Miracle-dz/UniFAR-FTUs huggingface dataset:
Nucleosynthesis in Self-Consistent Core-Collapse Supernova Models using Multidimensional Chimera Simulations

Observations of nuclear abundances in core-collapse supernova (CCSN) ejecta, highlighted by γ-ray [gamma-ray] observations of the Ti [titanium-44] spatial distribution in the nearby supernova remnants Cassiopeia A and SN 1987A, allow nucleosynthesis calculations to place powerful constraints on conditions deep in the interiors of supernovae and their progenitor stars. This ability to probe where direct observations cannot makes such calculations an invaluable tool for understanding the CCSN mechanism. Unfortunately, despite knowing for two decades that supernovae are intrinsically multi-dimensional events, discussions of CCSN nucleosynthesis have been predominantly based on spherically symmetric (1D) models, which employ a contrived energy source to launch an explosion and often ignore important neutrino effects. As part of the effort to bridge the gap between first-principles simulations of the explosion mechanism and observations of both supernovae and supernova remnants, this dissertation investigates CCSN nucleosynthesis with self-consistent, axisymmetric (2D) simulations using the multidimensional radiation-hydrodynamics code Chimera. These models represent a necessary improvement over their parameterized counterparts in characterizing the impact of the hydrodynamically unstable, neutrino-driven supernova explosion on the ejecta composition and distribution. Computational costs have traditionally constrained the evolution of the nuclear composition within multi-dimensional CCSN models to, at best, a 14-species αnetwork [alpha-network] capable of tracking only (α, γ) [(alpha,gamma)] reactions
Controllable Text-to-speech Synthesis Using Prosodic Features and Emotion Soft-label ∗

Text-to-speech (TTS) technology aims to generate human-like speech including both linguistic and para-linguistic information. Due to the fast development of deep learning models, synthetic speech is already well understandable from a linguistic perspective [1]. The next challenge is how to reproduce and control a diverse variety of para-linguistic information, such as emotions, in natural speech. In this paper, we aim to develop emotion-controllable TTS that can express diverse emotions. The conventional approaches to controlling emotion in synthetic speech can be separated into 2 categories: coarse-grained and Fine-grained emotion control. The “coarse-grained” emotion-control models condition TTS models with emotion labels [2, 3, 4]. And the “fine-grained” emotion-control models condition TTS models with emotion strength or weight [5, 6, 7]. However, these “fine-grained” emotion control models cannot control how emotion intensity is represented in speech. The prosodic features, such as energy or pitch, that represented emotion intensity cannot be controlled by these “fine-grained” emotion control models. The prosodic features vary from utterance to utterance even when the emotion strength is the same. In another word, current “fine-grained” emotion controlling models are not “fine” enough. Another method conditions a TTS model on prosodic features [8], but it cannot control emotion. To summarize, the conventional approaches can not express both inter-category and intro-category variation of speech emotion with at coarse-grained (i.e., emotion-level) and fine-grained level (i.e., prosodic features) at the same time. In this paper, we propose an emotion-controllable TTS model that enables both coarse-grained and fine-grained emotion control. To achieve coarsegrained emotion control, we introduce a speech emotion recognizer (SER) that estimates the speech emotion soft-label, which used for coarse-grained emotion control in inference stage, from the utterance-level prosodic features. To achieve finegrained emotion control, we also introduce a prosodic feature generator (PFG) that estimates the utterance-level prosodic features, which used for fine-grained emotion control in inference stage, from the estimated speech emotion soft-label.
Simulation-based Self-driving Car using Reinforcement Learning

—Reinforcement learning is an essential machine learning paradigm that is used to develop an AI through interaction with the environment and self-improvement by learning from their mistakes and per-ceiving the rewards obtained while doing appropriate actions. Its implementation in autonomous driving has not yet been widely applied. But it should be noted that it is difﬁcult to implement autonomous driving as a supervised learning problem. As such, our objective here is to simulate vehicle driving in a virtual environment using Reinforcement Learning to accomplish one of the ﬁrst steps in self-driving car problems i.e., navigating through a predeﬁned track. The simulated vehicle is only given distances to the walls of the track as input and based on the policy it develops, has to navigate the track without collision. We used a variant of Q-Learning (a Reinforcement Learning algorithm) called Deep Q-Network (DQN) to develop the aforementioned policy. DQN uses neural networks to approximate the policy function. The environment, as well as the vehicle simulation, is done in Unreal Engine 4.
Construction of Radiofrequency circuits for longer Coherence times in Rare-Earth Ions

An approach to the construction of high-frequency radiocircuits for ex- tending coherence-times in the Rare-Earth quantum computation scheme is presented, along with results from computer simulation of the radiofre- quency circuit using a program called QUCS. The results were experi- mentally verified and showed good correspondence with theory and sim- ulations. Problems to take into consideration when constructing high- frequency circuits are presented as well as possible solutions to them. A general background of the quantum-information field is also given as well as the necessary concepts needed in order to understand the background for the experiment.
THE IMPACT OF GLOBALIZATION ON ECONOMIC GROWTH: A STUDY ON SELECTED ASIAN COUNTRIES

The purpose of this research is to examine and investigate the impact of globalization on economic growth in selected countries of Asia. This research will investigate the impact of social, political and economic globalization on gross domestic product of the selected Asian countries. This research was done on 18 selected countries of Asia. This research used panel data analysis from 2011 to 2015 using convenience sampling and total observation of 100. Data was selected from KOF Index of Globalization. This research use social, political and economic globalization as independent variables and gross domestic product as dependent variable. E-views software was used to generate the regression result. The results reveal that social globalization has a significant impact on GDP while political and economic globalization has an insignificant impact on GDP. This research will benefit to economist, student, journalist and future researchers to understanding and acknowledge the impact of globalization on Asian’ economy growth. Future researches are recommended to use different sample such as European countries or Middle East country. Also they can use other variables such as FDI, export or import to measure the economic growth of selected countries.
Visual servoing of mobile robots for posture stabilization: from theory to experiments

On the basis of the kinematic model of a unicycle mobile robot in polar coordinates, an adaptive visual servoing strategy is proposed to regulate the mobile robot to its desired pose. By regarding the unknown depth as model uncertainty, the system error vector can be chosen as measurable signals that are reconstructed by a motion estimation technique. Then, an adaptive controller is carefully designed along with a parameter updating mechanism to compensate for the unknown depth information online. On the basis of Lyapunov techniques and LaSalle's invariance principle, rigorous stability analysis is conducted. Because the control law is elegantly designed on the basis of the polar‐coordinate‐based representation of error dynamics, the consequent maneuver behavior is natural, and the resulting path is short. Experimental results are provided to verify the performance of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd.
Universal Proposition Bank 2.0

Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argument analysis demands training data in the form of propbanks. Unfortunately, most languages simply do not have corresponding propbanks due to the high cost required to construct such resources. To overcome such challenges, Universal Proposition Bank 1.0 (UP1.0) was released in 2017, with high-quality propbank data generated via a two-stage method exploiting monolingual SRL and multilingual parallel data. In this paper, we introduce Universal Proposition Bank 2.0 (UP2.0), with significant enhancements over UP1.0: (1) propbanks with higher quality by using a state-of-the-art monolingual SRL and improved auto-generation of annotations; (2) expanded language coverage (from 7 to 9 languages); (3) span annotation for the decoupling of syntactic analysis; and (4) Gold data for a subset of the languages. We also share our experimental results that confirm the significant quality improvements of the generated propbanks. In addition, we present a comprehensive experimental evaluation on how different implementation choices impact the quality of the resulting data. We release these resources to the research community and hope to encourage more research on cross-lingual SRL.
Annotation of Communicative Functions of Short Feedback Tokens in Switchboard

There has been a lot of work on predicting the timing of feedback in conversational systems. However, there has been less focus on predicting the prosody and lexical form of feedback given their communicative function. Therefore, in this paper we present our preliminary annotations of the communicative functions of 1627 short feedback tokens from the Switchboard corpus and an analysis of their lexical realizations and prosodic characteristics. Since there is no standard scheme for annotating the communicative function of feedback we propose our own annotation scheme. Although our work is ongoing, our preliminary analysis revealed lexical tokens such as “yeah” are ambiguous and therefore lexical forms alone are not indicative of the function. Both the lexical form and prosodic characteristics need to be taken into account in order to predict the communicative function. We also found that feedback functions have distinguishable prosodic characteristics in terms of duration, mean pitch, pitch slope, and pitch range.
Customer Satisfaction, Loyalty, and Financial Performance in the Tourism and Hospitality Industry: Evidence from North Cyprus

This study investigates the relationship among customer satisfaction, loyalty and financial performance in the tourism and hospitality industry for the case of North Cyprus. The results indicate that SERVQUAL items have a positive effect on both customer satisfaction and customer loyalty. The study also demonstrates the positive effect of service quality factors on customer satisfaction and loyalty towards a hotel. Moreover, there are direct effects of customer satisfaction and loyalty on the financial performance of hotels while service quality factors affect the financial performance of the hotels indirectly. company a customer satisfaction human resource of the company in form on a positive relationship. customer the marketing in the
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish

The paper presents a tool for automatic marking up of quantifying expressions, their semantic features, and scopes. We explore the idea of using a BERT based neural model for the task (in this case HerBERT, a model trained specifically for Polish, is used). The tool is trained on a recent manually annotated Corpus of Polish Quantificational Expressions (Szymanik and Kieraś, 2022). We discuss how it performs against human annotation and present results of automatic annotation of 300 million sub-corpus of National Corpus of Polish. Our results show that language models can effectively recognise semantic category of quantification as well as identify key semantic properties of quantifiers, like monotonicity. Furthermore, the algorithm we have developed can be used for building semantically annotated quantifier corpora for other languages.
In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations

Summarization of doctor-patient conversations into clinical notes by medical scribes is an essential process for effective clinical care. Pre-trained transformer models have shown a great amount of success in this area, but the domain shift from standard NLP tasks to the medical domain continues to present challenges. We build upon several recent works to show that additional pre-training with in-domain medical conversations leads to performance gains for clinical summarization. In addition to conventional evaluation metrics, we also explore a clinical named entity recognition model for concept-based evaluation. Finally, we contrast long-sequence transformers with a common transformer model, BART. Overall, our findings corroborate research in non-medical domains and suggest that in-domain pre-training combined with transformers for long sequences are effective strategies for summarizing clinical encounters.
SPREGDPD: Stata module to estimate Spatial Panel Arellano-Bond Linear Dynamic Regression: Lag & Durbin Models

spregdpd estimate Spatial Panel Arellano-Bond Linear Dynamic Regression Models for both Spatial Lag and Durbin models spregdpd calculates Spatial Autocorrelation, Non Normality, Heteroscedasticity, and Marginal Effects and Elasticities, spregdpd can fit continuous and truncated dependent variables models.
Relationship between bronchopulmonary dysplasia phenotypes with high-resolution computed tomography score in early preterm infants

Objective To assess the relationship between high-resolution computed tomography (HRCT) abnormalities and clinical phenotypes of bronchopulmonary dysplasia (BPD). Methods A retrospective, single-center study was carried out at the Children’s Hospital of Fudan University between 2013 and 2020. Preterm infants born at ≤ 32 weeks’ gestation who were diagnosed with BPD and had HRCT between 40 and 50 weeks postmenstrual age (PMA)were included in the study. HRCT images from six pulmonary lobes were scored based on seven types of pulmonary lesions from two categories: hyperaeration lesions and parenchymal lesions. The hyperaeration score (HS) included scores of decreased attenuation, mosaic attenuation, and bulla/bleb, while the parenchymal score (PS) included those of linear lesion, consolidation, bronchial wall thickening, and bronchiectasis. All seven scores were summed up to create the total score (TS). One-way ANOVA testing or Kruskal-Wallis testing was adopted for the comparison of HRCT scores with BPD severity and clinical phenotypes. The correlation between HRCT scores and clinical phenotypes was evaluated by Spearman’s correlation analysis. Results A total of 81 cases were included in the study. Cases with more severe BPD had a higher TS (p = 0.01), HS (p = 0.02), PS (p = 0.02), mosaic attenuation score (p = 0.03), bulla/Bleb score (p = 0.03), and linear density score (p = 0.01). TS (r = 0.28), PS (r = 0.35), linear density (r = 0.34), and consolidation (r = 0.24) were correlated with pulmonary hypertension (PH). However, no HRCT score was significantly different between the patients with or without tracheobronchomalacia (TBM). BPD patients with a combination of lung parenchymal disease, PH, and TBM had the highest TS and HS. Conclusion HRCT scores correlated with BPD severity and PH in our study. HS might be a useful tool in the assessment of BPD severity while linear densities and consolidation might be helpful in predicting PH.
Nanopore-based metagenomic sequencing for the rapid and precise detection of pathogens among immunocompromised cancer patients with suspected infections

Cancer patients are at high risk of infections and infection-related mortality; thereby, prompt diagnosis and precise anti-infectives treatment are critical. This study aimed to evaluate the performance of nanopore amplicon sequencing in identifying microbial agents among immunocompromised cancer patients with suspected infections. This prospective study enlisted 56 immunocompromised cancer patients with suspected infections. Their body fluid samples such as sputum and blood were collected, and potential microbial agents were detected in parallel by nanopore amplicon sequencing and the conventional culture method. Among the 56 body fluid samples, 47 (83.9%) samples were identified to have at least one pathogen by nanopore amplicon sequencing, but only 25 (44.6%) samples exhibited a positive finding by culture. Among 31 culture-negative samples, nanopore amplicon sequencing successfully detected pathogens in 22 samples (71.0%). Nanopore amplicon sequencing showed a higher sensitivity in pathogen detection than that of the conventional culture method (83.9% vs. 44.6%, P<0.001), and this advantage both existed in blood samples (38.5% vs. 0%, P=0.039) and non-blood samples (97.7% vs. 58.1%, P<0.001). Compared with the culture method, nanopore amplicon sequencing illustrated more samples with bacterial infections (P<0.001), infections from fastidious pathogens (P=0.006), and co-infections (P<0.001). The mean turnaround time for nanopore amplicon sequencing was about 17.5 hours, which was shorter than that of the conventional culture assay. This study suggested nanopore amplicon sequencing as a rapid and precise method for detecting pathogens among immunocompromised cancer patients with suspected infections. The novel and high-sensitive method will improve the outcomes of immunocompromised cancer patients by facilitating the prompt diagnosis of infections and precise anti-infectives treatment.
Real-time distributed dispatch strategy for distribution transformer supply zone cluster based on cloud-edge collaboration architecture

Aiming at the problem of long-timescale prediction deviation in the distribution network, a fast regulation strategy of cloud-edge coordination based on an intelligent transformer supply zones edge consistency algorithm is proposed. The cloud makes the global initial optimal allocation to the edge transformer supply zone clusters, and then the cluster makes the secondary collaborative optimal allocation to the edge zones. A fast power interaction model within a cluster based on a consensus algorithm is established, and the micro-increase rate of dispatching cost is used as the consistency variable so that the cluster adjustment amount is optimally allocated to each transformer supply zone, and the total dispatch cost of all transformer supply zones is minimized. The simulation example verifies the effectiveness of the cloud-edge collaborative fast control strategy based on the intelligent station cluster edge consensus algorithm in this article.
Using a social-ecological systems perspective to identify context specific actions to build resilience in small scale fisheries in Mexico

To promote the resilience and sustainability of coastal social-ecological systems (SES), it is necessary to implement adaptive and participatory management schemes. Successful examples of adaptation to the rapid change in coastal SES exist, but the question of whether these cases may be scalable to other regions and contexts remains. To this end, the present study aimed to identify how successful management strategies implemented in a fishing cooperative in Baja California, Mexico, can be adapted to other coastal SES. In particular, this study aimed to understand whether adaptive co-management of Isla Natividad (IN) could be replicated in Isla Todos Santos (ITS), a biophysically similar coastal SES to IN but with different results with regard to fisheries management. We found that the resource systems and resources in both SESs were similar. However, there were substantial differences with regard to governance and resource users. In Isla Natividad, the level of organization orchestrated by the resource users has contributed to establishing rules and sanctions that have supported the sustainable use of fishery resources. On the contrary, in ITS, the number of resource users and their socioeconomic attributes have impeded the establishment of effective rules or sanctions. The results of this study suggest that the ITS governance system needs to be improved in order to adapt some of the IN management strategies to increase its adaptive capacity. To promote successful adaptive management, it is necessary to develop context-specific adaptive pathways that contribute to greater resilience in the SESs of this region and in other regions that face similar conditions.
Potential antagonistic relationship of fgf9 and rspo1 genes in WNT4 pathway to regulate the sex differentiation in Chinese giant salamander (Andrias davidianus)

Farmed chinese giant salamander (Andrias davidianus) was an important distinctive economically amphibian that exhibited male-biased sexual size dimorphism. Fgf9 and rspo1 genes antagonize each other in Wnt4 signal pathway to regulate mammalian gonadal differentiation has been demonstrated. However, their expression profile and function in A. davidianus are unclear. In this study, we firstly characterized fgf9 and rspo1 genes expression in developing gonad. Results showed that fgf9 expression level was higher in testes than in ovaries and increased from 1 to 6 years while rspo1 expression was higher in ovaries than in testes. In situ hybridization assay showed that both fgf9 and rspo1 genes expressed at 62 dpf in undifferentiated gonad, and fgf9 gene was mainly expressed in spermatogonia and sertoli cells in testis while strong positive signal of rspo1 was detected in granular cell in ovary. During sex-reversal, fgf9 expression was significantly higher in reversed testes and normal testes than in ovaries, and opposite expression pattern was detected for rspo1. When FH535 was used to inhibit Wnt/β-catenin pathway, expression of rspo1, wnt4 and β-catenin was down-regulated. Conversely, expression of fgf9, dmrt1, ftz-f1 and cyp17 were up-regulated. Furthermore, when rspo1 and fgf9 were knocked down using RNAi technology, respectively. We observed that female biased genes were down regulated in ovary primordial cells after rspo1 was knocked down, while the opposite expression profile was observed in testis primordial cells after fgf9 was knocked down. These results suggested that fgf9 and rspo1 played an antagonistic role to regulate sex differentiation in the process of the gonadal development and provided a foundation for further functional characterizations. The data also provided basic information for genome editing breeding to improve the Chinese giant salamander farming industry.
Tolerancing an optical freeform surface: an optical fabricator's perspective

Freeform optical shapes or optical surfaces that are designed with non-symmetric features are gaining popularity with lens designers and optical system integrators. Tolerances on a freeform optical design influence the optical fabrication process. Case studies and soft tolerance limits for easier fabrication will be discussed. This paper will also give a high level overview of a freeform optical fabrication process that includes generation, high speed VIBE polishing, sub-aperture figure correction and testing of freeform surfaces.