arXiv subjects

Lei Feng

Publications and source records attributed to Lei Feng.

At least 199 records · Page 11Linked to original sources

arXiv2021

Observation of a prethermal discrete time crystal

The conventional framework for defining and understanding phases of matter requires thermodynamic equilibrium. Extensions to non-equilibrium systems have led to surprising insights into the nature of many-body thermalization and the discovery of novel phases of matter, often catalyzed by driving the system periodically. The inherent heating from such Floquet drives can be tempered by including strong disorder in the system, but this can also mask the generality of non-equilibrium phases. In this work, we utilize a trapped-ion quantum simulator to observe signatures of a non-equilibrium driven phase without disorder: the prethermal discrete time crystal (PDTC). Here, many-body heating is suppressed not by disorder-induced many-body localization, but instead via high-frequency driving, leading to an expansive time window where non-equilibrium phases can emerge. We observe a number of key features that distinguish the PDTC from its many-body-localized disordered counterpart, such as the drive-frequency control of its lifetime and the dependence of time-crystalline order on the energy density of the initial state. Floquet prethermalization is thus presented as a general strategy for creating, stabilizing and studying intrinsically out-of-equilibrium phases of matter.

quant-ph↗

arXiv2020

MetaInfoNet: Learning Task-Guided Information for Sample Reweighting

Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this issue in the form of sample reweighting, by learning a meta weighting network that takes training losses as inputs to generate sample weights. In this paper, we advocate that choosing proper inputs for the meta weighting network is crucial for desired sample weights in a specific task, while training loss is not always the correct answer. In view of this, we propose a novel meta-learning algorithm, MetaInfoNet, which automatically learns effective representations as inputs for the meta weighting network by emphasizing task-related information with an information bottleneck strategy. Extensive experimental results on benchmark datasets with label noise or class imbalance validate that MetaInfoNet is superior to many state-of-the-art methods.

cs.LG↗

arXiv2020

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) model achieved state-of-the-art performance. Intuitively, better performance could be achieved if stronger SS strategies and SSL models are employed. Following this intuition, one might easily derive various effective noisy-label learning methods using different combinations of SS strategies and SSL models, which is, however, reinventing the wheel in essence. To prevent this problem, we propose SemiNLL, a versatile framework that combines SS strategies and SSL models in an end-to-end manner. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance. We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.

cs.LG↗

arXiv2020

Stringent constraints on the light boson model with supermassive black hole spin measurements

Massive bosons, such as light scalars and vector bosons, can lead to instabilities of rotating black holes by the superradiance effect, which extracts energy and angular momentum from rapidly-rotating black holes effectively. This process results in spinning-down of black holes and the formation of boson clouds around them. In this work, we used the masses and spins of supermassive black holes measured from the ultraviolet/optical or X-ray observations to constrain the model parameters of the light bosons. We find that the mass range of light bosons from $10^{-22}$ eV to $10^{-17}$ eV can be largely excluded by a set of supermassive black holes (including also the extremely massive ones OJ 287, Ton 618 and SDSS J140821.67+025733.2), particularly for the vector boson scenario, which eliminates a good fraction of the so-called fuzzy dark matter parameter regions. For the scalar bosons with self-interaction, most part of the mass range from $\sim 3 \times 10^{-19}$ eV to $10^{-17}$ eV with a decay constant $f_a > 10^{15}$ GeV can be excluded, which convincingly eliminate the QCD axions at these masses.

astro-ph.HE↗

arXiv2020

Provably Consistent Partial-Label Learning

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two novel PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.

cs.LG↗

arXiv2020

Progressive Identification of True Labels for Partial-Label Learning

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

cs.LG↗

arXiv2020

Plasma dark matter and electronic recoil events in XENON1T

Dark matter might be in the form of a dark plasma in the Milky Way halo. Specifically, we consider here a hidden sector consisting of a light `dark electron' and a much heavier `dark proton', each charged under an unbroken $U(1)'$ gauge symmetry. These self-interacting dark sector particles can also interact with ordinary matter via the kinetic mixing interaction, and lead to a signal in dark matter direct detection experiments. Indeed, keV electron recoils can arise quite naturally in such models from dark electron scattering off loosely bound atomic electrons. Here we examine the recently reported XENON1T excess in the context of such a plasma dark matter model. We find that the observed excess can be explained if kinetic mixing is in the approximate range: $10^{-12} \lesssim ε\lesssim 10^{-10}$. The allowed parameter space is consistent with astrophysical and cosmological constraints and consistent also with other direct detection experiments.

hep-ph↗

arXiv2020

Jet Sub-structure in Fireworks Emission from Non-uniform and Rotating Bose-Einstein Condensates

We show that jet emission from a Bose condensate with periodically driven interactions, a.k.a. "Bose fireworks", contains essential information on the condensate wavefunction, which is difficult to obtain using standard detection methods. We illustrate the underlying physics with two examples. When condensates acquire phase patterns from external potentials or from vortices, the jets display novel sub-structure, such as oscillations or spirals, in their correlations. Through a comparison of theory, numerical simulations and experiments, we show how one can quantitatively extract the phase and the helicity of a condensate from the emission pattern. Our work demonstrating the strong link between jet emission and the underlying quantum system, bears on the recent emphasis on jet sub-structure in particle physics.

cond-mat.quant-gas↗

arXiv2020

Anomalous bremsstrahlung and the structure of cosmic ray electron-positron fluxes at the GeV-TeV energy range

We reveal that the energy spectra of electrons-positrons in primary cosmic rays measured at atmosphere top have double structures: an excess component $Φ^s_{e^+}(E)=Φ^s_{e^-}(E)$ around $400 GeV$, which origins from a strong $e^+e^-$-source and the distorted background $Φ^0_{e^-}(E)$. We supposed that the difference between AMS-CALET and Fermi-LAT-DAMPE data origins from the energy loss of the fluxes due to the anomalous bremsstrahlung effect at a special window. The evolution of spectra under anomalous bremsstrahlung effect satisfies an improved electromagnetic cascade equation. The above spectra are parameterized and they can be regarded as the subjects exploring new physics. We suggest to check the previous applications of the Bethe-Heitler formula in the study of the propagation of high energy electrons and photons.

astro-ph.HE↗

arXiv2020

Combating noisy labels by agreement: A joint training method with co-regularization

Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels.

cs.CV↗

arXiv2020

Interpretation of the cosmic ray positron and electron excesses with an annihilating-decaying dark matter scenario

The precise measurements of energy spectra of cosmic ray positrons and/or electrons by recent experiments show clear excesses above 10 GeV. Moreover, a potential sharp spectral feature was suggested by the Dark Matter Particle Explorer (DAMPE) data. These results inspire quite a number of discussions on the connection with either the annihilation/decay of dark matter (DM) or the astrophysical origins. Here we discuss a DM scenario in which DM particles could annihilate and decay into standard model particle pairs simultaneously. In this model, the peak structure is due to the DM annihilation in a nearby subhalo and the broad positron/electron excesses are due to the decay of DM in the Milky Way. This model can reasonably explain the DAMPE and AMS-02 data of the total $e^+e^-$ spectra and the positron fraction, with model parameters being consistent with existing constraints. A simple realization of such a DM model is the spin-1 vector DM model.

hep-ph↗

arXiv2020

Two-Path Phonon-Interference Resonance Induces a Stop Band in Silicon Crystal Matrix by Embedded Nanoparticles Array

In this work, we report a new stop-band formation mechanism by performing the atomistic Green's function calculation and the wave-packet molecular dynamics simulation for a system with germanium-nanoparticle array embedded in a crystalline silicon matrix. When only a single nanoparticle is embedded, the local resonance, induced through destructive interference between two different phonon wave paths, gives rise to several sharp and significant transmittance dips. On the other hand, when the number of embedded nanoparticles further increases to ten, a stop band with complete phonon reflection is formed due to the two-path resonance Bragg-like phonon interference. The wave packet simulations further uncover that the stop band originates from the collective phonon resonances at the embedded nanoparticles. Compared with the traditional stop-band formation mechanism that is the single-path Bragg reflection, the resonance mechanism has a significant advantage in not requiring the strict periodicity in the embedded nanoparticles array. We also demonstrate that the stop band can significantly suppress thermal conductance in the low-frequency regime. Our work provides a robust, scalable. and easily modulable stop-band formation mechanism. which opens a new degree of freedom for phononics-related heat control.

cond-mat.mtrl-sci↗

arXiv2019

Searching for the possible signal of the photon-axionlike particle oscillation in the combined GeV and TeV spectra of supernova remnants

The conversion between photons and axionlike particles (ALPs) in the Milky Way magnetic field could result in the detectable oscillation phenomena in $γ$-ray spectra of Galactic sources. In this work, the GeV (Fermi-LAT) and TeV (MAGIC/VERITAS/H.E.S.S.) data of three bright supernova remnants (SNRs, ie. IC443, W51C and W49B) have been adopted together to search such the oscillation effect. Different from our previous analysis of the sole Fermi-LAT data of IC443, we do not find any reliable signal for the photon-ALP oscillation in the joint broadband spectrum of each SNR. The reason for the inconsistence is that in this work we use the latest revision (P8R3) of Fermi-LAT data, updated diffuse emission templates and the new version of the source catalog (4FGL), which lead to some modification of the GeV spectrum of IC443. Then we set constraints on ALP parameters based on the combined analysis of all the three sources. Though these constraints are somewhat weaker than limits from the CAST experiment and globular clusters, they are supportive of and complementary to these other results.

astro-ph.HE↗

arXiv2019

The Energy Dependence of Micro Black Hole Horizon in Quantum Gravity Theory

The energy dependence of the deflection angle is a common prediction in some quantum gravity theories when the impact parameters are much larger than the photon wavelength. For low energy photons, the deflection angle recovers to the prediction of GR. But it reduces to zero for infinite energy photons. In this paper, we develop an effective approach to calculate the trajectory of photons and other deflection-related quantities semiclassically by replacing $h_{μν}$ with $h_{μν} \times f(E)$ to include the correction of quantum gravity. This approach could provide more information for photons traveling in an external gravitational field. We compute the horizon of micro black hole with this method and find that they are all energy dependent and decrease to zero as the energy increases to infinity.

gr-qc↗

arXiv2019

Pattern formation in a driven Bose-Einstein Condensate

Pattern formation is ubiquitous in nature from morphogenesis and cloud formation to galaxy filamentation. More often than not, patterns arise in a medium driven far from equilibrium due to the interplay of dynamical instability and nonlinear wave mixing. We report, based on momentum and real space pattern recognition, formation of density patterns with two- (D$_2$), four- (D$_4$) and six-fold (D$_6$) symmetries in Bose-Einstein condensates (BECs) with atomic interactions driven at two frequencies. The symmetry of the pattern is controlled by the ratio of the frequencies. The D$_6$ density waves, in particular, arise from a resonant wave mixing process that coherently correlates and enhances the excitations that respect the symmetry.

cond-mat.quant-gas↗

arXiv2019

A speculation about a puzzled result in energy spectrum of cosmic-ray electrons around TeV energies

Nuclear Coulomb potential at completely ionized and extremely thin atmosphere can leaks to a macroscopic spatial scale. This effect is used to explain the difference between the energy spectra of cosmic-ray electrons around $1 TeV$ measured by different experimental groups. The result inspires us to review the traditional electromagnetic shower theory at the extreme conditions. It is also reminds us that the energy spectrum of cosmic-ray electrons, which are measured by Fermi-LAT and DAMPE at a higher altitude is more closer to a true signature.

astro-ph.HE↗

arXiv2019

Search for line signal candidates in the Fermi-LAT data

In order to search for the line-like signals in the Fermi-LAT data, we have analyzed totally 49152 regions of interest (ROIs) that cover the whole sky. No ROI displays a line signal with test statistic (TS) value above 25, while for 50 ROIs weak line-like excesses with ${\rm TS}>16$ are presented. The intrinsic significances of these potential signals are further reduced by the large trial factor introduced in such kind of analysis. For the largest TS value of 24.3 derived in our analysis, the corresponding global significance is only $0.54σ$. We thus do not find any significant line-like signal and set up constraints on the cross section of dark matter annihilating to gamma-ray lines, $\left<σ{v}\right>_{γγ}$.

astro-ph.HE↗

arXiv2019

A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification

Results from multiple diagnostic tests are usually combined to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic (ROC) curve is widely adopted to produce the optimal linear combination of multiple biomarkers. In the presence of large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. Complexity of this problem increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hypervolume Under Manifolds (HUM) for multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function or normal cumulative distribution function (CDF). With the above smooth approximations, efficient gradient-based algorithms can be employed to obtain better solution with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We also derive the asymptotic normality of the coefficient estimates. We conduct extensive simulations to examine our methods. Under different simulation scenarios, the proposed methods are compared with other existing methods and are shown to outperform them in terms of diagnostic accuracy. The proposed method is illustrated using two real medical data sets.

stat.ME↗