality of parallel processing by attempting to duplicate two results from previous studies: First, listeners perceive an illusory vowel in consonant clusters that are not permissible in their L1. Second, the illusory vowel is based on the characteristics of the preceding consonant, indicating that listeners integrate phonotactics and acoustic information. The experiment manipulated the number of candidates that the model can refer to, and the algorithm can be considered parallel when it allows models to use more than two candidates that are stored in memory. In addition, the transition probabilities between consonants were manipulated to represent the different phonotactics. The results showed that only the parallel processing condition reproduced the two observations above, supporting the psychological reality of parallel processing.
5.4: The Effects of 0.01% Atropine on Adult Myopes' Contrast Sensitivity

Purpose: Atropine at a low concentration is considered a safe and effective treatment to mitigate myopia progression. However, the potential unwanted side effects of administering atropine at a low dose on visual functions other than best corrected visual acuity has not been investigated. In this study, we investigate the short‐term (12, 16, and 20 h) and long‐term (1, 2, and 4 weeks) effects of 0.01% atropine (i.e., 0.1 mg/ml) on contrast sensitivity (CS) in patients with myopia.
Cardiopulmonary exercise testing has greater prognostic value than sarcopenia in oesophago‐gastric cancer patients undergoing neoadjuvant therapy and surgical resection

Sarcopenia (low skeletal muscle mass), myosteatosis (low skeletal muscle radiation‐attenuation) and fitness are independently associated with postoperative outcomes in oesophago‐gastric cancer. This study aimed to investigate (1) the effect of neoadjuvant therapy (NAT) on sarcopenia, myosteatosis and cardiopulmonary exercise testing (CPET), (2) the relationship between these parameters, and (3) their association with postoperative morbidity and survival.
Application of unmanned underwater vehicles in polar research

The importance of polar ice as vital components of the global ocean–climate system is widely recognized. In this paper, we demonstrate the importance and urgency of polar research, describe the primary characteristics of sea ice and ice shelves, and outline the current status and difficulties associated with sub-ice research. We highlight the importance of Unmanned Underwater Vehicles (UUVs) as important tools for oceanographic research. We present recent progress in UUV deployment in sub-ice research in the Arctic and the Antarctic, and review the latest international developments in UUV structure, navigation, payload, and field operation. Moreover, Chinese polar UUVs and their deployments in the polar regions are presented in detail. Key technologies and solutions regarding polar application of UUVs (e.g., sub-ice navigation and positioning, energy supply and data transmission, and sub-ice guidance and recovery) are discussed. Given the current worldwide attention on polar science, the potential future directions of UUV-related polar research (e.g., observations under Antarctic ice shelves, long-range surveys beneath Arctic sea ice and application of intelligent technology) are discussed. Citation :Zeng J B, Li S, Liu Y. Application of unmanned underwater vehicles in polar research. Adv Polar Sci, 2021, 32(3): 173-184, doi: 10.13679/j.advps. 2021.0018
A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.
Identification of Drought Tolerance on the Main Agronomic Traits for Rice (Oryza sativa L. ssp. japonica) Germplasm in China

Drought is a major abiotic factor restricting rice yield; therefore, to cope with this stress, 2030 japonica rice accessions from China and other countries were evaluated in Beijing in 2017 and 2018. This was the first time six agronomic traits in the large-scale germplasm of rice under lowland and upland conditions with an augmented randomized complete block design (ARCBD) were analysed. The genotypes revealing drought resistant grade (DRG) scores of 1, 1–3, 3 and 3–5 were considered drought-tolerant and comprised 10% of the assessed germplasm. These findings were consistent with the agglomerative hierarchical cluster (AHC) analysis that classified germplasm in nine clusters. The generated clusters were further grouped in A, B, C and D classes based on the stress response. Approximately half of the genotypes with an upland ecotype were distributed in drought-resistant class A (cluster VII and VI) and moderately resistant class B (VIII and IX). The majority of the genotypes from China, Korea and Japan fall in drought-susceptible classes C and D. Genotypes of DRG 1, 1–3 and 3 belonged to the clusters VII and VI. Finally, we screened out 42 elite genotypes including seven improved upland rice lines (D78, LB37-13, NSU77, Handao 385, Handao 306, SF83 and HF6-65-119), three upland released varieties (Liaogeng 27, Hanfeng 8 and IRAT109) and three traditional lowland cultivars (Hongmaodao, Weiguo 7 and Xiaohongbandao). These genotypes might be used as priority parents in drought-tolerant rice breeding programmes and some of them could be recommended directly to farmers in water-deficient rice areas of China.
Quantifying egg attendance behaviours of wild Asian houbara can improve artificial incubation outcomes

Artificial incubation is fundamental in ex situ avian conservation interventions for an increasing range of threatened species, but incubation parameters can differ between species. Both egg-turning angle and frequency are critical for successful embryonic development, but measuring these parameters in wild birds has historically been difficult. Using data-loggers inside artificial eggs that accurately record parental egg attendance behaviours, we quantified turning angles and rates throughout the incubation of 6 nesting wild Asian houbaras Chlamydotis macqueenii, a species bred in captivity in response to human over-exploitation. Wild females turned their eggs 0.6 times h−1 with a mean angle of 40.9° turn−1. Mimicking the patterns of wild birds (‘wild’ treatment), we applied a mean angular change of 40–50° turn once h−1 to artificially incubated eggs and compared this to eggs treated with an existing protocol (‘control’) of 120–130° per turn. Mean hatchability for the wild treatment (78.8%, n = 766) was similar to the control (76.6%, n = 1196). The wild treatment (n = 591) produced significantly heavier hatchlings (2.7%) than the control treatment (n = 893). However, chick growth rates (g d−1 and daily % mass change) and survival to Day 10 were not different between groups. The wild treatment demonstrates that turning angles affect embryo development and, perhaps, hatchability. Investigating natural incubation behaviours using egg-loggers and applying these patterns to artificial incubation appears efficacious to achieve optimal incubation protocols and reduce the need for ex situ trial and error refinement in conservation interventions of captive-bred threatened bird species.
Taiwan Min Nan (Taiwanese) Checked Tones Sound Change

The multifaced changes of Taiwan Min Nan (TMN) checked sandhi tones, S3 and S5 were investigated as well as the checked base tones, B3 and B5. Simultaneous EGG data, CQ_H and acoustic data, including duration, f0 offset at 80% vowel interval, and spectral tilt H1*-A3* from forty male and female speakers above 40 and under 30 years of age were analyzed. Though different measures progress at different paces, in general, as the coda stops [p, t, k, ʔ] from full stop closure, to energy damping and finally to complete deletion, vowel duration lengthening, f0 offset lowering, and more modal phonation were observed. Gender effects were found on f0 offset and CQ_H offset. The pace of progress is more advanced for base tone B5 with glottal coda stops. After coda deletion, the contexts conditioning the anticipatory co-articulation were removed and vowel and tone characteristics were modified to be similar to those found in open syllables.
The Application of Learnable STRF Kernels to the 2021 Fearless Steps Phase-03 SAD Challenge

We describe a deep-learning-based system developed for the Fearless Steps Phase-03 Speech Activity Detection (SAD) challenge. The system includes both learnable spectro-temporal receptive ﬁelds (STRFs) and unconstrained 2-dimensional convolutional kernels in the ﬁrst layer. Experiments show that the inclusion of learnable STRFs in the ﬁrst layer increases the system’s robustness to additive noise. Additionally, we found that utilizing SpecAugment during training improves generalization on unseen data. By incorporating these enhancements and others our system achieved the best score in the ofﬁcial SAD challenge.
Group Delay Based Re-Weighted Sparse Recovery Algorithms for Robust and High-Resolution Source Separation in DOA Framework

Sparse Recovery (SR) algorithms have been used widely for direction-of-arrival (DOA) estimation in spatially contiguous plane wave for their robust performance. But these algorithms have proven to be computationally costly. With a few sensors and at low SNRs, the noise dominates the data singular vectors and the sparse estimation of contiguous sources is incorrect. The magnitude spectrum-based re-weighted sparse recovery (RWSR) algorithms improve the robustness by re-weighting the sparse estimates. However, their efﬁciency degrades with decreasing the number of sensors at low SNRs. Therefore, this paper exhibits the signiﬁcance of the phase spectrum, in the form of group-delay, for sparse and robust source estimation using RWSR algorithms for spatially contiguous sources. Further, an optimal re-weighted methodology based on simultaneously minimizing average-root-mean-square-error and maximizing the probability of separation is also proposed. The simulation results are carried out for Gaussian noise to demonstrate the excellent performance of the proposed algorithms.
Recruitment and Employee Training and Organisational Resilience in Kenyan Hotels

This study sought to determine the role of human resource management practices in developing organizational resilience in star-rated hotels within Nairobi County, Kenya. Specifically, the study sought to assess the role of recruitment and employee training in developing organizational resilience. An embedded mixed research design was adopted. The study targeted 121 managers in star-rated hotels within the Nairobi region obtained through stratification sampling where questionnaires and interviews were conducted. A pre-test was done to test the research instrument for reliability and validity. Regression analysis findings proved a positive and significant effect between, recruitment and selection, training and organization resilience. Correlation analysis findings established that there were moderate to strong relationships between the study variables. Overall, Analysis of Variance showed that selected human resource management practices had a significant role in developing hotels’ organizational resilience. The study brought perspective on the risks and ways of developing resilient frameworks to guarantee business resilience. The findings of this study may be useful in developing hospitality resilience models for a local context.
A Maximum Likelihood Approach to SNR-Progressive Learning Using Generalized Gaussian Distribution for LSTM-Based Speech Enhancement

A maximum likelihood (ML) approach to characterizing regression errors in each target layer of SNR progressive learning (PL) using long short-term memory (LSTM) networks is proposed to improve performances of speech enhancement at low SNR levels. Each LSTM layer is guided to learn an intermediate target with a speciﬁc SNR gain. In contrast to using previously proposed minimum squared error criterion (MMSE-PL-LSTM) which leads to an un-even distribution and a broad dynamic range of the prediction errors, we model the errors with a generalized Gaussian distribution (GGD) at all intermediate layers in the newly proposed ML-PL-LSTM framework. The shape factors in GGD can be automatically updated when training the LSTM networks in a layer-wise manner to estimate the network parameters progressively. Tested on the CHiME-4 simulation set for speech enhancement in unseen noise conditions, the proposed ML-PL-LSTM approach outperforms MMSE-PL-LSTM in terms of both PESQ and STOI measures. Furthermore, when evaluated on the CHiME-4 real test set for speech recognition, using ML-enhanced speech also results in less word error rates than those obtained with MMSE-enhanced speech.
Disaster readiness’ influence on the impact of supply chain resilience and robustness on firms’ financial performance: a COVID-19 empirical investigation

This study investigates the impact of supply chain (SC) disaster readiness on SC resilience and robustness and the subsequent impact on firms’ financial performance in the context of the COVID-19 outbreak. Drawing on the dynamic capabilities view and organisational readiness for change theory, we provide a theoretical model and assess data gathered of 398 French firms using structural equation modelling. The findings corroborate the role of SC disaster readiness in setting the stage for resilience and robustness. In addition, the results indicate the positive influence of SC resilience on financial performance; however, the effects of SC robustness on performance are not demonstrated. Based on these results, we offer theoretical and practical implications for the extant literature and provide further avenues for research.
Pollinator Community Predicts Trait Matching between Oil-Producing Flowers and a Guild of Oil-Collecting Bees

The impact of pollinator community diversity on trait matching in plant-pollinator interactions is poorly studied, even though many mutualisms involve multiple interaction partners. We studied 10 communities in which one to three species of oil-collecting Rediviva bees pollinate the long-spurred, oil-producing flowers of Diascia “floribunda” to examine how pollinator diversity affects covariation of functional traits across sites and trait matching within sites. Floral spur length was significantly correlated with weighted grand mean foreleg length of the local bee community but not with foreleg length of individual bee species. The closeness of trait matching varied among populations and was inversely related to pollinator community diversity. For all bee species, trait matching was closest at sites characterized by exclusive pairwise interactions. Reduced trait matching associated with increased community diversity for individual pollinator species but close matching at the community level supports the importance of community context for shaping interacting traits of flowers and pollinators.
Role of College Environment on Students’ Soft Skills: Achievement Goal Structure

Abstract The development of undergraduate students’ soft skills is crucial because their chances to satisfactorily deal with the demands of various environments and interlocutors increases with the development of their soft skills. Based on the achievement goal theory, this study aimed to examine the role of the school environment in fostering students’ soft skills. Structural equation modeling was conducted to examine the relationships between five environmental factors—learning task, teacher support, extracurricular activities, interaction with teachers and peers, and soft skills. These findings positively predicted soft skills in college environments with development and competence growth, for both in-class and out-of-class contexts. In addition, the findings revealed that the most effective methods to facilitate students’ soft skills development included supporting extracurricular activities, engaging students in high-order learning tasks, facilitating peer learning, and providing structured guidance.