arXiv subjects

Nan Gao

Publications and source records attributed to Nan Gao.

At least 55 records · Page 3Linked to original sources

arXiv2022

Invariants and Gorenstein projective modules

Invariants with respect to recollements of the stable category of Gorenstein projective A-modules over an algebra A and stable equivalences are investigated. Specifically, the Gorenstein rigidity dimension is introduced. It is shown that the Gorenstein rigidity dimension is invariant with respect to both Morita equivalences and the stable equivalences of Gorenstein projective modules. As a consequence, the Gorenstein rigidity dimension is shown the invariant of derived equivalences. The Gorenstein rigidity dimension is compared along the recollements of the stable category of Gorenstein projective modules. Moreover, the bounds of Gorenstein rigidity dimension is given for several classes of algebras, respectively.

math.RT↗

arXiv2022

Lifting of recollements and Gorenstein projective modules

In the paper, we investigate the lifting of recollements with respect to Gorenstein-projective modules. Specifically, a homological ring epimorphism can induce a lifting of the recollement of the stable category of finitely generated Gorenstein-projective modules; the recollement of the bounded Gorenstein derived categories of some upper triangular matrix algebras can be lifted to the homotopy category of Gorenstein-projective modules. As a byproduct, we give a sufficient and necessary condition on the upper triangular matrix algebra T_{n}(A) to be of finite CM-type for an algebra A of finite CM-type.

math.RT↗

arXiv2022

Individual and Group-wise Classroom Seating Experience: Effects on Student Engagement in Different Courses

Seating location in the classroom can affect student engagement, attention and academic performance by providing better visibility, improved movement, and participation in discussions. Existing studies typically explore how traditional seating arrangements (e.g. grouped tables or traditional rows) influence students' perceived engagement, without considering group seating behaviours under more flexible seating arrangements. Furthermore, survey-based measures of student engagement are prone to subjectivity and various response bias. Therefore, in this research, we investigate how individual and group-wise classroom seating experiences affect student engagement using wearable physiological sensors. We conducted a field study at a high school and collected survey and wearable data from 23 students in 10 courses over four weeks. We aim to answer the following research questions: 1. How does the seating proximity between students relate to their perceived learning engagement? 2. How do students' group seating behaviours relate to their physiologically-based measures of engagement (i.e. physiological arousal and physiological synchrony)? Experiment results indicate that the individual and group-wise classroom seating experience is associated with perceived student engagement and physiologically-based engagement measured from electrodermal activity. We also find that students who sit close together are more likely to have similar learning engagement and tend to have high physiological synchrony. This research opens up opportunities to explore the implications of flexible seating arrangements and has great potential to maximize student engagement by suggesting intelligent seating choices in the future.

cs.HC↗

arXiv2022

Investigating the Effects of Mood & Usage Behaviour on Notification Response Time

Notifications are one of the most prevailing mechanisms on smartphones and personal computers to convey timely and important information. Despite these benefits, smartphone notifications demand individuals' attention and can cause stress and frustration when delivered at inopportune timings. This paper investigates the effect of individuals' smartphone usage behavior and mood on notification response time. We conduct an in-the-wild study with more than 18 participants for five weeks. Extensive experiment results show that the proposed regression model is able to accurately predict the response time of smartphone notifications using current user's mood and physiological signals. We explored the effect of different features for each participant to choose the most important user-oriented features in order to to achieve a meaningful and personalised notification response prediction. On average, our regression model achieved over all participants an MAE of 0.7764 ms and RMSE of 1.0527 ms. We also investigate how physiological signals (collected from E4 wristbands) are used as an indicator for mood and discuss the individual differences in application usage and categories of smartphone applications on the response time of notifications. Our research sheds light on the future intelligent notification management system.

cs.HC↗

arXiv2022

A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices

Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency. Specifically, we introduce a clean and parameter-refined attention module to enhance the information exchange between intent and slot, improving semantic accuracy by more than 2%. FAN can be implemented on different encoders and delivers more accurate models at every speed level. Our experiments on the Jetson Nano platform show that FAN inferences fifteen utterances per second with a small accuracy drop, showing its effectiveness and efficiency on edge devices.

cs.CL↗

arXiv2022

Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. Overall, the combined dataset could be used to analyse the relationships between indoor/outdoor climates and students' behaviours/mental states on campus, which provide opportunities for the future design of intelligent feedback systems to benefit both students and staff.

cs.HC↗

arXiv2022

Molecular Plasmon Hybridizition in Olefin Chains

With the continuous emergence of molecular and cluster devices or systems, the relationship between the plasmonic properties of multiple clusters and molecular interactions and the properties of the original single cluster or molecule becomes more and more important. Similar to plasmonic nanoparticle hybridization, there is also a hybrid phenomenon between two molecules with plasmon excitation modes. Using linear response time-dependent density functional theory (LR-TDDFT) and real-time propagation time-dependent density functional theory (RT-TDDFT) and combining the plasmonicity index (PI) and the transition contribution maps (TCM) methods we identify the plasmon excitation mode in the small molecular olefin chains with -OH and -NH2 groups and analyze the hybridization characteristics using charge transitions. The results show that for the plasmons in molecules, there are also plasmon hybridization mechanism exist when the two molecules coupling together. The TCM analysis shows that the plasmon modes and hybridization is a result of coexist of collective and single particle excitation. When there is extra charge depose in the molecules, as the electrons can moving in the whole molecules, the plasmon mode becomes stronger and the individual properties of the molecules maintains in the coupling. The study paves a way for molecule plasmon and the physics picture when the molecules are coupled together.

cond-mat.mes-hall↗

arXiv2021

Silting complexes and Gorenstein projective modules

We introduce Gorenstein silting modules (resp. complexes), and give the relation with the usual silting modules (resp. complexes). We show that Gorenstein silting modules are the module-theoretic counterpart of 2-term Gorenstein silting complexes; and partial Gorenstein silting modules are in bijection with τ_{G}-rigid modules for finite dimensional algebras of finite CM-type. We also give the relation between 2-term Gorenstein silting complexes, t-structures and torsion pair in module categories; and generalise the classical Brenner-Butler theorem to this setting; and characterise the global dimension of endomorphism algebras of 2-term Gorenstein silting complexes over an algebra A by terms of the Gorenstein global dimension of A.

math.AC↗

arXiv2021

Investigating the Reliability of Self-report Data in the Wild: The Quest for Ground Truth

Inferring human mental state (e.g., emotion, depression, engagement) with sensing technology is one of the most valuable challenges in the affective computing area, which has a profound impact in all industries interacting with humans. The self-report survey is the most common way to quantify how people think, but prone to subjectivity and various responses bias. It is usually used as the ground truth for human mental state prediction. In recent years, many data-driven machine learning models are built based on self-report annotations as the target value. In this research, we investigate the reliability of self-report surveys in the wild by studying the confidence level of responses and survey completion time. We conduct a case study (i.e., student engagement inference) by recruiting 23 students in a high school setting over a period of 4 weeks. Our participants volunteered 488 self-reported responses and data from their wearable sensors. We also find the physiologically measured student engagement and perceived student engagement are not always consistent. The findings from this research have great potential to benefit future studies in predicting engagement, depression, stress, and other emotion-related states in the field of affective computing and sensing technologies.

cs.HC↗

arXiv2021

Underwater metamaterial absorber with impedance-matched composite

By using a structured tungsten-polyurethane composite that is impedance-matched to water while simultaneously having a much slower longitudinal sound speed, we have theoretically designed, and experimentally realized, an underwater acoustic absorber exhibiting high absorption from 4 to 20 kHz, measured in a 5.6m times 3.6m water pool with the time-domain approach. The broadband functionality is achieved by optimally engineering the distribution of the Fabry-Perot resonances, based on an integration scheme, to attain impedance matching over a broad frequency range. The average thickness of the integrated absorber, 8.9 mm, is in the deep subwavelength regime (~λ/42 at 4 kHz) and close to the causal minimum thickness of 8.2 mm that is evaluated from the simulated absorption spectrum. The structured composite represents a new type of acoustic metamaterials that has high acoustic energy density and promises broad underwater applications.

physics.app-ph↗

arXiv2021

Generative Adversarial Networks for Spatio-temporal Data: A Survey

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

cs.LG↗

arXiv2020

A functorial approach to monomorphism categories for species I

We introduce a very general extension of the monomorphism category as studied by Ringel and Schmidmeier which in particular covers generalised species over locally bounded quivers. We prove that analogues of the kernel and cokernel functor send almost split sequences over the path algebra and the preprojective algebra to split or almost split sequences in the monomorphism category. We derive this from a general result on preservation of almost split morphisms under adjoint functors whose counit is a monomorphism. Despite of its generality, our monomorphism categories still allow for explicit computations as in the case of Ringel and Schmidmeier.

math.RT↗

arXiv2020

Transfer Learning for Thermal Comfort Prediction in Multiple Cities

HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage. Besides, thermal comfort is also crucial for well-being, health, and work productivity. Recently, data-driven thermal comfort models have got better performance than traditional knowledge-based methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model requires a large amount of self-reported thermal comfort data from indoor occupants which undoubtedly remains a challenge for researchers. In this research, we aim to tackle this data-shortage problem and boost the performance of thermal comfort prediction. We utilise sensor data from multiple cities in the same climate zone to learn thermal comfort patterns. We present a transfer learning based multilayer perceptron model from the same climate zone (TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art methods in accuracy, precision and F1-score.

cs.LG↗

arXiv2020

n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild

The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the response rate and quality of the survey are usually poor. As an alternative, in this paper, we investigate whether we can infer and predict engagement at multiple dimensions, just using sensors. We hypothesize that multidimensional student engagement can be translated into physiological responses and activity changes during the class, and also be affected by the environmental changes. Therefore, we aim to explore the following questions: Can we measure the multiple dimensions of high school student's learning engagement including emotional, behavioural and cognitive engagement with sensing data in the wild? Can we derive the activity, physiological, and environmental factors contributing to the different dimensions of student engagement? If yes, which sensors are the most useful in differentiating each dimension of the engagement? Then, we conduct an in-situ study in a high school from 23 students and 6 teachers in 144 classes over 11 courses for 4 weeks. We present the n-Gage, a student engagement sensing system using a combination of sensors from wearables and environments to automatically detect student in-class multidimensional learning engagement. Experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average MAE of 0.788 and RMSE of 0.975 using all the sensors. We also show a set of interesting findings of how different factors (e.g., combinations of sensors, school subjects, CO2 level) affect each dimension of the student learning engagement.

cs.HC↗

arXiv2020

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

cs.CV↗

arXiv2020

Ladders of recollements of abelian categories

Ladders of recollements of abelian categories are introduced, and used to address three general problems. Ladders of a certain height allow to construct recollements of triangulated categories, involving derived categories and singularity categories, from abelian ones. Ladders also allow to tilt abelian recollements, and ladders guarantee that properties like Gorenstein projective or injective are preserved by some functors in abelian recollements. Breaking symmetry is crucial in developing this theory.

math.RT↗

arXiv2020

Experimental Realization of Two-Dimensional Buckled Lieb lattice

Two-dimensional (2D) materials with a Lieb lattice can host exotic electronic band structures. Such a system does not exist in nature, and it is also difficult to obtain in the laboratory due to its structural instability. Here, we experimentally realized a 2D system composed of a tin overlayer on an aluminum substrate by molecular beam epitaxy. The specific arrangement of Sn atoms on the Al(100) surface, which benefits from favorable interface interactions, forms a stabilized buckled Lieb lattice. Our theoretical calculations indicate a partially broken nodal line loop protected by its mirror reflection symmetry and a topologically nontrivial insulating state with a spin-orbital coupling (SOC) effect in the band structure of this Lieb lattice. The electronic structure of this system has also been experimentally characterized by scanning tunnelling spectroscopy and angle-resolved photoemmision spectroscopy. Our work provides an appealing method for constructing 2D quantum materials based on the Lieb lattice.

cond-mat.mtrl-sci↗

arXiv2019

Data Augmentation for Deep Learning-based Radio Modulation Classification

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-of-the-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning algorithm. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and shorter the classification response time.

eess.SP↗