arXiv subjects

Zhuojun Li

Publications and source records attributed to Zhuojun Li.

14 recordsLinked to original sources

arXiv2026

HiSync: Spatio-Temporally Aligning Hand Motion from Wearable IMU and On-Robot Camera for Command Source Identification in Long-Range HRI

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) - determining who issued a command - is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI. https://github.com/OctopusWen/HiSync

cs.HC↗

arXiv2026

KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens

Existing touchscreen software keyboards prevent users from resting their hands, forcing slow and fatiguing index-finger tapping ("chicken typing") instead of familiar hands-down ten-finger typing. We present KeySense, a purely software solution that preserves physical keyboard motor skills. KeySense isolates intentional taps from resting-finger noise using cognitive-motor timing patterns, and then uses a fine-tuned LLM decoder to convert the resulting noisy letter sequence into the intended word. In controlled component tests, the decoder substantially outperforms two statistical baselines (top-1 accuracy 84.8% vs 75.7% and 79.3%). A 12-participant study shows clear ergonomic and performance benefits: compared with the conventional hover-style keyboard, users rated KeySense as markedly less physically demanding (NASA-TLX median 1.5 vs 4.0), and after brief practice typed significantly faster (WPM 28.3 vs 26.2, p < 0.01). These results indicate that KeySense enables accurate, efficient, and comfortable ten-finger text entry on commodity touchscreens without any extra hardware.

cs.HC↗

arXiv2025

CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning

Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effective strategy for enhancing safety at these urban junctures. Despite the growing popularity of Reinforcement Learning (RL) methods in optimizing TSC, these methods often prioritize driving efficiency over safety, thus failing to address the critical balance between these two aspects. Additionally, these methods usually need more interpretability. CounterFactual (CF) learning is a promising approach for various causal analysis fields. In this study, we introduce a novel framework to improve RL for safety aspects in TSC. This framework introduces a novel method based on CF learning to address the question: ``What if, when an unsafe event occurs, we backtrack to perform alternative actions, and will this unsafe event still occur in the subsequent period?'' To answer this question, we propose a new structure causal model to predict the result after executing different actions, and we propose a new CF module that integrates with additional ``X'' modules to promote safe RL practices. Our new algorithm, CFLight, which is derived from this framework, effectively tackles challenging safety events and significantly improves safety at intersections through a near-zero collision control strategy. Through extensive numerical experiments on both real-world and synthetic datasets, we demonstrate that CFLight reduces collisions and improves overall traffic performance compared to conventional RL methods and the recent safe RL model. Moreover, our method represents a generalized and safe framework for RL methods, opening possibilities for applications in other domains. The data and code are available in the github https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight.

cs.LG↗

arXiv2025

Computing with Smart Rings: A Systematic Literature Review

A smart ring is a wearable electronic device in the form of a ring that incorporates diverse sensors and computing technologies to perform a variety of functions. Designed for use with fingers, smart rings are capable of sensing more subtle and abundant hand movements, thus making them a good platform for interaction. Meanwhile, fingers are abundant with blood vessels and nerve endings and accustomed to wearing rings, providing an ideal site for continuous health monitoring through smart rings, which combine comfort with the ability to capture vital biometric data, making them suitable for all-day wear. We collected in total of 206 smart ring-related publications and conducted a systematic literature review. We provide a taxonomy regarding the sensing and feedback modalities, applications, and phenomena. We review and categorize these literatures into four main areas: (1) interaction - input, (2) interaction - output, (3) passive sensing - in body feature, (4) passive sensing - out body activity. This comprehensive review highlights the current advancements within the field of smart ring and identifies potential areas for future research.

cs.HC↗

arXiv2024

Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching

Safety alignment of large language models (LLMs) has been gaining increasing attention. However, current safety-aligned LLMs suffer from the fragile and imbalanced safety mechanisms, which can still be induced to generate unsafe responses, exhibit over-safety by rejecting safe user inputs, and fail to preserve general utility after safety alignment. To this end, we propose a novel post safety alignment (PSA) method to address these inherent and emerging safety challenges, including safety enhancement, over-safety mitigation, and utility preservation. In specific, we introduce \textsc{SafePatching}, a novel framework for comprehensive PSA, where two distinct safety patches are developed on the harmful data to enhance safety and mitigate over-safety concerns, and then seamlessly integrated into the target LLM backbone without compromising its utility. Extensive experiments on four representative aligned LLMs, including LLaMA-2/3, Gemma and Mistral, show that \textsc{SafePatching} achieves a more comprehensive PSA than baseline methods, further optimizing the balance between being helpful and harmless in current aligned LLMs. Also, \textsc{SafePatching} demonstrates its superiority in continual PSA scenarios.

cs.CL↗

arXiv2024

PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture

The data scarcity problem is a crucial factor that hampers the model performance of IMU-based human motion capture. However, effective data augmentation for IMU-based motion capture is challenging, since it has to capture the physical relations and constraints of the human body, while maintaining the data distribution and quality. We propose PoseAugment, a novel pipeline incorporating VAE-based pose generation and physical optimization. Given a pose sequence, the VAE module generates infinite poses with both high fidelity and diversity, while keeping the data distribution. The physical module optimizes poses to satisfy physical constraints with minimal motion restrictions. High-quality IMU data are then synthesized from the augmented poses for training motion capture models. Experiments show that PoseAugment outperforms previous data augmentation and pose generation methods in terms of motion capture accuracy, revealing a strong potential of our method to alleviate the data collection burden for IMU-based motion capture and related tasks driven by human poses.

cs.CV↗

arXiv2024

Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence

Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants. Previous works mainly focus on raising the emotion perception ability of them via naive fine-tuning on EI-related classification or regression tasks. However, this leads to the incomplete enhancement of EI and catastrophic forgetting of the general intelligence (GI). To this end, we first introduce \textsc{EiBench}, a large-scale collection of EI-related tasks in the text-to-text formation with task instructions that covers all three aspects of EI, which lays a solid foundation for the comprehensive EI enhancement of LLMs. Then a novel \underline{\textbf{Mo}}dular \underline{\textbf{E}}motional \underline{\textbf{I}}ntelligence enhancement method (\textbf{MoEI}), consisting of Modular Parameter Expansion and intra-inter modulation, is proposed to comprehensively enhance the EI of LLMs without compromise their GI. Extensive experiments on two representative LLM-based assistants, Flan-T5 and LLaMA-2-Chat, demonstrate the effectiveness of MoEI to improving EI while maintain GI.

cs.CL↗

arXiv2022

Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

Causal Emotion Entailment aims to identify causal utterances that are responsible for the target utterance with a non-neutral emotion in conversations. Previous works are limited in thorough understanding of the conversational context and accurate reasoning of the emotion cause. To this end, we propose Knowledge-Bridged Causal Interaction Network (KBCIN) with commonsense knowledge (CSK) leveraged as three bridges. Specifically, we construct a conversational graph for each conversation and leverage the event-centered CSK as the semantics-level bridge (S-bridge) to capture the deep inter-utterance dependencies in the conversational context via the CSK-Enhanced Graph Attention module. Moreover, social-interaction CSK serves as emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect candidate utterances with the target one, which provides explicit causal clues for the Emotional Interaction module and Actional Interaction module to reason the target emotion. Experimental results show that our model achieves better performance over most baseline models. Our source code is publicly available at https://github.com/circle-hit/KBCIN.

cs.CL↗

arXiv2022

Evidences for the exciton gas phase and its condensation in monolayer 1T-ZrTe2

The excitonic insulator (EI) is a Bose-Einstein condensation (BEC) of excitons bound by electron-hole interaction in a solid, which could support high-temperature BEC transition. The material realization of EI has been elusive, which is further challenged by the difficulty of distinguishing it from a conventional charge density wave (CDW) state. In the BEC limit, the pre-condensation exciton gas phase is a hallmark to distinguish EI from conventional CDW, yet direct experimental evidence has been lacking. Here we report a distinct correlated phase beyond the $2\times2$ CDW ground state emerging in epitaxially grown monolayer 1T-ZrTe2 and its investigation by angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM). The results show novel band- and energy-dependent folding behavior in a two-step process, evidenced by an exciton gas phase prior to its condensation into the final CDW state. The excellent agreement between experiments and theoretical predictions on the recovery of the pristine band structure by carrier-density-dependent suppression of the CDW state further corroborates the monolayer 1T-ZrTe2 as an EI. Our findings provide a versatile two-dimensional platform that allows tuning of the excitonic effect.

cond-mat.mtrl-sci↗

arXiv2019

Epitaxial growth and characterization of high quality Bi2O2Se thin films on SrTiO3 substrates by pulsed laser deposition

Recently, Bi2O2Se is discovered as a promising two-dimensional (2D) semiconductor for next generation electronics, due to its moderate bandgap size, high electron mobility and pronounced ambient stability. Meanwhile, it has been predicted that high quality Bi2O2Se-related heterostructures may possess exotic physical phenomena, such as piezoelectricity and topological superconductivity. Herein, we report the first successful heteroepitaxial growth of Bi2O2Se films on SrTiO3 substrates via pulsed laser deposition (PLD) method. Films obtained under optimal conditions show an epitaxial growth with the c axis perpendicular to the film surface and the a and b axes parallel to the substrate. The growth mode transition to three dimensional (3D) island from quasi-2D layer of the heteroepitaxial Bi2O2Se films on SrTiO3 (001) substrates is observed as prolonging deposition time of films. The maximum value of electron mobility reaches 160 cm2/V-1s-1 at room temperature in a 70nm-thick film. The thickness dependent mobility provides evidence that interface-scattering is likely to be the limiting factor for the relatively low electron mobility at low temperature, implying that the interface engineering as an effective method to tune the low temperature electron mobility. Our work suggests the epitaxial Bi2O2Se films grown by PLD are promising for both fundamental study and practical applications.

cond-mat.mtrl-sci↗

arXiv2019

Two-Gap Superconductivity in CaFe_{0.88}Co_{0.12}AsF Revealed by Temperature Dependence of the Lower Critical Field H_{c1}^c(T)

Gap symmetry and structure are crucial issues in understanding the superconducting mechanism of unconventional superconductors. Here we report an in-depth investigation on the out-of-plane lower critical field $H_{c1}^{c}$ of fluorine-based 1111 system superconductor CaFe$_{0.88}$Co$_{0.12}$AsF with $T_c$ = 21 K. A pronounced two-gap feature is revealed by the kink in the temperature dependent $H_{c1}^c(T)$ curve. The magnitudes of the two gaps are determined to be $Δ_1$ = 0.86 meV and $Δ_2$ = 4.48 meV, which account for 74% and 26% of the total superfluid density respectively. Our results suggest that the local antiferromagnetic exchange pairing picture is favored compared to the Fermi surface nesting scenario.

cond-mat.supr-con↗

arXiv2019

Single-crystal growth and extremely high H_c2 of 12442-type Fe-based superconductor KCa_2Fe_4As_4F_2

Millimeter sized single crystals of KCa_2Fe_4As_4F_2 were grown using a self-flux method. The chemical compositions and crystal structure were characterized carefully. Superconductivity with the critical transition T_c = 33.5 K was confirmed by both the resistivity and magnetic susceptibility measurements. Moreover, the upper critical field H_c2 was studied by the resistivity measurements under different magnetic fields. A rather steep increase for the in-plane H_c2^ab with cooling, dμ_0H_c2^a/dT|T_c = -50.9 T/K, was observed, indicating an extremely high upper critical field. Possible origins for this behavior were discussed. The findings in our work is a great promotion both for understanding the physical properties and applications of 12442-type Fe-based superconductors.

cond-mat.supr-con↗

arXiv2018

Possible structural origin of superconductivity in Sr-doped Bi2Se3

Doping bismuth selenide (Bi2Se3) with elements such as copper and strontium (Sr) can induce superconductivity, making the doped materials interesting candidates to explore potential topological superconducting behaviors. It was thought that the superconductivity of doped Bi2Se3 was induced by dopant atoms intercalated in van der Waals gaps. However, several experiments have shown that the intercalation of dopant atoms may not necessarily make doped Bi2Se3 superconducting. Thus, the structural origin of superconductivity in doped Bi2Se3 remains an open question. Herein, we combined material synthesis and characterization, high-resolution transmission electron microscopy, and first-principles calculations to study the doping structure of Sr-doped Bi2Se3. We found that the emergence of superconductivity is strongly related with n-type dopant atoms. Atomic-level energy-dispersive X-ray mapping revealed various n-type Sr dopants that occupy intercalated and interstitial positions. First-principles calculations showed that the formation energy of a specific interstitial Sr doping position depends strongly on Sr doping level. This site changes from a metastable position at low Sr doping level to a stable position at high Sr doping level. The calculation results explain why quenching is necessary to obtain superconducting samples when the Sr doping level is low and also why slow furnace cooling can yield superconducting samples when the Sr doping level is high. Our findings suggest that Sr atoms doped at interstitial locations, instead of those intercalated in van der Waals gaps, are most likely to be responsible for the emergence of superconductivity in Sr-doped Bi2Se3.

cond-mat.supr-con↗

arXiv2016

A combined method for synthesis of superconducting Cu doped Bi2Se3

We present a two-step technique for the synthesis of superconducting CuxBi2Se3. Cu0.15Bi2Se3 single crystals were synthesized using the melt-growth method. Although these samples are non-superconducting, they can be employed to generate high quality superconducting samples if used as precursors in the following electrochemical synthesis step. Samples made from Cu0.15Bi2Se3 reliably exhibit zero-resistance even under the non-optimal quenching condition, while samples made from pristine Bi2Se3 require fine tuning of the quenching conditions to achieve similar performance. Moreover, under the optimal quenching condition, the average superconducting shielding fraction was still lower in the samples made from pristine Bi2Se3 than in the samples made from Cu0.15Bi2Se3. These results suggest that the pre-doped Cu atoms facilitate the formation of a superconducting percolation network. We also discuss the useful clues that we gathered about the locations of Cu dopants that are responsible for superconductivity.

cond-mat.supr-con↗