arXiv subjects

Ray Chen

Publications and source records attributed to Ray Chen.

8 recordsLinked to original sources

arXiv2026

RISE: Interactive Visual Diagnosis of Fairness in Machine Learning Models

Evaluating fairness under domain shift is challenging because scalar metrics often obscure exactly where and how disparities arise. We introduce \textit{RISE} (Residual Inspection through Sorted Evaluation), an interactive visualization tool that converts sorted residuals into interpretable patterns. By connecting residual curve structures to formal fairness notions, RISE enables localized disparity diagnosis, subgroup comparison across environments, and the detection of hidden fairness issues. Through post-hoc analysis, RISE exposes accuracy-fairness trade-offs that aggregate statistics miss, supporting more informed model selection.

cs.LG↗

arXiv2025

MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data

Multi30k is frequently cited in the multimodal machine translation (MMT) literature, offering parallel text data for training and fine-tuning deep learning models. However, it is limited to four languages: Czech, English, French, and German. This restriction has led many researchers to focus their investigations only on these languages. As a result, MMT research on diverse languages has been stalled because the official Multi30k dataset only represents European languages in Latin scripts. Previous efforts to extend Multi30k exist, but the list of supported languages, represented language families, and scripts is still very short. To address these issues, we propose MultiScript30k, a new Multi30k dataset extension for global languages in various scripts, created by translating the English version of Multi30k (Multi30k-En) using NLLB200-3.3B. The dataset consists of over $30000$ sentences and provides translations of all sentences in Multi30k-En into Ar, Es, Uk, Zh\_Hans and Zh\_Hant. Similarity analysis shows that Multi30k extension consistently achieves greater than $0.8$ cosine similarity and symmetric KL divergence less than $0.000251$ for all languages supported except Zh\_Hant which is comparable to the previous Multi30k extensions ArEnMulti30k and Multi30k-Uk. COMETKiwi scores reveal mixed assessments of MultiScript30k as a translation of Multi30k-En in comparison to the related work. ArEnMulti30k scores nearly equal MultiScript30k-Ar, but Multi30k-Uk scores $6.4\%$ greater than MultiScript30k-Uk per split.

cs.CL↗

arXiv2025

ENLighten: Lighten the Transformer, Enable Efficient Optical Acceleration

Photonic computing has emerged as a promising substrate for accelerating the dense linear-algebra operations at the heart of AI, yet adoption for large Transformer models remains in its infancy. We identify two bottlenecks: (1) costly electro--optic conversions and data-movement overheads that erode energy efficiency as model sizes scale; (2) a mismatch between limited on-chip photonic resources and Transformer scale, which forces frequent reuse of photonic tensor cores and dilutes throughput gains. To address these challenges, we introduce a hardware--software co-design framework. First, we propose \texttt{Lighten}, a PTC-aware compression flow that post-hoc decomposes each Transformer weight matrix into a low-rank component plus a structured-sparse component aligned to photonic tensor-core granularity, without lengthy retraining. Second, we present \texttt{ENLighten}, a reconfigurable photonic accelerator with dynamically adaptive tensor cores, driven by broadband light redistribution, enabling fine-grained sparsity support and full power gating of inactive parts. On ImageNet, \texttt{Lighten} prunes a Base-scale Vision Transformer by 50\% with $\approx$1\% accuracy drop after only 3 epochs (about 1 hour) of fine-tuning. Deployed on \texttt{ENLighten}, it achieves a $2.5\times$ improvement in energy--delay product over the state-of-the-art photonic Transformer accelerator.

cs.ET↗

arXiv2024

Cutoff in total variation for the shelf shuffle

We analyze the mixing time of a popular shuffling machine known as the shelf shuffler. It is a modified version of a $2m$-handed riffle shuffle ($m=10$ in casinos) in which a deck of $n$ cards is split multinomially into $2m$ piles, the even-numbered piles are reversed, and then cards are dropped from piles proportionally to their sizes. We prove that $\frac{5}{4} \log_{2m} n$ shuffles are necessary and sufficient to mix in total variation, and a cutoff occurs with constant window size. We also determine the cutoff profile in terms of the total variation distance between two shifted normal random variables.

math.PR↗

arXiv2024

Incubating Advances in Integrated Photonics with Emerging Sensing and Computational Capabilities

As photonic technologies continue to grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities to research communities. Applications span across data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities growing endlessly. Here, we provide a review of state of the art integrated photonic sensors operating in near and mid infrared wavelength regions on various material platforms. Among different materials, architectures, and technologies leading the way for on chip sensors, we discuss optical sensing principles commonly applied to biochemical and gas sensing. Our focus is particularly on passive and active optical waveguides, including dispersion engineered metamaterial based structures an essential approach for enhancing the interaction between light and analytes in chip scale sensors. We harness a diverse array of cutting edge sensing technologies, heralding a revolutionary on chip sensing paradigm. Our arsenal includes refractive index based sensing, plasmonic, and spectroscopy, forging an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

physics.optics↗

arXiv2022

Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration

Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly in a mixed-signal manner. Tedious analog/digital (A/D) conversion cost significantly limits the overall system's energy efficiency. In this work, we devise an efficient analog activation unit with magnetic tunnel junction (MTJ)-based analog content-addressable memory (MACAM), simultaneously realizing nonlinear activation and A/D conversion in a fused fashion. To compensate for the nascent and therefore currently limited representation capability of MACAM, we propose to mix our analog activation unit with digital activation dataflow. A fully differential framework, SuperMixer, is developed to search for an optimized activation workload assignment, adaptive to various activation energy constraints. The effectiveness of our proposed methods is evaluated on a silicon photonic accelerator. Compared to standard activation implementation, our mixed activation system with the searched assignment can achieve competitive accuracy with $>$60% energy saving on A/D conversion and activation.

cs.ET↗

arXiv2019

City-level Geolocation of Tweets for Real-time Visual Analytics

Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We provide computational evaluations to demonstrate the superiority and utility of our geolocation prediction model within an interactive system.

cs.SI↗

arXiv2019

Coupling-enhanced Dual ITO Layer Electro-absorption Modulator in Silicon Photonics

Electro-optic signal modulation provides a key functionality in modern technology and information networks. Photonic integration has enabled not only miniaturizing photonic components, but also provided performance improvements due to co-design addressing both electrical and optical device rules. However, the millimeter-to-centimeter large footprint of many foundry-ready photonic electro-optic modulators significantly limits on-chip scaling density. To address these limitations, here we experimentally demonstrate a coupling-enhanced electro-absorption modulator by heterogeneously integrating a novel dual-gated indium-tin-oxide (ITO) phase-shifting tunable absorber placed at a silicon directional coupler region. Our experimental modulator shows a 2 dB extinction ratio for a just 4 um short device at 4 volt bias. Since no material nor optical resonances are deployed, this device shows spectrally broadband operation as demonstrated here across the entire C-band. In conclusion we demonstrate a modulator utilizing strong index-change from both real and imaginary part of active material enabling compact and high-performing modulators using semiconductor foundry-near materials.

physics.app-ph↗