arXiv subjects

Chao Chen

Publications and source records attributed to Chao Chen.

At least 559 records · Page 31Linked to original sources

arXiv2024

Observation of $D^+_s\to η^\prime μ^+ν_μ$, Precision Test of Lepton Flavor Universality with $D^+_s\to η^{(\prime)} \ell^+ν_\ell$, and First Measurements of $D^+_s\to η^{(\prime)}μ^+ν_μ$ Decay Dynamics

By analyzing 7.33 fb$^{-1}$ of $e^+e^-$ annihilation data collected at center-of-mass energies between 4.128 and 4.226 GeV with the BESIII detector, we report the observation of the semileptonic decay $D^+_s\to η^\prime μ^+ν_μ$, with a statistical significance larger than 10$σ$, and the measurements of the $D_s^+ \to ημ^+ν_μ$ and $D_s^+ \to η^\primeμ^+ν_μ$ decay dynamics for the first time. The branching fractions of $D_s^+ \to ημ^+ν_μ$ and $D_s^+ \to η^\primeμ^+ν_μ$ are determined to be $(2.235\pm0.051_{\rm stat}\pm0.052_{\rm syst})\%$ and $(0.801\pm0.055_{\rm stat}\pm0.028_{\rm syst})\%$, respectively, with precision improved by factors of 6.0 and 6.6 compared to the previous best measurements. Combined with the results for the decays $D_s^+ \to ηe^+ν_e$ and $D_s^+ \to η^\prime e^+ν_e$, the ratios of the decay widths are examined both inclusively and in several $\ell^+ν_\ell$ four-momentum transfer ranges. No evidence for lepton flavor universality violation is found within the current statistics. The products of the hadronic form factors $f_{+,0}^{η^{(\prime)}}(0)$ and the $c\to s$ Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ are determined. The results based on the two-parameter series expansion are $f^η_{+,0}(0)|V_{cs}| = 0.452\pm0.010_{\rm stat}\pm0.007_{\rm syst}$ and $f^{η^{\prime}}_{+,0}(0)|V_{cs}| = 0.504\pm0.037_{\rm stat}\pm0.012_{\rm syst}$, which help to constrain present models on $f_{+,0}^{η^{(\prime)}}(0)$. The forward-backward asymmetries are determined to be $\langle A_{\rm FB}^η\rangle=-0.059\pm0.031_{\rm stat}\pm0.005_{\rm syst}$ and $\langle A_{\rm FB}^{η^\prime}\rangle=-0.064\pm0.079_{\rm stat}\pm0.006_{\rm syst}$ for the first time, which are consistent with the theoretical calculation.

hep-ex↗

arXiv2024

Measurement of the $e^{+}e^{-} \to K_{S}^{0} K_{L}^{0} π^{0}$ cross sections from $\sqrt{s}=$ 2.000 to 3.080 GeV

Based on $e^{+}e^{-}$ collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performed for the process $e^{+}e^{-}\to K_{S}^{0} K_{L}^{0} π^{0}$. The results allow the Born cross sections of the process $e^{+}e^{-}\to K_{S}^{0} K_{L}^{0} π^{0}$, as well as its subprocesses $e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ and $K^{*}_{2}(1430)^{0}\bar{K}^{0}$ to be measured. The Born cross sections for $e^{+}e^{-}\to K_{S}^{0}K_{L}^{0}π^{0}$ are consistent with previous measurements by BaBar, but with substantially improved precision. The Born cross section lineshape of the process $e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ is consistent with a vector meson state around 2.2 GeV with a significance of 3.2$σ$. A Breit-Wigner fit determines its mass as $M_Y=(2164.7\pm9.1\pm3.1)~{\rm{MeV}}/c^{2}$ and its width as $Γ_{Y}=(32.4\pm21.0\pm1.8)~\rm{MeV}$.

hep-ex↗

arXiv2024

First observation of $Λ_c^+\rightarrowΛK^+π^0$ and evidence of $Λ_c^+\rightarrowΛK^+π^+π^-$

We present the first observation of the singly Cabibbo-suppressed decay $Λ_c^+ \rightarrow ΛK^+π^0$ with a significance of $5.7σ$ and the first evidence of $Λ_c^+ \rightarrow ΛK^+π^+π^-$ decay with a significance of $3.1σ$, based on $e^+e^-$ annihilation data recorded by the BESIII detector at the BEPCII collider. The data correspond to an integrated luminosity of $6.4~{\rm fb^{-1}}$, in the center-of-mass energy range from $4.600~{\rm GeV}$ to $4.950~{\rm GeV}$. We determine the branching fractions of $Λ_c^+ \rightarrow ΛK^+π^0$ and $Λ_c^+ \rightarrow ΛK^+π^+π^-$ relative to their Cabibbo-favored counterparts to be $\frac{\mathcal{B}(Λ_c^+ \rightarrow ΛK^+π^0)}{\mathcal{B}(Λ_c^+ \rightarrow Λπ^+π^0)} = (2.09\pm0.39_{\mathrm{stat.}}\pm0.07_{\mathrm{syst.}}) \times 10^{-2}$ and $\frac{\mathcal{B}(Λ_c^+ \rightarrow ΛK^+π^+π^-)}{\mathcal{B}(Λ_c^+ \rightarrow Λπ^+π^+π^-)} = (1.13\pm0.41_{\mathrm{stat.}}\pm0.06_{\mathrm{syst.}}) \times 10^{-2}$, respectively. Moreover, by combining our measured result with the world average of $\mathcal{B}(Λ^+_c\to Λπ^+π^0)$, we obtain the branching fraction $\mathcal{B}(Λ_c^+ \to ΛK^+π^0) = (1.49\pm0.27_{\mathrm{stat.}}\pm0.05_{\mathrm{syst.}}\pm0.08_{\mathrm{ref.}}) \times 10^{-3}$. This result significantly departs from theoretical predictions based on quark $SU(3)$ flavor symmetry, which is underpinned by the presumption of meson pair $S$-wave amplitude dominance.

hep-ex↗

arXiv2024

Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images

Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.

q-bio.NC↗

arXiv2024

An O(N) distributed-memory parallel direct solver for planar integral equations

Boundary value problems involving elliptic PDEs such as the Laplace and the Helmholtz equations are ubiquitous in mathematical physics and engineering. Many such problems can be alternatively formulated as integral equations that are mathematically more tractable. However, an integral-equation formulation poses a significant computational challenge: solving large dense linear systems that arise upon discretization. In cases where iterative methods converge rapidly, existing methods that draw on fast summation schemes such as the Fast Multipole Method are highly efficient and well-established. More recently, linear complexity direct solvers that sidestep convergence issues by directly computing an invertible factorization have been developed. However, storage and computation costs are high, which limits their ability to solve large-scale problems in practice. In this work, we introduce a distributed-memory parallel algorithm based on an existing direct solver named ``strong recursive skeletonization factorization.'' Specifically, we apply low-rank compression to certain off-diagonal matrix blocks in a way that minimizes computation and data movement. Compared to iterative algorithms, our method is particularly suitable for problems involving ill-conditioned matrices or multiple right-hand sides. Large-scale numerical experiments are presented to show the performance of our Julia implementation.

math.NA↗

arXiv2024

Search for a muonphilic scalar $X_{0}$ or vector $X_{1}$ via $J/ψ\toμ^+μ^-+\rm{invisible}$ decays at BESII

A light scalar $X_{0}$ or vector $X_{1}$ particles have been introduced as a possible explanation for the $(g-2)_μ$ anomaly and dark matter phenomena. Using $(8.998\pm 0.039)\times10^9$ $\jpsi $ events collected by the BESIII detector, we search for a light muon philic scalar $X_{0}$ or vector $X_{1}$ in the processes $J/ψ\toμ^+μ^- X_{0,1}$ with $X_{0,1}$ invisible decays. No obvious signal is found, and the upper limits on the coupling $g_{0,1}'$ between the muon and the $X_{0,1}$ particles are set to be between $1.1\times10^{-3}$ and $1.0\times10^{-2}$ for the $X_{0,1}$ mass in the range of $1<M(X_{0,1})<1000$ MeV$/c^2$ at 90$\%$ confidence level.

hep-ex↗

arXiv2024

Search for the production of deuterons and antideuterons in e^+e^- annihilation at center-of-mass energies between 4.13 and 4.70 GeV

Using a data sample of $e^+e^-$ collision data corresponding to an integrated luminosity of 19 fb$^{-1}$ collected with the BESIII detector at the BEPCII collider, we search for the production of deuterons and antideuterons via $e^+e^-\to ppπ^-\bar{d}+c.c.$ for the first time at center-of-mass energies between 4.13 and 4.70 GeV. No significant signal is observed and the upper limit of the $e^+e^-\to ppπ^-\bar{d}+c.c.$ cross section is determined to be from 9.0 to 145 fb depending on the center-of-mass energy at the $90\%$ confidence level.

hep-ex↗

arXiv2024

Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field

Accurate reconstruction of plant models for phenotyping analysis is critical for optimising sustainable agricultural practices in precision agriculture. Traditional laboratory-based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large-scale, direct phenotyping in real-world environments. This study explores the deployment of emerging robotics and digital technology in plant phenotyping to improve performance and efficiency. Three critical functional modules: environmental understanding, robotic motion planning, and in-situ phenotyping, are introduced to automate the entire process. Experimental results demonstrate the effectiveness of the system in agricultural environments. The pheno-robot system autonomously collects high-quality data by navigating around plants. In addition, the in-situ modelling model reconstructs high-quality plant models from the data collected by the robot. The developed robotic system shows high efficiency and robustness, demonstrating its potential to advance plant science in real-world agricultural environments.

cs.RO↗

arXiv2024

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting

Accurate solar power forecasting is crucial to integrate photovoltaic plants into the electric grid, schedule and secure the power grid safety. This problem becomes more demanding for those newly installed solar plants which lack sufficient data. Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities. In this paper, we propose a multi-modality fusion framework to integrate historical power data, numerical weather prediction, and satellite images, significantly improving forecast performance. We introduce a vector quantized framework that aligns modalities with varying information densities, striking a balance between integrating sufficient information and averting model overfitting. Our framework demonstrates strong zero-shot forecasting capability, which is especially useful for those newly installed plants. Moreover, we collect and release a multi-modal solar power (MMSP) dataset from real-world plants to further promote the research of multi-modal solar forecasting algorithms. Our extensive experiments show that our model not only operates with robustness but also boosts accuracy in both zero-shot forecasting and scenarios rich with training data, surpassing leading models. We have incorporated it into our eForecaster platform and deployed it for more than 300 solar plants with a capacity of over 15GW.

cs.LG↗

arXiv2024

Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting

Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain. Leading methods customize the transformer architecture from NLP and CV, utilizing a patching technique to convert continuous signals into segments. Yet, time series data are uniquely challenging due to significant distribution shifts and intrinsic noise levels. To address these two challenges,we introduce the Sparse Vector Quantized FFN-Free Transformer (Sparse-VQ). Our methodology capitalizes on a sparse vector quantization technique coupled with Reverse Instance Normalization (RevIN) to reduce noise impact and capture sufficient statistics for forecasting, serving as an alternative to the Feed-Forward layer (FFN) in the transformer architecture. Our FFN-free approach trims the parameter count, enhancing computational efficiency and reducing overfitting. Through evaluations across ten benchmark datasets, including the newly introduced CAISO dataset, Sparse-VQ surpasses leading models with a 7.84% and 4.17% decrease in MAE for univariate and multivariate time series forecasting, respectively. Moreover, it can be seamlessly integrated with existing transformer-based models to elevate their performance.

cs.LG↗

arXiv2024

An invariance constrained deep learning network for PDE discovery

The discovery of partial differential equations (PDEs) from datasets has attracted increased attention. However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise. Moreover, the selection principles for the candidate library to meet physical laws need to be further studied. The invariance is one of the fundamental laws for governing equations. In this study, we propose an invariance constrained deep learning network (ICNet) for the discovery of PDEs. Considering that temporal and spatial translation invariance (Galilean invariance) is a fundamental property of physical laws, we filter the candidates that cannot meet the requirement of the Galilean transformations. Subsequently, we embedded the fixed and possible terms into the loss function of neural network, significantly countering the effect of sparse data with high noise. Then, by filtering out redundant terms without fixing learnable parameters during the training process, the governing equations discovered by the ICNet method can effectively approximate the real governing equations. We select the 2D Burgers equation, the equation of 2D channel flow over an obstacle, and the equation of 3D intracranial aneurysm as examples to verify the superiority of the ICNet for fluid mechanics. Furthermore, we extend similar invariance methods to the discovery of wave equation (Lorentz Invariance) and verify it through Single and Coupled Klein-Gordon equation. The results show that the ICNet method with physical constraints exhibits excellent performance in governing equations discovery from sparse and noisy data.

cs.LG↗

arXiv2024

Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection

For a machine learning model deployed in real world scenarios, the ability of detecting out-of-distribution (OOD) samples is indispensable and challenging. Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples. The training-based methods require expensive training cost and rely on OOD samples which are not always available, while most training-free methods can not efficiently utilize the prior information from the training data. In this work, we propose an \textbf{O}ptimal \textbf{P}arameter and \textbf{N}euron \textbf{P}runing (\textbf{OPNP}) approach, which aims to identify and remove those parameters and neurons that lead to over-fitting. The main method is divided into two steps. In the first step, we evaluate the sensitivity of the model parameters and neurons by averaging gradients over all training samples. In the second step, the parameters and neurons with exceptionally large or close to zero sensitivities are removed for prediction. Our proposal is training-free, compatible with other post-hoc methods, and exploring the information from all training data. Extensive experiments are performed on multiple OOD detection tasks and model architectures, showing that our proposed OPNP consistently outperforms the existing methods by a large margin.

cs.LG↗

arXiv2024

Measurement of the Electromagnetic Transition Form-factors in the decays $η'\rightarrowπ^+π^-l^+l^-$

With a sample of $(10087\pm44)\times10^{6}$ $J/ψ$ events accumulated with the BESIII detector, we analyze the decays $η'\rightarrowπ^+π^-l^+l^-(l=e,$ $μ)$ via the process $J/ψ\rightarrowγη'$. The branching fractions are measured to be $\mathcal{B}(η'\rightarrowπ^+π^-e^+e^-)=(2.45\pm0.02(\rm{stat.})\pm0.08(\rm{syst.})) \times10^{-3}$ and $\mathcal{B}(η'\rightarrowπ^+π^-μ^+μ^-)=(2.16\pm0.12(\rm{stat.})\pm0.06(\rm{syst.}))\times10^{-5}$, and the ratio is $\frac{\mathcal{B}(η'\rightarrowπ^{+}π^{-}e^{+}e^{-})}{\mathcal{B}(η'\rightarrowπ^{+}π^{-}μ^{+}μ^{-})} = 113.4\pm0.9(\rm{stat.})\pm3.7(\rm{syst.})$. In addition, by combining the $η'\rightarrowπ^+π^-e^+e^-$ and $η'\rightarrowπ^+π^-μ^+μ^-$ decays, the slope parameter of the electromagnetic transition form factor is measured to be $b_{η'}=1.30\pm0.19\ (\mathrm{GeV}/c^{2})^{-2}$, which is consistent with previous measurements from BESIII and theoretical predictions from the VMD model. The asymmetry in the angle between the $π^+π^-$ and $l^+l^-$ decay planes, which has the potential to reveal the $CP$-violation originating from an unconventional electric dipole transition, is also investigated. The asymmetry parameters are determined to be $\mathcal{A}_{CP}(η'\rightarrowπ^+π^-e^+e^-)=(-0.21\pm0.73(\rm{stat.})\pm0.01(\rm{syst.}))\%$ and $\mathcal{A}_{CP}(η'\rightarrowπ^+π^-μ^+μ^-)=(0.62\pm4.71(\rm{stat.})\pm0.08(\rm{syst.}))\%$, implying that no evidence of $CP$-violation is observed at the present statistics. Finally, an axion-like particle is searched for via the decay $η'\rightarrowπ^+π^-a, a\rightarrow e^+e^-$, and upper limits of the branching fractions are presented for the mass assumptions of the axion-like particle in the range of $0-500\ \mathrm{MeV}/c^{2}$.

hep-ex↗

arXiv2024

Study of $e^{+}e^{-}\rightarrowπ^{+}π^{-}π^{0}$ at $\sqrt{s}$ from 2.00 to 3.08 GeV at BESIII

With the data samples taken at center-of-mass energies from 2.00 to 3.08 GeV with the BESIII detector at the BEPCII collider, a partial wave analysis on the $e^{+}e^{-}\rightarrowπ^{+}π^{-}π^{0}$ process is performed. The Born cross sections for $e^{+}e^{-}\rightarrowπ^{+}π^{-}π^{0}$ and its intermediate processes $e^{+}e^{-}\rightarrowρπ$ and $ρ(1450)π$ are measured as functions of $\sqrt{s}$. The results for $e^{+}e^{-}\rightarrowπ^{+}π^{-}π^{0}$ are consistent with previous results measured with the initial state radiation method within one standard deviation, and improve the uncertainty by a factor of ten. By fitting the line shapes of the Born cross sections for the $e^{+}e^{-}\rightarrowρπ$ and $ρ(1450)π$, a structure with mass $M = 2119\pm11\pm15\ {\rm MeV}/c^2$ and width $Γ=69\pm30\pm5 {\rm MeV}$ is observed with a significance of $5.9σ$, where the first uncertainties are statistical and the second ones are systematic. This structure can be intepreteted as an excited $ω$ state.

hep-ex↗

arXiv2024

Sketch and Refine: Towards Fast and Accurate Lane Detection

Lane detection is to determine the precise location and shape of lanes on the road. Despite efforts made by current methods, it remains a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. Proposal-based methods detect lanes by distinguishing and regressing a collection of proposals in a streamlined top-down way, yet lack sufficient flexibility in lane representation. Keypoint-based methods, on the other hand, construct lanes flexibly from local descriptors, which typically entail complicated post-processing. In this paper, we present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods. The motivation is that local directions of lanes are semantically simple and clear. At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers. Then we can build a set of lane proposals accordingly with moderate accuracy. At the "Refine" stage, we further optimize these proposals via a novel Lane Segment Association Module (LSAM), which allows adaptive lane segment adjustment. Last but not least, we propose multi-level feature integration to enrich lane feature representations more efficiently. Based on the proposed "Sketch and Refine" paradigm, we propose a fast yet effective lane detector dubbed "SRLane". Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9\%. The source code is available at: https://github.com/passerer/SRLane.

cs.CV↗

arXiv2024

A Survey of PPG's Application in Authentication

Biometric authentication prospered because of its convenient use and security. Early generations of biometric mechanisms suffer from spoofing attacks. Recently, unobservable physiological signals (e.g., Electroencephalogram, Photoplethysmogram, Electrocardiogram) as biometrics offer a potential remedy to this problem. In particular, Photoplethysmogram (PPG) measures the change in blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patients' blood oxygen saturation, heart rate, and other information to assist in diagnosing heart-related diseases. Since PPG signals contain a wealth of individual cardiac information, researchers have begun to explore their potential in cyber security applications. The unique advantages (simple acquisition, difficult to steal, and live detection) of the PPG signal allow it to improve the security and usability of the authentication in various aspects. However, the research on PPG-based authentication is still in its infancy. The lack of systematization hinders new research in this field. We conduct a comprehensive study of PPG-based authentication and discuss these applications' limitations before pointing out future research directions.

cs.CR↗

arXiv2024

First Measurement of the Decay Asymmetry in the pure W-boson-exchange Decay $Λ_{c}^{+}\toΞ^{0}K^{+}$

Based on $4.4~\text{fb}^{-1}$ of $e^{+}e^{-}$ annihilation data collected at the center-of-mass energies between $4.60$ and $4.70~\text{GeV}$ with the BESIII detector at the BEPCII collider, the pure \textit{W}-boson-exchange decay $Λ_{c}^{+}\toΞ^{0}K^{+}$ is studied with a full angular analysis. The corresponding decay asymmetry is measured for the first time to be $α_{Ξ^{0}K^{+}}=0.01\pm0.16({\rm stat.})\pm0.03({\rm syst.})$. This result reflects the non-interference effect between the $S$- and $P$-wave amplitudes. The phase shift between $S$- and $P$-wave amplitudes has two solutions, which are $δ_{p}-δ_{s}=-1.55\pm0.25({\rm stat.})\pm0.05({\rm syst.})~\text{rad}$ or $1.59\pm0.25({\rm stat.})\pm0.05({\rm syst.})~\text{rad}$.

hep-ex↗

arXiv2024

Search for the light hadron decay $χ_{c1}(3872) \to π^{+}π^{-}η$

With a data sample corresponding to an integrated luminosity of 11.5~fb$^{-1}$ collected with the BESIII detector operating at the BEPCII storage ring, for the first time the light hadron decay $χ_{c1}(3872) \rightarrow π^{+}π^{-}η$ is searched for. While no significant signal is observed, the upper limits at the 90\% confidence level for $σ[e^{+}e^{-} \rightarrow γχ_{c1}(3872)] \mathcal{B}[χ_{c1}(3872) \rightarrow π^{+}π^{-}η]$ at center-of-mass energies from 4.13 to 4.34 GeV are determined. By normalizing to the $χ_{c1}(3872)\toπ^+π^- J/ψ$ decay channel, a 90\% confidence level upper limit for the branching fraction ratio $\mathcal{R}=\mathcal{B}[χ_{c1}(3872) \rightarrowπ^{+}π^{-}η]/\mathcal{B}[χ_{c1}(3872) \rightarrow π^{+}π^{-} J/ψ] < 0.12$ is given. These measurements provide important inputs for understanding the internal structure of the $χ_{c1}(3872)$ resonance.

hep-ex↗