COV19IR : COVID-19 Domain Literature Information Retrieval

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions

Network Embedding Learning in Knowledge Graph

University of Technology Sydney. Faculty of Engineering and Information Technology.Knowledge Graph stores a large number of human knowledge facts in form of multi-relational network structure, is widely used as a core technique in real-world applications including search engine, question answering system, and recommender system. Knowledge Graph is used to provide extra info box for user query in Google search engine, the WolframAlpha site provides question answering service relying on Knowledge Graph, and the eBay uses Knowledge Graph as semantic enhance for their recommendation service. Motivated by several characteristics of Knowledge Graph including incompleteness, structural inferability, and semantical application enhancement, a few efforts have been put into the Knowledge Graph analysis area. Some works contribute to Knowledge Graph construction and maintenance through crowdsourcing. Some previous network embedding learning models show good performance on homogeneous network analysis, while the performance of directly using them on Knowledge Graph is limited because the multiple relationship information of the Knowledge Graph is ignored. Then, the concept of Knowledge Graph embedding learning is given, by learning representation for Knowledge Graph components including entities and relations, the latent semantic information is extracted into embedding representation. And the embedding techniques are also utilized in collaborative learning for Knowledge Graph and external application scenarios, the target is to use Knowledge Graph as a semantic enhancement to improve the performance of external applications. However, some problems still remain in Knowledge Graph completion, reasoning, and external application. First, a proper model is required for Knowledge Graph self-completion, and a proper integration solution is also required to add extra conceptual taxonomy information into the process of Knowledge Graph completion. Then, a framework to use sub-structure information of Knowledge Graph network into knowledge reasoning is needed. After that, a collaborative learning framework for knowledge graph completion and downstream machine learning tasks is needed to be designed. In this thesis, we take recommender systems as an example of downstream machine learning tasks. To address the aforementioned research problems, a few approaches are proposed in the works introduced in this thesis. • A bipartite graph embedding based Knowledge Graph completion approach for Knowledge Graph self-completion, each knowledge fact is represented in the form of bipartite graph structure for more reasonable triple inference. • An embedding based cross completion approach for completing the factual Knowledge Graph with additive conceptual taxonomy information, the components of factual Knowledge Graph and conceptual taxonomy, entities, relations, types, are jointly represented by embedding representation. • Two sub-structure based Knowledge Graph transitive relation embedding approaches for knowledge reasoning analysis based on Knowledge Graph sub-structure, the transitive structural information contained in Knowledge Graph network substructure is learned into relation embedding. • Two hierarchical collaborative embedding approaches for proper collaborative learning on Knowledge Graph and Recommender System through linking Knowledge Graph entities with Recommender items, then entities, relations, items, and users are represented by embedding in collaborative space. The main contributions of this thesis are proposing a few approaches which can be used in multiple Knowledge Graph related domains, Knowledge Graph completion, reasoning and application. Two approaches achieve more accurate Knowledge Graph completion, other two approaches model knowledge reasoning based on network substructure analysis, and the other approaches apply Knowledge Graph into a recommender system application

Inhomogeneous critical current in nanowire superconducting single-photon detectors

A superconducting thin film with uniform properties is the key to realize nanowire superconducting single-photon detectors (SSPDs) with high performance and high yield. To investigate the uniformity of NbN films, we introduce and characterize simple detectors consisting of short nanowires with length ranging from 100nm to 15{\mu}m. Our nanowires, contrary to meander SSPDs, allow probing the homogeneity of NbN at the nanoscale. Experimental results, endorsed by a microscopic model, show the strongly inhomogeneous nature of NbN films on the sub-100nm scale.Comment: 10 pages, 4 figure

Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives

Entailment trees have been proposed to simulate the human reasoning process of explanation generation in the context of open--domain textual question answering. However, in practice, manually constructing these explanation trees proves a laborious process that requires active human involvement. Given the complexity of capturing the line of reasoning from question to the answer or from claim to premises, the issue arises of how to assist the user in efficiently constructing multi--level entailment trees given a large set of available facts. In this paper, we frame the construction of entailment trees as a sequence of active premise selection steps, i.e., for each intermediate node in an explanation tree, the expert needs to annotate positive and negative examples of premise facts from a large candidate list. We then iteratively fine--tune pre--trained Transformer models with the resulting positive and tightly controlled negative samples and aim to balance the encoding of semantic relationships and explanatory entailment relationships. Experimental evaluation confirms the measurable efficiency gains of the proposed active fine--tuning method in facilitating entailment trees construction: up to 20\% improvement in explanatory premise selection when compared against several alternatives

Ultrasensitive N-photon interferometric autocorrelator

We demonstrate a novel method to measure the Nth-order (N=1, 2, 3, 4) interferometric autocorrelation with high sensitivity and temporal resolution. It is based on the combination of linear absorption and nonlinear detection in a superconducting nanodetector, providing much higher efficiency than methods based on all-optical nonlinearities. Its temporal resolution is only limited by the quasi-particle energy relaxation time, which is directly measured to be in the 20 ps range for the NbN films used in this work. We present a general model of interferometric autocorrelation with these nonlinear detectors and discuss the comparison with other approaches and possible improvements

Fuzzy logic based metric in software testing

How to provide cost-effective strategies for Software Testing has been one of the research focuses in Software Engineering for a long time. Many researchers in Software Engineering have addressed the effectiveness and quality metric of Software Testing, and many interesting results have been obtained. However, one issue of paramount importance in software testing &ndash; the intrinsic imprecise and uncertain relationships within testing metrics &ndash; is left unaddressed. To this end, a new quality and effectiveness measurement based on fuzzy logic is proposed. The software quality features and analogy-based reasoning are discussed, which can deal with quality and effectiveness consistency between different test projects. Experimental results are also provided to verify the proposed measurement.<br /

BEDRF: Bidirectional Edge Diffraction Response Function for Interactive Sound Propagation

We introduce bidirectional edge diffraction response function (BEDRF), a new approach to model wave diffraction around edges with path tracing. The diffraction part of the wave is expressed as an integration on path space, and the wave-edge interaction is expressed using only the localized information around points on the edge similar to a bidirectional scattering distribution function (BSDF) for visual rendering. For an infinite single wedge, our model generates the same result as the analytic solution. Our approach can be easily integrated into interactive geometric sound propagation algorithms that use path tracing to compute specular and diffuse reflections. Our resulting propagation algorithm can approximate complex wave propagation phenomena involving high-order diffraction, and is able to handle dynamic, deformable objects and moving sources and listeners. We highlight the performance of our approach in different scenarios to generate smooth auralization

Recent Advances in Heterogeneous Catalytic Hydrogenation of CO2 to Methane

With the accelerating industrialization, urbanization process, and continuously upgrading of consumption structures, the CO2 from combustion of coal, oil, natural gas, and other hydrocarbon fuels is unbelievably increased over the past decade. As an important carbon resource, CO2 gained more and more attention because of its converting properties to lower hydrocarbon, such as methane, methanol, and formic acid. Among them, CO2 methanation is considered to be an extremely efficient method due to its high CO2 conversion and CH4 selectivity. However, the CO2 methanation process requires high reaction temperatures (300–400°C), which limits the theoretical yield of methane. Thus, it is desirable to find a new strategy for the efficient conversion of CO2 to methane at relatively low reaction temperature, and the key issue is using the catalysts in the process. The advances in the noble metal catalysts, Ni-based catalysts, and Co-based catalysts, for catalytic hydrogenation CO2 to methane are reviewed in this paper, and the effects of the supports and the addition of second metal on CO2 methanation as well as the reaction mechanisms are focused