arXiv subjects

Tommy Nguyen

Publications and source records attributed to Tommy Nguyen.

13 recordsLinked to original sources

arXiv2026

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose a single answer and curate its citations. Source evaluation therefore passes from user to platform, yet what these systems surface is poorly characterized. We audited three free consumer products (ChatGPT, Perplexity, Google AI Overview) on twenty English mental health questions under two prompt conditions, with a subset of three also translated into six further languages of varying resource tiers. We recorded 15,942 citations across 1,140 responses and 1,713 unique domains, then classified every citation with a nine-category organizational typology applied by a deterministic classifier validated against human coding. Citations were heavily concentrated: the ten most-cited domains accounted for 43.6% of English citations, and government, commercial health, and academic sources were closely matched at roughly 22% each. Platforms differed little in typical citation volume but sharply in consistency and in the source types they favored. Explicitly requesting sources shifted composition only modestly. Non-English queries surfaced fewer citations and were routed to language-appropriate resources at significantly lower rates. We release the typology, classifier, and annotated corpus as reusable instruments for auditing generative health search.

cs.CY↗

arXiv2026

Sub-nanosecond control for spin-defect quantum memories with a low-cost, compact FPGA platform

Dynamical decoupling techniques are widely used to characterize and control the environments of solid-state quantum defects, enabling solid-state quantum memories and nanoscale quantum sensors. However, resolution is often limited by the timing granularity of control hardware, which can undersample narrow spectral features and distort extracted parameters. Here, we demonstrate sub-nanosecond timing control on an inexpensive FPGA-based platform by extending the open-source QICK (Quantum Instrumentation Control Kit) framework using a waveform-offset method. This approach achieves an effective timing resolution of 200~ps on an RF system-on-chip device without modification to the underlying hardware. We apply this capability to dynamical decoupling spectroscopy of nitrogen-vacancy centers in diamond, enabling precise extraction of hyperfine couplings of individual $^{13}\mathrm{C}$ nuclear spins and resolving spectral features that are otherwise undersampled. These results demonstrate that high-resolution, device-level characterization of spin-based quantum memories can be achieved using flexible, inexpensive control hardware, providing a scalable alternative to commercial arbitrary waveform generators.

quant-ph↗

arXiv2025

Deterministic Printing of Single Quantum Dots

The unique optical properties of quantum dots (QDs), size-tunable emission and high quantum yield, make them ideal candidates for applications in secure quantum communication, quantum computing, targeted single-cell and molecular tagging, and sensing. Scalable and deterministic heterointegration strategies for single QDs have, however, remained largely out of reach due to inherent material incompatibilities with conventional semiconductor manufacturing processes. To advance scalable photonic quantum device architectures, it is therefore crucial to adopt placement and heterointegration strategies that can address these challenges. Here, we present an electrohydrodynamic (EHD) printing model, single particle extraction electrodynamics (SPEED) printing, that exploits a novel regime of nanoscale dielectrophoretics to print and deterministically position single colloidal QDs. Using QDs solubilized in apolar solvents, this additive, zero-waste nanomanufacturing process overcomes continuum fluid surface energetics and stochastic imprecision that limited previous colloidal deposition strategies, achieving selective extraction and deposition of individual QDs at sub-zeptoliter volumes. Photoluminescence and autocorrelation function (g(2)) measurements confirm nanophotonic cavity-QD integration and single-photon emission from single printed QDs. By enabling deterministic placement of single quantum dots, this method provides a powerful, scalable, and sustainable platform for integrating complex photonic circuits and quantum light sources with nanoscale precision.

physics.optics↗

arXiv2024

AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models

The increasing deployment of AI models in critical applications has exposed them to significant risks from adversarial attacks. While adversarial vulnerabilities in 2D vision models have been extensively studied, the threat landscape for 3D generative models, such as Neural Radiance Fields (NeRF), remains underexplored. This work introduces \textit{AdvIRL}, a novel framework for crafting adversarial NeRF models using Instant Neural Graphics Primitives (Instant-NGP) and Reinforcement Learning. Unlike prior methods, \textit{AdvIRL} generates adversarial noise that remains robust under diverse 3D transformations, including rotations and scaling, enabling effective black-box attacks in real-world scenarios. Our approach is validated across a wide range of scenes, from small objects (e.g., bananas) to large environments (e.g., lighthouses). Notably, targeted attacks achieved high-confidence misclassifications, such as labeling a banana as a slug and a truck as a cannon, demonstrating the practical risks posed by adversarial NeRFs. Beyond attacking, \textit{AdvIRL}-generated adversarial models can serve as adversarial training data to enhance the robustness of vision systems. The implementation of \textit{AdvIRL} is publicly available at \url{https://github.com/Tommy-Nguyen-cpu/AdvIRL/tree/MultiView-Clean}, ensuring reproducibility and facilitating future research.

cs.CV↗

arXiv2024

Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects

3D Gaussian Splatting has advanced radiance field reconstruction, enabling high-quality view synthesis and fast rendering in 3D modeling. While adversarial attacks on object detection models are well-studied for 2D images, their impact on 3D models remains underexplored. This work introduces the Masked Iterative Fast Gradient Sign Method (M-IFGSM), designed to generate adversarial noise targeting the CLIP vision-language model. M-IFGSM specifically alters the object of interest by focusing perturbations on masked regions, degrading the performance of CLIP's zero-shot object detection capability when applied to 3D models. Using eight objects from the Common Objects 3D (CO3D) dataset, we demonstrate that our method effectively reduces the accuracy and confidence of the model, with adversarial noise being nearly imperceptible to human observers. The top-1 accuracy in original model renders drops from 95.4\% to 12.5\% for train images and from 91.2\% to 35.4\% for test images, with confidence levels reflecting this shift from true classification to misclassification, underscoring the risks of adversarial attacks on 3D models in applications such as autonomous driving, robotics, and surveillance. The significance of this research lies in its potential to expose vulnerabilities in modern 3D vision models, including radiance fields, prompting the development of more robust defenses and security measures in critical real-world applications.

cs.CV↗

arXiv2024

Large-Amplitude, Easy-Plane Spin-Orbit Torque Oscillators Driven by Out-of-Plane Spin Current: A Micromagnetic Study

Spin torque oscillators are spintronic devices that generate a periodic output signal from a non-periodic input, making them promising candidates for applications like microwave communications and neuromorphic computing. However, traditional spin torque oscillators suffer from a limited precessional cone angle and thermal stability, as well as a need for an applied bias magnetic field. We use micromagnetic simulations to demonstrate a novel spin torque oscillator that relies on spin-orbit effects in ferromagnets to overcome these limitations. The key mechanism behind this oscillator is the generation of an out-of-plane spin current, in which both the spin flow and the spin orientation are out-of-plane. The torque from this spin current enables easy-plane coherent magnetic precession with a large cone angle and high thermal stability over a micron-scale lateral area. Moreover, the precession occurs about an internal field in the free layer, thereby eliminating the need for an external bias field. We demonstrate the feasibility of an easy-plane spin-orbit torque oscillator at room temperature over a wide parameter space, including the ratio of the out-of-plane spin current to the conventional spin-Hall spin current, presenting exciting possibilities for this novel spintronic device.

cond-mat.mes-hall↗

arXiv2024

One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation

This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on multiple 2D images, offering a practical and scalable solution for enhancing model scalability and robustness. This generalizable method bridges the gap between 2D perturbations and 3D-like attack capabilities, making it suitable for real-world applications. Existing adversarial attacks may become ineffective when images undergo transformations like changes in lighting, camera position, or natural deformations. We address this challenge by crafting a single universal noise perturbation applicable to various object views. Experiments on diverse rendered 3D objects demonstrate the effectiveness of our approach. The universal perturbation successfully identified a single adversarial noise for each given set of 3D object renders from multiple poses and viewpoints. Compared to single-view attacks, our universal attacks lower classification confidence across multiple viewing angles, especially at low noise levels. A sample implementation is made available at https://github.com/memoatwit/UniversalPerturbation.

cs.CV↗

arXiv2024

Knight Watch: Ubiquitous Computing Enhancements To Sleep Quality With Acoustic Analysis

This project introduces a wearable, non-intrusive device for snoring detection and remediation, designed to be placed under or alongside a pillow. The device uses sensors and machine learning algorithms to detect snoring and employs gentle vibrations to prompt positional changes, thereby reducing snoring episodes. The device is capable of connecting via an API to a cloud-based platform for the analysis of snoring sleep patterns and environmental context. The paper details the development from concept to prototype, emphasizing the technical challenges, solutions, and alignment with ubiquitous computing in sleep quality improvement.

cs.HC↗

arXiv2021

Technical Report for HW2VEC -- A Graph Learning Tool for Automating Hardware Security

In this technical report, we present HW2VEC [11], an open-source graph learning tool for hardware security, and its implementation details (Figure 1). HW2VEC provides toolboxes for graph representation extraction in the form of Data Flow Graphs (DFGs) or Abstract Syntax Trees (ASTs) from hardware designs at RTL and GLN levels. Besides, HW2VEC also offers graph learning tools for representing hardware designs in vectors that preserve both structural features and behavioral features. To the best of our knowledge, HW2VEC is the first open-source research tool that supports applying graph learning methods to hardware designs in different abstraction levels for hardware security. We organize the remainder of this technical report as follows: Section 2 introduces the architecture of HW2VEC; Section 3 gives information about the use-case implementations; Section 4 provides the experimental results and demonstrates the performance of HW2VEC for two hardware security applications: HT detection and IP piracy detection; finally, Section 5 will conclude this report.

cs.CR↗

arXiv2021

HW2VEC: A Graph Learning Tool for Automating Hardware Security

The time-to-market pressure and continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to improve design flows has attracted research attention in the Electronic Design Automation (EDA) field. However, due to the lack of supporting tools, only a few existing works apply graph learning to resolve hardware security issues. To attract more attention, we propose HW2VEC, an open-source graph learning tool that lowers the threshold for newcomers to research hardware security applications with graphs. HW2VEC provides an automated pipeline for extracting a graph representation from a hardware design in various abstraction levels (register transfer level or gate-level netlist). Besides, HW2VEC users can automatically transform the non-Euclidean hardware designs into Euclidean graph embeddings for solving their problems. In this paper, we demonstrate that HW2VEC can achieve state-of-the-art performance on two hardware security-related tasks: Hardware Trojan Detection and Intellectual Property Piracy Detection. We provide the time profiling results for the graph extraction and the learning pipelines in HW2VEC.

cs.CR↗

arXiv2015

A New Metric for Quality of Network Community Structure

Modularity is widely used to effectively measure the strength of the community structure found by community detection algorithms. However, modularity maximization suffers from two opposite yet coexisting problems: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones. The latter tendency is known in the literature as the resolution limit problem. To address them, we propose to modify modularity by subtracting from it the fraction of edges connecting nodes of different communities and by including community density into modularity. We refer to the modified metric as Modularity Density and we demonstrate that it indeed resolves both problems mentioned above. We describe the motivation for introducing this metric by using intuitively clear and simple examples. We also prove that this new metric solves the resolution limit problem. Finally, we discuss the results of applying this metric, modularity, and several other popular community quality metrics to two real dynamic networks. The results imply that Modularity Density is consistent with all the community quality measurements but not modularity, which suggests that Modularity Density is an improved measurement of the community quality compared to modularity.

cs.SI↗

arXiv2013

Using Location-Based Social Networks to Validate Human Mobility and Relationships Models

We propose to use social networking data to validate mobility models for pervasive mobile ad-hoc networks (MANETs) and delay tolerant networks (DTNs). The Random Waypoint (RWP) and Erdos-Renyi (ER) models have been a popular choice among researchers for generating mobility traces of nodes and relationships between them. Not only RWP and ER are useful in evaluating networking protocols in a simulation environment, but they are also used for theoretical analysis of such dynamic networks. However, it has been observed that neither relationships among people nor their movements are random. Instead, human movements frequently contain repeated patterns and friendship is bounded by distance. We used social networking site Gowalla to collect, create and validate models of human mobility and relationships for analysis and evaluations of applications in opportunistic networks such as sensor networks and transportation models in civil engineering. In doing so, we hope to provide more human-like movements and social relationship models to researchers to study problems in complex and mobile networks.

cs.SI↗

arXiv2013

Social Ranking Techniques for the Web

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.

cs.SI↗