Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentence Level Fine-grained Emotion Computation Based on Dependency Syntax Improvement Dictionary

With the rapid development of Internet technology, emotion analysis has been widely used in various fields. However, the different types of emotion corresponding to multiple attribute words in text and the complex syntactic structure bring great challenges to the existing text emotion analysis methods. This paper proposes a sentence level fine-grained emotion computing approach based on dependency syntax improvement dictionary. Firstly, an attribute quad extraction algorithm is constructed based on the dependency relationship. Then we analyze the syntactic structure of the text, and study the influence of the combination mode of sentence pattern, inter-sentence relation, degree adverb and negative adverb on the sentence emotion. Lastly, a fine-grained emotion computing algorithm is designed to calculate the emotion value of sentences. Experiments on collected micro-blog data set show that our method compared with the original dictionaries, the F1 value of emotion classification reaches 81.37%.
Growth process of hydrogenated amorphous carbon films synthesized by atmospheric pressure plasma enhanced CVD using nitrogen and helium as a dilution gas

Hydrogenated amorphous carbon films with various thicknesses were synthesized by dielectric barrier discharge-based plasma deposition under atmospheric pressure diluted with nitrogen (N2) and helium (He) at various pulse frequencies. The C2H2/N2 film showed cauliflower-like-particles that grew bigger with the increase in film’s thickness. At 5 kHz, the film with a thickness of 2.7 µm and smooth surface was synthesized. On the other hand, the films synthesized from C2H2/He had a smooth surface and was densely packed with domed particles. The domed particles extended with the increase in the film thickness, enabling it to grow successfully to 37 µm with a smooth surface.
Biogeographic Patterns of Fungal Sub-Communities under Different Land-Use Types in Subtropical China

Revealing the regional distribution and diversity of fungal sub-communities under different land management practices is essential to conserve biodiversity and predict microbial change trends. In this study, a total of 19 tilled and 25 untilled soil samples across different land-use types were collected from subtropical China to investigate the differences between the spatial distribution patterns, diversity, and community assembly of fungal sub-communities using high-throughput sequencing technology. Our results found that anthropogenic disturbances significantly reduced the diversity of abundant taxa but significantly increased the diversity of rare taxa, suggesting that the small-scale intensive management of land by individual farmers is beneficial for fungal diversity, especially for the conservation of rare taxa. Abundant, intermediate, and rare fungal sub-communities were significantly different in tilled and untilled soils. Anthropogenic disturbances both enhanced the homogenization of fungal communities and decreased the spatial-distance–decay relationship of fungal sub-communities in tilled soils. Based on the null model approach, the changes in the assembly processes of the fungal sub-communities in tilled soils were found to shift consistently to stochastic processes, possibly as a result of the significant changes in the diversity of those fungal sub-communities and associated ecological niches in different land-use types. Our results provide support for the theoretical contention that fungal sub-communities are changed by different land management practices and open the way to the possibility of predicting those changes.
BRIDGING THE EMPLOYABILITY GAP BETWEEN HOTELS AND GRADUATES: THE IMMERSION METHODOLOGY

A challenging, competitive, and continuously changing environment which surrounds the field of hospitality management requires academia to inspire and facilitate student ownership of learning experiences. The orthodoxy of theoretical teaching and learning methodology in our HE systems has created a significant gap in industrial expectation of employing individuals who are creative and work ready. The purpose of the study is to determine the expectations of the industry in regards to what it expects hospitality program graduates to possess. Given the discrepancies that exist between industry expectations, this paper depicts the innovative epistemology of using an Immersion framework which encompasses experiential student-oriented learning in HE as the tool to bridge this gap. The research was devised through an extensive literature review and the industry’s expectation of its graduates was polled using a sample of 6 hotels comprising 3 five-star hotels and 3 four-star hotels within the vicinity of Kuala Lumpur. The convenience sampling method was used to gather a general estimation of hotel-employer feedback on their expectations of graduates of the hospitality industry. This paper concludes that HE learning should evolve from the monotonous and rote form of learning to one that is innovatively aligned with industry expectations of graduate performance and a number of key enablers were identified as necessary for HE to be able to embark on the successful application of the Immersion Methodology.
Functional L-Arginine Derivative as an Efficient Vector for Intracellular Protein Delivery for Potential Cancer Therapy

The utilization of cytosolic protein delivery is a promising approach for treating various diseases by replacing dysfunctional proteins. Despite the development of various nanoparticle-based intracellular protein delivery methods, the complicated chemical synthesis of the vector, loading efficiency and endosomal escape efficiency of proteins remain a great challenge. Recently, 9-fluorenylmethyloxycarbonyl (Fmoc)-modified amino acid derivatives have been used to self-assemble into supramolecular nanomaterials for drug delivery. However, the instability of the Fmoc group in aqueous medium restricts its application. To address this issue, the Fmoc ligand neighboring arginine was substituted for dibenzocyclooctyne (DBCO) with a similar structure to Fmoc to obtain stable DBCO-functionalized L-arginine derivative (DR). Azide-modified triethylamine (crosslinker C) was combined with DR to construct self-assembled DRC via a click chemical reaction for delivering various proteins, such as BSA and saporin (SA), into the cytosol of cells. The hyaluronic-acid-coated DRC/SA was able to not only shield the cationic toxicity, but also enhance the intracellular delivery efficiency of proteins by targeting CD44 overexpression on the cell membrane. The DRC/SA/HA exhibited higher growth inhibition efficiency and lower IC50 compared to DRC/SA toward various cancer cell lines. In conclusion, DBCO-functionalized L-arginine derivative represents an excellent potential vector for protein-based cancer therapy.
AN APPARATUS FOR LIGHT-LESS ARTIFICIAL GAUGE FIELDS AND NEW IMAGING TECHNIQUES

Title of dissertation: AN APPARATUS FOR LIGHT-LESS ARTIFICIAL GAUGE FIELDS AND NEW IMAGING TECHNIQUES Abigail Perry, Doctor of Philosophy, 2015 Dissertation directed by: Dr. I.B. Spielman and Professor S.L. Rolston Joint Quantum Institute, National Institute of Standards and Technology and University of Maryland Department of Physics The thesis presented has three components: experiments with artificial vector potentials, a new atom-chip apparatus designed and built for light-less fictitious gauge fields, and an imaging experiment. First, we introduce experiments with lightinduced vector potentials using two-photon Raman coupling to simulate charged particles using charge neutral Bose-Einstein condensates (BECs). Depending on the spatial and temporal properties of the engineered vector potential, it is possible for ultracold atoms to experience different variants of an effective Lorenz force such as; magnetic fields, electric fields, and spin-orbit coupling, via coupling between an atom’s internal spin and its linear momentum. In this context, we discuss the main focus of this thesis, the design and construction of an atom-chip apparatus for Rb BECs for experiments with light-less artificial gauge fields. Eliminating the source of heating due to spontaneous emission will open new paths to explore artificial gauge fields in alkali fermions and will be a step towards the realization of simulated topological insulators using ultracold atoms. Finally, we will describe in detail an imaging experiment performed on this new apparatus, the reconstruction of the two-dimensional column density of a BEC using multiple defocused images taken simultaneously. AN APPARATUS FOR LIGHT-LESS ARTIFICIAL GAUGE FIELDS AND NEW IMAGING TECHNIQUES by Abigail Reiko Perry Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2015 Advisory Committee: Dr. Ian Spielman, Advisor Professor Steve Rolston, Chair Professor Mario Dagenais, Dean’s Representative Professor Christopher Monroe Dr. Trey Porto © Copyright by Abigail Perry 2015
Evaluation and Students’ Self -assessment of Transversal Skills in Secondary School Mathematics. Case Study in Latvia

: To provide qualitative and sustainable education school reforms are taking place in many countries around the world. The transition to competence-based learning also alters expectations for learning outcomes. To ensure student-centred learning, it is necessary to equip them with transversal skills required to: address subject content challenges, use different teaching and learning methods; address everyday life problems. The aim of the study is to assess students' transversal skills in secondary school mathematics. There were 50 participants in the study, who ware all studying mathematics at an in-depth level. Transversal skills were measured in action with a test as well as with student self-assessment. While test results showed that students excel in interpretation skills, collaboration skills and digital skills, analy zation skills tested at high levels in students’ self - assessment. There is no strong correlation between student self-assessment and student performance evaluated by test. Consequently, it can be concluded that the overall development of transversal skills in secondary school mathematics are sufficient, but it is necessary to understand the inconsistency between self-assessment and performance evaluated by test so that the factors affecting the development of transversal skills can be further analysed.
Latent variable framework for modeling and separating single-channel acoustic sources

Auditory Scene Analysis refers to the human ability to extract different perceptual objects from a sound mixture. Replicating this ability in artificial systems has been an active area of research, related both to how one characterizes acoustic sources and separates sources from mixtures. The focus of this thesis is to develop models and algorithms that provide a framework to address these questions. The framework comprises latent variable models that employ hidden variables to model unobservable quantities. Such models are appropriate for obtaining representations of data that make hidden structure explicit. This work shows how one can utilize these ideas for the problem of source separation using single-channel audio signals. The proposed framework focuses on learning the time-frequency (TF) structure in a data-driven manner. TF representations of sounds are modeled by treating the energy in every TF bin as histogram counts of multiple draws. This formulation allows the extraction of the characteristic frequency structure of individual sources as latent components and models the sources as additive combinations of these components. The framework is then extended to incorporate the idea of sparse coding to overcome an important limitation of the basic model: an upper bound on the number of extractable components. Sparsity, imposed in the form of an entropic prior distribution, allows extraction of overcomplete sets of components that are more expressive and better characterize the sources. The statistical foundation of the framework makes it amenable to other extensions where known or hypothesized structure about the data can be easily incorporated by imposing appropriate prior distributions. Theoretical analysis of the proposed methods and algorithms for parameter inference are presented. Applications of the models to real-world problems are evaluated and discussed. The latent components learned from acoustic sources are used in a supervised setting for source separation and in a semi-supervised setting for denoising. Unlike approaches based on time-frequency masks that reconstruct partial spectral descriptions of sources by identifying time-frequency bins in which a source dominates, this approach reconstructs entire spectral descriptions of all sources. Various experimental results demonstrate the utility of the proposed framework.
Transfer Assurance for Machine Learning in Autonomous Systems

This paper introduces the concept of transfer assurance for Machine Learning (ML) components used as part of an autonomous system(AS). InpreviousworkwedevelopedthefirstapproachforassuringthesafetyofMLcomponentssuchthatacompelling safetycasecanbecreatedfortheirsafedeployment.DuringoperationitmaybenecessarytoupdateanMLcomponentbyre-trainingthemodelusingneworupdateddevelopmentdata.Ifmodelre-trainingisrequiredpost-deployment,the safetycasethatwascreatedfortheMLcomponentmaynolongerbevalid,sinceanewmodelhasbeencreatedthatcannolongerbeassuredtomeetitssafetyrequirements.Inparticular,thenatureofmachinelearntcomponentsmeansthatone maynotbeabletopredicthowevensmallchangesinthedevelopmentdatamayaffectthemodelanditsperformance.Asaresult,currentpracticewouldrequirethatafullassuranceassessmentisundertakenforthere-learnedmodel,andthata newsafetycaseiscreated.GiventhedesirabilityofupdatingMLcomponentsduringoperation,weseeitasimperativethattheassuranceprocessbecomemoreproportionatetothesizeofthechangethatismadetothemodel,whilstensuringthat assurancecanstillbedemonstrated.RetrainingMLcomponentsisknowntobeacostlyandcomplexprocessandassuchtechniquessuchastransferlearninghavebeendevelopedwhichaimtoreducethisburdenthroughincrementaldevelopment. Approachessuchastransferlearningprovideaninspirationforhowthechallengeofefficientlyassuringupdatedmodelscouldbeaddressedthroughunderstandingwhichaspectsofamodelmayhavebeenaffectedbychangestothedevelopment data.Werefertothisastransferassurance,wherepartsoftheassurancecaseforanMLcomponentcanremainfixedwhilstotherpartsarere-assessed.
TimesliceVis: an online system for analyzing temporal features in dynamic visualizations

To support stable dynamic graph layout and reveal time features hidden in the data, we propose a joint discovery method that extracts the skeleton of a dynamic graph by combining graph structural information and clustering algorithms, and explores time-sequence data features mainly based on graph community patterns. In addition, we propose a discrete exploration method that helps users better understand roles, connections, and interactions in the network, through dimensionality reduction views. We integrate these methods to design a visualization ana-lysis system called TimesliceVis, which helps users construct online dynamic graphs and explore and understand the temporal features of network graphs. Finally, through case studies on real-world data, we demonstrate the practicality and effectiveness of TimesliceVis in perceiving and interpreting network based on structural information.
Generative Adversarial Network for Prediction of Visual Deteriorations and NDE Maps for Bridge Decks

: Non-destructive evaluation (NDE) techniques are excellent at identifying subsurface deteriorations (e.g., cracks, delamination, and corrosion). NDE surveys of bridge decks are conducted periodically to identify causes and quantify the progress of deterioration. Many transportation authorities however rely on traditional visual assessment techniques for the generation of bridge deck condition indices. The primary limitation of visual assessment is that subsurface conditions and other hidden anomalies are not visible. Many transportation authorities do deploy NDE for condition assessment, however, few techniques are usually used. In this work, a generative deep learning approach is introduced for the two applications: (i) predicting future visual deterioration from past NDE data, and (ii) predicting current conditions map for an unknown NDE technique using a current condition map from a known NDE technique. This approach may be attractive to transportation authorities that may wish to use NDE condition maps to infer future visual deteriorations and other NDE condition maps.
Estimation and clustering in a semiparametric Poisson process stochastic block model for longitudinal networks

In this work, we introduce a Poisson process stochastic block model for recurrent interaction events, where each individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process whose intensity is driven by the individuals’ latent groups. The model is semiparametric as the intensities per group pair are modeled in a nonparametric way. First an identifiability result on the weights of the latent groups and the nonparametric intensities is established. Then we propose an estimation procedure, relying on a semiparametric version of a variational expectation-maximization algorithm. Two different versions of the method are proposed, using either histogram-type (with an adaptive choice of the partition size) or kernel intensity estimators. We also propose an integrated classification likelihood criterion to select the number of latent groups. Asymptotic consistency results are then explored, both for the estimators of the cumulative intensities per group pair and for the kernel procedures that estimate the intensities per group pair. Finally, we carry out synthetic experiments and analyse several real datasets to illustrate the strengths and weaknesses of our approach.
Bacterial Adhesion and the Role of Mouthwashes in Orthodontics: A Literature Review

Bacterial plaque can adhere to materials used in fixed orthodontic treatment. Several studies have reported the chemical control that mouthwashes provide with respect to the reduction of bacterial strains. The objective of the present investigation was to review the bibliographic evidence regarding bacterial adhesion that occurs during orthodontic treatment and the role of mouthwashes with respect to plaque control. Electronic search expressions were used in the following databases PubMed, Scopus, EBSCO, Science Direct and Virtual Health Library, retrieving a total of 43 articles on which this review was based. There is information available regarding oral mouthwashes, which allow the professional to prescribe or indicate an oral mouthwash with fewer side effects, but with the same antibacterial efficacy of chlorhexidine. In this sense, an interesting option is the rinses of natural origin, which have been successfully tested in experimental studies.
Ice Behavior under Cyclic Flexural Loading

Recent studies revealed that the flexural strength of S2 columnar-grained laboratory-grown freshwater and saline ice, loaded cyclically across the columns, increases linearly with stress amplitude, in a manner similar for both materials. The studies were conducted over the range of stress amplitudes from 0.1 to 2.6 MPa, frequencies of ~ 0.1 Hz and temperature of -10 ºC. The flexure experiments were conducted under reversed 4-point bending. Cyclic strengthening is attributed to the development of an internal back stress that opposes the applied stress and originates possibly from dislocation pileups or grain boundary sliding
Pulmonary Function Test and Obstructive Sleep Apnea Hypopnea Syndrome in Obese Adults: A Retrospective Study

Objective We explore risk factors related to severe obstructive sleep apnea (OSA) in obese patients, including pulmonary ventilation function, diffusion function, and impulse oscillometry (IOS) data. Methods The medical records of 207 obese patients who were prepared to undergo bariatric surgery in a hospital from May 2020 to September 2021 were retrospectively reviewed. Polysomnography (PSG), pulmonary ventilation function, diffusion function, and IOS parameters were collected according to the ethical standards of the institutional research committee (registration number: KYLL-202008-144). Logistic regression analysis was used to analyze the related independent risk factors. Results There were significantly statistical difference in a number of pulmonary ventilation and diffusion function parameters among the non-OSAHS group, the mild-to-moderate OSA group, and the severe OSA group. However, only airway resistance parameters R5%, R10%, R15%, R20%, R25%, and R35% increased with increasing OSA severity and were positively correlated with apnea hypopnea index (AHI). Age (P = 0.012, 1.104 (1.022, 1.192)), body mass index (P< 0.0001, 1.12 (1.057, 1.187)), gender (P = 0.003, 4.129 (1.625, 10.49)), and R25% (P = 0.007, 1.018 (1.005, 1.031)) were independent risk factors for severe OSA. In patients aged 35 to 60, RV/TLC (P = 0.029, 1.272 (1.025, 1.577)) is an independent risk factor for severe OSA. Conclusion R25% was an independent risk factor for severe OSA in obese individuals, while RV/TLC was also an independent risk factor in those aged 35 to 60. Pulmonary function tests (PFTs), particularly IOS levels, are recommended to assess severe OSA in obese patients.
Chinese Text Sentiment Analysis Based on BERT-BiGRU Fusion Gated Attention

: To address the problem that Word2vec static encoding cannot give accurate word vectors about contextual semantics and cannot solve the problem of multiple meanings of words, we propose to use the BERT pre-training model as a word embedding layer to obtain word vectors dynamically; we introduce the gating idea to improve on the traditional attention mechanism and propose BERT-BiGRU-GANet model. The model firstly uses the BERT pre-training model as the word vector layer to vectorize the input text by dynamic encoding; secondly, uses the bi-directional gated recursive unit model (BiGRU) to capture the dependencies between long discourse and further analyze the contextual semantics; finally, before output classification