Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological Arousal, Emotion, and Word Retrieval in Aphasia: Effects and Relationships

Physiological Arousal, Emotion, and Word Retrieval in Aphasia: Effects and Relationships Angela Lynne Johnson Department of Communication Disorders, BYU Master of Science People with aphasia are known to have poor word retrieval abilities in communicative tasks. It has also been reported that they have lower, non-optimal levels of physiological arousal, which may cause lower attention levels therefore contributing to poor performance on linguistic tasks. The purpose of this study was to investigate the relationship between physiological arousal and word retrieval in adults with aphasia and neurotypical adults when presented with emotional stimuli within a confrontational naming task. Participants included 6 people with aphasia and 15 neurotypical controls. All participants completed a confrontational naming task within 3 emotional conditions (neutral, positive, negative) and physiological measures (Heart Rate Variability, Skin Conductance) were taken simultaneously. No statistically significant results were found; however, numerical trends were identified in the data that may provide direction when designing future studies.
A Branch-and-Price Algorithm for the Vehicle Routing Problem with Drones

This paper considers a new variant of the vehicle routing problem with drones (VRPD), where multiple vehicles and drones work collaboratively to serve customers. Several practical constraints such as customers’ delivery deadlines and drones’ energy capacity are considered. Different from existing studies, we treat the number of drones taken by each vehicle as a decision variable instead of a given parameter, which provides more ﬂex-ibility for planning vehicle and drone routes. We also allow a drone to perform multiple back-and-forth trips when its paired vehicle stops at a customer node. We ﬁrst formulate this problem as a mixed-integer linear programming model, which is solvable by off-the-shelf commercial solvers. To tackle VRPD instances more efﬁciently, we next develop a set-partitioning model. To solve it, a branch-and-price algorithm is proposed, where a bidirectional labeling algorithm is used to solve the pricing problem. To speed up the algorithm, the cheapest insertion heuristic is developed for initial column generation, and a tabu search algorithm is ﬁrst applied before the exact labeling algorithm for ﬁnding desired columns in each iteration of the column generation process. Extensive numerical tests show that our algorithm can solve most instances within 25 customers to optimality in a short time frame and some instances of 35 customers to optimality within a three-hour time limit. Results also demonstrate that the allocation decisions of drones can help save the duration of all routes by 3.45% on average for 25-customer instances, compared to the case of ﬁxing the number of paired drones on each vehicle. In addition, sensitivity analyses show that multiple strategies, e.g., adopting batteries of a higher energy density and developing faster drones, can be applied to further improve the delivery efﬁciency of a truck-drone system.
Debugging the Internals of Convolutional Networks

The ﬁlters learned by Convolutional Neural Networks (CNNs) and the feature maps these ﬁlters compute are sensitive to convolution arithmetic. Several architectural choices that dictate this arithmetic can result in feature-map artifacts. These artifacts can interfere with the downstream task and impact the accuracy and robustness. We provide a number of visual-debugging means to surface feature-map artifacts and to analyze how they emerge in CNNs. Our means help analyze the impact of these artifacts on the weights learned by CNNs. Guided by our analysis, model developers can make informed architectural choices that can veriﬁably mitigate harmful artifacts and improve the model’s accuracy and its shift robustness.
Water in Circular Economy and Resilience (WICER) The Case of Phnom Penh, Cambodia

This case study is part of a series prepared by the World Bank’s Water Global Practice to highlight existing experiences in the water sector. The purpose of the series is to showcase one or more of the elements that can contribute toward a Water in Circular Economy and Resilience (WICER) system. This case focuses on the experience of Phnom Penh, Cambodia. Two of the main pillars of a circular economy are resource efficiency and zero (or minimum) waste. By significantly reducing water losses and improving operational efficiency, the Phnom Penh Water Supply Authority (PPWSA) is embracing circular economy principles toward a more sustainable future.
Understanding the learning dynamics of word2vec

Word embeddings are a common representation of linguistic data used in a variety of natural language processing (NLP) applications. However, it is not exactly clear what kind of information is being learnt during the training, and why it results in embeddings that can serve as effective inputs for a variety of downstream NLP tasks. This project studied how a variety of evaluation metrics change dynamically over the course of training in word2vec, including both intrinsic evaluators (word similarity and word analogy) and extrinsic evaluators (part-of-speech tagging and named entity recognition), as well as a set of linguistics diagnostics. We found that the metrics exhibited a number of clusters, including one cluster containing word similarity, part-of-speech tagging, and named entity recognition, which are tasks related to word categorisation. Another cluster contained word analogy, as well as proportion of neighbours which are synonyms or associations, which are tasks that rely more finely on the particular semantics of words. Together, these suggest that word2vec learns how to categorise words with similar properties, before fine-tuning the specific embeddings of words.
A study on the effectiveness of task manager board game as a training tool in managing project

Nowadays, games have become one of the useful tools in training. Many instructors choose to use games to enhance the way of delivering the subject. Failure to apply the suitable tool in training will lead to discouragement in learning and causing waste to the resources. An effective game will help the student understand the concept quickly. It can also help students to get involve in experiential learning where the student can manage and solve the problem as in the actual situation. This study will focus on the effectiveness of board game as a training tool for managing projects. This game has 4 tasks to be completed by students. They will be divided into a group of 4 or 5. Two methods are used in this study, pilot test, and post-test. These methods are chosen to analyze the effectiveness of using Task Manager Board Game as a teaching tool and the improvement of student’s knowledge in project management. Three sub-components assessed were motivation, user experience and learning using case studies on Kirk.
SQuAD2.0 with Conditional End Prediction and Character Embeddings

In this project, we developed a series of models for the question-answering task on the Stanford Question Answering 2.0 (SQuAD2.0) dataset. We worked with all aspects of a successful deep learning model in mind, from a proper encoding skeleton, to experimenting with different predictive layers, to augmenting data. Specifically, we focused on improving the baseline BiDAF model through character embeddings and a conditional pointer layer. Our results were mixed: while the dependent relationship of a start-and end-tokens showed to be of little importance; character embeddings revealed the importance of rich sub-word relationships; and a foray into data augmentation revealed some potential pitfalls in that area
Determining Predictors of Peer Relations: A Study on Youth inEthiopia, India, Peru, and Vietnam

Determining Predictors of Peer Relations: A Study on Youth in Ethiopia, India, Peru, and Vietnam Susanna Fullmer Department of Instructional Psychology and Technology, BYU Master of Science Across the world countries are currently striving to eliminate poverty, improve the quality of education, optimize well-being, among other areas of improvement. In an effort to help such improvements, a group called Young Lives ran a longitudinal study on youth in Ethiopia, India, Peru, and Vietnam that studied the many facets of poverty. The purpose of this study is to utilize the Young Lives dataset to determine how countries can more readily improve social-emotional skills by looking at important experiences in adolescents’ lives. Specifically, this study examines what factors increase a child’s ability to socialize with peers, which is shown to be linked to higher academic success as well as a fuller development into adulthood. In order to measure the ability to socialize with peers, Young Lives used the relationships with Peers Scale (RPS). I examined, through implementing structural equation modeling techniques, what factors significantly predict RPS scores, as well as which time point the factors are most predictive. I also inspected the psychometric properties of the RPS on the Young Lives’ population and observed measurement invariance across time and country in order to ensure this scale is a valid measure. Steps to improve relationships with peers can be taken by encouraging higher intrinsic locus of control, providing equal educational opportunities, improving safety conditions, providing nutritional education, and eliminating competition for resources.
An Optical Graphene-silicon Resonator Phase Shifter Suitable for Universal Linear Circuits Current Optics and Photonics

This paper describes the construction of a phase shifter with low loss and small volume. To construct it, we use the two graphene layers that are separated by a hexagonal boron nitride (hBN) and embedded in a silicon waveguide. The refractive index of the waveguide is adjusted by applying a bias voltage to the graphene sheet to create an optical phase shift. This waveguide is a compact device that only has a radius of 5 μm. It has a phase shift of 6π. In addition, the extinction ratio (ER) is 11.6 dB and the inser tion loss (IL) is 0.031 dB. Due to its unique characteristics, this device has great potential in silicon on-chip optical interconnection and all-optical multiple-input multiple-output processing.
Improving SQuAD Performance through Model Combination

Machine comprehension on text is an important task in natural language processing. The publishing of the Stanford Question Answering Dataset (SQuAD) has provided a large number of questions and answers through crowdsourcing that allow for a common testing dataset for machine comprehension models. I leverage strengths of existing models with various implementations by combining their outputs in order to produce predictions that incorporate knowledge from all models. I find that naive combinations of output layer decrease performance, but when combining the two models prior to the final output component, both F1 and EM increase past either model on its own. Because of the limitations of any particular model construction, and in particular choosing a single attention layer, model combination appears to be a promising avenue for improving performance.
Changes in the spatial patchiness of Pacific mackerel, Scomber japonicus, larvae with increasing age and size

Southwest Fisheries Science Center National Marine Fisheries SeNice. NOM P.O. Box 271. La Jolla. California 92038 terpreted in terms of species-specific differences in life history traits such as adult reproductive behavior and larval feeding ecology, size, growth, and mortality (Smith,1973; Hewitt, 1981; Koslow et aI., 1985; McGurk, 1987). Insight into the function of patchiness may be improved by comparing how patchiness changes with age and size for species with different life histories. In this note, we present patchiness-at-age and patchiness-at-size curves for Pacific mackerel, Scomber japonicuB, larvae and compare them with similar curves for other pelagic fish larvae.
FGF21 deficit is central in the metabolic pathophysiology of an ovine model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), affecting over 10% of women, is associated with insulin 21 resistance, obesity, dyslipidaemia, fatty liver and adipose tissue dysfunction. Its 22 pathogenesis is poorly understood and consequently treatment remains suboptimal. 23 Prenatally androgenized (PA) sheep, a clinically realistic model of PCOS, recapitulate the 24 metabolic problems associated with PCOS. Fibroblast Growth Factor 21 (FGF21) is a 25 metabolic hormone regulating lipid homeostasis, insulin sensitivity, energy balance and 26 adipose tissue function. We therefore investigated the role of FGF21 in the metabolic 27 phenotype of PA sheep. In adolescence PA sheep had decreased hepatic expression and 28 circulating concentrations of FGF21. Adolescent PA sheep show decreased FGF21 29 signalling in subcutaneous adipose tissue, increased hepatic triglyceride content, trend 30 towards reduced fatty acid oxidation capacity and increased hepatic expression of 31 inflammatory markers. These data parallel studies on FGF21 deficiency, suggesting that 32 FGF21 therapy during adolescence may represent a treatment strategy to mitigate 33 metabolic problems associated with PCOS.
Integrating the QANet Structure into the Default Model BiDAF for Question Answering

This project addresses the problem that the default BiDAF machine comprehension and question answering model is RNN based and thus slow as it is not parallelizable. This project aims to integrate the QANet model, which uses convolutions and self-attentions to form a model architecture that is faster than recurrent-based approaches, into the default BiDAF model to enhance the baseline scores by tuning existing baseline model hyperparameters and changing the model architectures by adding new layers. Findings were variable, with some successes in improving the score and some failures in testing. The best model, BiDAF (with Character Embedding), achieved 63.646(F1) / 60.254(EM), while two other variants of the QANet integrated model were 47.811(F1) / 47.811(EM) and 54.708(F1) / 51.784(EM) evaluated on the course test set.
Blockchain-Based Security Mechanisms for IoMT Edge Networks in IoMT-Based Healthcare Monitoring Systems

Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens’ quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT edge networks in an effective and efficient manner before they gain the trust of all involved stakeholders and reach their full potential in the market of next generation IoMT-based healthcare monitoring systems. In this context, blockchain technology has been foreseen by the industry and research community as a disruptive technology that can be integrated into novel security solutions for IoMT edge networks, as it can play a significant role in securing IoMT devices and resisting unauthorized access during data transmission (i.e., tamper-proof transmission of medical data). However, despite the fact that several blockchain-based security mechanisms have already been proposed in the literature for different types of IoT edge networks, there is a lack of blockchain-based security mechanisms for IoMT edge networks, and thus more effort is required to be put on the design and development of security mechanisms relying on blockchain technology for such networks. Towards this direction, the first step is the comprehensive understanding of the following two types of blockchain-based security mechanisms: (a) the very few existing ones specifically designed for IoMT edge networks, and (b) those designed for other types of IoT networks but could be possibly adopted in IoMT edge networks due to similar capabilities and technical characteristics. Therefore, in this paper, we review the state-of-the-art of the above two types of blockchain-based security mechanisms in order to provide a foundation for organizing research efforts towards the design and development of reliable blockchain-based countermeasures, addressing the pressing security challenges of IoMT edge networks in an effective and efficient manner.
Designing an Automatic Story Evaluation Metric

The rise of natural language models has led to their application to a plethora of complex tasks, including dialog systems and story generation. Such novel tasks necessitate automatic evaluation metrics for the plausibility of such machine-generated stories and thus aid in training these models. However, reference-based evaluation approaches such as BLEU and ROUGE do not perform well here because of the open-ended nature of story generation. Thus this paper proposes a novel framework for such an automatic story metric using emotional states and a commonsense model to measure the coherence and meaning-making ability of a story. Our model had the most success using a BERT-base-uncased with a linear layer on the Story Cloze task.
Style Transfer for VR

A popular task in computer vision is to try to synthesize the texture style of one image into another image, while preserving the content of the later. Different non-parametric algorithms like pixels resampling [1] [2] or image analogy [3] have been commonly used for texture synthesis. However, the latest advances in Convolutional Neural Networks (CNN) have revolutionized this topic, because instead of using the low-level features of the style image, they are able to extract high-level semantic information [4]. In this project, we want to extend the current applications of CNN style transfer to 360 videos and stereo videos, to see if the new synthesized videos are able to keep enough depth information. The goal is the be able to see in 3D in a head mounted device (HMD) the content of a video with a completely different style, which will be obtained from an style image of our choice.
Symbiotic, resilient, and rapidly transforming food supply chains in LMICs: supermarket and e-commerce revolutions helped by wholesale & logistics co-pivoting

FSCs (food supply chains) in developing countries (referred to here as LMICs, lowand middleincome countries) are transforming rapidly in the past several decades (Reardon et al. 2019; Barrett et al. forthcoming). FSCs are transforming along three stages: from "traditional" to "transitional" to "modern" (Reardon et al. 2012b). The main changes are spatial lengthening, consolidation, technological intensification, and movement along the product cycle from niche to commodities to differentiated products (and thus introduction of innovations).
Using QANet to Perform Question Answering on the SQuAD 2.0 Dataset

Machine comprehension and question answering (QA) systems are quickly becoming a staple of the modern world, with widespread commercial use from virtual assistants to search engines. The recently-proposed QANet model performs well on the earlier SQuAD 1.0 dataset, in large part due to its more parallelizable architecture which enables it to have significantly faster training and inference. In this project, we present our implementation of a custom QANet model and analyze our QANet model’s performance on the newer SQuAD 2.0 dataset, which includes unanswerable questions. We found that the QANet model underperformed and hypothesize that this is in large part due to class imbalance within the SQuAD 2.0 dataset as well as memory and compute limitations with our model architecture and training. We recommend several avenues for future work to explore to overcome these limitations and achieve better performance on the SQuAD 2.0 dataset.
F ORCE N ET : A G RAPH N EURAL N ETWORK FOR L ARGE -S CALE Q UANTUM C HEMISTRY C ALCULA - TIONS

With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based calculations. The key quantity to estimate is atomic forces, where the state-of-the-art Graph Neural Networks (GNNs) explicitly enforce basic physical constraints such as rotation-covariance. However, to strictly satisfy the physical constraints, existing models have to make tradeoffs between computational efficiency and model expressiveness. Here we explore an alternative approach. By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency. Physical constraints are implicitly imposed by training the models using physics-based data augmentation. To evaluate the approach, we carefully design a scalable and expressive GNN model, ForceNet, and apply it to OC20 (Chanussot et al., 2020), an unprecedentedly-large dataset of quantum physics calculations. Our proposed ForceNet is able to predict atomic forces more accurately than state-of-the-art physics-based GNNs while being faster both in training and inference. Overall, our promising results open up an exciting avenue for future research.1
Cross-domain Sentiment Classiﬁcation based on Adaptive Center Contrastive Learning

Sentiment analysis refers to automatically recognizing affects from natural language. Since people use different words in each context, it leads to the domain shift problem, which can cause a signiﬁcant decrease in training and testing different domains. In this work, we proposed an adaptive center contrastive learning (ACCL) method to tackle the problem. First, to learn semantically meaningful features and minimize inter-class variance, we introduced representation centers learned by center contrastive learning (CCL). Second, to reduce the representation discrepancy between the source and target domain, we proposed an adaptive contrastive learning (ACL) strategy, which used entropy-based pseudo-labels generation for high conﬁdence target domain samples and trained them with the CCL, which can learn a shared representation between source and target domain. We conducted experiments on a widely-used cross-domain sentiment analysis dataset - the Amazon review dataset. The ablation study demonstrated the effectiveness of the proposed method. Compared with other methods, our methods also showed satisfactory performance on many source and target domain pairs.
Incorporating Self-Attention and Character Embeddings in a Question Answering System

In this project, I extend and improve upon a baseline contextual question-answering BiDAF SQuAD model by using character-level embeddings, GRU encoding layers, and context-to-context self-attention. The existing embedding layer is augmented with the addition of character-level embeddings that are fed to a convolutional layer, which allows the model to deal with out-of-vocabulary tokens and infer more meaning from contexts and questions containing them. With the addition of a self-matching attention layer that receives question-aware context representations as input, the model is able to more effectively gather evidence from the entire context to help determine the answer. In addition, the baseline encoder layers are modified to use GRUs in place of LSTMs. The model is able to improve upon the baseline substantially, achieving EM and F1 scores of X and Y respectively
QA System with Transformer-XL

The automated question-answering system has always been an interesting and challenging task. In this project, we implemented a baseline BiDAF model with character-level embedding, a QANet model, and a QANet with transformer-XL model to explore their performance on the SQuAD 2.0 question-answering dataset. From the result of our experiments, the QANet reaches EM of 52.86 and F1 of 55.74, and the QANet with the transformer-XL reaches EM of 50.92 and F1 of 51.01 on the dev set.