Miracle-dz/UniFAR-FTUs huggingface dataset:
In the Dawn of an Early Invasion: No Genetic Diversity of Angiostrongylus cantonensis in Ecuador?

The nematode Angiostrongylus cantonensis has been reported worldwide. However, some basic questions remain unanswered about A. cantonensis in Ecuador: (1) Was the invasion of A. cantonensis in Ecuador unique, or did it occur in different waves? (2) Was this invasion as recent as historical records suggest? (3) Did this invasion come from other regions of South America or elsewhere? To address these issues, we assessed the genetic diversity of MT-CO1 gene sequences from isolates obtained in 11 of Ecuador’s 24 provinces. Our Bayesian inference phylogenetic tree recovered A. cantonensis as a well-supported monophyletic group. All 11 sequences from Ecuador were identical and identified as AC17a. The haplotype AC17a, found in Ecuador and the USA, formed a cluster with AC17b (USA), AC13 (Thailand), and AC12a-b (Cambodia). Notably, all the samples obtained in Ecuadorian provinces’ different geographic and climatic regions had no genetic difference. Despite the lack of genetic information on A. cantonensis in Latin America, except in Brazil, our finding differs from previous studies by its absence of gene diversity in Ecuador. We concluded that the invasion of A. cantonensis in Ecuador may have occurred: (1) as a one-time event, (2) recently, and (3) from Asia via the USA. Further research should include samples from countries neighboring Ecuador to delve deeper into this.
The Effect of Standardized Patient Education on 30-Day Hospital Readmissions for Heart Failure Patients in the Outpatient Setting

Problem: Heart failure (HF) is the second leading condition of hospital readmissions. Evidence shows that patient education on self-care and disease management can help reduce and prevent 30-day hospital readmissions. Registered nurse case managers (RN CMs) can help improve patients' ability to self-manage their condition and prevent 30-day hospital readmissions by applying a standard approach to patient education. Context: The Integrated Care Management (ICM) is an outpatient department that provides post-discharge patient calls. The ICM RN CMs utilized various HF patient education tools for patient teaching. The organization’s HF task force developed health-literate patient resources to be used uniformly across the system. Intervention: RN CMs will be trained on standardized HF resources. All HF patients eligible for ICM services recently discharged from the hospital will receive the newly standardized HF education from the RN CM. Measure: The targeted outcome is reducing the 30-day hospital readmission rate for HF patients by 2.3%. The process measure is to achieve 100% utilization of a documentation template applied to all HF patients receiving HF education. Results: There were no HF readmissions within the project time frame, thus achieving the target reduction of 2.3% for the 30-day HF hospital readmission rate from 11.9% to 9.6% %. Conclusions: Standardized HF patient education tools and documentation templates can streamline the management of HF patients after discharge from the hospital and reduce 30-day readmissions in this patient population.
Employer Image and Job Pursuit Intention in the New S-Curve Industries in Thailand: The Mediating Role of Organizational Attractiveness

This study examines the mechanism through which organizational attractiveness links the association between employer image and job pursuit intention in Thailand’s new S-curve industries. It makes a significant contribution to research that examines how the pool of possible and young applicants in the developing economies of newly emerging industries can be enhanced. Structural equation modeling is used for data analysis. The findings, based on 520 third- and fourth-year undergraduate students from 20 public universities across Thailand, show that employer image is positively associated with job pursuit intention. Additionally, the association between employer image and job pursuit intention is partially mediated by organizational attractiveness. This is demonstrated using signaling theory and the theory of reasoned action as the overarching framework. Our findings have practical business implications. Specifically, recruiters should give job seekers information about their organizations, especially in the areas of training, job opportunities, and career development. This action can have two effects: it increases the attraction of job seekers to particular organizations; and it can encourage them to apply for jobs at the same organizations.
Won’t You Be My Neighbor? Probing Informational Spread in Contextual Representations of Natural Language

Masked language models produce contextualized representations, where token embeddings encode information about its role and function in the overall sequence. However, the extent to which these representations reflect neighboring word identity at different offsets remains to be explored. In this project, we apply probing to extract mutual redundancy in contextual BERT word encodings. We first construct custom datasets of paired token embeddings over various layer-offset combinations from 10.4K documents from the HuggingFace Wikipedia dataset. We then fit several variations of linear models (using different initialization schema and dimensionalities) to predict context word identities from a center contextualized word encoding. In Experiment 1, we combine a 768 x 768 linear classifier (initialized to the identity matrix + Gaussian noise) with a the frozen weights of a 768 x 30522 classification matrix extracted from the BERT model. In Experiment 2, we fit a 768 x 30522 matrix created with Xavier initialization. In Experiment 3, we initialize our linear classifier using the matrix extracted from BERT, but do not freeze the weights. Across all experiments, we find that contextualized word encodings are substantially redundant, with simple linear models achieving relatively high prediction accuracy of neighboring word identities. The prediction accuracy is distributed asymmetrically, as neighboring tokens that come before the center word are predicted much more accurately. Moreover, the ability to predict more distant neighbors increases throughout the layers of BERT, with deeper layers enabling the predictions of further away words with higher accuracy. These preliminary findings are one of the first steps towards exploring interpretability of BERT encodings. Moreover, these results suggest that future work on redundancy in contextualized BERT embeddings could facilitate reduction of model complexity and word
A Replication Study to Assess the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

We investigate whether recurrent neural networks (RNNs, LSTMs) suffice to learn and represent non-local, context-dependent syntactic structures (paper reproduction of Linzen et al. (2016)). In addition, (1) we take inspiration from Gulordava et al. (2018) and evaluate whether LSTMs are able to provide accurate predictions in the absence of collocational information; (2) we probe models trained under direct supervision for other syntactic structures such as POS tags. We choose to reproduce tasks based on the same natural language structure, subject-verb agreement.
Online challenges that emerge as a public health issue for adolescents: Assessment of psychiatric comorbidity and the importance of parenting

Background: Online Challenge is neither an application nor an internet-based game; instead, users receive a link to it via chat groups on social media. Our aim is to identify the potential differences between the normal population and youth drawn to online challenges. These potential differences are the examining the parenting skills of parents of children who participate in the challenges and determining underlying psychopathologies through structured clinical interviews. Method: A questionnaire-based cross-sectional study was used to obtain further in-depth information on the comorbid psychopathology of children who play the BWC and on the parenting skills. The Development and Well Being Assessment and Alabama Parenting Questionnaire was applied to the children and their families in both the control and case groups. Results: Two groups of children and parents were recruited: a clinical sample (case) group (n = 34) and a community sample (control) group (n = 141). Attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), specific phobia and CD (conduct disorder) were significantly more frequent in the clinical sample than in the community sample. The median scores for the Alabama Parenting Questionnaire (APQ) parental involvement, positive parenting, and poor monitoring/supervision subscales were significantly lower in the clinical sample than in the community sample. Conclusion: As far as we know, this study is the first to examine comorbid psychopathologies of online challenge-style games and parenting skills. We believe that as research into these subject increases, it will assist mental health professionals to develop prevention strategies and to manage cases resulting from the Blue Whale Challenge (BWC) and other online challenges that pose a serious threat to mental health and that have driven many young people to suicide worldwide. Turan B / Arch Clin Psychiatry. 2021;48(6): 245-249
Final Project: Attention Analysis and Visualization on Multimodal Models

Visualization is a powerful tool for humans to understand and interpret large amounts of data. Attention mechanisms in natural language models appear like a prime target for visualization, but these can be spurious or uninterpretable in many models, such as recurrent neural networks. This project explores the ability for qualitative and quantitative analysis of attention in multimodal models, speciﬁcally Cai et al.’s sarcasm classiﬁer, to provide insight into decisions and highlight weak spots in the model’s performance.
Mitigating Abuse in Online Private Communication

The ability to communicate privately over the internet is now enjoyed by billions of people, due primarily to the adoption of end-to-end encryption by popular platforms like WhatsApp and iMessage. End-to-end encryption provides strong confidentiality and integrity guarantees, meaning the platform cannot read nor interfere with conversations between its users. Unfortunately these same properties hamper the ability of the platform to perform critical safety services on behalf of its users. We have seen in WhatsApp the negative effects of safety systems being unable to see message content directly: hate speech inciting lynchings, political disinformation campaigns, and child pornography rings are a few examples of abuse evading moderation found on WhatsApp. Such failings have led to calls for the end of private online communication.
A preliminary study of interpretable emergent language in visual referential games

Deep neural networks have achieved high accuracy in many computer vision and natural language processing tasks through their complicated structures and operations. But their formulation is numerical and the computation process cannot be interpreted by humans directly. Therefore they are often criticized for their inability to align with human language. In this project, we aim at discovering emergent language and how it evolves in multi-agent communication games. We design a visual referential game combining the ideas of the referential game (Lazaridou et al. [11]) and the sketch drawing game (Qiu et al. [16]). We consider both continuous and discrete communication types and propose a model which can be efficiently optimized by back-propagation. Experiments conducted on MNIST and CIFAR-10 have shown the efficiency and interpretability of this communication, with t-SNE of visual embedding as visualization. Our code is available at https://github.com/ran1812/pkucore-nonverbal_ communication . Our video is available at https://disk.pku. edu.cn: 443/link/
ParaGAN: A Cloud Training Framework for Generative Adversarial Networks

—Generative Adversarial Network (GAN) has shown tremendous success in synthesizing realistic photos and videos in recent years. However, training GAN to convergence is still a challenging task that requires significant computing power and is subject to training instability. To address these challenges, we propose ParaGAN, a cloud training framework for GAN optimized from both system and numerical perspectives. To achieve this, ParaGAN implements a congestion-aware pipeline for latency hiding, hardware-aware layout transformation for improved accelerator utilization, and an asynchronous update scheme to optimize system performance. Additionally, from a numerical perspective, we introduce an asymmetric optimization policy to stabilize training. Our preliminary experiments show that ParaGAN reduces the training time of BigGAN from 15 days to just 14 hours on 1024 TPUs, achieving 91% scaling efficiency. Moreover, we demonstrate that ParaGAN enables the generation of unprecedented high-resolution ( 1024 × 1024 ) images on BigGAN.
Meat Consumption, Cooking Practices, And Blood Harmane Concentrations In Essential Tremor: A Population-Based, Environmental Study In The Faroe Islands

4 INTRODUCTION . 6 METHODS . 23 RESULTS . 34 DEMOGRAPHIC CHARACTERISTICS OF STUDY POPULATION . 34 QUANTITY OF MEAT CONSUMED IN ET CASES AND CONTROLS . 40 MEAT COOKING PRACTICES IN ET CASES AND CONTROLS . 44 HARMANE LEVELS IN ET CASES AND CONTROLS . 47 DISCUSSION . 49 REFERENCES . 55
Deep Compositional Denoising on Frame Sequences

SMAPE 0.00848 0.00828 0.00808 DSSIM 0.00536 0.00561 0.00498 Figure 1: Visual results from the single-frame compositional denoiser [ZMV∗21], the multi-frame temporal kernel-predicting denoiser (KPAL [VRM∗18]) and our multi-frame compositional denoiser, showcasing the improved detail reconstruction from the temporal compositional denoiser over both counterparts. The first image was created by the Walt Disney Animation Studios (© Disney), and the second by Pixar Animation Studios (© Disney / Pixar).
Enhanced generalizable minBERT model for multiple downstream tasks

This project aims to 1) build a BERT model that could work for Sentiment Analysis and extend the model to multiple downstream tasks 2) explore and analyze performance impact from different improvement approaches. For part 1, we’ve built a BERT model that could work for three downstream tasks and got 0.348 average score as baseline. For part 2, we explored a few different approaches and finally got 0.663 average score for test Set
Intermittency at Earth's bow shock: Measures of turbulence in quasi-parallel and quasi-perpendicular shocks

Turbulent plasmas such as the solar wind and magnetosheath exhibit an energy cascade that is present across a broad range of scales, from the stirring scale at which energy is injected, down to the smallest scales where energy is dissipated through processes such as reconnection and wave–particle interactions. Recent observations of Earth's bow shock reveal a disordered or turbulent transition region exhibiting features of turbulent dissipation, like reconnecting current sheets. We used observations from magnetospheric multiscale (MMS) over four separate bow shock crossings of varying shock normal angle to characterize turbulence in the shock transition region and how it evolves toward the magnetosheath. These cases studies have been chosen to ensure validity of Taylor's hypothesis, which we discuss in depth. We observe the magnetic spectrum evolving by fitting power laws over many short intervals, finding that the power-law index in the shock transition region is separable from the upstream and downstream plasma, for both quasi-perpendicular and quasi-parallel shocks. Across the shock, we see a change in the breakpoint location between inertial and ion power-law slopes. We also observe the evolution of scale-independent kurtosis of magnetic fluctuations across the shock, finding a reduction of high kurtosis intervals downstream of the shock. Finally, we adapt a method for calculating correlation length to include a high-pass filter, allowing estimates for changes in correlation length across the shock. In a quasi-perpendicular shock, we find the correlation length to be significantly smaller in the magnetosheath than in solar wind; however, the opposite can occur for quasi-parallel shocks.
Why Demographics Matter in the Political Representation of Racial Minority Groups: A Standpoint Epistemology and Visibility Account

Do demographics matter in the political representation of racial minority groups, and if so, how should we understand and enhance our understanding of effective representation? This thesis aims to explore concepts surrounding how to best advocate for historically marginalized communities within the public policymaking process through the lens of standpoint epistemology and visibility. I begin by sharing my personal experiences as a young Asian American woman interested in politics, as well as a case study of two congressional representatives that serve similar constituencies, Representative Katie Porter and Young Kim. I will then introduce a theoretical approach termed standpoint epistemology, as first conceptualized by feminist scholars. Drawing on Karl Marx and W.E. B Du Bois’ work in class and race consciousness, I will highlight how standpoint theory emphasizes one’s social role in gaining knowledge, and how that connection can supplement the representation of constituencies with various standpoints and experiences. Next, I discuss the importance of diverse visibility in spaces such as public office, and the positive effects it has on fostering trust and inspiring a future generation of leaders. This understanding will be complemented by an analysis of recent Supreme Court cases that have affirmed the value of diversity. Lastly, I will reflect on the lessons learned from both accounts to an analysis on how demographics matter in representation, as seen through Representative Porter and Kim’s legislative records.
Hyperspectral image classification based on dimensionality reduction Gabor feature and decision Fusion

: Aiming at the problem of ignoring spatial features in traditional hyperspectral image classification algorithm, this paper proposed a hyperspectral image classification algorithm based on dimensionality reduction Gabor feature and decision fusion. Firstly, the adjacent and hypercorrelated spectral bands are intelligently grouped by coefficient correlation matrix. Then, Gabor features in each group are extracted from the PCA projection subspace to quantify the local direction and scale features. Then, the dimensionality of these feature subspaces is reduced by preserving the locality of the decomposition of nonnegative matrices. Finally, the reduced dimension features are classified by Gaussian mixture model, and the classification results are merged by decision fusion rules. Experimental results show that the proposed algorithm is superior to eight kinds of traditional and existing advanced classification algorithms.
Almost étale extensions

La theorie des revetements presque etales permet la comparaison entre les cohomologies cristalline et etale p-adique des schemas au-dessus d'un anneau de valuation discrete p-adique. Nous donnons une demonstration nouvelle du principal ingredient technique (theoreme de purete) et etendons ce resultat a toutes les singularites toroidales. Nous en deduisons une demonstration du theoreme de comparaison de Tsuji pour des schemas possedant ce genre de singularites incluant le cas de systemes locaux appropries. En chemin, nous sommes amene a etablir des resultats de finitude pour la cohomologie cristalline a coefficients dans de tels systemes locaux.
Coupled land-air parameterization scheme (LAPS) and non- hydrostatic mesoscale model (NMM) for use in agricultural planning

Characterization of climatic hazards for agriculture can be done using global circulation models (GCMs) and/or regional circulation models (RegGCMs). The GCMs provide credible information of climate, at least for subcontinental scales, while the RegCMs are used to determine specific characteristics of the weather in mesoscale. Regardless of whether these models provide meteorological data through either long-term or short-term runs, the land surface models are strong links between the underlying surface and the atmosphere. Recently they have been remarkably improved in the segment of the parameterization of turbulent fluxes inside and above the tall grass canopies, making them more relevant, in assessing how regional climate may affect agriculture. Except these schemes many other environmental/agricultural models (UV radiation, plant diseases, crop, irrigation models, etc.), linked with the new generation of non-hydrostatic mesoscale models, can provide highly sophisticated information for farmers and agricultural planners. In this paper we shortly describe environmental models, mostly designed in the Centre for Meteorology and Environmental Predictions, University of Novi Sad (Serbia). All of them are linked with the NMM non-hydrostatic mesoscale model for the purpose of an intensive use in agricultural planning. The description and comments are supported by the corresponding numerical simulations.
GeValDi: Generative Validation of Discriminative Models

The evaluation of machine learning (ML) models is a core tenet of trust-worthy use. Evaluation is typically done via a held-out dataset. However, such validation datasets often need to be large and are hard to procure; further, multiple models may perform equally well on such sets. To address these challenges, we offer GeValdi: an efficient method to validate discriminative classifiers by creating samples where such classifiers maximally differ. We highlight how such “maximally different samples” can be constructed via and leveraged to probe the failure mode of classifiers and offer a hierarchically-aware metric to further support fine-grained, comparative model evaluation.
Object Plane Deformation Due to Refraction in Two-Dimensional Underwater Motion Analysis

The purpose of this study was twofold: (a) to investigate the effects of selected experimental factors on the magnitude of the object plane deformation due to refraction, and (b) to discuss their practical implications in an effort to improve the applicability of the 2-D DLT method in the underwater motion analysis. The RMS and maximum object plane reconstruction errors of various experimental conditions were computed systematically. To isolate the error due to refraction from the experimental errors, the comparator coordinates (image plane coordinates) of the control points were computed based on a theoretical refraction model rather than actual digitizing. It was concluded from a series of object plane reconstruction that among the distance and angle factors of the experimental setting in the 2-D underwater motion analysis, the camera-to-interface distance and the interface-to-control-object distance are the two major factors affecting the magnitude of the object plane deformation. The other factors rev.
A N I MPROVED A UTOENCODER S TRUCTURE FOR I MAGE D IMENSION R EDUCTION AND C LUSTERING

Video frame clustering methods have introduced a new approach to video content analysis, besides traditional, sequential processing. Previous research has shown that using fully Convolutional Variational Autoencoders (CVAE) to perform dimension reduction on images ensures the efficiency of clustering. In this work, we propose another CVAE structure with Dense Neural Network layers and show its improvement from both theoretical and practical aspects. Further, multiple clustering methods and analyses are performed on the video frames based on the new dimension reduction process to provide real-world insights.