arXiv subjects

Chao Chen

Publications and source records attributed to Chao Chen.

At least 415 records · Page 23Linked to original sources

arXiv2024

Provable Robust Saliency-based Explanations

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the $\ell_p$ distance for stability assessment, which diverges from human perception. Besides, existing adversarial training (AT) associated with intensive computations may lead to an arms race. To address these challenges, we introduce a novel metric to assess the stability of top-$k$ salient features. We introduce R2ET which trains for stable explanation by efficient and effective regularizer, and analyze R2ET by multi-objective optimization to prove numerical and statistical stability of explanations. Moreover, theoretical connections between R2ET and certified robustness justify R2ET's stability in all attacks. Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods.

cs.LG↗

arXiv2024

Robust Blockwise Random Pivoting: Fast and Accurate Adaptive Interpolative Decomposition

The interpolative decomposition (ID) aims to construct a low-rank approximation formed by a basis consisting of row/column skeletons in the original matrix and a corresponding interpolation matrix. This work explores fast and accurate ID algorithms from comprehensive perspectives for empirical performance, including accuracy in both skeleton selection and interpolation matrix construction, efficiency in terms of asymptotic complexity and hardware efficiency, as well as rank adaptiveness. While many algorithms have been developed to optimize some of these aspects, practical ID algorithms proficient in all aspects remain absent. To fill in the gap, we introduce robust blockwise random pivoting (RBRP) that is asymptotically fast, hardware-efficient, and rank-adaptive, providing accurate skeletons and interpolation matrices comparable to the best existing ID algorithms in practice. Through extensive numerical experiments on various synthetic and natural datasets, we demonstrate the appealing empirical performance of RBRP from the aforementioned perspectives, as well as the robustness of RBRP to adversarial inputs.

math.NA↗

arXiv2024

Measurement of the Branching Fraction for the Decay $χ_{cJ}\to p\bar{p}ηπ^{0}$

Using $(2712.4\pm 14.3)\times10^6 ψ(3686)$ events collected by the BESIII detector operating at the BEPCII collider, we present the first observations of the decays $χ_{cJ}(J=0,1,2)\to p\bar{p}ηπ^{0}$. Their decay branching fractions are determined to be ${\cal B}(χ_{c0}\to p\bar{p}ηπ^{0})=({2.41 \pm 0.07 \pm 0.19}) \times 10^{-4}$, ${\cal B}(χ_{c1}\to p\bar{p}ηπ^{0})=({1.95 \pm 0.05 \pm 0.12}) \times 10^{-4}$, and ${\cal B}(χ_{c2}\to p\bar{p}ηπ^{0})=({1.31 \pm 0.05 \pm 0.08}) \times 10^{-4}$, where the first uncertainties are statistical and the second systematic.

hep-ex↗

arXiv2024

Study of $D_{s}^{+} \rightarrow f_{0}(980)ρ^+$ and $ϕπ^+$ decays through $D_{s}^{+}\rightarrow π^{+}π^{+}π^{-}π^{0}$

We perform the first amplitude analysis of $D^+_s \to π^+π^+π^-π^0$ decays, based on data samples of electron-positron collisions recorded with the BESIII detector at center-of-mass energies between 4.128 and 4.226~GeV, corresponding to an integrated luminosity of 7.33~fb$^{-1}$. We report the observation of $D_{s}^{+} \to f_0(980)ρ(770)^{+}$ with a statistical significance greater than 10$σ$ and determine the branching fractions $\mathcal{B}(D_s^+\toπ^+π^+π^-π^0|_{{\rm non}-η})=(2.04\pm0.08_{\rm stat.}\pm0.05_{\rm syst.})\%$ and $\mathcal{B}(D_s^+\toηπ^+)=(1.56\pm0.09_{\rm stat.}\pm0.04_{\rm syst.})\%$. Moreover, we measure the relative branching fraction between $ϕ\toπ^+π^-π^0$ and $ϕ\to K^+K^-$ to be $\frac{\mathcal{B}(ϕ(1020) \to π^+π^-π^0)}{\mathcal{B}(ϕ(1020) \to K^+K^-)}=0.230 \pm 0.014_{\rm stat.} \pm 0.010_{\rm syst.}$, which deviates from the world average value by more than $4σ$.

hep-ex↗

arXiv2024

Amplitude analysis and branching fraction measurement of the Cabibbo-favored decay $D^+ \to K^-π^+π^+π^0$

An amplitude analysis of the Cabibbo-favored decay $D^+ \to K^-π^+π^+π^0$ is performed, using 7.93 $\rm{fb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy of 3.773 GeV. The branching fractions of the intermediate processes are measured, with the dominant contribution $D^+ \to \bar{K}^{*}(892)^0ρ(770)^+$ observed to have a branching fraction of $(4.15\pm0.07_{\rm stat.}\pm0.17_{\rm syst.})\%$. With the detection efficiency derived from the amplitude analysis, the absolute branching fraction of $D^+ \to K^-π^+π^+π^0$ is measured to be $(6.06\pm0.04_{\rm stat.}\pm0.07_{\rm syst.})\%$.

hep-ex↗

arXiv2024

Search for $h_c \to π^+π^-J/ψ$ via $ψ(3686)\to π^0h_c$

Using $(2712.4 \pm 14.3) \times 10^6~ψ$(3686) events collected with the BESIII detector operating at the BEPCII collider, we search for the hadronic transition $h_c \to π^+π^-J/ψ$ via $ψ(3686)\to π^0 h_c$. No significant signal is observed. We set the most stringent upper limits to date on the branching fractions $\mathcal{B}(ψ(3686)\to π^0 h_c)\times\mathcal{B}(h_c\toπ^+π^-J/ψ)$ and $\mathcal{B}(h_c \to π^+π^-J/ψ)$ at the 90$\%$ confidence level, which are determined to be $6.7\times 10^{-7}$ and $9.4 \times10^{-4}$, respectively.

hep-ex↗

arXiv2024

Study of the semileptonic decay $D^0\rightarrow \bar{K}^0π^-e^+ν_e$

We report an improved study of the semileptonic decay $D^0 \rightarrow \bar{K}^0π^-e^+ν_{e}$ based on a sample of $7.9~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy of 3.773~GeV with the BESIII detector at the BEPCII collider. The branching fraction of this decay is measured to be $\mathcal{B}(D^0\rightarrow \bar{K}^0π^-e^+ν_{e}) = (1.444 \pm 0.022_{\rm stat} \pm 0.024_{\rm syst})\%$, which is the most precise to date, where the first uncertainty is statistical and the second is systematic. Based on investigation of the decay dynamics, we find that the decay is dominated by the $K^{*}(892)^-$ component and present an improved measurement of its branching fraction to be $\mathcal{B}(D^0\rightarrow K^{*}(892)^-e^+ν_e) = (2.039 \pm 0.032_{\rm stat} \pm 0.034_{\rm syst})\%$. We also determine the ratios of the hadronic form factors for the $K^{*}(892)^-e^+ν_e$ decay to be $r_{V} = V(0)/A_1(0) = 1.48 \pm 0.05_{\rm stat} \pm 0.02_{\rm syst}$ and $r_{2} = A_2(0)/A_1(0) = 0.70 \pm 0.04_{\rm stat} \pm 0.02_{\rm syst}$, where $V(0)$ is the vector form factor and $A_{1,2}(0)$ are the axial form factors. In addition, the $\bar{K}^0π^-$ $\mathcal{S}$-wave component is found to account for $(5.87 \pm 0.32_{\rm stat} \pm 0.16_{\rm syst})\%$ of the total decay rate, corresponding to a branching fraction of $\mathcal{B}[D^0\rightarrow (\bar{K}^0π^-)_{S-{\rm wave}}e^+ν_e] = (0.085 \pm 0.005_{\rm stat} \pm 0.003_{\rm syst})\%$.

hep-ex↗

arXiv2024

Measurement of the integrated luminosity of the data collected at 3.773 GeV by BESIII from 2021 to 2024

We present a measurement of the integrated luminosity of $e^+e^-$ collision data collected with the BESIII detector at the BEPCII collider at a center-of-mass energy of $E_{\rm cm} = 3.773$~GeV. The integrated luminosities of the data sets taken from December 2021 to June 2022, from November 2022 to June 2023, and from October 2023 to February 2024 are determined to be $4.995 \pm 0.019$~fb$^{-1}$, $8.157 \pm 0.031$~fb$^{-1}$, and $4.191 \pm 0.016$~fb$^{-1}$, respectively, by analyzing large angle Bhabha scattering events. The uncertainties are dominated by systematic effects and the statistical uncertainties are negligible. Our results provide essential input for future analyses and precision measurements.

hep-ex↗

arXiv2024

Study of the decay ψ(3686) \to Σ^{0}\barΣ^{0}ϕ

Using $(27.12\pm 0.14)\times 10^{8}$ $ψ(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decay $ψ(3686)\toΣ^{0}\barΣ^{0}ϕ$ is observed for the first time with a statistical significance of 7.6$σ$. Its branching fraction is measured to be $(2.64 \pm 0.32_{\textrm{stat}} \pm 0.12_{\textrm{sys}}) \times 10^{-6}$, where the first uncertainty is statistical and the second is systematic. In addition, we search for potential intermediate states in the $Σ^{0}ϕ$($\barΣ^{0}ϕ$) invariant mass distribution and a possible threshold enhancement in the $Σ^{0}\barΣ^{0}$ system, but no conclusive evidence of is observed.

hep-ex↗

arXiv2024

FedAH: Aggregated Head for Personalized Federated Learning

Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization. Personalized-head-based PFL is a common and effective PFL method that splits the model into a feature extractor and a head, where the feature extractor is collaboratively trained and shared, while the head is locally trained and not shared. However, retaining the head locally, although achieving personalization, prevents the model from learning global knowledge in the head, thus affecting the performance of the personalized model. To solve this problem, we propose a novel PFL method called Federated Learning with Aggregated Head (FedAH), which initializes the head with an Aggregated Head at each iteration. The key feature of FedAH is to perform element-level aggregation between the local model head and the global model head to introduce global information from the global model head. To evaluate the effectiveness of FedAH, we conduct extensive experiments on five benchmark datasets in the fields of computer vision and natural language processing. FedAH outperforms ten state-of-the-art FL methods in terms of test accuracy by 2.87%. Additionally, FedAH maintains its advantage even in scenarios where some clients drop out unexpectedly. Our code is open-accessed at https://github.com/heyuepeng/FedAH.

cs.LG↗

arXiv2024

SoftED: Metrics for Soft Evaluation of Time Series Event Detection

Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. This paper introduces SoftED metrics, a new set of metrics designed for soft evaluating event detection methods. They enable the evaluation of both detection accuracy and the degree to which their detections represent events. They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\% of experiments compared to the usual classification metrics. SoftED metrics were validated by domain specialists that indicated their contribution to detection evaluation and method selection.

cs.LG↗

arXiv2024

Search for lepton number violating decays of $D_s^+\to h^-h^0e^+e^+$

Based on 7.33 fb$^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector operating at the BEPCII collider at center-of-mass energies from 4.128 to 4.226 GeV, a search for the Majorana neutrino $ν_m$ is conducted in the lepton-number-violating decays of $D_s^+\to h^-h^0e^+e^+$. Here, $h^-$ represents a $K^-$ or $π^-$, and $h^0$ represents a $π^0$, $K_S^0$ or $ϕ$. No significant signal is observed, and the upper limits of their branching fractions at the 90\% confidence level are determined to be $\mathcal{B}(D_s^+\to ϕπ^-e^+e^+) < 6.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to ϕK^-e^+e^+) < 9.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to K_S^0π^-e^+e^+) < 1.3 \times 10^{-5}$, $\mathcal{B}(D_s^+\to K_S^0K^-e^+e^+) < 2.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to π^-π^0e^+e^+) < 2.9 \times 10^{-5}$ and $\mathcal{B}(D_s^+\to K^-π^0e^+e^+) < 3.4 \times 10^{-5}$. The Majorana neutrino is searched for with different mass assumptions within the range [0.20, 0.80] GeV$/c^2$ in the decay of $D_s^+\toϕe^+ν_m$ with $ν_m\toπ^-e^+$, and the upper limits of the branching fractions at the 90\% confidence level are at the level of $10^{-5}-10^{-2}$, depending on the mass of the Majorana neutrino.

hep-ex↗

arXiv2024

Self-Supervised Place Recognition by Refining Temporal and Featural Pseudo Labels from Panoramic Data

Visual place recognition (VPR) using deep networks has achieved state-of-the-art performance. However, most of them require a training set with ground truth sensor poses to obtain positive and negative samples of each observation's spatial neighborhood for supervised learning. When such information is unavailable, temporal neighborhoods from a sequentially collected data stream could be exploited for self-supervised training, although we find its performance suboptimal. Inspired by noisy label learning, we propose a novel self-supervised framework named TF-VPR that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods. Our method follows an iterative training paradigm which alternates between: (1) representation learning with data augmentation, (2) positive set expansion to include the current feature space neighbors, and (3) positive set contraction via geometric verification. We conduct auto-labeling and generalization tests on both simulated and real datasets, with either RGB images or point clouds as inputs. The results show that our method outperforms self-supervised baselines in recall rate, robustness, and heading diversity, a novel metric we propose for VPR. Our code and datasets can be found at https://ai4ce.github.io/TF-VPR/

cs.CV↗

arXiv2024

Model-independent determination of the strong-phase difference between $D^0$ and $\bar{D}^0 \to π^+π^-π^+π^-$ decays

Measurements of the strong-phase difference between $D^0$ and $\bar{D}^0\toπ^+π^-π^+π^-$ are performed in bins of phase space. The study exploits a sample of quantum-correlated $D\bar{D}$ mesons collected by the BESIII experiment in $e^+e^-$ collisions at a center-of-mass energy of 3.773~GeV, corresponding to an integrated luminosity of 2.93~fb$^{-1}$. Here, $D$ denotes a neutral charm meson in a superposition of flavor eigenstates. The reported results are valuable for measurements of the $C\!P$-violating phase $γ$ (also denoted $ϕ_3$) in $B^\pm \to DK^\pm$, $D \to π^+π^-π^+π^-$ decays, and the binning schemes are designed to provide good statistical sensitivity to this parameter. The expected uncertainty on $γ$ arising from the precision of the strong-phase measurements, when applied to very large samples of $B$-meson decays, is around $1.5^\circ$ or $2^\circ$, depending on the binning scheme. The binned strong-phase parameters are combined to give a value of $F_+^{4π} = 0.746 \pm 0.010 \pm 0.004$ for the $C\!P$-even fraction of $D^0 \to π^+π^-π^+π^-$ decays, which is around 30\% more precise than the previous best measurement of this quantity.

hep-ex↗

arXiv2024

Observation of $D^+\to f_0(500)μ^+ν_μ$ and study of $D^+\to π^+π^-\ell^+ν_\ell$ decay dynamics

Using $2.93~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy of 3.773 GeV,we investigate the semileptonic decays $D^+\to π^+π^- \ell^+ν_\ell$ ($\ell=e$ and $μ$). The $D^+\to f_0(500)μ^+ν_μ$ decay is observed for the first time. By analyzing simultaneously the differential decay rates of $D^+\to f_0(500) μ^+ν_μ$ and $D^+\to f_0(500) e^+ν_e$ in different $\ell^+ν_\ell$ four-momentum transfer intervals, the product of the relevant hadronic form factor $f^{f_0}_{+}(0)$ and the magnitude of the $c\to d$ Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined to be $f_{+}^{f_0} (0)|V_{cd}|=0.143\pm0.014_{\rm stat}\pm0.011_{\rm syst}$ for the first time. With the input of $|V_{cd}|$ from the global fit in the standard model, we determine $f_{+}^{f_0} (0)=0.63\pm0.06_{\rm stat}\pm0.05_{\rm syst}$. The absolute branching fractions of $D^+\to f_0(500)_{(π^+π^-)}μ^+ν_μ$ and $D^+\to ρ^0_{(π^+π^-)} μ^+ν_μ$ are determined as $(0.72\pm0.13_{\rm stat}\pm0.08_{\rm syst})\times10^{-3}$ and $(1.64\pm0.13_{\rm stat}\pm0.10_{\rm syst})\times 10^{-3}$. Combining these results with those of previous BESIII measurements on their semielectronic counterparts from the same data sample, we test lepton flavor universality by measuring the branching fraction ratios ${\mathcal B}_{D^+\to ρ^0 μ^+ν_μ}/{\mathcal B}_{D^+\to ρ^0 e^+ν_e}=0.88\pm0.10$ and ${\mathcal B}_{D^+\to f_0(500) μ^+ν_μ}/{\mathcal B}_{D^+\to f_0(500) e^+ν_e}=1.14\pm0.26$, which are compatible with the standard model expectation.

hep-ex↗

arXiv2024

Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions

The key to OOD detection has two aspects: generalized feature representation and precise category description. Recently, vision-language models such as CLIP provide significant advances in both two issues, but constructing precise category descriptions is still in its infancy due to the absence of unseen categories. This work introduces two hierarchical contexts, namely perceptual context and spurious context, to carefully describe the precise category boundary through automatic prompt tuning. Specifically, perceptual contexts perceive the inter-category difference (e.g., cats vs apples) for current classification tasks, while spurious contexts further identify spurious (similar but exactly not) OOD samples for every single category (e.g., cats vs panthers, apples vs peaches). The two contexts hierarchically construct the precise description for a certain category, which is, first roughly classifying a sample to the predicted category and then delicately identifying whether it is truly an ID sample or actually OOD. Moreover, the precise descriptions for those categories within the vision-language framework present a novel application: CATegory-EXtensible OOD detection (CATEX). One can efficiently extend the set of recognizable categories by simply merging the hierarchical contexts learned under different sub-task settings. And extensive experiments are conducted to demonstrate CATEX's effectiveness, robustness, and category-extensibility. For instance, CATEX consistently surpasses the rivals by a large margin with several protocols on the challenging ImageNet-1K dataset. In addition, we offer new insights on how to efficiently scale up the prompt engineering in vision-language models to recognize thousands of object categories, as well as how to incorporate large language models (like GPT-3) to boost zero-shot applications. Code is publicly available at https://github.com/alibaba/catex.

cs.CV↗

arXiv2024

Adversarial Vessel-Unveiling Semi-Supervised Segmentation for Retinopathy of Prematurity Diagnosis

Accurate segmentation of retinal images plays a crucial role in aiding ophthalmologists in diagnosing retinopathy of prematurity (ROP) and assessing its severity. However, due to their underdeveloped, thinner vessels, manual annotation in infant fundus images is very complex, and this presents challenges for fully supervised learning. To address the scarcity of annotations, we propose a semi supervised segmentation framework designed to advance ROP studies without the need for extensive manual vessel annotation. Unlike previous methods that rely solely on limited labeled data, our approach leverages teacher student learning by integrating two powerful components: an uncertainty weighted vessel unveiling module and domain adversarial learning. The vessel unveiling module helps the model effectively reveal obscured and hard to detect vessel structures, while adversarial training aligns feature representations across different domains, ensuring robust and generalizable vessel segmentations. We validate our approach on public datasets (CHASEDB, STARE) and an in-house ROP dataset, demonstrating its superior performance across multiple evaluation metrics. Additionally, we extend the model's utility to a downstream task of ROP multi-stage classification, where vessel masks extracted by our segmentation model improve diagnostic accuracy. The promising results in classification underscore the model's potential for clinical application, particularly in early-stage ROP diagnosis and intervention. Overall, our work offers a scalable solution for leveraging unlabeled data in pediatric ophthalmology, opening new avenues for biomarker discovery and clinical research.

cs.CV↗

arXiv2024

Measurement of Born cross sections of $e^+e^-\toΞ^0\barΞ^0$ and search for charmonium(-like) states at $\sqrt{s}$ = 3.51-4.95 GeV

Using $e^+e^-$ collision data collected by the BESIII detector at BEPCII corresponding to an integrated luminosity of 30 $\rm fb^{-1}$, we measure Born cross sections and effective form factors for the process $e^+e^-\toΞ^0\barΞ^0$ at forty-five center-of-mass energies between 3.51 and 4.95 GeV. The dressed cross section is fitted, assuming a power-law function plus a charmonium(-like) state, i.e., $ψ(3770)$, $ψ(4040)$, $ψ(4160)$, $ψ(4230)$, $ψ(4360)$, $ψ(4415)$ or $ψ(4660)$. No significant charmonium(-like) state decaying into $Ξ^0\barΞ^0$ is observed. Upper limits at the 90% confidence level on the product of the branching fraction and the electronic partial width are provided for each decay. In addition, ratios of the Born cross sections and the effective form factors for $e^+e^-\toΞ^0\barΞ^0$ and $e^+e^-\toΞ^-\barΞ^+$ are also presented to test isospin symmetry and the vector meson dominance model.

hep-ex↗