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Liang Guo

Publications and source records attributed to Liang Guo.

At least 55 records · Page 3Linked to original sources
arXiv2023

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.

eess.IV↗
arXiv2023

Incompressible limit of the Ericksen-Leslie hyperbolic liquid crystal model in compressible flow

Ericksen and Leslie proposed a hydrodynamic model for liquid crystals in the format of conservation laws in the 1960s. Their original model includes inertial and compressibility effects, which makes the model a coupled parabolic-hyperbolic system. In this paper we build up the connection between the compressible and incompressible parabolic-hyperbolic liquid crystal model in the framework of classical solutions. We first derive the scaled Ericksen-Leslie system with dimensionless numbers, including Mach, Reynolds, and Ericksen numbers. In particular, we introduce the so-called inertial constant $χ$ which characterizes the inertial effect of the liquid crystal molecular. Next, we establish the energy estimates uniform in the Mach number $ε$ for both the compressible system and its time-derivative system with small data. Then, we pass to the limit $ε\rightarrow 0$ in the compressible system, so that we establish the global classical solution of the incompressible system by the compactness arguments. Moreover, we also obtain the convergence rates associated with $L^2$-norm in the case of well-prepared initial data. This is the first result on the incompressible limit of the compressible parabolic-hyperbolic liquid crystal model and confirms the relations of different parabolic-hyperbolic liquid crystal model rigorously.

math.AP↗
arXiv2023

Manipulation of Giant Multipole Resonances via Vortex $γ$ Photons

Traditional photonuclear reactions primarily excite giant dipole resonances, making the measurement of isovector giant resonances with higher multipolarties a great challenge. In this work, the manipulation of collective excitations of different multipole transitions in nuclei via vortex $γ$ photons has been investigated. We develop the calculation method for photonuclear cross sections induced by the vortex $γ$ photon beam using the fully self-consistent random-phase approximation plus particle-vibration coupling (RPA+PVC) model based on Skyrme density functional. We find that the electromagnetic transitions with multipolarity $J< m_γ$ are forbidden for vortex $γ$ photons due to the angular momentum conservation, with $m_γ$ being the projection of total angular momentum of $γ$ photon on its propagation direction. For instance, this allows for probing the isovector giant quadrupole resonance without interference from dipole transitions using vortex $γ$ photons with $m_γ=2$. The electromagnetic transitions with $J>m_γ$ are strongly suppressed compared with the plane-wave-$γ$-photon case, and even vanish at specific polar angles. Therefore, the giant resonances with specific multipolarity can be extracted via vortex $γ$ photons. Moreover, the vortex properties of $γ$ photons can be meticulously diagnosed by measuring the nuclear photon-absorption cross section. Our method opens new avenues for photonuclear excitations, generation of coherent $γ$ photon laser and precise detection of vortex particles, and consequently, has significant impact on nuclear physics, nuclear astrophysics and strong laser physics.

nucl-th↗
arXiv2023

Anomalous Nernst effect induced terahertz emission in a single ferromagnetic film

By developing a bidirectional-pump terahertz (THz) emission spectroscopy, we reveal an anomalous Nernst effect (ANE) induced THz emission in a single ferromagnetic film. Based on the distinctive symmetry of the THz signals, ANE is unequivocally distinguished from the previously attributed ultrafast demagnetization and anomalous Hall effect mechanisms. A quantitative method is established to separate the different contributions, demonstrating a significant ANE contribution that even overwhelms other competing mechanisms. Our work not only clarifies the origin of the ferromagnetic-based THz emission, but also offers a fertile platform for investigating the ultrafast magnetism and THz spintronics.

cond-mat.mes-hall↗
arXiv2023

A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning

Modeling difficulty, time-varying model, and uncertain external inputs are the main challenges for energy management of fuel cell hybrid electric vehicles. In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintain the batteries' long-term operation, and extend the lifetime of the fuel cells system. Fuzzy Q-learning is a model-free reinforcement learning that can learn itself by interacting with the environment, so there is no need for modeling the fuel cells system. In addition, frequent startup of the fuel cells will reduce the remaining useful life of the fuel cells system. The proposed method suppresses frequent fuel cells startup by considering the penalty for the times of fuel cell startups in the reward of reinforcement learning. Moreover, applying fuzzy logic to approximate the value function in Q-Learning can solve continuous state and action space problems. Finally, a python-based training and testing platform verify the effectiveness and self-learning improvement of the proposed method under conditions of initial state change, model change and driving condition change.

cs.AI↗
arXiv2022

Multi-scale temporal-frequency attention for music source separation

In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS). Although previous methods have addressed the large receptive field modeling using various methods, the temporal and frequency correlations of the music spectrogram with repeated patterns have not been explicitly explored for the MSS task. In this paper, a temporal-frequency attention module is proposed to model the spectrogram correlations along both temporal and frequency dimensions. Moreover, a multi-scale attention is proposed to effectively capture the correlations for music signal. The experimental results on MUSDB18 dataset show that the proposed method outperforms the existing state-of-the-art systems with 9.51 dB signal-to-distortion ratio (SDR) on separating the vocal stems, which is the primary practical application of MSS.

eess.AS↗
arXiv2022

Fluid laminarization in protein-based high internal phase emulsions process

Protein-based high internal phase emulsions (HIPEs) have gained tremendous attention in diverse fields, but their mechanism in the emulsification process remains elusive. In this article, HIPEs were stabilized directly by food-grade proteins, depending on a self-organized process featuring a fluid laminarization. We elucidated that the emulsification with the rotor-stator mixer is a typical non-equilibrium process. The crucial factor for the process is related to the irreversible energy dissipation, while the internal phase volume fraction is the threshold determining the laminarization. The feasible explanation speculated that the transition corresponds to the dissipative structure, i.e., compressive droplets, arising from the spatiotemporal self-organization, to dissipate the turbulent kinetic energy. We found a new paradigm of dissipative structure, comprehending such structure in the HIPEs emulsification process, which is expected to pave the way for its industrial-scale production with the virtue of low-cost proteins.

cond-mat.soft↗
arXiv2022

A Bott periodicity theorem for $\ell^p$-spaces and the coarse Novikov conjecture at infinity

We formulate and prove a Bott periodicity theorem for an $\ell^p$-space ($1\leq p<\infty$). For a proper metric space $X$ with bounded geometry, we introduce a version of $K$-homology at infinity, denoted by $K_*^{\infty}(X)$, and the Roe algebra at infinity, denoted by $C^*_{\infty}(X)$. Then the coarse assembly map descents to a map from $\lim_{d\to\infty}K_*^{\infty}(P_d(X))$ to $K_*(C^*_{\infty}(X))$, called the coarse assembly map at infinity. We show that to prove the coarse Novikov conjecture, it suffices to prove the coarse assembly map at infinity is an injection. As a result, we show that the coarse Novikov conjecture holds for any metric space with bounded geometry which admits a fibred coarse embedding into an $\ell^p$-space. These include all box spaces of a residually finite hyperbolic group and a large class of warped cones of a compact space with an action by a hyperbolic group.

math.KT↗
arXiv2022

Coarse embeddings at infinity and generalized expanders at infinity

We introduce a notion of coarse embedding at infinity into Hilbert space for metric spaces, which is a weakening of the notion of fibred coarse embedding and a far generalization of Gromov's concept of coarse embedding. It turns out that a residually finite group admits a coarse embedding into Hilbert space if and only if one (or equivalently, every) box space of the group admits a coarse embedding at infinity into Hilbert space. Moreover, we introduce a concept of generalized expander at infinity and show that it is an obstruction to coarse embeddability at infinity.

math.OA↗
arXiv2022

A two-step backward compatible fullband speech enhancement system

Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fullband (48kHz) speech enhancement system is proposed in this paper. Compared to the existing fullband systems that utilizes perceptually motivated features to train the fullband speech enhancement using a single network structure, the proposed system is a two-step system ensuring good fullband speech enhancement quality while backward compatible to the existing wideband systems.

eess.AS↗
arXiv2021

K-theory of the maximal and reduced Roe algebras of metric spaces with A-by-CE coarse fibrations

Let $X$ be a discrete metric space with bounded geometry. We show that if $X$ admits an "A-by-CE coarse fibration", then the canonical quotient map $λ: C^*_{\max}(X)\to C^*(X)$ from the maximal Roe algebra to the Roe algebra of $X$, and the canonical quotient map $λ: C^*_{u, \max}(X)\to C^*_u(X)$ from the maximal uniform Roe algebra to the uniform Roe algebra of $X$, induce isomorphisms on $K$-theory. A typical example of such a space arises from a sequence of group extensions $\{1\to N_n\to G_n\to Q_n\to 1\}$ such that the sequence $\{N_n\}$ has Yu's property A, and the sequence $\{Q_n\}$ admits a coarse embedding into Hilbert space. This extends an early result of J. Špakula and R. Willett \cite{JR2013} to the case of metric spaces which may not admit a coarse embedding into Hilbert space. Moreover, it implies that the maximal coarse Baum-Connes conjecture holds for a large class of metric spaces which may not admit a fibred coarse embedding into Hilbert space.

math.OA↗
arXiv2021

One-pot green process to synthesize controllable surface terminations MXenes in molten salts

Surface terminations for 2D MXene have dramatic impacts on physicochemical properties. The commonly etching methods usually introduce -F surface termination or metallic into MXene. Here, we present a new molten salt assisted electrochemical etching (MS-E-etching) method to synthesize fluorine-free Ti3C2Tx without metallics. Due to performing electrons as reaction agent, the cathode reduction and anode etching can be spatially isolated, thus no metallic presents in Ti3C2Tx product. Moreover, the Tx surface terminations can be directly modified from -Cl to -O and/or -S in one pot process. The obtained -O terminated MXenes exhibited capacitance of 225 and 205 F/g at 1 and 10 A/g, confirming high reversibility of redox reactions. This one-pot process greatly shortens the modification procedures as well as enriches the surface functional terminations. More importantly, the recovered salt after synthesis can be recycled and reused, which brands it as a green sustainable method.

cond-mat.mtrl-sci↗
arXiv2021

Dissipative solutions to the compressible isentropic Navier-Stokes equations

The existence of dissipative solutions to the compressible isentropic Navier-Stokes equations was established in this paper. This notion was inspired by the concept of dissipative solutions to the incompressible Euler equations of Lions (\cite{Lions-1996}, Section 4.4). Our method is to recover such solutions by passing to the limits from approximated solutions, thanks to compactness argument.

math.AP↗
arXiv2020

Tailoring Topological Edge States with Photonic Crystal Nanobeam Cavities

The realization of topological edge states (TESs) in photonic systems has provided unprecedented opportunities for manipulating light in novel manners. The Su-Schrieffer-Heeger (SSH) model has recently gained significant attention and has been exploited in a wide range of photonic platforms to create TESs. We develop a photonic topological insulator strategy based on SSH photonic crystal nanobeam cavities. In contrast to the conventional photonic SSH schemes which are based on alternately tuned coupling strength in one-dimensional lattice, our proposal provides higher flexibility and allows tailoring TESs by manipulating mode coupling in a two-dimensional manner. We reveal that the proposed hole-array based nanobeams in a dielectric membrane can selectively tailor single or double TESs in the telecommunication region by controlling the coupling strength of the adjacent SSH nanobeams in both vertical and horizontal directions. Our finding provides an in-depth understanding of the SSH model, and allows an additional degree of freedom in exploiting the SSH model for integrated topological photonic devices with unique properties and functionalities.

physics.optics↗
arXiv2020

Space-time clustering of flash floods in a changing climate (China, 1950-2015)

The persistence over space and time of flash flood disasters -- flash floods that have caused either economical or life losses, or both -- is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyse the national inventory of flash floods disasters in China occurred in the period 1950-2015. Specifically, we investigated the spatio-temporal pattern distribution of the flash floods and their clustering behavior by using both global and local methods: the first, based on the Ripley's K-function, and the second on scan statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect precipitations trend. Due to the large spatial (the whole Chinese territory) and temporal scale of the dataset (66 years), we were able to capture whether certain clusters gather in specific locations and times, but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash floods compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash floods directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic changes.

stat.AP↗
arXiv2019

Subsampling Bias and The Best-Discrepancy Systematic Cross Validation

Statistical machine learning models should be evaluated and validated before putting to work. Conventional k-fold Monte Carlo Cross-Validation (MCCV) procedure uses a pseudo-random sequence to partition instances into k subsets, which usually causes subsampling bias, inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation. Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory, we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence, which ensures low subsampling bias and leads to more precise Expected-Prediction-Error estimates. Experiments with 156 benchmark datasets and three classifiers (logistic regression, decision tree and naive bayes) show that in general, our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18% and the variances around 26.73%. In comparison, the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58% and 11.85% respectively. The Leave-One-Out (LOO) can lower the EPE around 2.50% but its variances are much higher than the any other CV procedure. The computational time of our cross-validation procedure is just 8.64% of the MCCV, 8.67% of the stratified MCCV and 16.72% of the LOO. Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio. This makes our approach particularly pertinent when solving bioscience classification problems. Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism.

stat.ML↗
arXiv2014

A novel hohlraum with ultrathin depleted-uranium-nitride coating layer for low hard x-ray emission and high radiation temperature

An ultra-thin layer of uranium nitrides (UN) has been coated on the inner surface of the depleted uranium hohlraum (DUH), which has been proved by our experiment can prevent the oxidization of Uranium (U) effectively. Comparative experiments between the novel depleted uranium hohlraum and pure golden (Au) hohlraum are implemented on Shenguang III prototype laser facility. Under the laser intensity of 6*10^14 W/cm2, we observe that, the hard x-ray (> 1.8 keV) fraction of this uranium hohlraum decreases by 61% and the peak intensity of total x-ray flux (0.1 keV ~ 5.0 keV) increases by 5%. Radiation hydrodynamic code LARED is used to interpret the above observations. Our result for the first time indicates the advantage of the UN-coated DUH in generating the uniform x-ray field with a quasi Planckian spectrum and thus has important implications in optimizing the ignition hohlraum design.

physics.plasm-ph↗