Syncopation: Unifying Music Theory and Perception

PhDSyncopation is a fundamental feature of rhythm in music. However, the relationship between theory and perception is currently not well understood. This thesis is concerned with characterising this relationship and identifying areas where the theory is incomplete. We start with a review of relevant musicological background and theory. Next, we use psychophysical data to characterise the perception of syncopation for simple rhythms. We then analyse the predictions of current theory using this data and identify strengths and weaknesses in the theory. We then introduce further psychophysical data which characterises the perception of syncopation for simple rhythms at different tempi. This leads to revised theory and a new model of syncopation that is tempo-dependent.Joint Programme College Scholarshi

LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets

Plant-plant interactions and resource dynamics of Abies fabri and Picea brachytyla as affected by phosphorus fertilization

Although extensive research has been conducted on the temporal dynamics of plant-plant interactions, little is known about the effect of phosphorus (P) availability. In this study, Abies fabri and Picea brachytyla seedlings were collected from the late-stage Hailuogou glacier retreat area and grown under different P regimes (control and P fertilization) from year 2015 to 2016 in a common garden experiment to investigate whether plant-plant interactions are modulated by P availability. We found that P fertilization affected the relative competition intensity (RCI). Under control conditions in 2015, the growth of A. fabri was facilitated by the presence of P. brachytyla. Under P fertilization, the facilitative effect was more intensive: the leaf, stem and total biomass of A. fabri significantly increased under interspecific interaction compared with intraspecific interaction, but no effect was found in P. brachytyla. RCI showed similar tendencies both in 2015 and 2016. In addition, plant-plant interactions and P fertilization caused temporal variation in C, N, P and non-structural carbohydrate (NSC) contents. The growth of A. fabri greatly benefited from the presence of P. brachytyla when exposed to P fertilization and showed higher biomass, and C, N, P and NSC accumulations. Our results demonstrated interactive effects between environmental conditions (i.e. P availability) and plant-plant interactions that are closely related to resource accumulation.Peer reviewe

Different responses in leaf-level physiology to competition and facilitation under different soil types and N fertilization

Knowledge of how competition and facilitation affect photosynthetic traits and nitrogen metabolism contributes to understanding of plant-plant interaction mechanisms. We transplanted two larch species, Larix kaempferi and L. olgensis, to establish intra- and interspecific interaction experiments under different types of soil. Experiment 1: Two different soil types were selected, one from a c. twenty years old L. kaempferi plantation (named larch soil) and another from a secondary natural forest (named mixed forest soil). The experiment included three types of plant interactions (L kaempferi + L. kaempferi, L. olgensis + L. olgensis, and L. kaempferi + L. olgensis) and two soil types. Experiment 2: N fertilization was applied to larch soil. The experiment included the same three types of plant interactions as in Experiment 1 and two N treatments. The growth of L kaempferi was negatively affected by larch soil and accelerated by N fertilization, particularly under interspecific interaction. The effects of soil type combined with plant-plant interactions or N fertilization influenced the chlorophyll pigment content, net photosynthetic rate (Pn), photosynthetic N use efficiency (PNUE) and total non-structural carbohydrates of leaves (TNC). CM a/Chl b (ratio of chlorophyll a to chlorophyll b) was higher when the growth of L. kaempferi was facilitated by the presence of L olgensis in mixed forest soil. However, the ratio significantly declined when L. kaempferi confronted strong competition from L. olgensis in larch soil without N fertilization. Under N fertilization in larch soil, Chl a/Chl b of L. olgensis significantly increased by the presence of L. kaempferi. Plant-plant interactions and soil types affected the number of chloroplasts, especially in L. kaempferi, which had a greater number of chloroplasts under interspecific interactions than in monoculture when growing in mixed forest soil. L. olgensis enhanced its ability to absorb N-NO3- under interspecific interactions in larch N- soil, while L. kaempferi enhanced its ability to absorb N-NH4+ under interspecific competition in mixed forest soil. Competition or facilitation modified the photosynthetic traits and nitrogen metabolism depending on the type of soil. Differences in these physiological processes contribute to divergent performance among individuals growing under interspecific or intraspecific competition, or in isolation.Peer reviewe

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Distinct co-occurrence patterns and driving forces of rare and abundant bacterial subcommunities following a glacial retreat in the eastern Tibetan Plateau

Unraveling the dynamics and driving forces of abundant and rare bacteria in response to glacial retreat is essential for a deep understanding of their ecological and evolutionary processes. Here, we used Illumina sequencing datasets to investigate ecological abundance, successional dynamics, and the co-occurrence patterns of abundant and rare bacteria associated with different stages of soil development in the Hailuogou Glacier Chronosequence. Abundant taxa exhibited ubiquitous distribution and tight clustering, while rare taxa showed uneven distribution and loose clustering along the successional stages. Both abundant and rare subcommunities were driven by different factors during assembly: the interactions of biotic and edaphic factors were the main driving forces, although less important for rare taxa than for the abundant ones. In particular, the redundancy analysis and structural equation modeling showed that soil organic C, pH, and plant richness primarily affected abundant subcommunities, while soil N and pH were most influential for rare subcommunities. More importantly, variation partitioning showed that edaphic factors exhibited a slightly greater influence on both abundant (7.8%) and rare (4.5%) subcommunities compared to biotic factors. Both abundant and rare bacteria exhibited a more compact network topology at the middle than at the other chronosequence stages. The overlapping nodes mainly belonged to Proteobacteria and Acidobacteria in abundant taxa and Planctomycetia, Sphingobacteriia, and Phycisphaerae in rare taxa. In addition, the network analysis showed that the abundant taxa exhibited closer relationships and more influence on other co-occurrences in the community when compared to rare taxa. These findings collectively reveal divergent co-occurrence patterns and driving forces for abundant and rare subcommunities along a glacier forefield chronosequence in the eastern Tibetan Plateau.Peer reviewe

WavePF: A Novel Fusion Approach based on Wavelet-guided Pooling for Infrared and Visible Images

Infrared and visible image fusion aims to generate synthetic images simultaneously containing salient features and rich texture details, which can be used to boost downstream tasks. However, existing fusion methods are suffering from the issues of texture loss and edge information deficiency, which result in suboptimal fusion results. Meanwhile, the straight-forward up-sampling operator can not well preserve the source information from multi-scale features. To address these issues, a novel fusion network based on the wavelet-guided pooling (wave-pooling) manner is proposed, termed as WavePF. Specifically, a wave-pooling based encoder is designed to extract multi-scale image and detail features of source images at the same time. In addition, the spatial attention model is used to aggregate these salient features. After that, the fused features will be reconstructed by the decoder, in which the up-sampling operator is replaced by the wave-pooling reversed operation. Different from the common max-pooling technique, image features after the wave-pooling layer can retain abundant details information, which can benefit the fusion process. In this case, rich texture details and multi-scale information can be maintained during the reconstruction phase. The experimental results demonstrate that our method exhibits superior fusion performance over the state-of-the-arts on multiple image fusion benchmark

Software Testing with Large Language Model: Survey, Landscape, and Vision

Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products. As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent, and making it an area ripe for innovative approaches such as the use of LLMs. This paper provides a comprehensive review of the utilization of LLMs in software testing. It analyzes 52 relevant studies that have used LLMs for software testing, from both the software testing and LLMs perspectives. The paper presents a detailed discussion of the software testing tasks for which LLMs are commonly used, among which test case preparation and program repair are the most representative ones. It also analyzes the commonly used LLMs, the types of prompt engineering that are employed, as well as the accompanied techniques with these LLMs. It also summarizes the key challenges and potential opportunities in this direction. This work can serve as a roadmap for future research in this area, highlighting potential avenues for exploration, and identifying gaps in our current understanding of the use of LLMs in software testing.Comment: 20 pages, 11 figure