arXiv subjects

Hien Nguyen

Publications and source records attributed to Hien Nguyen.

At least 19 recordsLinked to original sources

arXiv2026

Spatial Mapping and Capacitor Trimming Developments to Improve Usable Pixel Yield in PRIMA FIRESS Kilo-Pixel Arrays

The Probe far-Infrared Mission for Astrophysics (PRIMA) will use 8 kilo-pixel kinetic inductance detector (KID) arrays in its spectrometer module. We present an improved resonant frequency to spatial position mapping system designed to preserve each array's mapping after transferring it from the mapping apparatus to the flight housing. Such a mapping is necessary for astronomical observations, and additionally allows us to laser trim the capacitive elements of KIDs to optimize resonance separation in frequency space. This increases the operating yield by eliminating collided resonances, reduces crosstalk, and reduces the sensitivity to frequency drift over time.

astro-ph.IM↗

arXiv2026

Radiation Total Dose for PRIMA: Cold Exposure with Alpha Particles

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a far-infrared (24-261 micron wavelengths) probe-class space observatory currently under Phase A study, which promises orders-of-magnitude improvement in mapping speed over its predecessors. PRIMA will field exquisitely sensitive kilopixel arrays of kinetic inductance detectors (KIDs) for the Far-Infrared Enhanced Survey Spectrometer (FIRESS) instrument. PRIMA will orbit in space at the Sun-Earth L2 point, where Planck found the energetic particle flux to be about 300/min/cm2. Thus, the possible effect of a high fluence of energetic particles on the detector sensitivity must be characterized. Previous work has suggested that bombardment of KIDs by ions can reduce the quasiparticle lifetime (Barends et. al. 2009), but the conditions of the experiment were not representative of a detector which is continuously held at sub-Kelvin temperatures in the energetic particle environment of L2 orbit. To better replicate the damage which would be produced by energetic particles in this environment, we developed a fully cryogenic irradiation experiment in which a stepper motor controls a screen which can block or reveal an alpha particle emitter. This setup can be used to irradiate aluminum KID arrays fabricated for FIRESS to well-controlled dose levels. In this work, we calculate the damage dose expected for a 5-year mission in L2 orbit, and we irradiate an array to approximately 62 percent of this level. Before and after irradiation, we measure the quasiparticle lifetimes, resonant frequencies, and quality factors of the detectors.

astro-ph.IM↗

arXiv2026

SemLT3D: Semantic-Guided Expert Distillation for Camera-only Long-Tailed 3D Object Detection

Camera-only 3D object detection has emerged as a cost-effective and scalable alternative to LiDAR for autonomous driving, yet existing methods primarily prioritize overall performance while overlooking the severe long-tail imbalance inherent in real-world datasets. In practice, many rare but safety-critical categories such as children, strollers, or emergency vehicles are heavily underrepresented, leading to biased learning and degraded performance. This challenge is further exacerbated by pronounced inter-class ambiguity (e.g., visually similar subclasses) and substantial intra-class diversity (e.g., objects varying widely in appearance, scale, pose, or context), which together hinder reliable long-tail recognition. In this work, we introduce SemLT3D, a Semantic-Guided Expert Distillation framework designed to enrich the representation space for underrepresented classes through semantic priors. SemLT3D consists of: (1) a language-guided mixture-of-experts module that routes 3D queries to specialized experts according to their semantic affinity, enabling the model to better disentangle confusing classes and specialize on tail distributions; and (2) a semantic projection distillation pipeline that aligns 3D queries with CLIP-informed 2D semantics, producing more coherent and discriminative features across diverse visual manifestations. Although motivated by long-tail imbalance, the semantically structured learning in SemLT3D also improves robustness under broader appearance variations and challenging corner cases, offering a principled step toward more reliable camera-only 3D perception.

cs.CV↗

arXiv2026

GazeQwen: Lightweight Gaze-Conditioned LLM Modulation for Streaming Video Understanding

Current multimodal large language models (MLLMs) cannot effectively utilize eye-gaze information for video understanding, even when gaze cues are supplied via visual overlays or text descriptions. We introduce GazeQwen, a parameter efficient approach that equips an open-source MLLM with gaze awareness through hidden-state modulation. At its core is a compact gaze resampler (~1-5 M trainable parameters) that encodes V-JEPA 2.1 video features together with fixation-derived positional encodings and produces additive residuals injected into selected LLM decoder layers via forward hooks. An optional second training stage adds low-rank adapters (LoRA) to the LLM for tighter integration. Evaluated on all 10 tasks of the StreamGaze benchmark, GazeQwen reaches 63.9% accuracy, a +16.1 point gain over the same Qwen2.5-VL-7B backbone with gaze as visual prompts and +10.5 points over GPT-4o, the highest score among all open-source and proprietary models tested. These results suggest that learning where to inject gaze within an LLM is more effective than scaling model size or engineering better prompts. All code and checkpoints are available at https://github.com/phamtrongthang123/gazeqwen .

cs.CV↗

arXiv2026

VietNormalizer: An Open-Source, Dependency-Free Python Library for Vietnamese Text Normalization in TTS and NLP Applications

We present VietNormalizer1, an open-source, zero-dependency Python library for Vietnamese text normalization targeting Text-to-Speech (TTS) and Natural Language Processing (NLP) applications. Vietnamese text normalization is a critical yet underserved preprocessing step: real-world Vietnamese text is densely populated with non-standard words (NSWs), including numbers, dates, times, currency amounts, percentages, acronyms, and foreign-language terms, all of which must be converted to fully pronounceable Vietnamese words before TTS synthesis or downstream language processing. Existing Vietnamese normalization tools either require heavy neural dependencies while covering only a narrow subset of NSW classes, or are embedded within larger NLP toolkits without standalone installability. VietNormalizer addresses these gaps through a unified, rule-based pipeline that: (1) converts arbitrary integers, decimals, and large numbers to Vietnamese words; (2) normalizes dates and times to their spoken Vietnamese forms; (3) handles VND and USD currency amounts; (4) expands percentages; (5) resolves acronyms via a customizable CSV dictionary; (6) transliterates non-Vietnamese loanwords and foreign terms to Vietnamese phonetic approximations; and (7) performs Unicode normalization and emoji/special-character removal. All regular expression patterns are pre-compiled at initialization, enabling high-throughput batch processing with minimal memory overhead and no GPU or external API dependency. The library is installable via pip install vietnormalizer, available on PyPI and GitHub at https://github.com/nghimestudio/vietnormalizer, and released under the MIT license. We discuss the design decisions, limitations of existing approaches, and the generalizability of the rule-based normalization paradigm to other low-resource tonal and agglutinative languages.

cs.CL↗

arXiv2026

VietSuperSpeech: A Large-Scale Vietnamese Conversational Speech Dataset for ASR Fine-Tuning in Chatbot, Customer Support, and Call Center Applications

We introduce VietSuperSpeech, a large-scale Vietnamese automatic speech recognition (ASR) dataset of 52,023 audio-text pairs totaling 267.39 hours, with a distinctive focus on casual conversational speech. Unlike existing Vietnamese ASR corpora that predominantly feature read speech, news narration, or audiobook content, VietSuperSpeech is sourced from four publicly accessible YouTube channels spanning everyday conversation, personal vlogging, overseas Vietnamese community dialogue, and informal commentary - the very speech styles encountered in real-world chatbot, customer support, call center, and hotline deployments. All audio is standardized to 16 kHz mono PCM WAV and segmented into 3-30 second utterances. Transcriptions are generated via pseudo-labeling using the Zipformer-30M-RNNT-6000h model (Nguyen, 2025) deployed through Sherpa-ONNX, pre-trained on 6,000 hours of Vietnamese speech. After quality filtering, the dataset is split into 46,822 training samples (240.67 hours) and 5,201 development/test samples (26.72 hours) with a fixed random seed. The text averages 266 characters per utterance, totaling 13.8 million fully diacritically marked Vietnamese characters. We demonstrate that VietSuperSpeech fills a critical gap in the Vietnamese ASR ecosystem: while corpora such as VLSP2020, VIET_BUD500, VietSpeech, FLEURS, VietMed, Sub-GigaSpeech2-Vi, viVoice, and Sub-PhoAudioBook provide broad coverage of formal and read speech, none specifically targets the casual, spontaneous register indispensable for conversational AI applications. VietSuperSpeech is publicly released at https://huggingface.co/datasets/thanhnew2001/VietSuperSpeech.

cs.SD↗

arXiv2026

Spectral response of SPHEREx

The Spectro Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is conducting the first all-sky near infrared spectral survey spanning 0.75 to 5.0um with resolving power R~35 to 130. Linear variable filters mounted in front of six H2RG detectors produce a position dependent spectral response across the focal plane. This paper presents the ground-based spectral calibration of SPHEREx, including the cryogenic apparatus, optical configuration, measurement strategy, analysis pipeline, and resulting calibration products. Monochromatic wavelength scans are used to derive the spectral response function, band center, and resolving power for every pixel. Band centers are measured to better than 1nm for Bands 1 through 4 (0.75 to 3.82um) and better than 10nm for Bands 5 and 6 (3.82 to 5.0um). Out-of-band leakage is negligible for detectors above 1.64um and is present at the percent level below this wavelength. The resolving power is measured to within 5% and agrees with design expectations to within 10%. An on-sky spectrum of the Cat's Eye Nebula (NGC 6543) constructed from repeated observations provides in-flight verification and shows agreement between ground calibrated response and astrophysical emission features. Calibration products, including per-pixel band center and resolving power maps, are released through IPAC to support community use of SPHEREx data. The absolute spectral calibration will continue to improve through in-flight measurements, with further reductions in uncertainty expected for the longest-wavelength bands.

astro-ph.IM↗

arXiv2025

The Far-Infrared Enhanced Survey Spectrometer (FIRESS) for PRIMA: Approach and Estimated Performance

We present the architectural concept for the Far-Infrared Enhanced Survey Spectrometer (FIRESS) for the Probe Mission for far-IR Astrophysics (PRIMA). FIRESS spans the 24--235 micron range with four R ~ 100 slit-fed grating modules, each coupling to a 24 (spatial) by 84 (spectral) pixel array of kinetic inductance detectors (KIDs). All four arrays are read out simultaneously, and a point source of interest can be coupled to two of the four bands at a time. A Fourier transform module can be engaged over a portion of the FIRESS slits to create a high-resolution mode in which the light is intercepted, processed by the interferometer then reinserted into the path to the grating modules for detection. We provide a simulation and description of the technique that will be used to obtain high-resolution spectra. We identify the most important system requirements imposed by the detector system, finding that they are met with the existing design. Finally, we present our performance modeling, including both direct estimates given our current design status, as well as durable guidelines for developing general-observer programs.

astro-ph.IM↗

arXiv2024

FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation

Developing an interpretable system for generating reports in chest X-ray (CXR) analysis is becoming increasingly crucial in Computer-aided Diagnosis (CAD) systems, enabling radiologists to comprehend the decisions made by these systems. Despite the growth of diverse datasets and methods focusing on report generation, there remains a notable gap in how closely these models' generated reports align with the interpretations of real radiologists. In this study, we tackle this challenge by initially introducing Fine-Grained CXR (FG-CXR) dataset, which provides fine-grained paired information between the captions generated by radiologists and the corresponding gaze attention heatmaps for each anatomy. Unlike existing datasets that include a raw sequence of gaze alongside a report, with significant misalignment between gaze location and report content, our FG-CXR dataset offers a more grained alignment between gaze attention and diagnosis transcript. Furthermore, our analysis reveals that simply applying black-box image captioning methods to generate reports cannot adequately explain which information in CXR is utilized and how long needs to attend to accurately generate reports. Consequently, we propose a novel explainable radiologist's attention generator network (Gen-XAI) that mimics the diagnosis process of radiologists, explicitly constraining its output to closely align with both radiologist's gaze attention and transcript. Finally, we perform extensive experiments to illustrate the effectiveness of our method. Our datasets and checkpoint is available at https://github.com/UARK-AICV/FG-CXR.

cs.CV↗

arXiv2024

GazeSearch: Radiology Findings Search Benchmark

Medical eye-tracking data is an important information source for understanding how radiologists visually interpret medical images. This information not only improves the accuracy of deep learning models for X-ray analysis but also their interpretability, enhancing transparency in decision-making. However, the current eye-tracking data is dispersed, unprocessed, and ambiguous, making it difficult to derive meaningful insights. Therefore, there is a need to create a new dataset with more focus and purposeful eyetracking data, improving its utility for diagnostic applications. In this work, we propose a refinement method inspired by the target-present visual search challenge: there is a specific finding and fixations are guided to locate it. After refining the existing eye-tracking datasets, we transform them into a curated visual search dataset, called GazeSearch, specifically for radiology findings, where each fixation sequence is purposefully aligned to the task of locating a particular finding. Subsequently, we introduce a scan path prediction baseline, called ChestSearch, specifically tailored to GazeSearch. Finally, we employ the newly introduced GazeSearch as a benchmark to evaluate the performance of current state-of-the-art methods, offering a comprehensive assessment for visual search in the medical imaging domain. Code is available at \url{https://github.com/UARK-AICV/GazeSearch}.

cs.CV↗

arXiv2024

Development of an ultra-sensitive 210-micron array of KIDs for far-IR astronomy

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a proposed space observatory which will use arrays of thousands of kinetic inductance detectors (KIDs) to perform low- and moderate-resolution spectroscopy throughout the far-infrared. The detectors must have noise equivalent powers (NEPs) at or below 0.1 aW/sqrt(Hz) to be subdominant to noise from sky backgrounds and thermal noise from PRIMA's cryogenically cooled primary mirror. Using a Radio Frequency System on a Chip for multitone readout, we measure the NEPs of detectors on a flight-like array designed to observe at a wavelength of 210 microns. We find that 92% of the KIDs measured have an NEP below 0.1 aW/sqrt(Hz) at a noise frequency of 10 Hz.

astro-ph.IM↗

arXiv2024

SPHEREx: NASA's Near-Infrared Spectrophotmetric All-Sky Survey

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron At the end of its two-year mission, SPHEREx will provide 0.75-to-5 micron spectra of each 6.2"x6.2" pixel on the sky - 14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test and a summary of the data processing, analysis, and distribution plans.

astro-ph.IM↗

arXiv2023

Spin-Orbital Coupling in All-Inorganic Metal-Halide Perovskites: the Hidden Force that Matters

Highlighted with improved long-term thermal and environmental stability, all-inorganic metal halide perovskites exhibit tunable physical properties, cost-effective synthesis, and satisfactory optoelectronic performance, attracting increasing research interests worldwide. However, a less explored feature of these materials is their strong spin-orbit coupling (SOC), which is the hidden force influencing not only band structure but also properties including magnetoresistance, spin lifetime and singlet-triplet splitting. This review provides an overview of the fundamental aspects and the latest progress of the SOC and debate regarding Rashba effects in all-inorganic metal halide perovskites, providing critical insights into the physical phenomena and potential applications. Meanwhile, crystal structures and photophysics of all-inorganic perovskite are discussed in the context of SOC, along with the related experimental and characterization techniques. Furthermore, a recent understanding of the band topology in the all-inorganic halide perovskites is introduced to push the boundary even further for the novel applications of all-inorganic halide perovskites. Finally, an outlook is given on the potential directions of breakthroughs via leveraging the SOC in halide perovskites.

cond-mat.mtrl-sci↗

arXiv2023

Modeling of cosmic rays and near-IR photons in aluminum KIDs

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is working to develop kinetic inductance detectors (KIDs) that can meet the sensitivity targets of a far-infrared spectrometer on a cryogenically cooled space telescope. An important ingredient for achieving high sensitivity is increasing the fractional-frequency responsivity. Here we present a study of the responsivity of aluminum KIDs fabricated at the Jet Propulsion Laboratory. Specifically, we model the KID's temporal response to pair-breaking excitations in the framework of the Mattis-Bardeen theory, incorporating quasiparticle recombination dynamics and the pair-breaking efficiency. Using a near-IR laser, we measure time-resolved photon pulses and fit them to our model, extracting the time-resolved quasiparticle density and the quasiparticle recombination lifetime. Comparing the fit to the known energy of the laser provides a measurement of the pair-breaking efficiency. In addition to photon-sourced excitations, it is important to understand the KID's response to phonon-sourced excitations from cosmic rays. We measure the rate of secondary cosmic rays detected by our devices, and predict the dead time due to cosmic rays for an array in L2 orbit. This work provides confidence in KIDs' robustness to cosmic ray events in the space environment.

astro-ph.IM↗

arXiv2023

High-sensitivity Kinetic Inductance Detector Arrays for the Probe Far-Infrared Mission for Astrophysics

Far-infrared (far-IR) astrophysics missions featuring actively cooled telescopes will offer orders of magnitude observing speed improvement at wavelengths where galaxies and forming planetary systems emit most of their light. The PRobe far-Infrared Mission for Astrophysics (PRIMA), which is currently under study, emphasizes low and moderate resolution spectroscopy throughout the far-IR. Full utilization of PRIMA's cold telescope requires far-IR detector arrays with per-pixel noise equivalent powers (NEPs) at or below 1 x 10-19 W/rtHz. We are developing low-volume Aluminum kinetic inductance detector (KID) arrays to reach these sensitivities. We will present on the development of our long-wavelength (210 um) array approach, with a focus on multitone measurements of our 1,008-pixel arrays. We measure an NEP below 1 x 10-19 W/rtHz for 73 percent of our pixels.

astro-ph.IM↗

arXiv2023

Controllable Group Choreography using Contrastive Diffusion

Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications. The ability to generate synchronized and visually appealing group dance motions that are aligned with music opens up opportunities in many fields such as entertainment, advertising, and virtual performances. However, most of the recent works are not able to generate high-fidelity long-term motions, or fail to enable controllable experience. In this work, we aim to address the demand for high-quality and customizable group dance generation by effectively governing the consistency and diversity of group choreographies. In particular, we utilize a diffusion-based generative approach to enable the synthesis of flexible number of dancers and long-term group dances, while ensuring coherence to the input music. Ultimately, we introduce a Group Contrastive Diffusion (GCD) strategy to enhance the connection between dancers and their group, presenting the ability to control the consistency or diversity level of the synthesized group animation via the classifier-guidance sampling technique. Through intensive experiments and evaluation, we demonstrate the effectiveness of our approach in producing visually captivating and consistent group dance motions. The experimental results show the capability of our method to achieve the desired levels of consistency and diversity, while maintaining the overall quality of the generated group choreography. The source code can be found at https://aioz-ai.github.io/GCD

cs.CV↗

arXiv2023

I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining where a radiologist looks, typically through tasks such as detection, segmentation, or classification. However, these approaches are often designed as black-box models, lacking interpretability. In this paper, we introduce Interpretable Artificial Intelligence (I-AI) a novel and unified controllable interpretable pipeline for decoding the intense focus of radiologists in CXR diagnosis. Our I-AI addresses three key questions: where a radiologist looks, how long they focus on specific areas, and what findings they diagnose. By capturing the intensity of the radiologist's gaze, we provide a unified solution that offers insights into the cognitive process underlying radiological interpretation. Unlike current methods that rely on black-box machine learning models, which can be prone to extracting erroneous information from the entire input image during the diagnosis process, we tackle this issue by effectively masking out irrelevant information. Our proposed I-AI leverages a vision-language model, allowing for precise control over the interpretation process while ensuring the exclusion of irrelevant features. To train our I-AI model, we utilize an eye gaze dataset to extract anatomical gaze information and generate ground truth heatmaps. Through extensive experimentation, we demonstrate the efficacy of our method. We showcase that the attention heatmaps, designed to mimic radiologists' focus, encode sufficient and relevant information, enabling accurate classification tasks using only a portion of CXR. The code, checkpoints, and data are at https://github.com/UARK-AICV/IAI

cs.CV↗

arXiv2023

High-sensitivity transition-edge-sensed bolometers: improved speed and characterization with AC and DC bias

We report on efforts to improve the speed of low-G far-infrared transition-edged-sensed bolometers. We use a fabrication process that does not require any dry etch steps to reduce heat capacity on the suspended device and measure a reduction in the detector time constant. However, we also measure an increase in the temperature-normalized thermal conductance (G), and a corresponding increase in the noise-equivalent power (NEP). We employ a new near-IR photon-noise technique using a near-IR laser to calibrate the frequency-domain multiplexed AC system and compare the results to a well-understood DC circuit. We measure an NEP white noise level of 0.8 aW/rtHz with a 1/f knee below 0.1 Hz and a time constant of 3.2 ms.

physics.app-ph↗