arXiv subjects

Duo Xu

Publications and source records attributed to Duo Xu.

At least 55 records · Page 3Linked to original sources

arXiv2024

MusicAOG: an Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music

In addressing the challenge of interpretability and generalizability of artificial music intelligence, this paper introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and granularities. Utilizing a hierarchical and-or graph representation, the model employs nodes and edges to encapsulate a broad spectrum of musical elements, including structures, textures, rhythms, and harmonies. This hierarchical approach expands the representability across various scales of music. This representation serves as the foundation for an energy-based model, uniquely tailored to learn musical concepts through a flexible algorithm framework relying on the minimax entropy principle. Utilizing an adapted Metropolis-Hastings sampling technique, the model enables fine-grained control over music generation. A comprehensive empirical evaluation, contrasting this novel approach with existing methodologies, manifests considerable advancements in interpretability and controllability. This study marks a substantial contribution to the fields of music analysis, composition, and computational musicology.

cs.SD↗

arXiv2023

Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System

The objective of this study is to develop a deep-learning based detection and diagnosis technique for carotid atherosclerosis using a portable freehand 3D ultrasound (US) imaging system. A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system. A U-Net segmentation network was firstly applied to extract the carotid artery on 2D transverse frame, then a novel 3D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume. Furthermore, a convolutional neural network was used to classify healthy and diseased cases qualitatively. 3D volume analysis methods including longitudinal image acquisition and stenosis grade measurement were developed to obtain the clinical metrics quantitatively. The proposed system achieved sensitivity of 0.714, specificity of 0.851 and accuracy of 0.803 respectively for diagnosis of carotid atherosclerosis. The automatically measured stenosis grade illustrated good correlation (r=0.762) with the experienced expert measurement. The developed technique based on 3D US imaging can be applied to the automatic diagnosis of carotid atherosclerosis. The proposed deep-learning based technique was specially designed for a portable 3D freehand US system, which can provide more convenient carotid atherosclerosis examination and decrease the dependence on clinician's experience.

eess.IV↗

arXiv2023

Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models

Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion probabilistic models (DDPMs), to predict the interstellar radiation field (ISRF) strength based on three-band dust emission at 4.5 \um, 24 \um, and 250 \um. We adopt magnetohydrodynamic simulations from the STARFORGE (STAR FORmation in Gaseous Environments) project that model star formation and giant molecular cloud (GMC) evolution. We generate synthetic dust emission maps matching observed spectral energy distributions in the Monoceros R2 (MonR2) GMC. We train DDPMs to estimate the ISRF using synthetic three-band dust emission. The dispersion between the predictions and true values is within a factor of 0.1 for the test set. We extended our assessment of the diffusion model to include new simulations with varying physical parameters. While there is a consistent offset observed in these out-of-distribution simulations, the model effectively constrains the relative intensity to within a factor of 2. Meanwhile, our analysis reveals weak correlation between the ISRF solely derived from dust temperature and the actual ISRF. We apply our trained model to predict the ISRF in MonR2, revealing a correspondence between intense ISRF, bright sources, and high dust emission, confirming the model's ability to capture ISRF variations. Our model robustly predicts radiation feedback distribution, even in complex, poorly constrained ISRF environments like those influenced by nearby star clusters. However, precise ISRF predictions require an accurate training dataset mirroring the target molecular cloud's unique physical conditions.

astro-ph.GA↗

arXiv2023

CMR exploration II -- filament identification with machine learning

We adopt magnetohydrodynamics (MHD) simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varying physical conditions. We conduct radiative transfer using RADMC-3D to generate synthetic dust emission of CMR filaments. We use the previously developed machine learning technique CASI-2D along with the diffusion model to identify the location of CMR filaments in dust emission. Both models showed a high level of accuracy in identifying CMR filaments in the test dataset, with detection rates of over 80% and 70%, respectively, at a false detection rate of 5%. We then apply the models to real Herschel dust observations of different molecular clouds, successfully identifying several high-confidence CMR filament candidates. Notably, the models are able to detect high-confidence CMR filament candidates in Orion A from dust emission, which have previously been identified using molecular line emission.

astro-ph.GA↗

arXiv2023

$ \mathrm{Sr}_{4}\mathrm{Al}_{2}\mathrm{O}_{7}$: A New Sacrificial Layer with High Water Dissolution Rate for the Synthesis of Freestanding Oxide Membranes

Freestanding perovskite oxide membranes have drawn great attention recently since they offer exceptional structural tunability and stacking ability, providing new opportunities in fundamental research and potential device applications in silicon-based semiconductor technology. Among different types of sacrificial layers, the $ \mathrm{(Ca, Sr, Ba)}_{3}\mathrm{Al}_{2}\mathrm{O}_{6}$ compounds are most widely used since they can be dissolved in water and prepare high-quality perovskite oxide membranes with clean and sharp surfaces and interfaces. However, the typical transfer process takes a long time (up to hours) in obtaining millimeter-size freestanding membranes, let alone realize wafer-scale samples with high yield. Here, we introduce a new member of the $ \mathrm{SrO-}\mathrm{Al}_{2}\mathrm{O}_{3}$ family,$ \mathrm{Sr}_{4}\mathrm{Al}_{2}\mathrm{O}_{7},$, and demonstrate its high dissolution rate, about 10 times higher than that of $ \mathrm{Sr}_{3}\mathrm{Al}_{2}\mathrm{O}_{6}$. The high-dissolution-rate of $ \mathrm{Sr}_{4}\mathrm{Al}_{2}\mathrm{O}_{7}$ is most likely related to the more discrete Al-O networks and higher concentration of water-soluble Sr-O species in this compound. Our work significantly facilitates the preparation of freestanding membranes and sheds light on the integration of multifunctional perovskite oxides in practical electronic devices.

cond-mat.mtrl-sci↗

arXiv2023

An Order-Complexity Model for Aesthetic Quality Assessment of Homophony Music Performance

Although computational aesthetics evaluation has made certain achievements in many fields, its research of music performance remains to be explored. At present, subjective evaluation is still a ultimate method of music aesthetics research, but it will consume a lot of human and material resources. In addition, the music performance generated by AI is still mechanical, monotonous and lacking in beauty. In order to guide the generation task of AI music performance, and to improve the performance effect of human performers, this paper uses Birkhoff's aesthetic measure to propose a method of objective measurement of beauty. The main contributions of this paper are as follows: Firstly, we put forward an objective aesthetic evaluation method to measure the music performance aesthetic; Secondly, we propose 10 basic music features and 4 aesthetic music features. Experiments show that our method performs well on performance assessment.

cs.SD↗

arXiv2023

Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. We adopt magnetohydrodynamic simulations with different global magnetic field strengths and large-scale dynamics, i.e., noncolliding and colliding GMCs. We train a diffusion model on both mass surface density maps and their corresponding mass-weighted number density maps from different viewing angles for all the simulations. We compare the diffusion model performance with a more traditional empirical two-component and three-component power-law fitting method and with a more traditional neural network machine learning approach (CASI-2D). We conclude that the diffusion model achieves an order of magnitude improvement on the accuracy of predicting number density compared to that by other methods. We apply the diffusion method to some example astronomical column density maps of Taurus and the Infrared Dark Clouds (IRDCs) G28.37+0.07 and G35.39-0.33 to produce maps of their mean volume densities.

astro-ph.GA↗

arXiv2023

CMR exploration I -- filament structure with synthetic observations

In this paper, we carry out a pilot parameter exploration for the collision-induced magnetic reconnection (CMR) mechanism that forms filamentary molecular clouds. Following Kong et al. (2021), we utilize Athena++ to model CMR in the context of resistive magnetohydrodynamics (MHD), considering the effect from seven physical conditions, including the Ohmic resistivity ($η$), the magnetic field ($B$), the cloud density ($ρ$), the cloud radius $R$, the isothermal temperature $T$, the collision velocity $v_x$, and the shear velocity $v_z$. Compared to their fiducial model, we consider a higher and a lower value for each one of the seven parameters. We quantify the exploration results with five metrics, including the density probability distribution function ($ρ$-PDF), the filament morphology (250 $μ$m dust emission), the $B$-$ρ$ relation, the dominant fiber width, and the ringiness that describes the significance of the ring-like sub-structures. The exploration forms straight and curved CMR-filaments with rich sub-structures that are highly variable in space and time. The variation translates to fluctuation in all the five metrics, reflecting the chaotic nature of magnetic reconnection in CMR. A temporary $B\proptoρ$ relation is noticeable during the first 0.6 Myr. Overall, the exploration provides useful initial insights to the CMR mechanism.

astro-ph.GA↗

arXiv2023

An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores

Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work are as follows: first, we put forward a homophony music score aesthetic model to objectively evaluate the quality of music score as a baseline model; second, we put forward eight basic music features and four music aesthetic features.

cs.SD↗

arXiv2022

Generalizing LTL Instructions via Future Dependent Options

In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. An important problem in RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instructions not observed in the training. However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefficiency and infeasibility or sub-optimality of the found solutions. In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efficiency and optimality. To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. In order to propagate the rewards of satisfying future subgoals back more efficiently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.

cs.AI↗

arXiv2022

Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds

We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to infer the orientation of magnetic fields in sub-/trans- Alfvenic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code RADMC-3d to model 12CO and 13CO (J = 1-0) line emission from the simulated clouds and then train a CASI-3D model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained CASI-3D model is able to infer magnetic field directions with low error (< 10deg for sub-Alfvenic samples and <30deg for trans-Alfvenic samples). We furthermore test the performance of CASI-3D on a real sub-/trans- Alfvenic region in Taurus. The CASI-3D prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three-times higher angular resolution than the Planck map.

astro-ph.GA↗

arXiv2022

A Census of Outflow to Magnetic Field Orientations in Nearby Molecular Clouds

We define a sample of 200 protostellar outflows showing blue and redshifted CO emission in the nearby molecular clouds Ophiuchus, Taurus, Perseus and Orion to investigate the correlation between outflow orientations and local, but relatively large-scale, magnetic field directions traced by Planck 353 GHz dust polarization. At high significance (p~1e-4), we exclude a random distribution of relative orientations and find that there is a preference for alignment of projected plane of sky outflow axes with magnetic field directions. The distribution of relative position angles peaks at ~30deg and exhibits a broad dispersion of ~50deg. These results indicate that magnetic fields have dynamical influence in regulating the launching and/or propagation directions of outflows. However, the significant dispersion around perfect alignment orientation implies that there are large measurement uncertainties and/or a high degree of intrinsic variation caused by other physical processes, such as turbulence or strong stellar dynamical interactions. Outflow to magnetic field alignment is expected to lead to a correlation in the directions of nearby outflow pairs, depending on the degree of order of the field. Analyzing this effect we find limited correlation, except on relatively small scales < 0.5 pc. Furthermore, we train a convolutional neural network to infer the inclination angle of outflows with respect to the line of sight and apply it to our outflow sample to estimate their full 3D orientations. We find that the angles between outflow pairs in 3D space also show evidence of small-scale alignment.

astro-ph.GA↗

arXiv2022

Size-dependent transient nature of localized turbulence in transitional channel flow

It has been reported that a fully localized turbulent band in channel flow becomes sustained when the Reynolds number is above a threshold. Here we show evidences that turbulent bands are of a transient nature instead. When the band length is controlled to be fixed, lifetimes of turbulent bands appear to be stochastic and exponentially distributed, a sign of a memoryless transient nature. Besides increasing with the Reynolds number, the mean lifetime also strongly increases with the band length. Given that the band length always changes over time in real channel flow, this size dependence may translate into a time dependence, which needs to be taken into account when clarifying the relationship between channel flow transition and the directed percolation universality class.

physics.flu-dyn↗

arXiv2022

Magnetic surface on nonmagnetic bulk of electride Hf2S

Recent experiment reported the self-passivated electride Hf2S with excellent stability and continuous electrocatalytic ability [S. H. Kang et al., Sci. Adv. 6, eaba7416 (2020)]. Starting from its 2H-type layered structure, we have studied the electronic, magnetic, and transport properties of the electride Hf2S in the monolayer and multilayer forms by combining first-principles electronic structure calculations and Kubo formula approach. Our calculations indicate that these thin films of Hf2S electride are both dynamically and thermodynamically stable. Astonishingly, the calculations further show that the outmost Hf atoms and the surface electron gas of the Hf2S multilayers are spin polarized, while the inner Hf atoms and the electron gas in the interlayer regions remain nonmagnetic. Due to the magnetic surface, the multilayer Hf2S exhibits many unusual transport properties such as the surface anomalous Hall effect and the electric-field-induced layer Hall effect. Our theoretical predictions on Hf2S call for future experimental verification.

cond-mat.mtrl-sci↗

arXiv2022

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies

Teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments is a challenging problem. We consider that user defines every task by a linear temporal logic (LTL) formula. However, some causal dependencies in complex environments may be unknown to the user in advance. Hence, when human user is specifying instructions, the robot cannot solve the tasks by simply following the given instructions. In this work, we propose a hierarchical reinforcement learning (HRL) framework in which a symbolic transition model is learned to efficiently produce high-level plans that can guide the agent efficiently solve different tasks. Specifically, the symbolic transition model is learned by inductive logic programming (ILP) to capture logic rules of state transitions. By planning over the product of the symbolic transition model and the automaton derived from the LTL formula, the agent can resolve causal dependencies and break a causally complex problem down into a sequence of simpler low-level sub-tasks. We evaluate the proposed framework on three environments in both discrete and continuous domains, showing advantages over previous representative methods.

cs.RO↗

arXiv2022

On the Steady-State Behavior of Finite-Control-Set MPC with an Application to High-Precision Power Amplifiers

Motivated by increasing precision requirements for switched power amplifiers, this paper addresses the problem of model predictive control (MPC) design for discrete-time linear systems with a finite control set (FCS). Typically, existing solutions for FCS-MPC penalize the output tracking error and the control input rate of change, which can lead to arbitrary switching among the available discrete control inputs and unpredictable steady-state behavior. To improve the steady-state behavior of FCS-MPC, in this paper we design a cost function that penalizes the tracking error with respect to a state and input steady-state limit cycle. We prove that if a suitable terminal cost is added to the FCS-MPC algorithm convergence to the limit cycle is ensured. The developed methodology is validated in direct switching control of a power amplifier for high-precision motion systems, where it significantly improves the steady-state output current ripple.

eess.SY↗

arXiv2022

Measurement error of tracer-based velocimetry in single-phase turbulent flows with inhomogeneous refractive indices

Inhomogeneous refractive index fields lead to errors in optical flow velocity measurements. Former respective studies are mostly in quasi two-dimensional flows, and attribute the measurement errors to spatial gradients in the refractive index field, while less attention has been paid to flows with three-dimensional refractive index fields which usually change in space and in time. In this study, ray tracing simulations were carried out in a three-dimensional flow, which is from a direct numerical simulation of single-phase turbulent mixing of two fluids. Given the data of the numerical simulation as reference, the ray tracing simulation is used to quantify the measurement errors of the flow velocity and flow acceleration for tracer-based velocimetry, i.e. particle tracking velocimetry in this study. The errors of both flow velocity and flow acceleration are attributed to the spatial and the spatio-temporal gradients of the refractive indices, respectively, which are closely inherited from flow characteristics. While the dominant type of error depends on the studied flow, the main measurement error for the considered turbulent mixing flow is caused by the random error. When the maximum spatial difference of the refractive indices is about 10 to the power of -6, the relative random measurement error is about 1 % in velocity and about 200 % in acceleration, respectively. When the maximum index difference is about 0.01 (water), the relative random measurement errors of velocity and acceleration are 2000 % and 100,000%, respectively, for the flow considered in this study.

physics.flu-dyn↗

arXiv2022

Hydrogenation induced magnetic and electronic transitions in monolayer electride Gd$_2$C: A first-principles study

The recently synthesized two-dimensional electride Gd$_2$C was proposed to be a ferromagnetic metal that possesses multiple pairs of Weyl points and may display a large anomalous Hall conductivity [Liu \textit{et al.}, Phys. Rev. Lett. \textbf{125}, 187203 (2020)]. In view of its layered structure, here we carry out first-principles studies on the magnetic and electronic properties of Gd$_2$C in the ultrathin monolayer limit. We find that monolayer Gd$_2$C remains ferromagnetic like the bulk form and the hydrogenation can effectively tune its magnetism and electronic structure. With one-sided coverage of hydrogen atoms, monolayer Gd$_2$C becomes a half-metal with one spin channel around the Fermi level. For two-sided hydrogenation, monolayer Gd$_2$C transforms to an antiferromagnetic insulator with a band gap of 0.8 eV. Our studies show that monolayer electride Gd$_2$C can perform multiple magnetic and electronic transitions with different levels of hydrogenation and may be also adopted to construct a planar heterojunction with selective area adsorption of hydrogen atoms, which has promising applications in future electronic and spintronic devices.

cond-mat.mtrl-sci↗