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This paper presents CleanGraph, an interactive web-based tool designed to facilitate the refinement and completion of knowledge graphs. Maintaining the reliability of knowledge graphs, which are grounded in high-quality and error-free facts, is crucial for real-world applications such as question-answering and information retrieval systems. These graphs are often automatically assembled from textual sources by extracting semantic triples via information extraction. However, assuring the quality of these extracted triples, especially when dealing with large or low-quality datasets, can pose a significant challenge and adversely affect the performance of downstream applications. CleanGraph allows users to perform Create, Read, Update, and Delete (CRUD) operations on their graphs, as well as apply models in the form of plugins for graph refinement and completion tasks. These functionalities enable users to enhance the integrity and reliability of their graph data. A demonstration of CleanGraph and its source code can be accessed at https://github.com/nlp-tlp/CleanGraph under the MIT License.

Although a few approaches are proposed to convert relational databases to graphs, there is a genuine lack of systematic evaluation across a wider spectrum of databases. Recognising the important issue of query mapping, this paper proposes an approach Rel2Graph, an automatic knowledge graph construction (KGC) approach from an arbitrary number of relational databases. Our approach also supports the mapping of conjunctive SQL queries into pattern-based NoSQL queries. We evaluate our proposed approach on two widely used relational database-oriented datasets: Spider and KaggleDBQA benchmarks for semantic parsing. We employ the execution accuracy (EA) metric to quantify the proportion of results by executing the NoSQL queries on the property knowledge graph we construct that aligns with the results of SQL queries performed on relational databases. Consequently, the counterpart property knowledge graph of benchmarks with high accuracy and integrity can be ensured. The code and data will be publicly available. The code and data are available at github\footnote{https://github.com/nlp-tlp/Rel2Graph}.

In this paper we present the first investigation into the effectiveness of Large Language Models (LLMs) for Failure Mode Classification (FMC). FMC, the task of automatically labelling an observation with a corresponding failure mode code, is a critical task in the maintenance domain as it reduces the need for reliability engineers to spend their time manually analysing work orders. We detail our approach to prompt engineering to enable an LLM to predict the failure mode of a given observation using a restricted code list. We demonstrate that the performance of a GPT-3.5 model (F1=0.80) fine-tuned on annotated data is a significant improvement over a currently available text classification model (F1=0.60) trained on the same annotated data set. The fine-tuned model also outperforms the out-of-the box GPT-3.5 (F1=0.46). This investigation reinforces the need for high quality fine-tuning data sets for domain-specific tasks using LLMs., Comment: 8 pages, 3 tables

Virtual Reality (VR) users often encounter postural instability, i.e., balance issues, which can be a significant impediment to universal usability and accessibility, particularly for those with balance impairments. Prior research has validated imbalance issues, but little effort has been made to mitigate them. We recruited 39 participants (with balance impairments: 18, without balance impairments: 21) to examine the effect of various vibrotactile feedback techniques on balance in virtual reality, specifically spatial vibrotactile, static vibrotactile, rhythmic vibrotactile, and vibrotactile feedback mapped to the center of pressure (CoP). Participants completed standing visual exploration and standing reach and grasp tasks. According to within-subject results, each vibrotactile feedback enhanced balance in VR significantly (p < .001) for those with and without balance impairments. Spatial and CoP vibrotactile feedback enhanced balance significantly more (p < .001) than other vibrotactile feedback. This study presents strategies that might be used in future virtual environments to enhance standing balance and bring VR closer to universal usage., Comment: 10 pages, 7 figures. arXiv admin note: text overlap with arXiv:2202.04743

The objective of this study is to investigate the impact of several auditory feedback modalities on gait (i.e., walking patterns) in virtual reality (VR). Prior research has substantiated gait disturbances in VR users as one of the primary obstacles to VR usability. However, minimal research has been done to mitigate this issue. We recruited 39 participants (with mobility impairments: 18, without mobility impairments: 21) who completed timed walking tasks in a real-world environment and the same tasks in a VR environment with various types of auditory feedback. Within-subject results showed that each auditory condition significantly improved gait performance while in VR (p < .001) compared to the no auditory condition in VR for both groups of participants with and without mobility impairments. Moreover, spatial audio improved gait performance significantly (p < .001) compared to other auditory conditions for both groups of participants. This research could help to make walking in VR more accessible for people with and without mobility impairments., Comment: 10 pages, 6 figures

This research aims to examine the effects of various vibrotactile feedback techniques on gait (i.e., walking patterns) in virtual reality (VR). Prior studies have demonstrated that gait disturbances in VR users are significant usability barriers. However, adequate research has not been performed to address this problem. In our study, 39 participants (with mobility impairments: 18, without mobility impairments: 21) performed timed walking tasks in a real-world environment and identical activities in a VR environment with different forms of vibrotactile feedback (spatial, static, and rhythmic). Within-group results revealed that each form of vibrotactile feedback improved gait performance in VR significantly (p < .001) relative to the no vibrotactile condition in VR for individuals with and without mobility impairments. Moreover, spatial vibrotactile feedback increased gait performance significantly (p < .001) in both participant groups compared to other vibrotactile conditions. The findings of this research will help to make real walking in VR more accessible for those with and without mobility impairments., Comment: 13 pages, 7 figures

Virtual Reality (VR) users often experience postural instability, i.e., balance problems, which could be a major barrier to universal usability and accessibility for all, especially for persons with balance impairments. Prior research has confirmed the imbalance effect, but minimal research has been conducted to reduce this effect. We recruited 42 participants (with balance impairments: 21, without balance impairments: 21) to investigate the impact of several auditory techniques on balance in VR, specifically spatial audio, static rest frame audio, rhythmic audio, and audio mapped to the center of pressure (CoP). Participants performed two types of tasks - standing visual exploration and standing reach and grasp. Within-subject results showed that each auditory technique improved balance in VR for both persons with and without balance impairments. Spatial and CoP audio improved balance significantly more than other auditory conditions. The techniques presented in this research could be used in future virtual environments to improve standing balance and help push VR closer to universal usability., Comment: 10 pages

Sufficient conditions are provided under which the log-likelihood ratio test statistic fails to have a limiting chi-squared distribution under the null hypothesis when testing between one and two components under a general two-component mixture model, but rather tends to infinity in probability. These conditions are verified when the component densities describe continuous-time, discrete-statespace Markov chains and the results are illustrated via a parametric bootstrap simulation on an analysis of the migrations over time of a set of corporate bonds ratings. The precise limiting distribution is derived in a simple case with two states, one of which is absorbing which leads to a right-censored exponential scale mixture model. In that case, when centred by a function growing logarithmically in the sample size, the statistic has a limiting distribution of Gumbel extreme-value type rather than chi-squared., Comment: To be published in Econometrics and Statistics

Entity Typing (ET) is the process of identifying the semantic types of every entity within a corpus. In contrast to Named Entity Recognition, where each token in a sentence is labelled with zero or one class label, ET involves labelling each entity mention with one or more class labels. Existing entity typing models, which operate at the mention level, are limited by two key factors: they do not make use of recently-proposed context-dependent embeddings, and are trained on fixed context windows. They are therefore sensitive to window size selection and are unable to incorporate the context of the entire document. In light of these drawbacks we propose to incorporate context using transformer-based embeddings for a mention-level model, and an end-to-end model using a Bi-GRU to remove the dependency on window size. An extensive ablative study demonstrates the effectiveness of contextualised embeddings for mention-level models and the competitiveness of our end-to-end model for entity typing.

The characteristics of spontaneous emission can be strongly modified by the mode structure of the vacuum. In waveguide quantum-electrodynamics based on photonic crystals, this modification is exploited to engineer atom-photon interactions near a band edge, but the physics of coupling to an entire band has not yet been explored in experiments. Using ultracold atoms in an optical lattice, we study the decay dynamics of matter-wave quantum emitters coupled to a single band of an effective photonic crystal waveguide structure with tunable characteristics. Depending on the ratio between vacuum coupling and bandwidth, we observe a transition from irreversible decay to fully oscillatory dynamics linked to the interplay of matter-wave bound states near the band edges, whose spatial structure we characterize. Our results shed light on the emergence of coherence in an open quantum system in a controllable environment, and are of relevance for the understanding of vacuum-induced decay phenomena in photonic systems., Comment: 7 pages, 5 figures; Revised version combining main text and supplemental materials, accepted for publication

Lexical normalisation (LN) is the process of correcting each word in a dataset to its canonical form so that it may be more easily and more accurately analysed. Most lexical normalisation systems operate at the character-level, while word-level models are seldom used. Recent language models offer solutions to the drawbacks of word-level LN models, yet, to the best of our knowledge, no research has investigated their effectiveness on LN. In this paper we introduce a word-level GRU-based LN model and investigate the effectiveness of recent embedding techniques on word-level LN. Our results show that our GRU-based word-level model produces greater results than character-level models, and outperforms existing deep-learning based LN techniques on Twitter data. We also find that randomly-initialised embeddings are capable of outperforming pre-trained embedding models in certain scenarios. Finally, we release a substantial lexical normalisation dataset to the community.

We present an overview of our triple extraction system for the ICDM 2019 Knowledge Graph Contest. Our system uses a pipeline-based approach to extract a set of triples from a given document. It offers a simple and effective solution to the challenge of knowledge graph construction from domain-specific text. It also provides the facility to visualise useful information about each triple such as the degree, betweenness, structured relation type(s), and named entity types.

A fast Bayesian method that seamlessly fuses classification and hypothesis testing via discriminant analysis is developed. Building upon the original discriminant analysis classifier, modelling components are added to identify discriminative variables. A combination of cake priors and a novel form of variational Bayes we call reverse collapsed variational Bayes gives rise to variable selection that can be directly posed as a multiple hypothesis testing approach using likelihood ratio statistics. Some theoretical arguments are presented showing that Chernoff-consistency (asymptotically zero type I and type II error) is maintained across all hypotheses. We apply our method on some publicly available genomics datasets and show that our method performs well in practice for its computational cost. An R package VaDA has also been made available on Github., Comment: Under Review

One of the paradigms of a small quantum system in a dissipative environment is the decay of an excited atom undergoing spontaneous photon emission into the fluctuating quantum electrodynamic vacuum. Recent experiments have demonstrated that the gapped photon dispersion in periodic structures can give rise to novel spontaneous-decay behavior including the formation of dissipative bound states. So far, these effects have been restricted to the optical domain. Here, we experimentally demonstrate similar behavior in a system of artificial atoms in an optical lattice that decay by emitting matter-wave, rather than optical, radiation into free space. By controlling the vacuum coupling and excitation energy, we directly observe exponential and partly reversible, non-Markovian dynamics and detect a tunable bound state containing evanescent matter waves for emission at negative excitation energies. Our system provides a flexible platform for the emulation of open-system quantum electrodynamics and studies of dissipative many-body physics with ultracold atoms., Comment: 15 Pages, 5 figures

We introduce a new class of priors for Bayesian hypothesis testing, which we name "cake priors". These priors circumvent Bartlett's paradox (also called the Jeffreys-Lindley paradox); the problem associated with the use of diffuse priors leading to nonsensical statistical inferences. Cake priors allow the use of diffuse priors (having one's cake) while achieving theoretically justified inferences (eating it too). We demonstrate this methodology for Bayesian hypotheses tests for scenarios under which the one and two sample t-tests, and linear models are typically derived. The resulting Bayesian test statistic takes the form of a penalized likelihood ratio test statistic. By considering the sampling distribution under the null and alternative hypotheses we show for independent identically distributed regular parametric models that Bayesian hypothesis tests using cake priors are Chernoff-consistent, i.e., achieve zero type I and II errors asymptotically. Lindley's paradox is also discussed. We argue that a true Lindley's paradox will only occur with small probability for large sample sizes.

Behavior analogous to that of spontaneous emission in photonic band gap materials has been predicted for an atom-optical system consisting of an atom confined in a well of a state-dependent optical lattice that is coupled to free space through an internal-state transition [de Vega et al., Phys. Rev. Lett. 101, 260404 (2008)]. Using the Weisskopf-Wigner approach and considering a one-dimensional geometry, we analyze the properties of this system in detail, including the evolution of the lattice-trapped population, the momentum distribution of emitted matter waves, and the detailed structure of an evanescent matter-wave state below the continuum boundary. We compare and contrast our findings for the transition from Markovian to non-Markovian behaviors to those previously obtained for three dimensions., Comment: 10 pages, 4 figures

Precise control of magnetic fields is a frequent challenge encountered in experiments with atomic quantum gases. Here we present a simple method for performing in-situ monitoring of magnetic fields that can readily be implemented in any quantum-gas apparatus in which a dedicated field-stabilization approach is not possible. The method, which works by sampling several Rabi resonances between magnetically field sensitive internal states that are not otherwise used in a given experiment, can be integrated with standard measurement sequences at arbitrary fields. For a condensate of $^{87}$Rb atoms, we demonstrate the reconstruction of Gauss-level bias fields with an accuracy of tens of microgauss and with millisecond time resolution. We test the performance of the method using measurements of slow resonant Rabi oscillations on a magnetic-field sensitive transition, and give an example for its use in experiments with state-selective optical potentials., Comment: 5 pages, 5 figures

Diffraction phenomena usually can be formulated in terms of a potential that induces the redistribution of a wave's momentum. Using an atomic Bose-Einstein condensate coupled to the orbitals of a state-selective optical lattice, we investigate a hitherto unexplored nonadiabatic regime of diffraction in which no diffracting potential can be defined, and in which the adiabatic dressed states are strongly mixed. We show how, in the adiabatic limit, the observed coupling between internal and external dynamics gives way to standard Kapitza-Dirac diffraction of atomic matter waves. We demonstrate the utility of our scheme for atom interferometry and discuss prospects for studies of dissipative superfluid phenomena., Comment: 5 pages, 4 figures slightly shortened text and compressed fig. 2

The TGF and Energetic Thunderstorm Rooftop Array (TETRA) is an array of NaI scintillators located at rooftop level on the campus of Louisiana State University in Baton Rouge, Louisiana. From July 2010 through March 2014, TETRA has detected 28 millisecond-duration bursts of gamma-rays at energies 50 keV - 2 MeV associated with nearby (< 8 km) thunderstorms. The ability to observe ground-level Terrestrial Gamma Flashes from close to the source allows a unique analysis of the storm cells producing these events. The results of the initial analysis will be presented., Comment: Paper presented at XV International Conference on Atmospheric Electricity, 15-20 June 2014, Norman, Oklahoma

Specificity is important for extracting collocations, keyphrases, multi-word and index terms [Newman et al. 2012]. It is also useful for tagging, ontology construction [Ryu and Choi 2006], and automatic summarization of documents [Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency and inverse-document frequency (TF-IDF) are typically used to do this, but fail to take advantage of the semantic relationships between terms [Church and Gale 1995]. The result is that general idiomatic terms are mistaken for specific terms. We demonstrate use of relational data for estimation of term specificity. The specificity of a term can be learned from its distribution of relations with other terms. This technique is useful for identifying relevant words or terms for other natural language processing tasks.

Terrestrial Gamma ray Flashes (TGFs) -- very short, intense bursts of electrons, positrons, and energetic photons originating from terrestrial thunderstorms -- have been detected with satellite instruments. TETRA, an array of NaI(Tl) scintillators at Louisiana State University, has now been used to detect similar bursts of 50 keV to over 2 MeV gamma rays at ground level. After 2.6 years of observation, twenty-four events with durations 0.02- 4.2 msec have been detected associated with nearby lightning, three of them coincident events observed by detectors separated by ~1000 m. Nine of the events occurred within 6 msec and 3 miles of negative polarity cloud-to-ground lightning strokes with measured currents in excess of 20 kA. The events reported here constitute the first catalog of TGFs observed at ground level in close proximity to the acceleration site., Comment: To be published in Journal of Geophysical Research: Space Phys. 118, 1

Software diversity protects against a modern-day exploits such as code-reuse attacks. When an attacker designs a code-reuse attack on an example executable, it relies on replicating the target environment. With software diversity, the attacker cannot reliably replicate their target. This is a security benefit which can be applied to massive-scale software distribution. When applied to large-scale communities, an invested attacker may perform analysis of samples to improve the chances of a successful attack (M. Franz). We present a general NOP-insertion algorithm which can be expanded and customized for security, performance, or other costs. We demonstrate an improvement in security so that a code-reuse attack based on any one variant has minimal chances of success on another and analyse the costs of this method. Alternately, the variants may be customized to meet performance or memory overhead constraints. Deterministic diversification allows for the flexibility to balance these needs in a way that doesn't exist in a random online method.

Let X_1, ..., X_n be independent and identically distributed random vectors with a log-concave (Lebesgue) density f. We first prove that, with probability one, there exists a unique maximum likelihood estimator of f. The use of this estimator is attractive because, unlike kernel density estimation, the method is fully automatic, with no smoothing parameters to choose. Although the existence proof is non-constructive, we are able to reformulate the issue of computation in terms of a non-differentiable convex optimisation problem, and thus combine techniques of computational geometry with Shor's r-algorithm to produce a sequence that converges to the maximum likelihood estimate. For the moderate or large sample sizes in our simulations, the maximum likelihood estimator is shown to provide an improvement in performance compared with kernel-based methods, even when we allow the use of a theoretical, optimal fixed bandwidth for the kernel estimator that would not be available in practice. We also present a real data clustering example, which shows that our methodology can be used in conjunction with the Expectation--Maximisation (EM) algorithm to fit finite mixtures of log-concave densities. An R version of the algorithm is available in the package LogConcDEAD -- Log-Concave Density Estimation in Arbitrary Dimensions., Comment: 31 pages, 5 figures