arXiv subjects

Lei Feng

Publications and source records attributed to Lei Feng.

At least 181 records · Page 10Linked to original sources

arXiv2022

Mitigating Neural Network Overconfidence with Logit Normalization

Detecting out-of-distribution inputs is critical for safe deployment of machine learning models in the real world. However, neural networks are known to suffer from the overconfidence issue, where they produce abnormally high confidence for both in- and out-of-distribution inputs. In this work, we show that this issue can be mitigated through Logit Normalization (LogitNorm) -- a simple fix to the cross-entropy loss -- by enforcing a constant vector norm on the logits in training. Our method is motivated by the analysis that the norm of the logit keeps increasing during training, leading to overconfident output. Our key idea behind LogitNorm is thus to decouple the influence of output's norm during network optimization. Trained with LogitNorm, neural networks produce highly distinguishable confidence scores between in- and out-of-distribution data. Extensive experiments demonstrate the superiority of LogitNorm, reducing the average FPR95 by up to 42.30% on common benchmarks.

cs.LG↗

arXiv2022

Interactive Protocols for Classically-Verifiable Quantum Advantage

Achieving quantum computational advantage requires solving a classically intractable problem on a quantum device. Natural proposals rely upon the intrinsic hardness of classically simulating quantum mechanics; however, verifying the output is itself classically intractable. On the other hand, certain quantum algorithms (e.g. prime factorization via Shor's algorithm) are efficiently verifiable, but require more resources than what is available on near-term devices. One way to bridge the gap between verifiability and implementation is to use "interactions" between a prover and a verifier. By leveraging cryptographic functions, such protocols enable the classical verifier to enforce consistency in a quantum prover's responses across multiple rounds of interaction. In this work, we demonstrate the first implementation of an interactive quantum advantage protocol, using an ion trap quantum computer. We execute two complementary protocols -- one based upon the learning with errors problem and another where the cryptographic construction implements a computational Bell test. To perform multiple rounds of interaction, we implement mid-circuit measurements on a subset of trapped ion qubits, with subsequent coherent evolution. For both protocols, the performance exceeds the asymptotic bound for classical behavior; maintaining this fidelity at scale would conclusively demonstrate verifiable quantum advantage.

quant-ph↗

arXiv2022

Nebula-Relay Hypothesis: The Chirality of Biological Molecules in Molecular Clouds

The chiral puzzle of biological molecules is thought to be closely related to the origin of life and is still a mystery so far. Previously, we proposed a new model on the origin of life, Nebula-Relay hypothesis, which assumed that the life on Earth originated at the planetary system of the Sun's predecessor star and then filled in the pre-solar nebula after its death. As primitive lives existed in the pre-solar nebula for a long time, did the chiral biomolecules form during this period? We explore such a possibility in this work and find that the ultra-low temperature environment of molecular clouds is beneficial to generating the chiral polymer chain of biological molecules.

physics.pop-ph↗

arXiv2022

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

This paper studies weakly supervised domain adaptation(WSDA) problem, where we only have access to the source domain with noisy labels, from which we need to transfer useful information to the unlabeled target domain. Although there have been a few studies on this problem, most of them only exploit unidirectional relationships from the source domain to the target domain. In this paper, we propose a universal paradigm called GearNet to exploit bilateral relationships between the two domains. Specifically, we take the two domains as different inputs to train two models alternately, and asymmetrical Kullback-Leibler loss is used for selectively matching the predictions of the two models in the same domain. This interactive learning schema enables implicit label noise canceling and exploits correlations between the source and target domains. Therefore, our GearNet has the great potential to boost the performance of a wide range of existing WSDL methods. Comprehensive experimental results show that the performance of existing methods can be significantly improved by equipping with our GearNet.

cs.LG↗

arXiv2022

Explanation of nearby SNRs for primary electron excess and proton spectral bump

Several groups have reported a possible excess of primary electrons at high energies with the joint fit of the positron fraction and total electron/positron spectra. With the latest release of high-precision electron/positron spectra measured by AMS-02, we further confirm this excess by fitting $ΔΦ$ $\rm(i.e., Φ_{e^-}-Φ_{e^+})$ data in this work. Then we investigate the contribution of a single nearby supernova remnant to the primary electron excess and find that Monogem can reasonably account for this excess. Moreover, we predict that the electron spectrum may harden again at a few TeVs due to Vela's contribution. DAMPE, which can accurately measure electrons at TeV scale, is expected to provide the robust test of this new spectral feature in the near future. Finally, we fit the proton spectrum data of DAMPE with Monogem or Loop I. We find that both the primary electron excess and the proton spectral bump could be mainly generated by Monogem.

astro-ph.HE↗

arXiv2022

Pointwise Binary Classification with Pairwise Confidence Comparisons

To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some consider using pairwise but not pointwise labels, when pointwise labels are not accessible due to privacy, confidentiality, or security reasons. However, as a pairwise label denotes whether or not two data points share a pointwise label, it cannot be easily collected if either point is equally likely to be positive or negative. Thus, in this paper, we propose a novel setting called pairwise comparison (Pcomp) classification, where we have only pairs of unlabeled data that we know one is more likely to be positive than the other. Firstly, we give a Pcomp data generation process, derive an unbiased risk estimator (URE) with theoretical guarantee, and further improve URE using correction functions. Secondly, we link Pcomp classification to noisy-label learning to develop a progressive URE and improve it by imposing consistency regularization. Finally, we demonstrate by experiments the effectiveness of our methods, which suggests Pcomp is a valuable and practically useful type of pairwise supervision besides the pairwise label.

cs.LG↗

arXiv2021

With False Friends Like These, Who Can Notice Mistakes?

Adversarial examples crafted by an explicit adversary have attracted significant attention in machine learning. However, the security risk posed by a potential false friend has been largely overlooked. In this paper, we unveil the threat of hypocritical examples -- inputs that are originally misclassified yet perturbed by a false friend to force correct predictions. While such perturbed examples seem harmless, we point out for the first time that they could be maliciously used to conceal the mistakes of a substandard (i.e., not as good as required) model during an evaluation. Once a deployer trusts the hypocritical performance and applies the "well-performed" model in real-world applications, unexpected failures may happen even in benign environments. More seriously, this security risk seems to be pervasive: we find that many types of substandard models are vulnerable to hypocritical examples across multiple datasets. Furthermore, we provide the first attempt to characterize the threat with a metric called hypocritical risk and try to circumvent it via several countermeasures. Results demonstrate the effectiveness of the countermeasures, while the risk remains non-negligible even after adaptive robust training.

cs.LG↗

arXiv2021

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific $\infty$-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries.

cs.LG↗

arXiv2021

Edge computing for cyber-physical systems: A systematic mapping study emphasizing trustworthiness

Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

cs.DC↗

arXiv2021

Observations of Forbush Decreases of cosmic ray electrons and positrons with the Dark Matter Particle Explorer

The Forbush Decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections (CMEs) or high-speed streams from coronal holes. It has been mainly explored with ground-based neutron monitors network which indirectly measure the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmosphere atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relative small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic ray electrons and positrons have just been investigated by the PAMELA experiment in the low energy range ($<5$ GeV) with limited statistics. In this paper, we study the FD event occurred in September, 2017, with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hours are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier and weak one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy-dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections.

astro-ph.HE↗

arXiv2021

Investigating the dark matter minispikes with the gamma-ray signal from the halo of M31

Recently, the evidence for gamma-ray emission has been found in the $Fermi$-LAT observation for the outer halo of Andromeda galaxy (M31). The dark matter (DM) annihilation offers a possible explanation on the gamma-ray radiation. In this work, we focus on the dark matter annihilation within minispikes around intermediate-mass black holes (IMBHs) with masses ranging from $100~\mathrm{M_\odot}$ to $10^6~\mathrm{M_\odot}$. When the thermal annihilation relic cross section $\left\langle σv \right\rangle = 3 \times 10^{-26}~\mathrm {cm} ^{3}\;\mathrm {s} ^{-1}$ is adopted, we conduct an investigation on the population of IMBHs in the spherical halo area of M31. We find that there could be more than 65 IMBHs with masses of $ 100~ \mathrm{M_\odot}$ surrounded by the DM minispikes as the remnants of Population III stars in the M31 spherical halo, and it is almost impossible for the existence of minspikes around IMBHs with masses above $10^4~ \mathrm{M_\odot}$ which could be formed by the collapse of primordial cold gas, for both dark matter annihilation channels $b\bar{b}$ and $τ^{+}τ^{-}$. The properties of dark matter have been further explored with the simulation of these two scenarios for IMBHs formation.

astro-ph.HE↗

arXiv2021

Mirror Dark Matter and Electronic Recoil Events in XENON1T

Recently, the XENON1T experiment has reported the possible detection of an excess in the electronic recoil spectrum. Such an excess may indicate the presence of new physics. In this work, we suggest that the scattering of mirror electrons with ordinary electrons through photon$-$mirror photon kinetic mixing with parameter $ε\sim 10^{-12}(n_{\rm e'}/0.2{\rm cm^{-3}})^{-1/2}({v_{\rm c}^0/5\times 10^{9}~{\rm cm~s^{-1}}})^{1/2}$ may account for the excess electronic recoil events in XENON1T, where $n_{\rm e'}$ is the density of mirror electron and $v_{\rm c}^0$ is the cutoff velocity of the mirror electron arriving at the earth. Interestingly, this parameter to interpret the excess of XENON1T electronic recoil spectrum are consistent with the constrains of Darkside50.

hep-ph↗

arXiv2021

GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. However, most of the existing methods only construct a single global item graph shared by all the users and regrettably ignore the diverse tastes between different user groups. Inspired by the success of local models for recommendation, this paper provides the first attempt to investigate multiple local item graphs along with a global item graph for graph-based recommendation models. We argue that recommendation on global and local graphs outperforms that on a single global graph or multiple local graphs. Specifically, we propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs), which simultaneously captures both the global and local user tastes. By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.

cs.IR↗

arXiv2021

A common origin of muon g-2 anomaly, Galaxy Center GeV excess and AMS-02 anti-proton excess in the NMSSM

The supersymmetric model is one of the most attractive extensions of the Standard Model of particle physics. In light of the most recently reported anomaly of the muon g-2 measurement by the FermiLab E989 experiment, and the excesses of gamma rays at the Galactic center observed by Fermi-LAT space telescope, as well as the antiproton excess observed by the Alpha Magnetic Spectrometer, we propose to account for all these anomalies or excesses in the Next-to-Minimal Supersymmetric Standard Model. Considering various experimental constraints including the Higgs mass, B-physics, collider data, dark matter relic density and direct detections, we find that a $\sim 60$ GeV bino-like neutralino is able to successfully explain all these observations. Our scenario can be sensitively probed by future direct detection experiments.

hep-ph↗

arXiv2021

Multi-Class Classification from Single-Class Data with Confidences

Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i.e., the class-posterior probabilities for all the classes) are available. Specifically, we propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. Instead of constructing a boundary between the given class and other classes, our method can conduct discriminative classification between all the classes even if no data from the other classes are provided. We further theoretically and experimentally show that our method can be Bayes-consistent with a simple modification even if the provided confidences are highly noisy. Then, we provide an extension of our method for the case where data from a subset of all the classes are available. Experimental results demonstrate the effectiveness of our methods.

cs.LG↗

arXiv2021

Nebula-Relay Hypothesis: Primitive Life in Nebula and Origin of Life on Earth

A modified version of panspermia theory, named Nebula-Relay hypothesis or local panspermia, is introduced to explain the origin of life on Earth. Primitive life, acting as the seeds of life on Earth, originated at pre-solar epoch through physicochemical processes and then filled in the pre-solar nebula after the death of pre-solar star. Then the history of life on the Earth can be divided into three epochs: the formation of primitive life in the pre-solar epoch; pre-solar nebula epoch; the formation of solar system and the Earth age of life. The main prediction of our model is that primitive life existed in the pre-solar nebula (even in the current nebulas) and the celestial body formed therein (i.e. solar system).

physics.pop-ph↗

arXiv2021

Constraining $U(1)_{L_μ-L_τ}$ charged dark matter model for muon $g-2$ anomaly with AMS-02 electron and positron data

Very recently, the Fermi-Lab reported the new experimental combined results on the magnetic momentum of muon with a 4.2$σ$ discrepancy compared with the expectation of the Standard Model \cite{Fermi_Lab}. A new light gauge boson $X$ in the $L_μ-L_τ$ model provides a good explanation for the $g-2$ anomaly. A Dirac fermion dark matter with a large $L_μ-L_τ$ charge can explain both the $g-2$ anomaly and the dark matter relic density \cite{Asai_2021}. In this work, we focus on the case that the mass of the dark matter is larger than the mass of muon (i.e. $m_Ψ > m_μ$) for which the channel $ΨΨ\rightarrow μ^- μ^+$ opens. Although the cross section $(σv)_{μ^{-}μ^{+}}$ is smaller by a factor of $1/q_Ψ^2$ ($q_Ψ$ represents the $L_μ-L_τ$ charge of the dark matter) compared with the channel $ΨΨ\rightarrow XX \rightarrow νν\barν\barν$, the resulting secondary electrons and positrons could imprint on their spectra above GeV energies due to the reacceleration effect of cosmic ray propagation. We use the AMS-02 measurements of electrons and positrons to constrain the annihilation cross section of the channel $ΨΨ\rightarrow μ^{-}μ^{+}$, which rules out part of the parameter space of the large $L_μ-L_τ$ charged dark matter model to account for the muon $g-2$ anomaly.

hep-ph↗

arXiv2021

Learning from Similarity-Confidence Data

Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data, where we aim to learn an effective binary classifier from only unlabeled data pairs equipped with confidence that illustrates their degree of similarity (two examples are similar if they belong to the same class). To solve this problem, we propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate. To alleviate potential overfitting when flexible models are used, we further employ a risk correction scheme on the proposed risk estimator. Experimental results demonstrate the effectiveness of the proposed methods.

stat.ML↗