arXiv subjects

Minh Nguyen

Publications and source records attributed to Minh Nguyen.

At least 73 records · Page 4Linked to original sources

arXiv2023

Deterministic/Fragmented-Stochastic Exchange for Large Scale Hybrid DFT Calculations

We develop an efficient approach to evaluate range-separated exact exchange for grid or plane-wave based representations within the Generalized Kohn-Sham DFT (GKS-DFT) framework. The Coulomb kernel is fragmented in reciprocal space, and we employ a mixed deterministic-stochastic representation, retaining long wavelength (low-$k$) contributions deterministically and using a sparse ("fragmented") stochastic basis for the high-$k$ part. Coupled with a projection of the Hamiltonian onto a subspace of valence and conduction states from a prior local-DFT calculation, this method allows for the calculation of long-range exchange of large molecular systems with hundreds and potentially thousands of coupled valence states delocalized over millions of grid points. We find that even a small number of valence and conduction states is sufficient for converging the HOMO and LUMO energies of the GKS-DFT. Excellent tuning of long-range separated hybrids (RSH) is easily obtained in the method for very large systems, as exemplified here for the chlorophyll hexamer of Photosystem II with 1,320 electrons.

physics.chem-ph↗

arXiv2023

Arbitrary structured quantum emission with a multifunctional imaging metalens

Structuring light emission from single-photon emitters in multiple degrees of freedom is of a great importance for quantum information processing towards higher dimensions. However, traditional control of emission from quantum light sources relies on the use of multiple bulky optical elements or nanostructured resonators with limited functionalities, constraining the potential of multi-dimensional tailoring. Here we introduce the use of an ultrathin polarisation-beam-splitting metalens for the arbitrary structuring of quantum emission at room temperature. Owing to the complete and independent polarisation and phase control at a single meta-atom level, the designed metalens enables simultaneous imaging of quantum emission from ultra-bright defects in hexagonal boron nitride and imprinting of an arbitrary wavefront onto orthogonal polarisation states of the sources. The hybrid quantum metalens enables simultaneous manipulation of multiple degrees of freedom of a quantum light source, including directionality, polarisation, and orbital angular momentum. The demonstrated arbitrary wavefront shaping of quantum emission in multiple degrees of freedom could unleash the full potential of solid-state SPEs for their use as high-dimensional quantum sources for advanced quantum photonic applications.

physics.optics↗

arXiv2023

DNN-based Denial of Quality of Service Attack on Software-defined Hybrid Edge-Cloud Systems

In order to satisfy diverse quality-of-service (QoS) requirements of complex real-time video applications, civilian and tactical use cases are employing software-defined hybrid edge-cloud systems. One of the primary QoS requirements of such applications is ultra-low end-to-end latency for video applications that necessitates rapid frame transfer between end-devices and edge servers using software-defined networking (SDN). Failing to guarantee such strict requirements leads to quality degradation of video applications and subsequently mission failure. In this paper, we show how a collaborative group of attackers can exploit SDN's control communications to launch Denial of Quality of Service (DQoS) attack that artificially increases end-to-end latency of video frames and yet evades detection. In particular, we show how Deep Neural Network (DNN) model training on all or partial network state information can help predict network packet drop rates with reasonable accuracy. We also show how such predictions can help design an attack model that can inflict just the right amount of added latency to the end-to-end video processing that is enough to cause considerable QoS degradation but not too much to raise suspicion. We use a realistic edge-cloud testbed on GENI platform for training data collection and demonstration of high model accuracy and attack success rate.

cs.NI↗

arXiv2023

Optimized Attenuated Interaction: Enabling Stochastic Bethe-Salpeter Spectra for Large Systems

We develop an improved stochastic formalism for the Bethe-Salpeter equation, based on an exact separation of the effective-interaction $W$ to two parts, $W=(W-v_W)+v_W$ where the latter is formally any translationally-invariant interaction $v_W(r-r')$. When optimizing the fit of $v_W$ exchange kernel to $W$, by using a stochastic sampling of $W$, the difference $W-v_W$ becomes quite small. Then, in the main BSE routine, this small difference is stochastically sampled. The number of stochastic samples needed for an accurate spectrum is then largely independent of system size. While the method is formally cubic in scaling, the scaling prefactor is small due to the constant number of stochastic orbitals needed for sampling $W$.

physics.chem-ph↗

arXiv2023

Time-dependent Mandel Q parameter analysis for a hexagonal boron nitride single photon source

The time-dependent Mandel Q parameter, Q(T), provides a measure of photon number variance for a light source as a function of integration time. Here, we use Q(T) to characterise single photon emission from a quantum emitter in hexagonal boron nitride (hBN). Under pulsed excitation a negative Q parameter was measured, indicating photon antibunching at an integration time of 100 ns. For larger integration times Q is positive and the photon statistics become super-Poissonian, and we show by comparison with a Monte Carlo simulation for a three-level emitter that this is consistent with the effect of a metastable shelving state. Looking towards technological applications for hBN single photon sources, we propose that Q(T) provides valuable information on the intensity stability of single photon emission. This is useful in addition to the commonly used $g^{(2)}(τ)$ function for the complete characterisation of a hBN emitter.

quant-ph↗

arXiv2023

On the spectral theory for first-order systems without the unique continuation property

We consider the differential equation $Ju'+qu=wf$ on the real interval $(a,b)$ when $J$ is a constant, invertible skew-Hermitian matrix and $q$ and $w$ are matrices whose entries are distributions of order zero with $q$ Hermitian and $w$ non-negative. In this situation it may happen that there is no existence and uniqueness theorem for balanced solutions of a given initial value problem. We describe the set of solutions the equation does have and establish that the adjoint of the minimal operator is still the maximal operator, even though unique continuation of balanced solutions fails.

math.CA↗

arXiv2022

A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography

Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to transform sparse 2-D projection measurements to a 3-D reconstruction by training on a dataset of known similar objects. However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete. This becomes a chicken-and-egg problem: high-quality reconstructions cannot be generated without deep learning, and the deep neural network cannot be learned without the reconstructions. This work develops and validates a self-supervised probabilistic deep learning technique, the physics-informed variational autoencoder, to solve this problem. A dataset consisting solely of sparse projection measurements from each object is used to jointly reconstruct all objects of the set. This approach has the potential to allow visualization of fragile samples with x-ray computed tomography. We release our code for reproducing our results at: https://github.com/vganapati/CT_PVAE .

cs.CV↗

arXiv2022

MTet: Multi-domain Translation for English and Vietnamese

We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation. MTet consists of 4.2M high-quality training sentence pairs and a multi-domain test set refined by the Vietnamese research community. Combining with previous works on English-Vietnamese translation, we grow the existing parallel dataset to 6.2M sentence pairs. We also release the first pretrained model EnViT5 for English and Vietnamese languages. Combining both resources, our model significantly outperforms previous state-of-the-art results by up to 2 points in translation BLEU score, while being 1.6 times smaller.

cs.CL↗

arXiv2022

Gapped-filtering for efficient Chebyshev expansion of the density projection operator

We develop the gapped-filtering method, whereby a short Chebyshev expansion accurately represents the density-matrix operator. The method optimizes the Chebyshev coefficients to give the correct density matrix at all energies except within the gapped region where there are no eigenstates. Gapped filtering reduces the number of required terms in the Chebyshev expansion compared to traditional expansion methods, as long as one knows or can determine efficiently the HOMO and LUMO positions. The reduction is especially noticeable (factors of 2-3) when high accuracy is sought. To exemplify the method, we use gapped-filtering to increase the efficiency of stochastic-GW calculations.

physics.comp-ph↗

arXiv2022

Folding over Neural Networks

Neural networks are typically represented as data structures that are traversed either through iteration or by manual chaining of method calls. However, a deeper analysis reveals that structured recursion can be used instead, so that traversal is directed by the structure of the network itself. This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training as recursion scheme patterns. In turn, we promote a coherent implementation of neural networks that delineates between their structure and semantics, allowing for compositionality in both how they are built and how they are trained.

cs.PL↗

arXiv2022

A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries

Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses.

q-bio.NC↗

arXiv2022

Bethe Salpeter Equation Spectra for Very Large Systems

We present a highly efficient method for the extraction of optical properties of very large molecules via the Bethe-Salpeter equation. The crutch of this approach is the calculation of the action of the effective Coulombic interaction, $W$, through a stochastic TD Hartree propagation, which uses only 10 stochastic orbitals rather than propagating the full sea of occupied states. This leads to a scaling that is at most cubic in system size, with trivial MPI parallelization. We apply this new method to calculate the spectra and electronic density of the dominant excitons of a carbon-nanohoop bound fullerene system with 520 electrons, using less than 4000 core hours.

physics.chem-ph↗

arXiv2022

A Pipeline to Understand Emerging Illness via Social Media Data Analysis: A Case Study on Breast Implant Illness

Background: A new illness could first come to the public attention over social media before it is medically defined, formally documented or systematically studied. One example is a phenomenon known as breast implant illness (BII) that has been extensively discussed on social media, though vaguely defined in medical literature. Objectives: The objective of this study is to construct a data analysis pipeline to understand emerging illness using social media data, and to apply the pipeline to understand key attributes of BII. Methods: We conducted a pipeline of social media data analysis using Natural Language Processing (NLP) and topic modeling. We extracted mentions related to signs/symptoms, diseases/disorders and medical procedures using the Clinical Text Analysis and Knowledge Extraction System (cTAKES) from social media data. We mapped the mentions to standard medical concepts. We summarized mapped concepts to topics using Latent Dirichlet Allocation (LDA). Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results: Our pipeline identified topics related to toxicity, cancer and mental health issues that are highly associated with BII. Our pipeline also shows that cancers, autoimmune disorders and mental health problems are emerging concerns associated with breast implants based on social media discussions. The pipeline also identified mentions such as rupture, infection, pain and fatigue as common self-reported issues among the public, as well as toxicity from silicone implants. Conclusions: Our study could inspire future work studying the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using NLP techniques, and demonstrates the potential of using social media information to better understand similar emerging illnesses.

cs.IR↗

arXiv2022

MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks

Deep neural network (DNN) based approaches have been intensively studied to improve video quality thanks to their fast advancement in recent years. These approaches are designed mainly for desktop devices due to their high computational cost. However, with the increasing performance of mobile devices in recent years, it became possible to execute DNN based approaches in mobile devices. Despite having the required computational power, utilizing DNNs to improve the video quality for mobile devices is still an active research area. In this paper, we propose an open-source mobile platform, namely MoViDNN, to evaluate DNN based video quality enhancement methods, such as super-resolution, denoising, and deblocking. Our proposed platform can be used to evaluate the DNN based approaches both objectively and subjectively. For objective evaluation, we report common metrics such as execution time, PSNR, and SSIM. For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposed platform is available publicly at https://github.com/cd-athena/MoViDNN

cs.CV↗

arXiv2021

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

q-bio.PE↗

arXiv2021

Compressive Features in Offline Reinforcement Learning for Recommender Systems

In this paper, we develop a recommender system for a game that suggests potential items to players based on their interactive behaviors to maximize revenue for the game provider. Our approach is built on a reinforcement learning-based technique and is trained on an offline data set that is publicly available on an IEEE Big Data Cup challenge. The limitation of the offline data set and the curse of high dimensionality pose significant obstacles to solving this problem. Our proposed method focuses on improving the total rewards and performance by tackling these main difficulties. More specifically, we utilized sparse PCA to extract important features of user behaviors. Our Q-learning-based system is then trained from the processed offline data set. To exploit all possible information from the provided data set, we cluster user features to different groups and build an independent Q-table for each group. Furthermore, to tackle the challenge of unknown formula for evaluation metrics, we design a metric to self-evaluate our system's performance based on the potential value the game provider might achieve and a small collection of actual evaluation metrics that we obtain from the live scoring environment. Our experiments show that our proposed metric is consistent with the results published by the challenge organizers. We have implemented the proposed training pipeline, and the results show that our method outperforms current state-of-the-art methods in terms of both total rewards and training speed. By addressing the main challenges and leveraging the state-of-the-art techniques, we have achieved the best public leaderboard result in the challenge. Furthermore, our proposed method achieved an estimated score of approximately 20% better and can be trained faster by 30 times than the best of the current state-of-the-art methods.

cs.AI↗

arXiv2021

Tempering stochastic density functional theory

We introduce a tempering approach with stochastic density functional theory (sDFT), labeled t-sDFT, which reduces the statistical errors in the estimates of observable expectation values. This is achieved by rewriting the electronic density as a sum of a "warm" component complemented by "colder" correction(s). Since the "warm" component is larger in magnitude but faster to evaluate, we use many more stochastic orbitals for its evaluation than for the smaller-sized colder correction(s). This results in a significant reduction of the statistical fluctuations and the bias compared to sDFT for the same computational effort. We the method's performance on large hydrogen-passivated silicon nanocrystals (NCs), finding a reduction in the systematic error in the energy by more than an order of magnitude, while the systematic errors in the forces are also quenched. Similarly, the statistical fluctuations are reduced by factors of around 4-5 for the total energy and around 1.5-2 for the forces on the atoms. Since the embedding in t-sDFT is fully stochastic, it is possible to combine t-sDFT with other variants of sDFT such as energy-window sDFT and embedded-fragmented sDFT.

physics.comp-ph↗

arXiv2021

Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query

Most neuroimaging experiments are under-powered, limited by the number of subjects and cognitive processes that an individual study can investigate. Nonetheless, over decades of research, neuroscience has accumulated an extensive wealth of results. It remains a challenge to digest this growing knowledge base and obtain new insights since existing meta-analytic tools are limited to keyword queries. In this work, we propose Text2Brain, a neural network approach for coordinate-based meta-analysis of neuroimaging studies to synthesize brain activation maps from open-ended text queries. Combining a transformer-based text encoder and a 3D image generator, Text2Brain was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published neuroimaging studies. We demonstrate that Text2Brain can synthesize anatomically-plausible neural activation patterns from free-form textual descriptions of cognitive concepts. Text2Brain is available at https://braininterpreter.com as a web-based tool for retrieving established priors and generating new hypotheses for neuroscience research.

q-bio.NC↗