arXiv subjects

Qi Lin

Publications and source records attributed to Qi Lin.

At least 19 recordsLinked to original sources

arXiv2026

SIGMA-ASL: Sensor-Integrated Multimodal Dataset for Sign Language Recognition

Automatic sign language recognition (SLR) has become a key enabler of inclusive human-computer interaction, fostering seamless communication between deaf individuals and hearing communities. Despite significant advances in multimodal learning, existing SLR research remains dominated by vision-based datasets, which are limited by sensitivity to lighting and occlusion, privacy concerns, and a lack of cross-modal diversity. To address these challenges, we introduce SIGMA-ASL, a large-scale multimodal dataset for SLR. The dataset integrates an Azure Kinect RGB-D camera, a millimeter-wave (mmWave) radar, and two wrist-worn inertial measurement units (IMUs) to capture complementary visual, radio-reflection, and kinematic information. Collected in a controlled studio environment with 20 participants performing 160 common American sign language (ASL) signs, SIGMA-ASL provides 93,545 temporally synchronized word-level multimodal clips. A unified sensing framework achieves millisecond-level alignment across modalities, enabling reliable sensor fusion and cross-modal learning. We further design standardized preprocessing pipelines and benchmarking protocols under both user-dependent and user-independent settings, offering a comprehensive foundation for evaluating single and multimodal SLR. Extensive experiments validate the dataset's quality and demonstrate its potential as a valuable resource for developing robust, privacy-preserving, and ubiquitous sign language recognition systems.

cs.HC↗

arXiv2026

CRB-Based Waveform Optimization for MIMO ISAC Systems With One-Bit ADCs

This paper studies the transmit waveform optimization for a quantized multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, where one-bit analog-to-digital converters (ADCs) are employed to enable a low-cost and power-efficient hardware implementation. Focusing on the parameter estimation task, we propose two novel Cram\'er-Rao bounds (CRBs) for both point-like target (PT) and extended target (ET) to characterize the impact of quantization distortion on the estimation accuracy, where associated estimation methods are also developed to approach these theoretical CRBs. Moreover, with the goal of jointly enhancing the sensing and communication performances, we formulate the bi-criterion ISAC waveform optimization problem by minimizing the derived CRB objectives subject to a communication symbol error probability (SEP) constraint and a total power constraint, which, due to the high nonlinearity of the one-bit CRBs, are extremely nonconvex. To yield a high-quality suboptimal solution, we develop an efficient alternating direction method of multipliers (ADMM) framework which exploits the majorization-minimization (MM) technique to address the nonconvex issue. Simulation results verify that the one-bit CRBs are tight for characterizing the quantized estimation performance and the proposed estimation methods also show clear performance advantages over the existing benchmark schemes. Furthermore, a flexible trade-off between the CRB and the SEP performance can be achieved by the developed ADMM framework, demonstrating the effectiveness of the optimized ISAC waveform.

eess.SP↗

arXiv2026

O^3-LSM: Maximizing Disaggregated LSM Write Performance via Three-Layer Offloading

Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) have been optimized and redesigned for disaggregated storage via techniques such as compaction offloading to reduce the network I/Os between compute and storage. However, the constrained memory space and slow flush at the compute node severely limit the overall write throughput of existing optimizations. In this paper, we propose O3-LSM, a fundamental new LSM-KVS architecture, that leverages the shared Disaggregated Memory (DM) to support a three-layer offloading, i.e., memtable Offloading, flush Offloading, and the existing compaction Offloading. Compared to the existing disaggregated LSM-KVS with compaction offloading only, O3-LSM maximizes the write performance by addressing the above issues. O3-LSM first leverages a novel DM-Optimized Memtable to achieve dynamic memtable offloading, which extends the write buffer while enabling fast, asynchronous, and parallel memtable transmission. Second, we propose Collaborative Flush Offloading that decouples the flush control plane from execution and supports memtable flush offloading at any node with dedicated scheduling and global optimizations. Third, O3-LSM is further improved with the Shard-Level Optimization, which partitions the memtable into shards based on disjoint key-ranges that can be transferred and flushed independently, unlocking parallelism across shards. Besides, to mitigate slow lookups in the disaggregated setting, O3-LSM also employs an adaptive Cache-Enhanced Read Delegation mechanism to combine a compact local cache with DM-assisted memtable delegated read. Our evaluation shows that O3-LSM achieves up to 4.5X write, 5.2X range query, and 1.8X point lookup throughput improvement, and up to 76% P99 latency reduction compared with Disaggregated-RocksDB, CaaS-LSM, and Nova-LSM.

cs.DB↗

arXiv2025

StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems

Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often system specific, require manual glue, and degrade under changes. Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task, which limits cross-system reuse, constrains exploration, and weakens validation. We present StorageXTuner, an LLM agent-driven auto-tuning framework for heterogeneous storage engines. StorageXTuner separates concerns across four agents - Executor (sandboxed benchmarking), Extractor (performance digest), Searcher (insight-guided configuration exploration), and Reflector (insight generation and management). The design couples an insight-driven tree search with layered memory that promotes empirically validated insights and employs lightweight checkers to guard against unsafe actions. We implement a prototype and evaluate it on RocksDB, LevelDB, CacheLib, and MySQL InnoDB with YCSB, MixGraph, and TPC-H/C. Relative to out-of-the-box settings and to ELMo-Tune, StorageXTuner reaches up to 575% and 111% higher throughput, reduces p99 latency by as much as 88% and 56%, and converges with fewer trials.

cs.DB↗

arXiv2025

Efficient Third-Harmonic Generation via Strong Coupling of Quasi-Guided Modes

Improving the conversion efficiency is critical for advancing nonlinear photonic devices, enabling applications in ultrafast optics, quantum light sources, and high-resolution imaging. Herein, we present a bilayer waveguide integrated with a periodic square nanocube array to enhance third-harmonic generation (THG) efficiency. This structure achieves strong coupling between TE- and TM-polarized quasi-guided mode (QGM) resonances, thereby enabling simultaneous dynamic control of both resonant Q-factor and coupling strength through incident polarization angle. The distinct avoided crossing observed in both reflection spectra and eigenfrequency diagrams indicates the strong coupling regime. This mechanism, facilitated by the inter-mode energy exchange within the hybridized system, results in an enhancement of Q-factor by two orders of magnitude when compared to conventional guided-mode resonances. Simulation results verify an unprecedented maximum Q-factor of 10^12 for the upper-branch mode. Leveraging strong-coupling-induced field enhancement, the system attains THG conversion efficiency of order 10^-2, demonstrating unprecedented nonlinear performance.

physics.optics↗

arXiv2025

Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction

Complex interactions among agents present a significant challenge for autonomous driving in real-world scenarios. Recently, a promising approach has emerged, which formulates the interactions of agents as a level-k game framework. It effectively decouples agent policies by hierarchical game levels. However, this framework ignores both the varying driving complexities among agents and the dynamic changes in agent states across game levels, instead treating them uniformly. Consequently, redundant and error-prone computations are introduced into this framework. To tackle the issue, this paper proposes a metric, termed as Trajectory Entropy, to reveal the game status of agents within the level-k game framework. The key insight stems from recognizing the inherit relationship between agent policy uncertainty and the associated driving complexity. Specifically, Trajectory Entropy extracts statistical signals representing uncertainty from the multimodality trajectory prediction results of agents in the game. Then, the signal-to-noise ratio of this signal is utilized to quantify the game status of agents. Based on the proposed Trajectory Entropy, we refine the current level-k game framework through a simple gating mechanism, significantly improving overall accuracy while reducing computational costs. Our method is evaluated on the Waymo and nuPlan datasets, in terms of trajectory prediction, open-loop and closed-loop planning tasks. The results demonstrate the state-of-the-art performance of our method, with precision improved by up to 19.89% for prediction and up to 16.48% for planning.

cs.AI↗

arXiv2025

V-VAE: A Variational Auto Encoding Framework Towards Fine-Grained Control over Human-Like Chat

With the continued proliferation of Large Language Model (LLM) based chatbots, there is a growing demand for generating responses that are not only linguistically fluent but also consistently aligned with persona-specific traits in conversations. However, existing role-play and persona-based chat approaches rely heavily on static role descriptions, coarse-grained signal space, and low-quality synthetic data, which fail to capture dynamic fine-grained details in human-like chat. Human-like chat requires modeling subtle latent traits, such as emotional tone, situational awareness, and evolving personality, which are difficult to predefine and cannot be easily learned from synthetic or distillation-based data. To address these limitations, we propose a Verbal Variational Auto-Encoding (V-VAE) framework, containing a variational auto-encoding module and fine-grained control space which dynamically adapts dialogue behaviour based on fine-grained, interpretable latent variables across talking style, interaction patterns, and personal attributes. We also construct a high-quality dataset, HumanChatData, and benchmark HumanChatBench to address the scarcity of high-quality data in the human-like domain. Experiments show that LLMs based on V-VAE consistently outperform standard baselines on HumanChatBench and DialogBench, which further demonstrates the effectiveness of V-VAE and HumanChatData.

cs.CL↗

arXiv2025

ELMo-Tune-V2: LLM-Assisted Full-Cycle Auto-Tuning to Optimize LSM-Based Key-Value Stores

Log-Structured Merge-tree-based Key-Value Store (LSM-KVS) is a foundational storage engine serving diverse modern workloads, systems, and applications. To suit varying use cases, LSM-KVS allows a vast configuration space that controls core parameters like compaction, flush, and cache sizes, each consuming a shared pool of CPU, Memory, and Storage resources. Navigating the LSM-KVS configuration space necessitates knowledge of the impact of each configuration on the expected workload and underlying hardware. Beyond expensive and time-intensive human-expert-based tuning, existing LSM-KVS tuning solutions focus on tuning with specific workload expectations while limited to a narrow subset of parameters. This paper introduces ELMo-Tune-V2, a framework that integrates Large Language Models (LLMs) at its foundation to demonstrate the potential of applying modern LLMs in data system optimization problems. ELMo-Tune-V2 leverages the contextual reasoning, cross-domain, and generative capabilities of LLMs to perform 1) self-navigated characterization and modeling of LSM-KVS workloads, 2) automatic tuning across a broad parameter space using cross-domain knowledge, and 3) real-time dynamic configuration adjustments for LSM-KVS. ELMo-Tune-V2 integrates three innovations: LLM-based workload synthesis for adaptive benchmark generation, feedback-driven iterative fine-tuning for configuration refinement, and real-time tuning to handle evolving workloads. Through detailed evaluation using RocksDB under several real-world applications across diverse scenarios, ELMo-Tune-V2 achieves performance improvements up to ~14X our YCSB benchmarks compared against default RocksDB configurations, and our end-to-end tests with upper-level applications, NebulaGraph and Kvrocks, demonstrate performance gains of 34% and 26%, respectively.

cs.DB↗

arXiv2025

Polycyclic Aromatic Hydrocarbon and the Ultraviolet Extinction Bump at the Cosmic Dawn

First detected in 1965, the mysterious ultraviolet (UV) extinction bump at 2175 Angstrom is the most prominent spectroscopic feature superimposed on the interstellar extinction curve. Its carrier remains unidentified over the past six decades ever since its first detection, although many candidate materials have been proposed. Widely seen in the interstellar medium (ISM) of the Milky Way as well as several nearby galaxies, this bump was recently also detected by the James Webb Space Telescope (JWST) at the cosmic dawn in JADES-GS-z6-0, a distant galaxy at redshift z~6.71, corresponding to a cosmic age of just 800 million years after the Big Bang. Differing from that of the known Galactic and extragalactic interstellar sightlines which always peak at ~2175 Angstrom, the bump seen at z~6.71 in JADES-GS-z6-0 peaks at an appreciably longer wavelength of ~2263 Angstrom and is the narrowest among all known Galactic and extragalactic extinction bumps. Here we show that the combined electronic absorption spectra quantum-chemically computed for a number of polycyclic aromatic hydrocarbon (PAH) molecules closely reproduce the bump detected by JWST in JADES-GS-z6-0. This suggests that PAH molecules have already been pervasive in the Universe at an epoch when asymptotic giant branch stars have not yet evolved to make dust.

astro-ph.GA↗

arXiv2024

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models

The rapid development of large language models (LLMs) and large vision models (LVMs) have propelled the evolution of multi-modal AI systems, which have demonstrated the remarkable potential for industrial applications by emulating human-like cognition. However, they also pose significant ethical challenges, including amplifying harmful content and reinforcing societal biases. For instance, biases in some industrial image generation models highlighted the urgent need for robust fairness assessments. Most existing evaluation frameworks focus on the comprehensiveness of various aspects of the models, but they exhibit critical limitations, including insufficient attention to content generation alignment and social bias-sensitive domains. More importantly, their reliance on pixel-detection techniques is prone to inaccuracies. To address these issues, this paper presents INFELM, an in-depth fairness evaluation on widely-used text-to-image models. Our key contributions are: (1) an advanced skintone classifier incorporating facial topology and refined skin pixel representation to enhance classification precision by at least 16.04%, (2) a bias-sensitive content alignment measurement for understanding societal impacts, (3) a generalizable representation bias evaluation for diverse demographic groups, and (4) extensive experiments analyzing large-scale text-to-image model outputs across six social-bias-sensitive domains. We find that existing models in the study generally do not meet the empirical fairness criteria, and representation bias is generally more pronounced than alignment errors. INFELM establishes a robust benchmark for fairness assessment, supporting the development of multi-modal AI systems that align with ethical and human-centric principles.

cs.CV↗

arXiv2024

Dynamic control of polarization conversion based on borophene nanostructures in optical communication bands

Polarized light has various potential applications in the communication bands, including optical communication, polarization imaging, quantum emission, and quantum communication. However, optimizing polarization control requires continuous improvements in areas such as dynamic tunability, materials, and efficiency. In this work, we propose a borophene-based structure capable of converting linearly polarized light into arbitrarily polarized light through the coherent excitation of localized surface plasmons (LSPs) in optical communication band. Furthermore, a double-layer borophene structure can be achieved by placing a second borophene array at the top of the first one with a 90{\deg} relative rotation of their crystalline plane. The rotation direction of the polarization state of the reflected light can be switched by independently controlling the carrier concentration of the two-layer borophene. Finally, a dipole source is used to realize the emission of polarized light, which is two orders of magnitude higher than the emission rate in free space, and the polarization state can be dynamically controlled by manipulating the carrier concentration. Our study is simple and compact, with potential applications in the fields of polarizers, polarization detectors, and quantum emitters.

physics.optics↗

arXiv2024

Active formation of Friedrich-Wintgen bound states in the continuum in dielectric dimerized grating borophene heterostructure

The Friedrich-Wintgen bound state in the continuum (FW BIC) provides a unique approach for achieving high quality factor (Q-factor) resonance, which has attracted wide attention and promoted the development of various applications. However, the FW BIC is usually considered as accident BIC resulting from the continuous parameters tuning, and a systematic approach to generate the FW BIC is still lacking. To address this, a method of actively forming FW BIC by matching the damping rate and resonance frequency of the coupling mode is proposed. As a proof-of-principle example, we propose a dielectric dimerized grating borophene heterostructure that generates a FW BIC near the commercially important communication wavelength. The coupling system comprises an electrically tunable borophene plasmon mode and a BIC supported by a dielectric dimer grating that can be attributed to the Brillouin zone folding. More interestingly, the BIC can be excited by the localized borophene plasmon (LBP) mode through near-field coupling as LBP mode can be considered as the dipole source. The interaction between them can further form the FW BIC, and support electromagnetically induced transparency (EIT)-like with maximum group index up to 2043, indicating its great potential for slow light applications. Our results provide a promising strategy and theoretical support for the generation of FW BIC in active plasmonic optical devices.

physics.optics↗

arXiv2023

Achieving coherent perfect absorption based on flat-band plasmonic Friedrich-Wintgen BIC in borophene metamaterials

Many applications involve the phenomenon of a material absorbing electromagnetic radiation. By exploiting wave interference, the efficiency of absorption can be significantly enhanced. Here, we propose Friedrich-Wintgen bound states in the continuum (F-W BICs) based on borophene metamaterials to realize coherent perfect absorption with a dual-band absorption peak in commercially important communication bands. The metamaterials consist of borophene gratings and a borophene sheet that can simultaneously support a Fabry-Perot plasmon resonance and a guided plasmon mode. The formation and dynamic modulation of the F-W BIC can be achieved by adjusting the width or carrier density of the borophene grating, while the strong coupling leads to the anti-crossover behavior of the absorption spectrum. Due to the weak angular dispersion originating from the intrinsic flat-band characteristic of the deep sub-wavelength periodic structure, the proposed plasmonic system exhibits almost no change in wavelength and absorption at large incident angles (within 70 degrees). In addition, we employ the temporal coupled-mode theory including near- and far-field coupling to obtain strong critical coupling, successfully achieve coherent perfect absorption, and can realize the absorption switch by changing the phase difference between the two coherent beams. Our findings can offer theoretical support for absorber design and all-optical tuning.

physics.optics↗

arXiv2023

Serving Deep Learning Model in Relational Databases

Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS). The potential relation-centric architecture aims to represent a large-scale tensor computation through relational operators. While each of these architectures demonstrates promise in specific use scenarios, we identify urgent requirements for seamless integration of these architectures and the middle ground in-between these architectures. We delve into the gaps that impede the integration and explore innovative strategies to close them. We present a pathway to establish a novel RDBMS for enabling a broad class of data-intensive DL inference applications.

cs.DB↗

arXiv2023

Polycyclic Aromatic Hydrocarbon Molecules and the 2175 Angstrom Interstellar Extinction Bump

The exact nature of the 2175 Angstrom extinction bump, the strongest spectroscopic absorption feature superimposed on the interstellar extinction curve, remains unknown ever since its discovery in 1965. Popular candidate carriers for the extinction bump include nano-sized graphitic grains and polycyclic aromatic hydrocarbon (PAH) molecules. To quantitatively evaluate PAHs as a possible carrier, we perform quantum chemical computations for the electronic transitions of 30 compact, pericondensed PAH molecules and their cations as well as anions with a wide range of sizes from 16 to 96 C atoms and a mean size of 43 C atoms. It is found that a mixture of such PAHs, which individually exhibit sharp absorption features, show a smooth and broad absorption band that resembles the 2175 Angstrom interstellar extinction bump. Arising from \pi^{*} --\pi, the width and intensity of the absorption bump for otherwise randomly-selected and uniformly-weighted PAH mixtures, do not vary much with PAH sizes and charge states, whereas the position shifts to longer wavelengths as PAH size increases. While the computed bump position, with the computational uncertainty taken into account, appears to agree with that of the interstellar extinction bump, the computed width is considerably broader than the interstellar bump if the molecules are uniformly weighted. It appears that, to account for the observed bump width, one has to resort to PAH species of specific sizes and structures.

astro-ph.GA↗

arXiv2023

From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces

Much of what we remember is not due to intentional selection, but simply a by-product of perceiving. This raises a foundational question about the architecture of the mind: How does perception interface with and influence memory? Here, inspired by a classic proposal relating perceptual processing to memory durability, the level-of-processing theory, we present a sparse coding model for compressing feature embeddings of images, and show that the reconstruction residuals from this model predict how well images are encoded into memory. In an open memorability dataset of scene images, we show that reconstruction error not only explains memory accuracy but also response latencies during retrieval, subsuming, in the latter case, all of the variance explained by powerful vision-only models. We also confirm a prediction of this account with 'model-driven psychophysics'. This work establishes reconstruction error as a novel signal interfacing perception and memory, possibly through adaptive modulation of perceptual processing.

q-bio.NC↗

arXiv2022

Polaritonic coherent perfect absorption based on strong critical coupling between quasi-bound state in the continuum and exciton

Enhancement of light-matter interactions is of great importance for many nanophotonic devices, and one way to achieve it is to feed energy perfectly to the strongly coupled system. Here, we propose gap-perturbed dimerized gratings based on bulk WS2 for flexible control of the strong coupling between quasi-bound state in the continuum (quasi-BIC) and exciton. The simulation results show that when a gap perturbation is introduced into the system resulting in the Brillouin zone folding, BIC transforms into quasi-BIC whose quality factor (Q-factor) is related to the value of gap perturbation. The strong coupling results in the anti-crossover behavior of the absorption spectra, and thus a Rabi splitting energy of 0.235 eV is obtained. Temporal coupled-mode theory is employed to analyze the strong coupling system, and the absorption of the system could be enhanced, by modulating the damping rate of quasi-BIC to make the system satisfy the strong critical coupling condition. Furthermore, polaritonic coherent perfect absorption is achieved by using the two-port source excitation. This work could provide ideas for enhancing light-matter interactions and a promising prospect for polariton-based light-emitting or lasing devices.

physics.optics↗

arXiv2022

Tunable strong plasmon-exciton coupling based on borophene and deep subwavelength perovskite grating

Two-dimensional materials support deeply confined and tunable plasmonic modes, which have great potential for achieving device miniaturization and flexible manipulation. In this paper, we propose a diffraction-unlimited system composed of borophene layer and perovskite grating to investigate the strong coupling between the borophene guiding plasmon (BGP) and perovskite exciton (PE) mode. The resonant energy of BGP mode could be electrically tuned to match the energy of PE mode, and a remarkable Rabi splitting is attained under zero-detuning condition. The splitting energy could reach 230 meV due to the strong field enhancement provided by BGP mode. Consequently, an active reflective phase modulation with 1.76{\pi} range is achieved by dynamically manipulating the detuning. Furthermore, by increasing the distance between the borophene layer and perovskite grating, a parity-time symmetry breaking could be observed with the vanished energy splitting. Our results deepen the understanding of light-matter interaction at the sub-wavelength scale and provide a guideline for designing active plasmonic devices.

physics.optics↗