arXiv subjects

Gang Chen

Publications and source records attributed to Gang Chen.

At least 307 records · Page 17Linked to original sources

arXiv2022

Speed up the inference of diffusion models via shortcut MCMC sampling

Diffusion probabilistic models have generated high quality image synthesis recently. However, one pain point is the notorious inference to gradually obtain clear images with thousands of steps, which is time consuming compared to other generative models. In this paper, we present a shortcut MCMC sampling algorithm, which balances training and inference, while keeping the generated data's quality. In particular, we add the global fidelity constraint with shortcut MCMC sampling to combat the local fitting from diffusion models. We do some initial experiments and show very promising results. Our implementation is available at https://github.com//vividitytech/diffusion-mcmc.git.

cs.CV↗

arXiv2022

Revisiting the centrality definition and observable centrality dependence of relativistic heavy-ion collisions in PACIAE model

We improve the centrality definition in impact parameter in PACIAE model responding the fact reported by the ALICE, ATLAS, and CMS collaborations that the maximum impact parameter in heavy ion collisions should be extended to 20 $fm$. Meanwhile the PACIAE program is updated to a new version of PACIAE 2.2.2 with convenience of studying the elementary nuclear collisions, proton-nucleus collisions, and the nucleus-nucleus collisions in one unified program version. The new impact parameter definition together with the optical Glauber model calculated impact parameter bin, $N_{part}$, and $N_{coll}$ in proton-nucleus and nucleus-nucleus collisions at relativistic energies are consistent with the improved MC-Glauber model ones within the error bar. The charged-particle pseudorapidity and the transverse momentum distributions in Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV simulated by PACIAE 2.2.2 well reproduce the ALICE experimental data.

nucl-th↗

arXiv2022

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL -- representation learning and label disambiguation -- in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

cs.LG↗

arXiv2022

Unbiased Knowledge Distillation for Recommendation

As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model. However, we find such a standard distillation paradigm would incur serious bias issue -- popular items are more heavily recommended after the distillation. This effect prevents the student model from making accurate and fair recommendations, decreasing the effectiveness of RS. In this work, we identify the origin of the bias in KD -- it roots in the biased soft labels from the teacher, and is further propagated and intensified during the distillation. To rectify this, we propose a new KD method with a stratified distillation strategy. It first partitions items into multiple groups according to their popularity, and then extracts the ranking knowledge within each group to supervise the learning of the student. Our method is simple and teacher-agnostic -- it works on distillation stage without affecting the training of the teacher model. We conduct extensive theoretical and empirical studies to validate the effectiveness of our proposal. We release our code at: https://github.com/chengang95/UnKD.

cs.IR↗

arXiv2022

The Metaverse Data Deluge: What Can We Do About It?

In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

cs.HC↗

arXiv2022

MSRL: Distributed Reinforcement Learning with Dataflow Fragments

Reinforcement learning (RL) trains many agents, which is resource-intensive and must scale to large GPU clusters. Different RL training algorithms offer different opportunities for distributing and parallelising the computation. Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e.g. policy network updates) on GPU workers. Fundamentally, current systems lack abstractions that decouple RL algorithms from their execution. We describe MindSpore Reinforcement Learning (MSRL), a distributed RL training system that supports distribution policies that govern how RL training computation is parallelised and distributed on cluster resources, without requiring changes to the algorithm implementation. MSRL introduces the new abstraction of a fragmented dataflow graph, which maps Python functions from an RL algorithm's training loop to parallel computational fragments. Fragments are executed on different devices by translating them to low-level dataflow representations, e.g. computational graphs as supported by deep learning engines, CUDA implementations or multi-threaded CPU processes. We show that MSRL subsumes the distribution strategies of existing systems, while scaling RL training to 64 GPUs.

cs.LG↗

arXiv2022

Stripe order and spin dynamics in triangular-lattice antiferromagnet KErSe$_{2}$: A single-crystal study with a theoretical description

The rare-earth triangular-lattice chalcogenide is a great platform for exploring both spin liquids and novel magnetic orders with anisotropic spin interactions and magnetic frustrations. Here, we report the thermodynamic and neutron scattering measurements of rare-earth triangular-lattice chalcogenide KErSe$_{2}$, using single-crystal samples. Our experiments revealed a long-range stripe order below 0.2 K. Although the magnetic order was three-dimensional, magnetic excitations exhibited negligible modulation along the z direction, indicating very weak interlayer coupling. Furthermore, magnetic excitation developed a well-defined spin-wave dispersion with a gap of $\sim$0.03 meV at M points. Both the stripe order and spin-wave excitations could be quantitatively understood from the anisotropic spin interactions of the Er$^{3+}$ Kramers doublets.

cond-mat.str-el↗

arXiv2022

Linear scaling of lepton charge asymmetry in $W^\pm$ production in ultra-relativistic nuclear collisions

The lepton charge asymmetry in $W^\pm$ production in the nuclear collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV is investigated with a parton and hadron cascade model PACIAE. Recently published ALICE and the ATLAS data of lepton charge asymmetry are well reproduced. An interesting linear scaling behavior is observed in the lepton charge asymmetry as a function of the collision system valence quark number asymmetry among the different size of nuclear collision systems at $\sqrt{s_{\rm NN}}=5.02$ TeV. This linear scaling behavior may serve as an additional constraint on the PDF (nPDF) extractions.

hep-ph↗

arXiv2022

A New Global Divergence Free and Pressure-Robust HDG Method for Tangential Boundary Control of Stokes Equations

In [ESAIM: M2AN, 54(2020), 2229-2264], we proposed an HDG method to approximate the solution of a tangential boundary control problem for the Stokes equations and obtained an optimal convergence rate for the optimal control {that reflects its global regularity}. However, the error estimates depend on the pressure, and the velocity is not divergence free. The importance of pressure-robust numerical methods for fluids was addressed by John et al. [SIAM Review, 59(2017), 492-544]. In this work, we devise a new HDG method to approximate the solution of the Stokes tangential boundary control problem; the HDG method is also of independent interest for solving the Stokes equations. This scheme yields a $\mathbf{H}(\mathrm{div})$ conforming, globally divergence free, and pressure-robust solution. To the best of our knowledge, this is the first time such a numerical scheme has been obtained for an optimal boundary control problem for the Stokes equations. We also provide numerical experiments to show the performance of the new HDG method and the advantage over the non pressure-robust scheme.

math.NA↗

arXiv2022

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods. Code and data are available at: https://github.com/hbzju/SoLar .

cs.LG↗

arXiv2022

Magnetic field effects in an octupolar quantum spin liquid candidate

Quantum spin liquid (QSL) is a disordered state of quantum-mechanically entangled spins commonly arising from frustrated magnetic dipolar interactions. However, QSL in some pyrochlore magnets can also come from frustrated magnetic octupolar interactions. Although the key signature for both dipolar and octupolar interaction-driven QSL is the presence of a spin excitation continuum (spinons) arising from the spin quantum number fractionalization, an external magnetic field-induced ferromagnetic order will transform the spinons into conventional spin waves in a dipolar QSL. By contrast, in an octupole QSL, the spin waves carry octupole moments that do not couple, in the leading order, to the external magnetic field or to neutron moments but will contribute to the field dependence of the heat capacity. Here we use neutron scattering to show that the application of a large external magnetic field to Ce2Zr2O7, an octupolar QSL candidate, induces an Anderson-Higgs transition by condensing the spinons into a static ferromagnetic ordered state with octupolar spin waves invisible to neutrons but contributing to the heat capacity. Our theoretical calculations also provide a microscopic, qualitative understanding for the presence of octupole scattering at large wavevectors in Ce2Sn2O7 pyrochlore, and its absence in Ce2Zr2O7. Therefore, our results identify Ce2Zr2O7 as a strong candidate for an octupolar U (1) QSL, establishing that frustrated magnetic octupolar interactions are responsible for QSL properties in Ce-based pyrochlore magnets.

cond-mat.str-el↗

arXiv2022

Gapless triangular-lattice spin-liquid candidate in PrZnAl$_{11}$O$_{19}$

A quantum spin liquid (QSL) is an exotic state in which electron spins are highly entangled, yet keep fluctuating even at zero temperature. Experimental realization of model QSLs has been challenging due to imperfections, such as antisite disorder, strain, and extra or a lack of interactions in real materials compared to the model Hamiltonian. Here we report the magnetic susceptibility, thermodynamic, inelastic neutron scattering (INS), and muon-spin relaxation studies on a polycrystalline sample of PrZnAl$_{11}$O$_{19}$, where the Pr$^{3+}$ ions form an ideal two-dimensional triangular lattice. Our results demonstrate that this system does not order nor freeze, but keep fluctuating down to 50 mK despite large antiferromagnetic couplings ($\sim$ -10 K). Furthermore, the INS and specific-heat data suggest that PrZnAl$_{11}$O$_{19}$ is best described as a gapless QSL.

cond-mat.str-el↗

arXiv2022

Interplay of itinerant electrons and Ising moments in a hybrid honeycomb quantum magnet TmNi$_3$Al$_9$

The interplay between itinerant electrons and local magnetic moments in quantum materials brings about rich and fascinating phenomena and stimulates various developments in the theoretical framework. In this work, thermodynamic, electric transport, and neutron diffraction measurements were performed on a newly synthesized honeycomb lattice magnet TmNi$_3$Al$_9$. Based on the experimental data, a magnetic field temperature phase diagram was constructed, exhibiting three essentially different magnetic regions. Below ${T_{\rm N}=2.97 \pm 0.02}\ \rm K$ Tm$^{3+}$ moments order antiferromagnetically in zero field. We found that the Tm$^{3+}$ ions form a pseudo-doublet ground state with the Ising-like moments lying normal to the two-dimensional honeycomb layers. Application of a magnetic field along the easy axis gradually suppresses the antiferromagnetic order in favor of an induced ferromagnetic state above the critical field ${B_c=0.92 \pm 0.05}\ \rm T$. In the vicinity of $B_c$, a strong enhancement of the quantum spin fluctuations was observed. The quantum Ising nature of the local moments and the coupling to itinerant electrons are discussed.

cond-mat.str-el↗

arXiv2022

Universal excitonic superexchange in spin-orbit-coupled Mott insulators

We point out the universal presence of the excitonic superexchange in spin-orbit-coupled Mott insulators. It is observed that, the restriction to the lowest spin-orbit-entangled "$J$" states may sometimes be insufficient to characterize the microscopic physics, and the virtual excitonic processes via the upper "$J$" states provide an important correction to the superexchange. We illustrate this excitonic superexchange from a two-dimensional $5d$ iridate Sr$_2$IrO$_4$ and explain its physical consequences such as the orbital-like coupling to the external magnetic flux and the nonlinear magnetic susceptibility. The universal presence of the excitonic superexchange in other spin-orbit-coupled Mott insulators such as $3d$ Co-based Kitaev magnets and even $f$ electron rare-earth magnets is further discussed.

cond-mat.str-el↗

arXiv2022

Fractionalization on the Surface: Is Type-II Terminated $1T$-TaS$_2$ Surface an Anomalously Realized Spin Liquid?

The type-II terminated $1T$-TaS$_2$ surface of a three-dimensional $1T$-TaS$_2$ bulk material realizes the effective spin-$1/2$ degree of freedom on each David star cluster with ${\mathcal{T}^2=-1}$ such that the time-reversal symmetry is realized anomalously, despite the fact that bulk three-dimensional $1T$-TaS$_2$ material has an even number of electrons per unit cell with ${\mathcal{T}^2=+1}$. This surface is effectively viewed as a spin-$1/2$ triangular lattice magnet, except with a fully gapped topological bulk. We further propose this surface termination realizes a spinon Fermi surface spin liquid with the surface fractionalization but with a nonexotic three-dimensional bulk. We analyze possible experimental consequences, especially the surface spectroscopic measurements, of the type-II terminated surface spin liquid.

cond-mat.str-el↗

arXiv2022

Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

The proposal of Pseudo-Lidar representation has significantly narrowed the gap between visual-based and active Lidar-based 3D object detection. However, current researches exclusively focus on pushing the accuracy improvement of Pseudo-Lidar by taking the advantage of complex and time-consuming neural networks. Seldom explore the profound characteristics of Pseudo-Lidar representation to obtain the promoting opportunities. In this paper, we dive deep into the pseudo Lidar representation and argue that the performance of 3D object detection is not fully dependent on the high precision stereo depth estimation. We demonstrate that even for the unreliable depth estimation, with proper data processing and refining, it can achieve comparable 3D object detection accuracy. With this finding, we further show the possibility that utilizing fast but inaccurate stereo matching algorithms in the Pseudo-Lidar system to achieve low latency responsiveness. In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy. The evaluation on the KITTI benchmark shows that the presented system achieves competitive accuracy to the state-of-the-art approaches with only 23 ms computing, showing it is a suitable candidate for deploying to real car-hold applications.

cs.CV↗

arXiv2022

Significant reduction in semiconductor interface resistance via interfacial atomic mixing

The contact resistance between two dissimilar semiconductors is determined by the carrier transmission through their interface. Despite the ubiquitous presence of interfaces, quantitative simulation of charge transport across such interfaces is difficult, limiting the understanding of interfacial charge transport. This work employs Green's functions to study the charge transport across representative Si/Ge interfaces. For perfect interfaces, it is found that the transmittance is small and the contact resistance is high, not only because the mismatch of carrier pockets makes it hard to meet the momentum conservation requirement, but also because of the incompatible symmetries of the Bloch wave functions of the two sides. In contrast, atomic mixing at the interface increases the carrier transmittance as the interface roughness opens many nonspecular transmission channels, which greatly reduces the contact resistance compared with the perfect interface. Specifically, we show that disordered interfaces with certain symmetries create more nonspecular transmission. The insights from our study will benefit the future design of high-performance heterostructures with low contact resistance.

cond-mat.mes-hall↗

arXiv2022

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretation of itself with formal language. Experiments with real datasets show the proposed neural network can obtain highly accurate fault diagnosis results with good computation efficiency. Additionally, the embedded formal language of the neuron network can provide explanations about the decision process, thus achieve interpretable fault diagnosis.

cs.LG↗