Power Study on Testing Epidemic Alternatives

Detecting change points in epidemic models has been studied by many scholars. Yao (1993) summarized five existing test statistics in the literature. Out of those test statistics, it was observed that the likelihood ratio statistic showed its standout power. However, all of the existing test statistics are based on an assumption that population variance is known, which is an unrealistic assumption in practice. To avoid assuming known population variance, a new test statistic for detecting epidemic models is studied in this thesis. The new test statistic is a parameter-free test statistic which is more powerful compared to the existing test statistics. Different sample sizes and lengths of epidemic durations are used for the power comparison purpose. Monte Carlo simulation is used to find the critical values of the new test statistic and to perform the power comparison. Based on the Monte Carlo simulation result, it can be concluded that the sample size and the length of the duration have some effect on the power of the tests. It can also be observed that the new test statistic studied in this thesis has higher power than the existing test statistics do in all of cases

Hand-tracking Object Interaction System

At this point, VR, AR and MR technologies are thriving in different industries and business. They are increasing substantial productivity for large enterprises like Boeing and Ford at this point. Therefore, it’s well recognised that in future, VR, AR and MR will dive deeper into people\u27s life, especially in to-business area. With VR, AR and MR being applied deeperly, more use cases and scenarios will show up. However, from interaction design and user experience perspective, current interactions for VR, AR and MR is still very primitive and not user-centered enough along with several major problems. Problems include limited accessibility, being harmful to muscles, violations to social norms, lack of measurable precsions and so on. This might keep VR/MR from further influencing the industries and the world. This project aims to offer an emerging gesture interaction system in VR that can help users manipulate virtual objects more confidently, more efficiently and more fluently with natural gestures and hand-tracking ability. More importantly, this project serves as an interaction template for different industries, that different industries can create their own interaction systems in terms of vertical needs based off this project. Final design aims to help define the next level of VR and MR user experience

Deep Recurrent Generative Decoder for Abstractive Text Summarization

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201

Mapping the repository landscape : harnessing similarity with RepoSim and RepoSnipy

The rapid growth of scientific software development has led to the emergence of large and complex codebases, making it challenging to search, find, and compare software repositories within the scientific research community. In this paper, we propose a solution by leveraging deep learning techniques to learn embeddings that capture semantic similarities among repositories. Our approach focuses on identifying repositories with similar semantics, even when their code fragments and documentation exhibit different syntax. To address this challenge, we introduce two complementary open-source tools: RepoSim and RepoSnipy. RepoSim is a command-line toolbox designed to represent repositories at both the source code and documentation levels. It utilizes the UniXcoder pre-trained language model, which has demonstrated remarkable performance in code-related understanding tasks. RepoSnipy is a web-based neural semantic search engine that utilizes the powerful capabilities of RepoSim and offers a user-friendly search interface, allowing researchers and practitioners to query public repositories hosted on GitHub and discover semantically similar repositories. RepoSim and RepoSnipy empower researchers, developers, and practitioners by facilitating the comparison and analysis of software repositories. They not only enable efficient collaboration and code reuse but also accelerate the development of scientific software.Postprin

DiffuRec: A Diffusion Model for Sequential Recommendation

Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items' latent aspects and users' diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this paper, we make the very first attempt to adapt Diffusion model to SR and propose DiffuRec, for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect user's multiple interests and item's various aspects adaptively. In diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterwards, the item representation is fed into an Approximator for target item representation reconstruction. In reversion phase, based on user's historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin

DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction

Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem aimed at automating protein enrichment analysis to eliminate the current reliance on labor-intensive wet-lab methods. Currently, popular methods primarily combine protein-related information and Gene Ontology (GO) terms to generate final functional predictions. For example, protein sequences, structural information, and protein-protein interaction networks are integrated as prior knowledge to fuse with GO term embeddings and generate the ultimate prediction results. However, these methods are limited by the difficulty in obtaining structural information or network topology information, as well as the accuracy of such data. Therefore, more and more methods that only use protein sequences for protein function prediction have been proposed, which is a more reliable and computationally cheaper approach. However, the existing methods fail to fully extract feature information from protein sequences or label data because they do not adequately consider the intrinsic characteristics of the data itself. Therefore, we propose a sequence-based hierarchical prediction method, DeepGATGO, which processes protein sequences and GO term labels hierarchically, and utilizes graph attention networks (GATs) and contrastive learning for protein function prediction. Specifically, we compute embeddings of the sequence and label data using pre-trained models to reduce computational costs and improve the embedding accuracy. Then, we use GATs to dynamically extract the structural information of non-Euclidean data, and learn general features of the label dataset with contrastive learning by constructing positive and negative example samples. Experimental results demonstrate that our proposed model exhibits better scalability in GO term enrichment analysis on large-scale datasets.Comment: Accepted in BIOKDD'2