arXiv subjects

Yu-Feng Li

Publications and source records attributed to Yu-Feng Li.

At least 91 records · Page 5Linked to original sources

arXiv2024

Expected geoneutrino signal at JUNO using local integrated 3-D refined crustal model

Geoneutrinos serve as a potent tool for comprehending the radiogenic power and composition of Earth. Although geoneutrinos have been observed in prior experiments, the forthcoming generation of experiments,such as JUNO, will be necessary for fully harnessing their potential. Precise prediction of the crustal contribution is vital for interpreting particlephysics measurements in the context of geo-scientific inquiries. Nonetheless, existing models such as JULOC and GIGJ have limitations in accurately forecasting the crustal contribution. This paper introduces JULOCI, the novel 3-D integrated crustal model of JUNO, which employs seismic, gravity, rock sample, and heat flow data to precisely estimate the geoneutrino signal of the lithosphere. The model indicates elevated concentrations of uranium and thorium in southern China, resulting in unexpectedly strong geoneutrino signals.The accuracy of JULOC-I, coupled with a decade of experimental data, affords JUNO the opportunity to test multiple mantle models. Once operational, JUNO can validate the model predictions and enhance the precision of mantle measurements. All in all, the improved accuracy ofJULOC-I represents a substantial stride towards comprehending the geochemical distribution of the South China crust, offering a valuable tool for investigating the composition and evolution of the Earth through geoneutrinos.

physics.geo-ph↗

arXiv2023

Interactive Reweighting for Mitigating Label Quality Issues

Label quality issues, such as noisy labels and imbalanced class distributions, have negative effects on model performance. Automatic reweighting methods identify problematic samples with label quality issues by recognizing their negative effects on validation samples and assigning lower weights to them. However, these methods fail to achieve satisfactory performance when the validation samples are of low quality. To tackle this, we develop Reweighter, a visual analysis tool for sample reweighting. The reweighting relationships between validation samples and training samples are modeled as a bipartite graph. Based on this graph, a validation sample improvement method is developed to improve the quality of validation samples. Since the automatic improvement may not always be perfect, a co-cluster-based bipartite graph visualization is developed to illustrate the reweighting relationships and support the interactive adjustments to validation samples and reweighting results. The adjustments are converted into the constraints of the validation sample improvement method to further improve validation samples. We demonstrate the effectiveness of Reweighter in improving reweighting results through quantitative evaluation and two case studies.

cs.HC↗

arXiv2023

How Re-sampling Helps for Long-Tail Learning?

Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a classical and widely used approach for addressing class imbalance issues. Unfortunately, recent studies claim that re-sampling brings negligible performance improvements in modern long-tail learning tasks. This paper aims to investigate this phenomenon systematically. Our research shows that re-sampling can considerably improve generalization when the training images do not contain semantically irrelevant contexts. In other scenarios, however, it can learn unexpected spurious correlations between irrelevant contexts and target labels. We design experiments on two homogeneous datasets, one containing irrelevant context and the other not, to confirm our findings. To prevent the learning of spurious correlations, we propose a new context shift augmentation module that generates diverse training images for the tail class by maintaining a context bank extracted from the head-class images. Experiments demonstrate that our proposed module can boost the generalization and outperform other approaches, including class-balanced re-sampling, decoupled classifier re-training, and data augmentation methods. The source code is available at https://www.lamda.nju.edu.cn/code_CSA.ashx.

cs.CV↗

arXiv2023

Investigating the Limitation of CLIP Models: The Worst-Performing Categories

Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks. It is usually expected that satisfactory overall accuracy can be achieved across numerous domains through well-designed textual prompts. However, we found that their performance in the worst categories is significantly inferior to the overall performance. For example, on ImageNet, there are a total of 10 categories with class-wise accuracy as low as 0\%, even though the overall performance has achieved 64.1\%. This phenomenon reveals the potential risks associated with using CLIP models, particularly in risk-sensitive applications where specific categories hold significant importance. To address this issue, we investigate the alignment between the two modalities in the CLIP model and propose the Class-wise Matching Margin (\cmm) to measure the inference confusion. \cmm\ can effectively identify the worst-performing categories and estimate the potential performance of the candidate prompts. We further query large language models to enrich descriptions of worst-performing categories and build a weighted ensemble to highlight the efficient prompts. Experimental results clearly verify the effectiveness of our proposal, where the accuracy on the worst-10 categories on ImageNet is boosted to 5.2\%, without manual prompt engineering, laborious optimization, or access to labeled validation data.

cs.CV↗

arXiv2023

LAMDA-SSL: Semi-Supervised Learning in Python

LAMDA-SSL is open-sourced on GitHub and its detailed usage documentation is available at https://ygzwqzd.github.io/LAMDA-SSL/. This documentation introduces LAMDA-SSL in detail from various aspects and can be divided into four parts. The first part introduces the design idea, features and functions of LAMDA-SSL. The second part shows the usage of LAMDA-SSL by abundant examples in detail. The third part introduces all algorithms implemented by LAMDA-SSL to help users quickly understand and choose SSL algorithms. The fourth part shows the APIs of LAMDA-SSL. This detailed documentation greatly reduces the cost of familiarizing users with LAMDA-SSL toolkit and SSL algorithms.

cs.LG↗

arXiv2022

Enhancement of the screening effect in semiconductor detectors in the presence of the neutrino magnetic moment

The theoretical framework of the neutrino electron excitation at low energies including the screening effect in semiconductor detectors is developed for the first time, both in the Standard Model and in the presence of the neutrino magnetic moment. We explore the contribution of the screening effect of semiconductors to the neutrino electron excitation based on the linear response theory and calculate the corresponding numerical results. We show that excitation rates from the neutrino magnetic moment are dramatically enhanced {by} the screening effect and the sensitivity can be significantly improved to the level of $10^{-13}\,μ_{\rm B}$, much better than the current {laboratory and astrophysical} limits.

hep-ph↗

arXiv2022

Robust Deep Semi-Supervised Learning: A Brief Introduction

Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient. Recently, SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications as these benchmarks provide perfect unlabeled data, while in realistic scenarios, unlabeled data could be corrupted. Many researchers have pointed out that after exploiting corrupted unlabeled data, SSL suffers severe performance degradation problems. Thus, there is an urgent need to develop SSL algorithms that could work robustly with corrupted unlabeled data. To fully understand robust SSL, we conduct a survey study. We first clarify a formal definition of robust SSL from the perspective of machine learning. Then, we classify the robustness threats into three categories: i) distribution corruption, i.e., unlabeled data distribution is mismatched with labeled data; ii) feature corruption, i.e., the features of unlabeled examples are adversarially attacked; and iii) label corruption, i.e., the label distribution of unlabeled data is imbalanced. Under this unified taxonomy, we provide a thorough review and discussion of recent works that focus on these issues. Finally, we propose possible promising directions within robust SSL to provide insights for future research.

cs.LG↗

arXiv2022

USB: A Unified Semi-supervised Learning Benchmark for Classification

Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with TorchSSL.

cs.LG↗

arXiv2022

A Survey on Extreme Multi-label Learning

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML.

cs.LG↗

arXiv2022

Neutrinoless double beta decay in the minimal type-I seesaw model: How the enhancement or cancellation happens?

We discuss the contribution of right-handed neutrinos (RHNs) to the effective neutrino mass of the neutrinoless double beta decay within the minimal type-I seesaw model using the intrinsic seesaw relation of neutrino mass and mixing parameters and the relative mass dependence of the nuclear matrix elements. In the viable parameter space, we find the possibilities of both the enhancement and cancellation to the effective neutrino mass from RHNs. The bounds on the parameter space of the RHNs can be determined with the effective neutrino mass extracted from neutrinoless double beta decay experiments.

hep-ph↗

arXiv2022

Probing neutrino magnetic moments and the XENON1T excess with coherent elastic solar neutrino scattering

The detection potential of the active neutrino magnetic moment ($ν_{a}$MM) and the active sterile transition magnetic moment ($ν_{s}$MM) of the solar neutrino CE$ν$NS process in future Dark Matter direct detection experiments is studied and compared with the respective allowed range to explain the Xenon1T excess. We find that the sensitivity to the $ν_{a}$MM approaches to the level between $10^{-10}μ_B$ and $10^{-11}μ_B$, which is dominantly limited by the detection threshold. On the other hand, the future solar neutrino CE$ν$NS detection would be powerful in probing the $ν_{s}$MM for the sterile neutrino mass below 10 MeV, which could unambiguously test the $ν_{s}$MM explanation of the Xenon1T excess. The sensitivity in the general framework with both $ν_{a}$MM and $ν_{s}$MM contributions is also derived.

hep-ph↗

arXiv2022

Constraining Light Mediators via Detection of Coherent Elastic Solar Neutrino Nucleus Scattering

Dark matter (DM) direct detection experiments are entering the multiple-ton era and will be sensitive to the coherent elastic neutrino nucleus scattering (CE$ν$NS) of solar neutrinos, enabling the possibility to explore contributions from new physics with light mediators at the low energy range. In this paper we consider light mediator models (scalar, vector and axial vector) and the corresponding contributions to the solar neutrino CE$ν$NS process. Motivated by the current status of new generation of DM direct detection experiments and the future plan, we study the sensitivity of light mediators in DM direct detection experiments of different nuclear targets and detector techniques. The constraints from the latest $^8$B solar neutrino measurements of XENON-1T are also derived. Finally, We show that the solar neutrino CE$ν$NS process can provide stringent limitation on the $ L_μ-L_τ $ model with the vector mediator mass below 100 MeV, covering the viable parameter space of the solution to the $ (g-2)_μ$ anomaly.

hep-ph↗

arXiv2022

Prospects for the detection of the Diffuse Supernova Neutrino Background with the experiments SK-Gd and JUNO

The advent of gadolinium-loaded Super-Kamiokande (SK-Gd) and of the soon-to-start JUNO liquid scintillator detector marks a substantial improvement in the global sensitivity for the Diffuse Supernova Neutrino Background (DSNB). The present article reviews the detector properties most relevant for the DSNB searches in both experiments and estimates the expected signal and background levels. Based on these inputs, we evaluate the sensitivity of both experiments individually and combined. Using a simplified statistical approach, we find that both SK-Gd and JUNO have the potential to reach $>$3$σ$ evidence of the DSNB signal within 10 years of measurement. The combined results are likely to enable a $5σ$ discovery of the DSNB signal within the next decade.

astro-ph.HE↗

arXiv2022

Model-Independent Determination of Isotopic Cross Sections per Fission for Reactor Antineutrinos

Model-independent reactor isotopic cross sections per fission are determined by global fits of the reactor antineutrino data from High-Enriched Uranium (HEU) reactor rates, Low-Enriched Uranium (LEU) reactor rates, and reactor fuel evolution data. Taking account of the implicit quasi-linear relationship between the fission fractions of $^{239}\rm{Pu}$ and $^{241}\rm{Pu}$ in the LEU reactor data, the Inverse-Beta-Decay (IBD) yields and their correlations of the fissionable isotopes $^{235}\rm{U}$, $^{238}\rm{U}$, and Pu's are obtained. The data-driven isotopic IBD yields provide an anomaly-free model for the reactor isotopic cross sections per fission, where better than 1\% accuracy of the expected reactor IBD yields can be achieved for future experiments.

nucl-th↗

arXiv2022

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Transit Timing Variation (TTV) of hot Jupiters provides direct observational evidence of planet tidal dissipation. Detecting tidal dissipation through TTV needs high precision transit timings and long timing baselines. In this work, we predict and discuss the potential scientific contribution of SiTian Survey in detecting and analyzing exoplanet TTV. We develop a tidal dissipation detection pipeline for SiTian Survey that aims at time-domain astronomy with 72 1-meter optical telescopes. The pipeline includes the modules of light curve deblending, transit timing obtaining, and TTV modeling. SiTian is capable to detect more than 25,000 exoplanets among which we expect $\sim$50 sources showing evidence of tidal dissipation. We present detection and analysis of tidal dissipating targets, based on simulated SiTian light curves of XO-3b and WASP-161b. The transit light curve modeling gives consistent results within 1$σ$ to input values of simulated light curves. Also, the parameter uncertainties predicted by Monte-Carlo Markov Chain are consistent with the distribution obtained from simulating and modeling the light curve 1000 times. The timing precision of SiTian observations is $\sim$ 0.5 minutes with one transit visit. We show that differences between TTV origins, e.g., tidal dissipation, apsidal precession, multiple planets, would be significant, considering the timing precision and baseline. The detection rate of tidal dissipating hot Jupiters would answer a crucial question of whether the planet migrates at an early formation stage or random stages due to perturbations, e.g., planet scattering, secular interaction. SiTian identified targets would be constructive given that the sample would extend tenfold.

astro-ph.EP↗

arXiv2021

Collective neutrino oscillations in moving and polarized matter

We consider neutrino evolution master equations in dense moving and polarized matter consisted of electrons, neutrons, protons and neutrinos. We also take into account the neutrino magnetic moment interaction with a magnetic field. We point out the mechanisms responsible for the neutrino spin precession and provide the expressions for the corresponding interaction Hamiltonians that should be taken into account in theoretical treatments of collective neutrino oscillations.

hep-ph↗

arXiv2021

Prototypical Classifier for Robust Class-Imbalanced Learning

Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise and class imbalance. While both learning with noisy labels and class-imbalanced learning have received tremendous attention, existing works mainly focus on one of these two training set biases. To fill the gap, we propose \textit{Prototypical Classifier}, which does not require fitting additional parameters given the embedding network. Unlike conventional classifiers that are biased towards head classes, Prototypical Classifier produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. By leveraging this appealing property, we can easily detect noisy labels by thresholding the confidence scores predicted by Prototypical Classifier, where the threshold is dynamically adjusted through the iteration. A sample reweghting strategy is then applied to mitigate the influence of noisy labels. We test our method on CIFAR-10-LT, CIFAR-100-LT and Webvision datasets, observing that Prototypical Classifier obtains substaintial improvements compared with state of the arts.

cs.CV↗

arXiv2021

Dash: Semi-Supervised Learning with Dynamic Thresholding

While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use either all unlabeled examples or the unlabeled examples with a fixed high-confidence prediction during the training progress. However, it is possible that too many correct/wrong pseudo labeled examples are eliminated/selected. In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models. The selection is performed at each updating iteration by only keeping the examples whose losses are smaller than a given threshold that is dynamically adjusted through the iteration. Our proposed approach, Dash, enjoys its adaptivity in terms of unlabeled data selection and its theoretical guarantee. Specifically, we theoretically establish the convergence rate of Dash from the view of non-convex optimization. Finally, we empirically demonstrate the effectiveness of the proposed method in comparison with state-of-the-art over benchmarks.

cs.LG↗