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Tri Nguyen

Publications and source records attributed to Tri Nguyen.

At least 55 records · Page 3Linked to original sources
arXiv2023

GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowdsourced questions from Latinx caregivers and noncaregivers as well as caregivers and noncaregivers from other demographics. We examine variations in question--asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open--ended questions that are representative of those asked by Latinx caregivers.

cs.CL↗
arXiv2023

Synthetic Gaia DR3 surveys from the FIRE cosmological simulations of Milky-Way-mass galaxies

The third data release (DR3) of Gaia has provided a five-fold increase in the number of radial velocity measurements of stars, as well as a stark improvement in parallax and proper motion measurements. To help with studies that seek to test models and interpret Gaia DR3, we present nine Gaia synthetic surveys, based on three solar positions in three Milky-Way-mass galaxies of the Latte suite of the Fire-2 cosmological simulations. These synthetic surveys match the selection function, radial velocity measurements, and photometry of Gaia DR3, adapting the code base Ananke, previously used to match the Gaia DR2 release in Sanderson et al. 2020. The synthetic surveys are publicly available and can be found at http://ananke.hub.yt/. Similarly to the previous release of Ananke, these surveys are based on cosmological simulations and thus able to model non-equilibrium dynamical effects, making them a useful tool in testing and interpreting Gaia DR3.

astro-ph.GA↗
arXiv2023

Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-to-Action

Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and are increasingly being employed in diverse fields. One such promising application is in operations and maintenance (O&M). O&M tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or under high-stress situations. By marrying the advantages of superimposing virtual objects onto the physical world, and generating human-like text using GPT, we can revolutionize O&M operations. This study introduces a system that combines AR, Optical Character Recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in O&M tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=15) is conducted to illustrate the findings and answer the research questions. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggests a reduction in cognitive load and an increase in trust when executing the same operations using the AR and AI system.

cs.HC↗
arXiv2023

Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.

gr-qc↗
arXiv2023

Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization

Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific ``label confusion'' layers and co-train the two parts in a parameter-coupled manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria are intuitive and work well in practice. Nonetheless, theoretical understanding of the CCEM criterion has been limited. The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented. Our analysis reveals for the first time that the CCEM can indeed correctly identify the annotators' confusion characteristics and the desired ``ground-truth'' neural classifier under realistic conditions, e.g., when only incomplete annotator labeling and finite samples are available. Second, based on the insights learned from our analysis, two regularized variants of the CCEM are proposed. The regularization terms provably enhance the identifiability of the target model parameters in various more challenging cases. A series of synthetic and real data experiments are presented to showcase the effectiveness of our approach.

cs.LG↗
arXiv2023

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantially enhance the clustering accuracy. However, beyond empirical successes, there is a lack of understanding of DCC. In addition, many DCC paradigms are sensitive to annotation noise, but performance-guaranteed noisy DCC methods have been largely elusive. This work first takes a deep look into a recently emerged logistic loss function of DCC, and characterizes its theoretical properties. Our result shows that the logistic DCC loss ensures the identifiability of data membership under reasonable conditions, which may shed light on its effectiveness in practice. Building upon this understanding, a new loss function based on geometric factor analysis is proposed to fend against noisy annotations. It is shown that even under $\textit{unknown}$ annotation confusions, the data membership can still be $\textit{provably}$ identified under our proposed learning criterion. The proposed approach is tested over multiple datasets to validate our claims.

cs.LG↗
arXiv2023

On the Impact of Aspect Ratio and Other Geometric Effects on the Stability of Rectangular Thermosiphons

Single-phase natural circulation thermosiphon loops have been attracting increased interest as they represent the prototype of passive safety systems. However, the stability properties of thermosiphon loops, which can affect and compromise their functionality, are still actively investigated. Traditionally, the stability analysis of thermosiphon loops has been simplified to one-dimensional (1D) calculations, on the argument that the flow would be mono-dimensional when the diameter of the pipe D is orders of magnitude smaller than the length of the loop Lt. However, at lower Lt/D ratios, rectangular thermosiphon loops show that the flow presents 3D effect, which also has been confirmed by stability analyses in toroidal loops. In this paper, we performed a series of high-fidelity simulations using the spectral element code Nek5000 to investigate the stability behavior of the flow in rectangular thermosiphon loops. A wide range of Lt/D ratio from 10 to 200 has been considered and the results show many different outcomes compared to previous 1D analytical calculations or stability theory. Moreover, we analyzed the flow in rectangular thermosiphon loops using Proper Orthogonal Decomposition (POD) and we observed that the cases without flow reversal are characterized by swirl modes typical of bent pipes and high-frequency oscillation of the related time coefficients obtained by Galerkin projection. However, the swirl mode was not observed in cases with flow reversals, these cases are characterized by symmetric flow field at 2nd POD mode and the similarity of low-frequency oscillation in the projection of POD modes.

physics.flu-dyn↗
arXiv2023

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.

cs.CV↗
arXiv2023

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.

cs.MA↗
arXiv2022

Perspectives on Negative Research Results in Pervasive Computing

Not all research leads to fruitful results; trying new ways or methods may surpass the state of the art, but sometimes the hypothesis is not proven or the improvement is insignificant. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. However, failure to succeed is not a failure to progress. It is essential to create platforms for sharing insights, experiences, and lessons learned when conducting research in pervasive computing so that the same mistakes are not repeated. And sometimes, a problem is a symptom of discovering new research challenges. Based on the collective input of the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022), co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022), this paper presents a comprehensive discussion on perspectives on publishing negative results and lessons learned in pervasive computing.

cs.DC↗
arXiv2022

EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head. Both contracting and expanding paths are synergetically learned by a collaboration head. We have benchmarked our proposed EmbryosFormer on two datasets: a public dataset with mouse embryos with 8-cell stage and an in-house dataset with human embryos with 4-cell stage. Source code: https://github.com/UARK-AICV/Embryos.

cs.CV↗
arXiv2022

Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

Dwarf galaxies are small, dark matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter -- understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. We introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.

astro-ph.CO↗
arXiv2022

Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach

Nonnegative matrix factorization (NMF) often relies on the separability condition for tractable algorithm design. Separability-based NMF is mainly handled by two types of approaches, namely, greedy pursuit and convex programming. A notable convex NMF formulation is the so-called self-dictionary multiple measurement vectors (SD-MMV), which can work without knowing the matrix rank a priori, and is arguably more resilient to error propagation relative to greedy pursuit. However, convex SD-MMV renders a large memory cost that scales quadratically with the problem size. This memory challenge has been around for a decade, and a major obstacle for applying convex SD-MMV to big data analytics. This work proposes a memory-efficient algorithm for convex SD-MMV. Our algorithm capitalizes on the special update rules of a classic algorithm from the 1950s, namely, the Frank-Wolfe (FW) algorithm. It is shown that, under reasonable conditions, the FW algorithm solves the noisy SD-MMV problem with a memory cost that grows linearly with the amount of data. To handle noisier scenarios, a smoothed group sparsity regularizer is proposed to improve robustness while maintaining the low memory footprint with guarantees. The proposed approach presents the first linear memory complexity algorithmic framework for convex SD-MMV based NMF. The method is tested over a couple of unsupervised learning tasks, i.e., text mining and community detection, to showcase its effectiveness and memory efficiency.

eess.SP↗
arXiv2022

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.

cs.NI↗
arXiv2022

Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry

Buoyancy effect on low-flow condition convective heat transfer of non-conventional coolants, such as liquid metal and molten salts, is a crucial safety factor to advanced reactors under transient or accidental scenarios. The distinct heat transfer characteristics of non-unitary Prandtl fluids and the inherent complexity of the low-flow mixed convection phenomena requires the development of novel turbulent and heat transfer models that are adaptive to different spatiotemporal scales involved in the mixed convection heat transfer. In this work, direct numerical simulation of low-flow mixed convection is carried out at low-to-unitary Prandtl numbers that are of industrial interest. Time-averaged statistics, turbulent Prandtl number, as well as time signals are analyzed to investigate mixed convection phenomenon. From the time-averaged statistics, buoyant plume altered velocity boundary layer as well as the intensity of the fluctuation near both walls and channel centerline. Buoyancy effect also rendered different degree of convective heat transfer enhancement and impairment depends on Prandtl and Richardson number. Analysis of time series was conducted on the sodium mixed convection case to emphasize on the low-Pr mixed convection behavior at transition region. Resulting power spectra density and wavelet spectrogram suggests possible large convective structure in transition region. Future work will focus on providing broader data coverage on Pr-Re-Ri parameter space to facilitate more comprehensive analysis of mixed convection.

physics.flu-dyn↗
arXiv2022

Direct Numerical Simulation of High Prandtl Number Fluid Flow in the Downcomer of an Advanced Reactor

The passive safety is a crucial feature of advanced nuclear reactor (Gen IV) design. During loss of power scenarios, the downcomer plays a crucial role. The fluid-flow behavior in the downcomer can involve forced to mixed to natural convection and characterizing the heat transfer for these changing regimes is a daunting challenge. High-resolution heat transfer numerical database can potentially support the development of precise and affordable reduced resolution heat transfer models. These models can be designed based on a multiscale hierarchy developed as part of the recently DOE-funded center of excellence for thermal-fluids applications in nuclear energy. In this paper, the downcomer is simplified to heated parallel plates and High Prandtl number fluid (FLiBe) is considered for all simulations. The calculations are performed for a wide range of Richardson number from 0 to 400 at 2 different FLiBe Prandtl numbers (12 and 24) which result in 40 simulated Cases in total. Time averaged and time series statistics as well as Nusselt number correlations are investigated to illuminate the mixed convection behaviors. The calculated database will be instrumental in understanding the flow behaviors in the downcomer. Ultimately, we aimed to evaluate existing heat transfer correlations and some modifications will be proposed for Cases where no satisfactory choice is available.

physics.flu-dyn↗
arXiv2022

Direct Numerical Simulation of high Prandtl number fluids and supercritical carbon dioxide canonical flows using the spectral element method

The design of advanced nuclear reactors (Gen IV) involves an array of challenging fluid-flow issues that affect safety and performance. Currently, these problems are addressed in an ad-hoc manner at varying scales which are time-consuming and expensive. The creation of a high-resolution heat transfer numerical database has the potential to help develop to accurate and inexpensively reduced resolution heat transfer models. Such models can help address industrial-driven issues associated with the heat transfer behavior of advanced reactors. The models can be developed using the multiscale hierarchy developed as part of the recently DOE-funded center of excellence for thermal-fluids applications in nuclear energy. Ultimately this can lead to fast-running reliable models, thus accelerating the deployment of advanced reactors. In this paper, we performed a series of Direct Numerical Simulation using the spectral element codes Nek5000 and NekRS to investigate heat transfer in mixed convection conditions. First, we investigate the heat transfer of the flow in heated parallel plates for high Prandtl number fluids. The calculated database will eventually be used to evaluate existing heat transfer correlations and some modifications will be proposed for cases where no satisfactory choice is available. We have also investigated the heated transfer alteration phenomena in a straight heated tube for supercritical carbon dioxide (sCO2). The low-Mach-number approximation is used to decouple thermal and dynamic pressure, as pressure drop is negligible in this problem. The properties of sCO2 are calculated using multi-region polynomials. We observed that the heat transfer deterioration occurred in combination with the property changes of sCO2 and the depreciation of turbulence kinetic energy (TKE) for upward flow. Whereas, in downward flow, the heat transfer is enhanced thanks to the increase of TKE.

physics.flu-dyn↗