arXiv subjects

Nan Gao

Publications and source records attributed to Nan Gao.

At least 37 records · Page 2Linked to original sources

arXiv2025

InfoBFR: Real-World Blind Face Restoration via Information Bottleneck

Blind face restoration (BFR) is a highly challenging problem due to the uncertainty of data degradation patterns. Current BFR methods have realized certain restored productions but with inherent neural degradations that limit real-world generalization in complicated scenarios. In this paper, we propose a plug-and-play framework InfoBFR to tackle neural degradations, e.g., prior bias, topological distortion, textural distortion, and artifact residues, which achieves high-generalization face restoration in diverse wild and heterogeneous scenes. Specifically, based on the results from pre-trained BFR models, InfoBFR considers information compression using manifold information bottleneck (MIB) and information compensation with efficient diffusion LoRA to conduct information optimization. InfoBFR effectively synthesizes high-fidelity faces without attribute and identity distortions. Comprehensive experimental results demonstrate the superiority of InfoBFR over state-of-the-art GAN-based and diffusion-based BFR methods, with around 70ms consumption, 16M trainable parameters, and nearly 85% BFR-boosting. It is promising that InfoBFR will be the first plug-and-play restorer universally employed by diverse BFR models to conquer neural degradations.

cs.CV↗

arXiv2025

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types.

q-bio.QM↗

arXiv2025

Influence of conjugated structure for tunable molecular plasmons in peropyrene and its derivatives

Advances in research have sparked an increasing curiosity in understanding the plasmonic excitation properties of molecular-scale systems. Polycyclic aromatic hydrocarbons, as the fundamental building blocks of graphene, have been documented to possess plasmonic properties through experimental observations, making them prime candidates for investigation. By doping different elements, the conjugated structure of the molecule can be altered. In this study, the plasmonic excitation properties influenced by conjugated structures in peropyrene and its derivatives are investigated through first-principles calculations that combine the plasmonicity index, generalized plasmonicity index and transition contribution maps. For molecular plasmonic excitation, the conjugated structure can influence the oscillation modes of valence electrons, which is pivotal in yielding distinct field enhancement characteristics. Furthermore, charge doping can lead to a certain degree of alteration in the conjugated structures, and the doping of elements will result in varying degrees of such alteration, thereby initiating different trends in the evolution of plasmonic resonance. This further enhances the tunability of molecular plasmonic resonance. The results provide novel insights into the development and utilization of molecular plasmonic devices in practical applications.

cond-mat.mes-hall↗

arXiv2024

Vertical Emission of Blue Light from a Symmetry Breaking Plasmonic Nanocavity-Emitter System Supporting Bound States in the Continuum

The concept of photonic bound states in the continuum (BICs), introduced in structured metallic surface cavities, provides a crucial mechanism for designing plasmonic open-resonant cavities with high quality (high-Q) factors, making significant advances in plasmonic nanophotonics. However, the two major bottlenecks for plasmonic nanocavities: enhancing emission and big beam divergence for quantum emitters, due to the strong intrinsic Ohmic losses of metals. Here, we propose and realize a σh symmetry-breaking plasmonic honeycomb nanocavities (PHC) that support quasi-BIC resonance modes with high-Q factors. Our anodic oxidation-engineered strategy breaks out-of-plane symmetry while preserving in-plane symmetry, enabling the PHC to exhibit collective plasmonic lattice resonances (PLR) couplings and achieve Q-factors exceeding 106. Experimentally, we couple perovskite quantum dots (PQDs) to the PHC, demonstrating effective tuning of their emission properties and beam quality in the blue spectral region, achieving a 32-fold emission enhancement by suppress Ohmic loss and the life time of quantum emitters, simultaneously realize vertical emission in the 2.556 - 2.638 eV region, with a far-field hexagonal beam shape and a full width at half maximum of 12.6 degree under optimal coupling conditions. Furthermore, we demonstrate topological band inversion characterized by Zak phase transitions by continuously tuning the system parameters, confirming that the PHC supports topologically non-trivial q-BIC due to PLR coupling. The PHC presents itself as a promising next-generation, high-brightness nanoscale light source matrix, which can be directly scaled up to cover a wide wavelength range from UV to IR.

physics.optics↗

arXiv2024

Biodynamic Analysis of Alpine Skiing with a Skier-Ski-Snow Interaction Model

This study establishes a skier-ski-snow interaction (SSSI) model that integrates a 3D full-body musculoskeletal model, a flexible ski model, a ski-snow contact model, and an air resistance model. An experimental method is developed to collect kinematic and kinetic data using IMUs, GPS, and plantar pressure measurement insoles, which are cost-effective and capable of capturing motion in large-scale field conditions. The ski-snow interaction parameters are optimized for dynamic alignment with snow conditions and individual turning techniques. Forward-inverse dynamics simulation is performed using only the skier's posture as model input and leaving the translational degrees of freedom (DOFs) between the pelvis and the ground unconstrained. The effectiveness of our model is further verified by comparing the simulated results with the collected GPS and plantar pressure data. The correlation coefficient between the simulated ski-snow contact force and the measured plantar pressure data is 0.964, and the error between the predicted motion trajectory and GPS data is 0.7%. By extracting kinematic and kinetic parameters from skiers of different skill levels, quantitative performance analysis helps quantify ski training. The SSSI model with the parameter optimization algorithm of the ski-snow interaction allows for the description of skiing characteristics across varied snow conditions and different turning techniques, such as carving and skidding. Our research advances the understanding of alpine skiing dynamics, informing the development of training programs and facility designs to enhance athlete performance and safety.

physics.soc-ph↗

arXiv2024

A functorial approach to monomorphism categories II: Indecomposables

We investigate the (separated) monomorphism category $\operatorname{mono}(Q,Λ)$ of a quiver $Q$ over an Artin algebra $Λ$. We construct an epivalence from $\overline{\operatorname{mono}}(Q,Λ)$ to $\operatorname{rep}(Q,\overline{\operatorname{mod}}\, Λ)$, where $\operatorname{mod}Λ$ is the category of finitely generated modules and $\overline{\operatorname{mod}}\, Λ$ and $\overline{\operatorname{mono}}(Q,Λ)$ denote the respective injectively stable categories. Furthermore, if $Q$ has at least one arrow, then we show that this is an equivalence if and only if $Λ$ is hereditary. In general, it induces a bijection between indecomposable objects in $\operatorname{rep}(Q,\overline{\operatorname{mod}}\, Λ)$ and non-injective indecomposable objects in $\operatorname{mono}(Q,Λ)$. We show that the generalized Mimo-construction, an explicit minimal right approximation into $\operatorname{mono}{(Q,Λ)}$, gives an inverse to this bijection. Using this, we describe the indecomposables in the monomorphism category of a radical-square-zero Nakayama algebra, and give a bijection between the indecomposables in the monomorphism category of two artinian uniserial rings of Loewy length $3$ with the same residue field. These results are proved using free monads on an abelian category, in order to avoid the technical combinatorics arising from quiver representations. The setup also specializes to representations of modulations. In particular, we obtain new results on the singularity category of the algebras $H$ which were introduced by Geiss, Leclerc, and Schröer in order to extend their results relating cluster algebras and Lusztig's semicanonical basis to symmetrizable Cartan matrices. We also recover results on the $ι$quivers algebras which were introduced by Lu and Wang to realize $ι$quantum groups via semi-derived Hall algebras.

math.RT↗

arXiv2024

AngleSizer: Enhancing Spatial Scale Perception for the Visually Impaired with an Interactive Smartphone Assistant

Spatial perception, particularly at small and medium scales, is an essential human sense but poses a significant challenge for the blind and visually impaired (BVI). Traditional learning methods for BVI individuals are often constrained by the limited availability of suitable learning environments and high associated costs. To tackle these barriers, we conducted comprehensive studies to delve into the real-world challenges faced by the BVI community. We have identified several key factors hindering their spatial perception, including the high social cost of seeking assistance, inefficient methods of information intake, cognitive and behavioral disconnects, and a lack of opportunities for hands-on exploration. As a result, we developed AngleSizer, an innovative teaching assistant that leverages smartphone technology. AngleSizer is designed to enable BVI individuals to use natural interaction gestures to try, feel, understand, and learn about sizes and angles effectively. This tool incorporates dual vibration-audio feedback, carefully crafted teaching processes, and specialized learning modules to enhance the learning experience. Extensive user experiments validated its efficacy and applicability with diverse abilities and visual conditions. Ultimately, our research not only expands the understanding of BVI behavioral patterns but also greatly improves their spatial perception capabilities, in a way that is both cost-effective and allows for independent learning.

cs.HC↗

arXiv2024

Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior Modeling

Mobile sensing plays a crucial role in generating digital traces to understand human daily lives. However, studying behaviours like mood or sleep quality in smartphone users requires carefully designed mobile sensing strategies such as sensor selection and feature construction. This process is time-consuming, burdensome, and requires expertise in multiple domains. Furthermore, the resulting sensing framework lacks generalizability, making it difficult to apply to different scenarios. In the research, we propose an automated mobile sensing strategy for human behaviour understanding. First, we establish a knowledge base and consolidate rules for data collection and effective feature construction. Then, we introduce the multi-granular human behaviour representation and design procedures for leveraging large language models to generate strategies. Our approach is validated through blind comparative studies and usability evaluation. Ultimately, our approach holds the potential to revolutionise the field of mobile sensing and its applications.

cs.HC↗

arXiv2024

Understanding Physiological Responses of Students Over Different Courses

Student engagement plays a vital role in academic success with high engagement often linked to positive educational outcomes. Traditionally, student engagement is measured through self-reports, which are both labour-intensive and not real-time. An emerging alternative is monitoring physiological signals such as Electrodermal Activity (EDA) and Inter-Beat Interval (IBI), which reflect students' emotional and cognitive states. In this research, we analyzed these signals from 23 students wearing Empatica E4 devices in real-world scenarios. Diverging from previous studies focused on lab settings or specific subjects, we examined physiological synchrony at the intra-student level across various courses. We also assessed how different courses influence physiological responses and identified consistent temporal patterns. Our findings show unique physiological response patterns among students, enhancing our understanding of student engagement dynamics. This opens up possibilities for tailoring educational strategies based on unobtrusive sensing data to optimize learning outcomes.

cs.HC↗

arXiv2024

GesGPT: Speech Gesture Synthesis With Text Parsing from ChatGPT

Gesture synthesis has gained significant attention as a critical research field, aiming to produce contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic information present in the text, leading to less expressive and meaningful gestures. In this letter, we propose GesGPT, a novel approach to gesture generation that leverages the semantic analysis capabilities of large language models , such as ChatGPT. By capitalizing on the strengths of LLMs for text analysis, we adopt a controlled approach to generate and integrate professional gestures and base gestures through a text parsing script, resulting in diverse and meaningful gestures. Firstly, our approach involves the development of prompt principles that transform gesture generation into an intention classification problem using ChatGPT. We also conduct further analysis on emphasis words and semantic words to aid in gesture generation. Subsequently, we construct a specialized gesture lexicon with multiple semantic annotations, decoupling the synthesis of gestures into professional gestures and base gestures. Finally, we merge the professional gestures with base gestures. Experimental results demonstrate that GesGPT effectively generates contextually appropriate and expressive gestures.

cs.CL↗

arXiv2024

Illuminating the Unseen: Investigating the Context-induced Harms in Behavioral Sensing

Behavioral sensing technologies are rapidly evolving across a range of well-being applications. Despite its potential, concerns about the responsible use of such technology are escalating. In response, recent research within the sensing technology has started to address these issues. While promising, they primarily focus on broad demographic categories and overlook more nuanced, context-specific identities. These approaches lack grounding within domain-specific harms that arise from deploying sensing technology in diverse social, environmental, and technological settings. Additionally, existing frameworks for evaluating harms are designed for a generic ML life cycle, and fail to adapt to the dynamic and longitudinal considerations for behavioral sensing technology. To address these gaps, we introduce a framework specifically designed for evaluating behavioral sensing technologies. This framework emphasizes a comprehensive understanding of context, particularly the situated identities of users and the deployment settings of the sensing technology. It also highlights the necessity for iterative harm mitigation and continuous maintenance to adapt to the evolving nature of technology and its use. We demonstrate the feasibility and generalizability of our framework through post-hoc evaluations on two real-world behavioral sensing studies conducted in different international contexts, involving varied population demographics and machine learning tasks. Our evaluations provide empirical evidence of both situated identity-based harm and more domain-specific harms, and discuss the trade-offs introduced by implementing bias mitigation techniques.

cs.HC↗

arXiv2024

A Unified Editing Method for Co-Speech Gesture Generation via Diffusion Inversion

Diffusion models have shown great success in generating high-quality co-speech gestures for interactive humanoid robots or digital avatars from noisy input with the speech audio or text as conditions. However, they rarely focus on providing rich editing capabilities for content creators other than high-level specialized measures like style conditioning. To resolve this, we propose a unified framework utilizing diffusion inversion that enables multi-level editing capabilities for co-speech gesture generation without re-training. The method takes advantage of two key capabilities of invertible diffusion models. The first is that through inversion, we can reconstruct the intermediate noise from gestures and regenerate new gestures from the noise. This can be used to obtain gestures with high-level similarities to the original gestures for different speech conditions. The second is that this reconstruction reduces activation caching requirements during gradient calculation, making the direct optimization on input noises possible on current hardware with limited memory. With different loss functions designed for, e.g., joint rotation or velocity, we can control various low-level details by automatically tweaking the input noises through optimization. Extensive experiments on multiple use cases show that this framework succeeds in unifying high-level and low-level co-speech gesture editing.

cs.HC↗

arXiv2024

DiffMAC: Diffusion Manifold Hallucination Correction for High Generalization Blind Face Restoration

Blind face restoration (BFR) is a highly challenging problem due to the uncertainty of degradation patterns. Current methods have low generalization across photorealistic and heterogeneous domains. In this paper, we propose a Diffusion-Information-Diffusion (DID) framework to tackle diffusion manifold hallucination correction (DiffMAC), which achieves high-generalization face restoration in diverse degraded scenes and heterogeneous domains. Specifically, the first diffusion stage aligns the restored face with spatial feature embedding of the low-quality face based on AdaIN, which synthesizes degradation-removal results but with uncontrollable artifacts for some hard cases. Based on Stage I, Stage II considers information compression using manifold information bottleneck (MIB) and finetunes the first diffusion model to improve facial fidelity. DiffMAC effectively fights against blind degradation patterns and synthesizes high-quality faces with attribute and identity consistencies. Experimental results demonstrate the superiority of DiffMAC over state-of-the-art methods, with a high degree of generalization in real-world and heterogeneous settings. The source code and models will be public.

cs.CV↗

arXiv2023

Critiquing Self-report Practices for Human Mental and Wellbeing Computing at Ubicomp

Computing human mental and wellbeing is crucial to various domains, including health, education, and entertainment. However, the reliance on self-reporting in traditional research to establish ground truth often leads to methodological inconsistencies and susceptibility to response biases, thus hindering the effectiveness of modelling. This paper presents the first systematic methodological review of self-reporting practices in Ubicomp within the context of human mental and wellbeing computing. Drawing from existing survey research, we establish guidelines for self-reporting in human wellbeing studies and identify shortcomings in current practices at Ubicomp community. Furthermore, we explore the reliability of self-report as a means of ground truth and propose directions for improving ground truth measurement in this field. Ultimately, we emphasize the urgent need for methodological advancements to enhance human mental and wellbeing computing.

cs.HC↗

arXiv2023

Chiral topological whispering gallery modes formed by gyromagnetic photonic crystals

We explore a hexagonal cavity that supports chiral topological whispering gallery (CTWG) modes, formed by a gyromagnetic photonic crystal. This mode is a special type of topologically protected optical mode that can propagate in photonic crystals with chiral direction. Finite element method simulations show that discrete edge states exist in the topological band gap due to the coupling of chiral edge states and WG modes. Since the cavity only supports edge state modes with group velocity in only one direction, it can purely generate traveling modes and be immune to interference modes. In addition, we introduced defects and disorder to test the robustness of the cavity, demonstrating that the CTWG modes can be effectively maintained under all types of perturbations. Our topological cavity platform offers useful prototype of robust topological photonic devices. The existence of this mode can have important implications for the design and application of optical devices.

physics.optics↗

arXiv2022

Gorenstein silting modules and Gorenstein projective modules

(Partial) Gorenstein silting modules are introduced and investigated. It is shown that for finite dimensional algebras of finite CM-type, partial Gorenstein silting modules are in bijection with τ_G-rigid modules; Gorenstein silting modules are the module-theoretic counterpart of 2-term Gorenstein silting complexes; and the relation between 2-term Gorenstein silting complexes, t-structures and torsion pairs in module categories. Furthermore, the corresponding version of the classical Brenner-Butler theorem in this setting are characterised; and the upper bound of the global dimension of endomorphism algebras of 2-term Gorenstein silting complexes over an algebra A are also characterised by terms of the Gorenstein global dimension of A.

math.RT↗

arXiv2022

(Gorenstein) silting modules in recollements

In the paper, we focus on the silting properties and the combinatorial properties of silting and Gorenstein, which is called Gorenstein silting, where the main tools used are recollements of module categories and tensor products. For a ring A and its idempotent ideal J, we show that an A/J-module T is a silting A-module if and only if T is a silting A/J-module. For the finite dimensional k-algebras, with k a field, we show that the tensor products of silting modules are still silting. We also show that the (partial) Gorenstein silting properties can be glued by the recollements of module categories of Noetherian rings. As a consequence, we glue the Gorenstein silting modules of an upper triangular matrix Gorenstein ring by those of the involved rings.

math.RT↗

arXiv2022

From Gorenstein derived equivalences to stable functors of Gorenstein projective modules

In the paper, we mainly connect the Gorenstein derived equivalence and stable functors of Gorenstein projective modules. Specially, we prove that a Gorenstein derived equivalence between CM-finite algebras A and B can induce a stable functor between the factor categories A-mod/A-Gproj and B-mod\B-Gproj. Furthermore, the above stable functor is an equivalence when A and B are Gorenstein.

math.RT↗