arXiv subjects

Yi Liao

Publications and source records attributed to Yi Liao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Probing dimension-8 SMEFT operators through neutral meson mixing

We investigate the impact of effective interactions of dimension-8 (dim-8) operators in the standard model effective field theory (SMEFT) on neutral meson mixing, focusing on the $K^0-\bar K^0$, $B_{d,s}-\bar B_{d,s}$, and $D^0-\bar D^0$ systems. Within the framework of the low energy effective field theory (LEFT), each system is governed by eight dim-6 operators, with four originating at tree level from dim-6 SMEFT operators and the other four from dim-8 SMEFT operators. Notably, in certain UV complete models those dim-8 operators instead of the dim-6 ones are generated at the leading order. Our analysis focuses on those dim-8 operators and includes their one-loop QCD renormalization group running effects. By leveraging the LEFT master formula we impose stringent constraints on the effective scales associated with these dim-8 operators. We find that neutral meson mixing can probe an effective scale up to 80 TeV for some operators, surpassing the constraints imposed on other dim-8 operators by other observables. Lastly, we present a UV complete model capable of generating dim-8 operators at the leading order, thus offering a unique perspective on the interplay between different operator dimensions in probing new physics phenomena.

hep-ph↗

arXiv2025

Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation. Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences. We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (eg, common sense). For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it. Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation. Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.

cs.CL↗

arXiv2025

Baryon number violating hydrogen decay

Most studies on baryon number violating (BNV) processes in the literature focus on free or bound nucleons in nuclei, with limited attention given to the decay of bound atoms. Given that hydrogen is the most abundant atom in the universe, it is particularly intriguing to investigate the decay of hydrogen atom as a means to probe BNV interactions. In this study, for the first time, we employ a robust effective field theory (EFT) approach to estimate the decay widths of two-body decays of hydrogen atom into standard model particles, by utilizing the constraints on the EFT cutoff scale derived from conventional nucleon decay processes. We integrate low energy effective field theory (LEFT), chiral perturbation theory (ChPT), and standard model effective field theory (SMEFT) to formulate the decay widths in terms of the LEFT and SMEFT Wilson coefficients (WCs), respectively. By applying the bounds on the WCs from conventional nucleon decays, we provide a conservative estimate on hydrogen BNV decays. Our findings indicate that the bounds on the inverse partial widths of all dominant two-body decays exceed $10^{44}$ years. Among these modes, the least constrained diphoton decay $\Hy\to γγ$ might be astrophysically interesting, although the monochromatic photon signal from our Sun is difficult to detect with current near-Earth telescopes.

hep-ph↗

arXiv2024

Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models

In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when making their classification decisions. A novel cascading neuron abandoning back-propagation algorithm is designed to trace neurons in all layers of a CNN that involve in making its prediction to address the problem of significant interference from abandoned neurons. Firstly, a Neuron Abandoning Back-Propagation (NA-BP) module is proposed to generate Back-Propagated Feature Maps (BPFM) by using the inverse function of the intermediate layers of CNN models, on which the neurons not used for decision-making are abandoned. Meanwhile, the cascading NA-BP modules calculate the tensors of importance coefficients which are linearly combined with the tensors of BPFMs to form the NAFlow. Secondly, to be able to visualize attention flow for similarity metric-based CNN models, a new channel contribution weights module is proposed to calculate the importance coefficients via Jacobian Matrix. The effectiveness of the proposed NAFlow is validated on nine widely-used CNN models for various tasks of general image classification, contrastive learning classification, few-shot image classification, and image retrieval.

cs.CV↗

arXiv2024

Revisiting general dark matter-bound-electron interactions

In this Letter we revisit general dark matter (DM)-bound-electron interactions studied previously in the influential work [R. Catena {\it et al.,} Atomic responses to general dark matter-electron interactions, Phys. Rev. Res. 2, 033195 (2020)] For the most general DM-electron nonrelativistic or relativistic interactions for DM with spin up to 1, we find the average ionization matrix element squared can be organized into three terms, each of which is a product of a DM response function ($a_{0,1,2}$) and a linear combination ($\widetilde W_{0,1,2}$) of the four atomic response functions ($W_{1,2,3,4}$) given in that work, $ \widetilde W_0 = W_1, \, \widetilde W_1 = |\pmb{v}_0^\perp|^2 W_1 - 2 {m_e\, \pmb{q}\cdot \pmb{v}_0^\perp \over \pmb{q}^2} W_2 + W_3,\, \widetilde W_2 = { (\pmb{q}\cdot \pmb{v}_0^\perp)^2 \over \pmb{q}^2} W_1 - 2 {m_e\, \pmb{q}\cdot \pmb{v}_0^\perp \over \pmb{q}^2} W_2 + {m_e^2 \over \pmb{q}^2}W_4$. Furthermore, we find a crucial minus sign was missed for the calculation of $W_2$ in that work, which has significant phenomenological consequences when explaining experimental bounds on specific DM scenarios. Due to the corrected sign, there can be significant cancellations between the $W_2$ and $W_{3,4}$ terms, so that $\widetilde W_{1,2}$ are dominated by the usual response function $W_1$ in some cases. Many DM scenarios involving DM or electron axial-vector current can yield $W_2$ and thus are potentially affected by the sign. As an example, we show that the recent XENON1T constraint on the fermionic DM anapole moment is weakened by a factor of 2 or so. We also present a complete list of NR operators for spin-1 DM and compute their contributions to the DM response functions.

hep-ph↗

arXiv2024

SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers

Over the past few years, vision transformers (ViTs) have consistently demonstrated remarkable performance across various visual recognition tasks. However, attempts to enhance their robustness have yielded limited success, mainly focusing on different training strategies, input patch augmentation, or network structural enhancements. These approaches often involve extensive training and fine-tuning, which are time-consuming and resource-intensive. To tackle these obstacles, we introduce a novel approach named Spatial Autocorrelation Token Analysis (SATA). By harnessing spatial relationships between token features, SATA enhances both the representational capacity and robustness of ViT models. This is achieved through the analysis and grouping of tokens according to their spatial autocorrelation scores prior to their input into the Feed-Forward Network (FFN) block of the self-attention mechanism. Importantly, SATA seamlessly integrates into existing pre-trained ViT baselines without requiring retraining or additional fine-tuning, while concurrently improving efficiency by reducing the computational load of the FFN units. Experimental results show that the baseline ViTs enhanced with SATA not only achieve a new state-of-the-art top-1 accuracy on ImageNet-1K image classification (94.9%) but also establish new state-of-the-art performance across multiple robustness benchmarks, including ImageNet-A (top-1=63.6%), ImageNet-R (top-1=79.2%), and ImageNet-C (mCE=13.6%), all without requiring additional training or fine-tuning of baseline models.

cs.CV↗

arXiv2024

Comprehensive constraints on fermionic dark matter-quark tensor interactions in direct detection experiments

Effective field theory (EFT) provides a model-independent framework for interpreting the results of dark matter (DM) direct detection experiments. In this study, we demonstrate that the two fermionic DM-quark tensor operators, $(\barχ iσ^{μν} γ^5 χ) (\bar{q} σ_{μν}q)$ and $(\barχ σ^{μν} χ) (\bar{q} σ_{μν} q)$, can contribute to the DM electric and magnetic dipole moments via nonperturbative QCD effects, in addition to the well-studied contact DM-nucleon operators. We then investigate the constraints on these two operators by considering both the contact and the dipole contributions using the XENON1T nuclear recoil and Migdal effect data. We also recast other existing bounds on the DM dipole operators, derived from electron and nuclear recoil measurements in various direct detection experiments, as constraints on the two tensor operators. For $m_χ\lesssim 1\,\rm GeV$, our results significantly extend the reach of constraints on the DM-quark tensor operators to masses as low as $5\,\rm MeV$, with the bound exceeding that obtained by the Migdal effect with only contact interactions by an order of magnitude or so. In particular, for the operator $(\barχ σ^{μν}iγ_5 χ) (\bar{q} σ_{μν}q)$ with DM mass $m_χ\gtrsim 10\,\rm GeV$, the latest PandaX constraint on the DM electric dipole moment puts more stringent bounds than the previous direct detection limit.

hep-ph↗

arXiv2024

A systematic investigation on dark matter-electron scattering in effective field theories

In this paper, we systematically investigate the general dark matter-electron interactions within the framework of effective field theories (EFT). We consider both the non-relativistic (NR) EFT and the relativistic EFT descriptions of the interactions with the spin of dark matter (DM) up to one, i.e., the scalar ($ϕ$), fermion ($χ$), and vector $(X)$ DM scenarios. We first collect the leading-order NR EFT operators describing the DM-electron interactions, and construct especially the NR operators for the vector DM case. Next, we consider all possible leading-order relativistic EFT operators including those with a photon field and perform the NR reduction to match them onto the NR EFT. Then we rederive the DM-bound-electron scattering rate within the NR EFT framework and find that the matrix element squared, which is the key input that encodes the DM and atomic information, can be compactly decomposed into three terms. Each term is a product of a DM response function $(a_{0,1,2})$, which is essentially a factor of Wilson coefficients squared, and its corresponding generalized atomic response function ($\widetilde W_{0,1,2}$). Lastly, we employ the electron recoil data from the DM direct detection experiments (including XENON10, XENON1T, and PandaX-4T) to constrain all the non-relativistic and relativistic operators in all three DM scenarios. We set strong bounds on the DM-electron interactions in the sub-GeV region. Particularly, we find that the latest PandaX-4T S2-only data provide stringent constraints on dark matter with a mass greater than approximately 20 MeV, surpassing those from the previous XENON10 and XENON1T experiments.

hep-ph↗

arXiv2024

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training

The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. To address this, we propose a soft deduplication method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. Central to our approach is the concept of "data commonness", a metric we introduce to quantify the degree of duplication by measuring the occurrence probabilities of samples using an n-gram model. Empirical analysis shows that this method significantly improves training efficiency, achieving comparable perplexity scores with at least a 26% reduction in required training steps. Additionally, it enhances average few-shot downstream accuracy by 1.77% when trained for an equivalent duration. Importantly, this approach consistently improves performance, even on rigorously deduplicated datasets, indicating its potential to complement existing methods and become a standard pre-training process for LLMs.

cs.CL↗

arXiv2024

Stability of the de-Sitter universe: One-loop nonlocal $f(R)$ gravity

With the method of the background field expansion, we investigate the one-loop quantization of the Euclidean nonlocal $f(R)$ model in the de-Sitter universe. We obtain the ghost-free condition (GFC) based on the transformation from the Jordan frame to the Einstein frame and the classical stability condition (CSC) satisfied $f^{(0)}_{RR}-ϕ_0F^{(0)}_{RR}<0$. We present the on-shell and off-shell one-loop effective action and quantum stability condition (QSC) by utilizing the generalized zeta-function. We find that under the fulfillment of GFC, CSC and QSC are inconsistent.

gr-qc↗

arXiv2023

Dark Sector Effective Field Theory

We introduce the effective field theory of two different light dark particles interacting with the standard model (SM) light states in a single vertex, termed dark sector effective field theory (DSEFT). We focus on the new light particles with spin up to 1 and being real in essence, namely, new real scalars $ϕ$ and $S$, Majorana fermions $χ$ and $ψ$, and real vectors $X_μ$ and $V_μ$. In the framework of low energy effective field theory with QED and QCD symmetry, the DSEFT can be classified into six categories, including the scalar-scalar-SM ($ϕS$-SM), fermion-fermion-SM ($χψ$-SM), vector-vector-SM ($X V$-SM), scalar-fermion-SM ($ϕχ$-SM), scalar-vector-SM ($ϕX$-SM), and fermion-vector-SM ($χX$-SM) cases. For each case, we construct the effective operator basis up to canonical dimension 7, which will cover most interesting phenomenology at low energy. As a phenomenological example, we investigate the longstanding neutron lifetime anomaly through the neutron dark decay modes $n \to χϕ\text{ or } χX$ from the effective interactions in the fermion-scalar-SM or fermion-vector-SM case. When treating the light fermion as a dark matter candidate, we also explore the constraints from DM-neutron annihilation signal at Super-Kamiokande. We find the neutron dark decay in each scenario can accommodate the anomaly, at the same time, without contradicting with the Super-Kamiokande limit.

hep-ph↗

arXiv2023

Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification

Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based methods were proposed as powerful interpretation tools, making the prediction of deep learning models more explainable, transparent, and trustworthy. However, all the CAM-based methods (e.g., CAM, Grad-CAM, and Relevance-CAM) can only be used for interpreting CNN models with fully-connected (FC) layers as a classifier. It is worth noting that many deep learning models classify images without FC layers, e.g., few-shot learning image classification, contrastive learning image classification, and image retrieval tasks. In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed, which can interpret deep learning models without FC layers as a classifier. In the proposed FAM algorithm, the channel-wise contribution weights are derived from the similarity scores between two image embeddings. The activation maps are linearly combined with the corresponding normalized contribution weights, forming the explanation map for visualization. The quantitative and qualitative experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.

cs.CV↗

arXiv2022

One-loop Matching of Scotogenic Model onto Standard Model Effective Field Theory up to Dimension 7

The scotogenic neutrino seesaw model is a minimal extension of the standard model with three $\mathbb{Z}_2$-odd right-handed singlet fermions $N$ and one $\mathbb{Z}_2$-odd Higgs doublet $η$ that can accommodate the tiny neutrino mass and provide a dark matter candidate in a unified picture. Due to lack of experimental signatures for electroweak scale new physics, it is appealing to assume these new particles are well above the electroweak scale and take the effective field theory approach to study their effects on low energy observables. In this work we apply the recently developed functional matching formalism to the one-loop matching of the model onto the standard model effective field theory up to dimension seven for the case when all new states $N$ and $η$ are heavy to be integrated out. This is a realistic example which has no tree-level matching due to the $\mathbb{Z}_2$ symmetry. Using the matching results, we analyze their phenomenological implications for several physical processes, including the lepton number violating effect, the CDF $W$ mass excess, and the lepton flavor violating decays like $μ\to eγ$ and $μ\to 3e$.

hep-ph↗

arXiv2022

Nanoporous Structure of Sintered Metal Powder Heat Exchanger in Dilution Refrigeration: A Numerical Study

We use LAMMPS to randomly pack hard spheres to simulate the heat exchanger, where the hard spheres represent sintered metal particles in the heat exchanger. We simulated the heat exchanger under different sphere radii and different packing fractions of the metal particle and researched pore space. To improve the performance of the heat exchanger, we adopted this simulation method to explore when the packing fraction is 65%, the optimal sintering particle radius in the heat exchanger is 30~35nm.

physics.comp-ph↗

arXiv2022

Extending low energy effective field theory with a complete set of dimension-7 operators

We present a complete and independent set of dimension-7 operators in the low energy effective field theory (LEFT) where the dynamical degrees of freedom are the standard model five quarks and all of the neutral and charged leptons. All operators are non-Hermitian and are classified according to their baryon ($ΔB$) and lepton ($ΔL$) numbers violated. Including Hermitian-conjugated operators, there are in total $3168$, $750$, $588$, $712$ operators with $(ΔB,ΔL)=(0,0)$, $(0,\pm 2)$, $(\pm 1,\mp 1)$, $(\pm 1,\pm 1)$ respectively. We perform the tree-level matching with the standard model effective field theory (SMEFT) up to dimension-7 (dim-7) operators in both LEFT and SMEFT. As a phenomenological application we study the effective neutrino-photon interactions due to dim-7 lepton number violating operators that are induced and much enhanced at one loop from dim-6 operators that in turn are matched from dim-7 SMEFT operators. We compare the cross sections of various neutrino-photon scattering with their counterparts in the standard model and highlight the new features. Finally we illustrate how these effective interactions could arise from ultraviolet completion.

hep-ph↗

arXiv2022

A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis

18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients. Reconstructing the low-dose PET (L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective way that both reduces the radiation exposure and remains diagnostic accuracy. In this paper, we propose a resource-efficient deep learning framework for L-PET reconstruction and analysis, referred to as transGAN-SDAM, to generate F-PET from corresponding L-PET, and quantify the standard uptake value ratios (SUVRs) of these generated F-PET at whole brain. The transGAN-SDAM consists of two modules: a transformer-encoded Generative Adversarial Network (transGAN) and a Spatial Deformable Aggregation Module (SDAM). The transGAN generates higher quality F-PET images, and then the SDAM integrates the spatial information of a sequence of generated F-PET slices to synthesize whole-brain F-PET images. Experimental results demonstrate the superiority and rationality of our approach.

eess.IV↗

arXiv2021

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora. In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks. The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy, Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti) and natural language inference (XNLI).

cs.CL↗

arXiv2021

A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-grained visual categorization (ultra-FGVC), which targets at identifying subclasses with extremely similar patterns, has not received much attention. In ultra-FGVC datasets, the samples per category are always scarce as the granularity moves down, which will lead to overfitting problems. Moreover, the difference among different categories is too subtle to distinguish even for professional experts. Motivated by these issues, this paper proposes a novel compositional feature embedding and similarity metric (CECS). Specifically, in the compositional feature embedding module, we randomly select patches in the original input image, and these patches are then replaced by patches from the images of different categories or masked out. Then the replaced and masked images are used to augment the original input images, which can provide more diverse samples and thus largely alleviate overfitting problem resulted from limited training samples. Besides, learning with diverse samples forces the model to learn not only the most discriminative features but also other informative features in remaining regions, enhancing the generalization and robustness of the model. In the compositional similarity metric module, a new similarity metric is developed to improve the classification performance by narrowing the intra-category distance and enlarging the inter-category distance. Experimental results on two ultra-FGVC datasets and one FGVC dataset with recent benchmark methods consistently demonstrate that the proposed CECS method achieves the state of-the-art performance.

cs.CV↗