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Lei Li

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preprint2026arXiv

ChipLingo: A Systematic Training Framework for Large Language Models in EDA

With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-driven engineering domain. Although large language models (LLMs) have shown strong general capabilities, applying them directly to EDA remains challenging due to limited domain expertise, cross-tool knowledge confusion, and degraded retrieval-augmented generation (RAG) performance after domain training. To address these issues, this paper presents ChipLingo, a systematic training pipeline for domain-adapted LLMs tailored to EDA scenarios. ChipLingo consists of three stages: domain corpus construction with multi-source data curation and QA augmentation, domain-adaptive pretraining with comparisons of different parameter training strategies, and instruction alignment with RAG scenario training under diverse retrieval conditions. We also curate an internal benchmark, EDA-Bench, covering representative EDA tool scenarios, with plans for public release. Experiments show that ChipLingo-8B achieves 59.7% accuracy on EDA-Bench, outperforming the same-scale base model and some larger general-purpose models. ChipLingo-32B reaches 70.02%, approaching leading closed-source commercial models. Further analysis shows that QA augmentation improves domain performance, Partial FT offers a better balance between adaptation and general capability retention than LoRA, and explicit RAG scenario training mitigates the decline in retrieval utilization after domain training. These results demonstrate the practical value of systematic domain training for knowledge-intensive EDA tasks and provide a foundation for future EDA agents and external-knowledge-driven systems.

preprint2025arXiv

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

preprint2025arXiv

Training Report of TeleChat3-MoE

TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems.

preprint2024arXiv

A multi-objective optimization framework for terrain modification based on a combined hydrological and earthwork cost-benefit

The escalating risk of urban inundation has drawn increased attention to urban stormwater management. This study proposes a multi-objective optimization for terrain modification, combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with digital elevation model (DEM)-based hydrological cost factor analysis. To reduce the precipitation erosive forces and runoff kinetic energy, the resulting framework offers the possibility of efficiently searching numerous solutions for trade-off sets that meet three conflicting objectives: minimizing maximum flow velocity, maximizing runoff path length and minimizing earthwork costs. Our application case study in Høje Taastrup, Denmark, demonstrates the ability of the optimization framework to iteratively generate diversified modification scenarios, which form the reference for topography planning. The three individual objective preferred solutions, a balanced solution, and twenty solutions under regular ordering are selected and visualized to determine the limits of the optimization and the cost-effectiveness tendency. Integrating genetic algorithms with DEM-based hydrological analysis demonstrates the potential to consider more complicated hydrological benefit objectives with open-ended characteristics. It provides a novel and efficient way to optimize topographic characteristics for improving holistic stormwater management strategies.

preprint2024arXiv

Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design

Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequence and structure are strongly correlated and they together determine its function. In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based on automatically detected functional sites. NAEPro is powered by an interleaving network of attention and equivariant layers, which can capture global correlation in a whole sequence and local influence from nearest amino acids in three dimensional (3D) space. Such an architecture facilitates effective yet economic message passing at two levels. We evaluate our model and several strong baselines on two protein datasets, $β$-lactamase and myoglobin. Experimental results show that our model consistently achieves the highest amino acid recovery rate, TM-score, and the lowest RMSD among all competitors. These findings prove the capability of our model to design protein sequences and structures that closely resemble their natural counterparts. Furthermore, in-depth analysis further confirms our model's ability to generate highly effective proteins capable of binding to their target metallocofactors. We provide code, data and models in Github.

preprint2024arXiv

Investigation of the $ΔI = 1/2$ rule and test of CP violation through the measurement of decay asymmetry parameters in $Ξ^-$ decays

Using $(10087\pm44)\times 10^{6}$ $J/ψ$ events collected with the BESIII detector, numerous $Ξ^-$ and $Λ$ decay asymmetry parameters are simultaneously determined from the process $J/ψ\to Ξ^- \barΞ^+ \to Λ(pπ^-) π^- \barΛ(\bar{n} π^0) π^+$ and its charge-conjugate channel. The precisions of $α_0$ for $Λ\to nπ^0$ and $\barα_0$ for $\barΛ \to \bar{n}π^0$ compared to world averages are improved by factors of 4 and 1.7, respectively. The ratio of decay asymmetry parameters of $Λ\to nπ^0$ to that of $Λ\to pπ^-$, $\langle α_0 \rangle/ \langle α_{Λ-} \rangle $, is determined to be $ 0.873 \pm 0.012^{+0.011}_{-0.010}$, where the first and the second uncertainties are statistical and systematic, respectively. The ratio is smaller than unity more than $5σ$, which signifies the existence of the $ΔI = 3/2$ transition in $Λ$ for the first time. Beside, we test for CP violation in $Ξ^- \to Λπ^-$ and in $Λ\to n π^{0}$ with the best precision to date.

preprint2024arXiv

Some Grönwall inequalities for a class of discretizations of time fractional equations on nonuniform meshes

We consider the completely positive discretizations of fractional ordinary differential equations (FODEs) on nonuniform meshes. Making use of the resolvents for nonuniform meshes, we first establish comparison principles for the discretizations. Then we prove some discrete Grönwall inequalities using the comparison principles and careful analysis of the solutions to the time continuous FODEs. Our results do not have any restrictions on the step size ratio. The Grönwall inequalities for dissipative equations can be used to obtain the uniform-in-time error control and decay estimates of the numerical solutions. The Grönwall inequalities are then applied to subdiffusion problems and the time fractional Allen-Cahn equations for illustration.

preprint2023arXiv

BuildSeg: A General Framework for the Segmentation of Buildings

Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality. While current research using different types of convolutional and transformer networks has considerably improved the performance on this task, even more accurate segmentation methods for buildings are desirable for applications such as automatic mapping. In this study, we propose a general framework termed \emph{BuildSeg} employing a generic approach that can be quickly applied to segment buildings. Different data sources were combined to increase generalization performance. The approach yields good results for different data sources as shown by experiments on high-resolution multi-spectral and LiDAR imagery of cities in Norway, Denmark and France. We applied ConvNeXt and SegFormer based models on the high resolution aerial image dataset from the MapAI-competition. The methods achieved an IOU of 0.7902 and a boundary IOU of 0.6185. We used post-processing to account for the rectangular shape of the objects. This increased the boundary IOU from 0.6185 to 0.6189.

preprint2023arXiv

Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential totackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targetssimultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT).

preprint2023arXiv

Personalized Prompt Learning for Explainable Recommendation

Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning), and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.

preprint2023arXiv

Search for hidden-charm tetraquark with strangeness in $e^{+}e^{-}\rightarrow K^+ D_{s}^{-} D^{0}+c.c.$

We report a search for a heavier partner of the recently observed $Z_{cs}(3985)^{-}$ state, denoted as $Z_{cs}^{\prime -}$, in the process $e^{+} e^{-}\rightarrow K^{+}D_{s}^{*-}D^{* 0}+c.c.$, based on $e^+e^-$ collision data collected at the center-of-mass energies of $\sqrt{s}=4.661$, 4.682 and 4.699 GeV with the BESIII detector. The $Z_{cs}^{\prime -}$ is of interest as it is expected to be a candidate for a hidden-charm and open-strange tetraquark. A partial-reconstruction technique is used to isolate $K^+$ recoil-mass spectra, which are probed for a potential contribution from $Z_{cs}^{\prime -}\to D_{s}^{*-}D^{* 0}$ ($c.c.$). We find an excess of $Z_{cs}^{\prime -}\rightarrow D_{s}^{*-}D^{*0}$ ($c.c.$) candidates with a significance of $2.1σ$, after considering systematic uncertainties, at a mass of $(4123.5\pm0.7_\mathrm{stat.}\pm4.7_\mathrm{syst.})\ \mathrm{MeV}/c^{2}$. As the data set is limited in size, the upper limits are evaluated at the 90\% confidence level on the product of the Born cross sections ($σ^{\mathrm{Born}}$) and the branching fraction ($\mathcal{B}$) of $Z_{cs}^{\prime-}\rightarrow D_{s}^{*-}D^{* 0}$, under different assumptions of the $Z_{cs}^{\prime -}$ mass from 4.120 to 4.140 MeV and of the width from 10 to 50 MeV at the three center-of-mass energies. The upper limits of $σ^{\rm Born}\cdot\mathcal{B}$ are found to be at the level of $\mathcal{O}(1)$ pb at each energy. Larger data samples are needed to confirm the $Z_{cs}^{\prime -}$ state and clarify its nature in the coming years.

preprint2023arXiv

Self-Supervised Speech Denoising Using Only Noisy Audio Signals

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://github.com/liqingchunnnn/Only-Noisy-Training

preprint2023arXiv

Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels

Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization problem of this task is inherently challenging, requiring well-designed constraints. In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints. Firstly, we extend a conventional non-convex energy function as an intensity constraint and implement it with U-Net. For shape constraint, synthetic images are generated from anatomical labels via image-to-image translation, as shape supervision for the segmentation network. Moreover, augmentation invariance is applied to facilitate the segmentation network to learn the latent features in terms of shape. We evaluated the proposed framework using the public datasets from MICCAI2019 MSCMR Challenge and achieved promising results on cardiac MRIs with Dice scores of 0.5737, 0.7796, and 0.6287 in Myo, LV, and RV, respectively.

preprint2023arXiv

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

preprint2022arXiv

$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme ($ \text{T}^3 $OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle's observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed $ \text{T}^3 $OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

preprint2022arXiv

$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation

Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its generation quality, they still need the help of an autoregressive model for training to overcome the one-to-many multi-modal phenomenon in the dataset, limiting their applications. In this paper, we propose $\textit{latent}$-GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

preprint2022arXiv

3D Textured Shape Recovery with Learned Geometric Priors

3D textured shape recovery from partial scans is crucial for many real-world applications. Existing approaches have demonstrated the efficacy of implicit function representation, but they suffer from partial inputs with severe occlusions and varying object types, which greatly hinders their application value in the real world. This technical report presents our approach to address these limitations by incorporating learned geometric priors. To this end, we generate a SMPL model from learned pose prediction and fuse it into the partial input to add prior knowledge of human bodies. We also propose a novel completeness-aware bounding box adaptation for handling different levels of scales and partialness of partial scans.

preprint2022arXiv

A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation

Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon measures, which takes into account both transportation and weight change. Spherical WFR distance is a projected version of WFR distance for probability measures so that the space of Radon measures equipped with WFR can be viewed as metric cone over the space of probability measures with spherical WFR. Compared to the case for Wasserstein distance, the understanding of geodesics under the spherical WFR is less clear and still an ongoing research focus. In this paper, we develop a deep learning framework to compute the geodesics under the spherical WFR metric, and the learned geodesics can be adopted to generate weighted samples. Our approach is based on a Benamou-Brenier type dynamic formulation for spherical WFR. To overcome the difficulty in enforcing the boundary constraint brought by the weight change, a Kullback-Leibler (KL) divergence term based on the inverse map is introduced into the cost function. Moreover, a new regularization term using the particle velocity is introduced as a substitute for the Hamilton-Jacobi equation for the potential in dynamic formula. When used for sample generation, our framework can be beneficial for applications with given weighted samples, especially in the Bayesian inference, compared to sample generation with previous flow models.

preprint2022arXiv

A projection--less approach to Rickart Jordan structures

The main goal of this paper is to introduce and explore an appropriate notion of weakly Rickart JB$^*$-triples. We introduce weakly order Rickart JB$^*$-triples, and we show that a C$^*$-algebra $A$ is a weakly (order) Rickart JB$^*$-triple precisely when it is a weakly Rickart C$^*$-algebra. We also prove that the Peirce-2 subspace associated with a tripotent in a weakly order Rickart JB$^*$-triple is a Rickart JB$^*$-algebra in the sense of Ayupov and Arzikulov. By extending a classical property of Rickart C$^*$-algebras, we prove that every weakly order Rickart JB$^*$-triple is generated by its tripotents.

preprint2022arXiv

A splitting Hamiltonian Monte Carlo method for efficient sampling

We propose a splitting Hamiltonian Monte Carlo (SHMC) algorithm, which can be computationally efficient when combined with the random mini-batch strategy. By splitting the potential energy into numerically nonstiff and stiff parts, one makes a proposal using the nonstiff part of $U$, followed by a Metropolis rejection step using the stiff part that is often easy to compute. The splitting allows efficient sampling from systems with singular potentials (or distributions with degenerate points) and/or with multiple potential barriers. In our SHMC algorithm, the proposal only based on the nonstiff part in the splitting is generated by the Hamiltonian dynamics, which can be potentially more efficient than the overdamped Langevin dynamics. We also use random batch strategies to reduce the computational cost to $\mathcal{O}(1)$ per time step in generating the proposals for problems arising from many-body systems and Bayesian inference, and prove that the errors of the Hamiltonian induced by the random batch approximation is $\mathcal{O}(\sqrt{Δt})$ in the strong and $\mathcal{O}(Δt)$ in the weak sense, where $Δt$ is the time step. Numerical experiments are conducted to verify the theoretical results and the computational efficiency of the proposed algorithms in practice.

preprint2022arXiv

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track~2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR.

preprint2022arXiv

Amplitude analysis and branching fraction measurement of the decay $D_{s}^{+} \to K^+π^+π^-$

Using $6.32$ fb$^{-1}$ of $e^{+}e^{-}$ collision data collected at the center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we perform an amplitude analysis of the decay $D^+_s \to K^+π^+π^-$ and determine the amplitudes of the various intermediate states. The absolute branching fraction of $D^+_s\to K^+π^+π^-$ is measured to be ($6.11\pm0.18_{\rm stat.}\pm0.11_{\rm syst.})\times 10^{-3}$. The branching fractions of the dominant intermediate processes $D_{s}^{+} \to K^+ρ^0, ρ^0 \to π^+π^-$ and $D_{s}^{+} \to K^*(892)^0π^+, K^*(892)^0 \to K^+π^-$ are determined to be $(1.96\pm0.19_{\rm stat.}\pm0.23_{\rm syst.})\times 10^{-3}$ and $(1.85\pm0.12_{\rm stat.}\pm0.13_{\rm syst.})\times 10^{-3}$, respectively. The intermediate resonances $f_0(500)$, $f_0(980)$, and $f_0(1370)$ are observed for the first time in this channel.

preprint2022arXiv

Amplitude analysis and branching-fraction measurement of $D_{s}^{+} \to π^{+}π^{0}η^{\prime}$

Using data collected with the BESIII detector in $e^+e^-$ collisions at center-of-mass energies between 4.178 and 4.226 GeV and corresponding to 6.32~fb$^{-1}$ of integrated luminosity, we report the amplitude analysis and branching-fraction measurement of the $D^+_s \to π^+ π^0 η^{\prime}$ decay. We find that the dominant intermediate process is $D^+_s \toρ^+ η^{\prime}$ and the significances of other resonant and nonresonant processes are all less than $3σ$. The upper limits on the branching fractions of $S$-wave and $P$-wave nonresonant components are set to $0.10\%$ and $0.74\%$ at the $90\%$ confidence level, respectively. In addition, the branching fraction of the $D^+_s \to π^+ π^0 η^{\prime}$ decay is measured to be $(6.15\pm0.25(\rm stat.)\pm0.18(\rm syst.))\%$, which receives significant contribution only from $D_s^+\to ρ^+η^{\prime}$ according to the amplitude analysis.

preprint2022arXiv

Analytical results for the superflow of spin-orbit-coupled Bose-Einstein condensates in optical lattices

In this paper, we show that for sufficiently strong atomic interactions, there exist analytical solutions of current-carrying nonlinear Bloch states at the Brillouin zone edge to the model of spin-orbit-coupled Bose-Einstein condensates (BECs) with symmetric spin interaction loaded into optical lattices. These simple but generic exact solutions provide an analytical demonstration of some intriguing properties which have neither an analog in the regular BEC lattice systems nor in the uniform spin-orbit-coupled BEC systems. It is an analytical example for understanding the superfluid and other related properties of the spin-orbit-coupled BEC lattice systems.

preprint2022arXiv

AutoCellLibX: Automated Standard Cell Library Extension Based on Pattern Mining

Custom standard cell libraries can improve the final quality of the corresponding VLSI designs but properly customizing standard cell libraries remains challenging due to the complex characteristics of the VLSI designs. This paper presents an automatic standard-cell library extension framework, AutoCellLibX. It can find a set of standard cell cluster pattern candidates from the post-technology mapping gate-level netlist, with the consideration of standard cell characteristics and technology mapping constraints, based on our high-efficiency frequent subgraph mining algorithm. Meanwhile, to maximize the area benefit of standard cell customization for the given gate-level netlist, AutoCellLibX includes our proposed pattern combination algorithm which can iteratively find a set of gate-level patterns from numerous candidates as the extension part of the given initial standard cell library. To the best of our knowledge, AutoCellLibX is the first automated standard cell extension framework that closes the optimization loop between the analysis of gate-level netlist and standard cell library customization for VLSI design productivity. The experiments with FreePDK45 library and benchmarks from various domains show that AutoCellLibX can generate the library extension with up to 5 custom standard cells within 1.1 hours for each of the 31 benchmark designs and the resultant extension of the standard cell library can save design area by 4.49% averagely.

preprint2022arXiv

Automatic Fatou Property of Law-invariant Risk Measures

In the paper we investigate automatic Fatou property of law-invariant risk measures on a rearrangement-invariant function space $\mathcal{X}$ other than $L^\infty$. The main result is the following characterization: Every real-valued, law-invariant, coherent risk measure on $\mathcal{X}$ has the Fatou property at every random variable $X\in \mathcal{X}$ whose negative tails have vanishing norm (i.e., $\lim_n\|X\mathbf{1}_{\{X\leq -n\}}\|=0$) if and only if $\mathcal{X}$ satisfies the Almost Order Continuous Equidistributional Average (AOCEA) property, namely, $\mathrm{d}(\mathcal{CL}(X),\mathcal{X}_a) =0$ for any $X\in \mathcal{X}_+$, where $ \mathcal{CL}(X)$ is the convex hull of all random variables having the same distribution as $X$ and $\mathcal{X}_a=\{X\in\mathcal{X}:\lim_n \|X\mathbf{1}_{ \{|X|\geq n\} }\| =0\}$. As a consequence, we show that under the AOCEA property, every real-valued, law-invariant, coherent risk measure on $\mathcal{X}$ admits a tractable dual representation at every $X\in \mathcal{X}$ whose negative tails have vanishing norm. Furthermore, we show that the AOCEA property is satisfied by most classical model spaces, including Orlicz spaces, and therefore the foregoing results have wide applications.

preprint2022arXiv

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segmentation from multi-sequence CMR with a novel auto-weighted supervision framework, where the interactions among different supervised layers are explored under a task-specific objective using reinforcement learning. Furthermore, we design a coarse-to-fine framework to boost the small myocardial pathology region segmentation with shape prior knowledge. The coarse segmentation model identifies the left ventricle myocardial structure as a shape prior, while the fine segmentation model integrates a pixel-wise attention strategy with an auto-weighted supervision model to learn and extract salient pathological structures from the multi-sequence CMR data. Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods. Our method is promising in advancing the myocardial pathology assessment on multi-sequence CMR data. To motivate the community, we have made our code publicly available via https://github.com/soleilssss/AWSnet/tree/master.

preprint2022arXiv

Completely Spin-Decoupled Geometric Phase of Metasurface

Metasurfaces have provided unprecedented degree of freedom (DOF) in manipulating electromagnetic (EM) waves. Geometric phase can be readily obtained by rotating the meta-atom of metasurfaces. Nevertheless, such geometric phases are usually spin-coupled, with the same magnitude but opposite signs for left_ and right_handed circularly polarized (LCP,RCP) waves. To achieve independent control on LCP and RCP waves, it is crucial to obtain spin-decoupled geometric phases. In this paper, we propose to obtain completely spin-decoupled geometric phases by engineering surface current paths on meta-atoms. Based on the rotational Doppler effect, the rotation manner is firstly analyzed and it is found that the essence of generating geometric phase lies in the rotation of surface current paths on meta-atoms. Since the induced surface currents paths under LCP and RCP waves always start oppositely and are mirror-symmetrical with each other, it is natural that the geometric phases be with the same magnitude and opposite signs when the meta-atoms are rotated. To obtain spin-decoupled geometric phases, the start point of induced surface current under one spin should be rotated by an angle while that under the other spin by another different angle. In this way, LCP and RCP waves can acquire different geometric phase changes and spin-decoupled geometric phase can be imparted by metasurfaces. Proof-of-principle prototypes were designed, fabricated and measured. Both the simulation and experiment results verify spin-decoupled geometric phases. This work provides a robust means of obtaining spin-dependent geometric phase and will further adds up to the metasurface DOF in manipulating EM waves.

preprint2022arXiv

Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation

How to learn highly compact yet effective sentence representation? Pre-trained language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. Moreover, there is a big performance gap between large and small models. In this paper, we propose Homomorphic Projective Distillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality. We evaluate our method with different model sizes on both semantic textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show that our method achieves 2.7-4.5 points performance gain on STS tasks compared with previous best representations of the same size. In SR tasks, our method improves retrieval speed (8.2$\times$) and memory usage (8.0$\times$) compared with state-of-the-art large models.

preprint2022arXiv

Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

The expansion of renewable energy could help realizing the goals of peaking carbon dioxide emissions and carbon neutralization. Some existing grid dispatching methods integrating short-term renewable energy prediction and reinforcement learning (RL) have been proved to alleviate the adverse impact of energy fluctuations risk. However, these methods omit the long-term output prediction, which leads to stability and security problems on the optimal power flow. This paper proposes a confidence estimation Transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching (Conformer-RLpatching). Conformer-RLpatching predicts long-term active output of each renewable energy generator with an enhanced Transformer to boost the performance of hybrid energy grid dispatching. Furthermore, a confidence estimation method is proposed to reduce the prediction error of renewable energy. Meanwhile, a dispatching necessity evaluation mechanism is put forward to decide whether the active output of a generator needs to be adjusted. Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score by 25.8% and achieves a better total reward compared with the golden medal team in the power grid dispatching competition sponsored by State Grid Corporation of China under the same simulation environment. Codes are outsourced in https://github.com/buptlxh/Conformer-RLpatching.

preprint2022arXiv

Consecutive Decoding for Speech-to-text Translation

Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose COnSecutive Transcription and Translation (COSTT), an integral approach for speech-to-text translation. The key idea is to generate source transcript and target translation text with a single decoder. It benefits the model training so that additional large parallel text corpus can be fully exploited to enhance the speech translation training. Our method is verified on three mainstream datasets, including Augmented LibriSpeech English-French dataset, IWSLT2018 English-German dataset, and TED English-Chinese dataset. Experiments show that our proposed COSTT outperforms or on par with the previous state-of-the-art methods on the three datasets. We have released our code at \url{https://github.com/dqqcasia/st}.

preprint2022arXiv

Contextual Representation Learning beyond Masked Language Modeling

How do masked language models (MLMs) such as BERT learn contextual representations? In this work, we analyze the learning dynamics of MLMs. We find that MLMs adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations, which limits the efficiency and effectiveness of MLMs. To address these issues, we propose TACO, a simple yet effective representation learning approach to directly model global semantics. TACO extracts and aligns contextual semantics hidden in contextualized representations to encourage models to attend global semantics when generating contextualized representations. Experiments on the GLUE benchmark show that TACO achieves up to 5x speedup and up to 1.2 points average improvement over existing MLMs. The code is available at https://github.com/FUZHIYI/TACO.

preprint2022arXiv

Cross section measurements of the processes $e^+e^- \rightarrow ωπ^{0}$ and $ωη$ at center-of-mass energies between 3.773 and 4.701 GeV

The Born cross sections of the processes $e^+e^- \rightarrow ωπ^{0}$ and $e^+e^- \rightarrow ωη$ are measured at center-of-mass energies between 3.773 and 4.701 GeV using a total integrated luminosity of 22.7 fb$^{-1}$ collected with the BESIII detector operating at the BEPCII collider. A simple $s^{-n}$ dependence for the continuum process can describe the measured Born cross sections. No significant contributions from the $ψ(4160)$, $Y(4230)$, $Y(4360)$, $ψ(4415)$, $Y(4660)$ resonances are found, which indicates relative small branching fractions for these resonances into the $ωπ^{0}$ and $ωη$ final states.

preprint2022arXiv

Cross-modal Contrastive Learning for Speech Translation

How can we learn unified representations for spoken utterances and their written text? Learning similar representations for semantically similar speech and text is important for speech translation. To this end, we propose ConST, a cross-modal contrastive learning method for end-to-end speech-to-text translation. We evaluate ConST and a variety of previous baselines on a popular benchmark MuST-C. Experiments show that the proposed ConST consistently outperforms the previous methods on, and achieves an average BLEU of 29.4. The analysis further verifies that ConST indeed closes the representation gap of different modalities -- its learned representation improves the accuracy of cross-modal speech-text retrieval from 4% to 88%. Code and models are available at https://github.com/ReneeYe/ConST.

preprint2022arXiv

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of registration or label fusion procedures. In this work, we design a novel cross-modality MAS framework, which uses available atlases from a certain modality to segment a target image from another modality. To boost the computational efficiency of the framework, both the image registration and label fusion are achieved by well-designed deep neural networks. For the atlas-to-target image registration, we propose a bi-directional registration network (BiRegNet), which can efficiently align images from different modalities. For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image. SimNet can learn multi-scale information for similarity estimation to improve the performance of label fusion. The proposed framework was evaluated by the left ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets, respectively. Results have shown that the framework is effective for cross-modality MAS in both registration and label fusion.

preprint2022arXiv

Deep Computational Model for the Inference of Ventricular Activation Properties

Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiogram (ECG) with ground truth properties to train the inference model, where specific patient information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.

preprint2022arXiv

Deepfake Network Architecture Attribution

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models' architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det.

preprint2022arXiv

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction. To remedy this, we present a novel distillation framework for training a light-weight student model to perform trading volume prediction given historical transaction data. Specifically, we turn the regression model into a probabilistic forecasting model, by training models to predict a Gaussian distribution to which the trading volume belongs. The student model can thus learn from the teacher at a more informative distributional level, by matching its predicted distributions to that of the teacher. Two correlational distillation objectives are further introduced to encourage the student to produce consistent pair-wise relationships with the teacher model. We evaluate the framework on a real-world stock volume dataset with two different time window settings. Experiments demonstrate that our framework is superior to strong baseline models, compressing the model size by $5\times$ while maintaining $99.6\%$ prediction accuracy. The extensive analysis further reveals that our framework is more effective than vanilla distillation methods under low-resource scenarios.

preprint2022arXiv

Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

Sequence-to-sequence learning naturally has two directions. How to effectively utilize supervision signals from both directions? Existing approaches either require two separate models, or a multitask-learned model but with inferior performance. In this paper, we propose REDER (Reversible Duplex Transformer), a parameter-efficient model and apply it to machine translation. Either end of REDER can simultaneously input and output a distinct language. Thus REDER enables reversible machine translation by simply flipping the input and output ends. Experiments verify that REDER achieves the first success of reversible machine translation, which helps outperform its multitask-trained baselines by up to 1.3 BLEU.

preprint2022arXiv

Enhancing Cross-lingual Transfer by Manifold Mixup

Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated with the cross-lingual representation discrepancy. To achieve better cross-lingual transfer performance, we propose the cross-lingual manifold mixup (X-Mixup) method, which adaptively calibrates the representation discrepancy and gives a compromised representation for target languages. Experiments on the XTREME benchmark show X-Mixup achieves 1.8% performance gains on multiple text understanding tasks, compared with strong baselines, and significantly reduces the cross-lingual representation discrepancy.

preprint2022arXiv

Ergodicity and long-time behavior of the Random Batch Method for interacting particle systems

We study the geometric ergodicity and the long time behavior of the Random Batch Method for interacting particle systems, which exhibits superior numerical performance in recent large-scale scientific computing experiments. We show that for both the interacting particle system (IPS) and the random batch interacting particle system (RB-IPS), the distribution laws converge to their respective invariant distributions exponentially, and the convergence rate does not depend on the number of particles $N$, the time step $τ$ for batch divisions or the batch size $p$. Moreover, the Wasserstein distance between the invariant distributions of the IPS and the RB-IPS is bounded by $O(\sqrtτ)$, showing that the RB-IPS can be used to sample the invariant distribution of the IPS accurately with greatly reduced computational cost.

preprint2022arXiv

First Observation of the Semileptonic Decay $Λ_c^+\rightarrow pK^- e^+ν_e$

Using $4.5~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data samples collected at the center-of-mass energies ranging from 4.600~GeV to 4.699~GeV with the BESIII detector at the BEPCII collider, a first study of the semileptonic decays $Λ_c^+\rightarrow pK^-e^+ν_e$, $Λ_c^+\rightarrow Λ(1520) e^+ν_e$ and $Λ_c^+\rightarrow Λ(1405) e^+ν_e$ is performed. The $Λ_c^+\rightarrow pK^-e^+ν_e$ decay is observed with a significance of $8.2σ$ and the branching fraction is measured to be $\mathcal{B}(Λ_c^+\rightarrow pK^- e^+ν_e)=(0.88\pm0.17_{\rm stat.}\pm0.07_{\rm syst.})\times 10^{-3}$. We also report evidence of $Λ_c^+\rightarrow Λ(1520)e^+ν_e$ and $Λ_c^+\rightarrow Λ(1405)e^+ν_e$ with significances of $3.3σ$ and $3.2σ$, respectively, and measure $\mathcal B(Λ^+_c\rightarrow Λ(1520)e^+ν_e)=(1.02\pm0.52_{\rm stat.}\pm0.11_{\rm syst.})\times10^{-3}$ and $\mathcal B(Λ^+_c\rightarrow Λ(1405)[\rightarrow pK^-]e^+ν_e)=(0.42\pm0.19_{\rm stat.}\pm0.04_{\rm syst.})\times10^{-3}$. Combining these with the inclusive semileptonic $Λ_c^+$ branching fraction measured by BESIII, the relative fraction is determined to be $[\mathcal{B}(Λ_c^+\rightarrow pK^-e^+ν_e)/\mathcal{B}(Λ_c^+\rightarrow X e^+ν_e)]=(2.1\pm0.4_{\rm stat.}\pm0.2_{\rm syst.})\%$, which provides a clear confirmation that semileptonic $Λ_c^+$ decays are not saturated by the $Λ\ell^+ν_{\ell}$ final state.

preprint2022arXiv

Fluctuation suppression and enhancement in interacting particle systems

We investigate in this work the effects of interaction on the fluctuation of empirical measures. The systems with positive definite interaction potentials tend to exhibit smaller fluctuation compared to the fluctuation in standard Monte Carlo sampling while systems with negative definite potentials tend to exhibit larger fluctuation. Moreover, if the temperature goes to zero, the fluctuation for positive definite kernels in the long time tends to vanish to zero, while the fluctuation for negative definite kernels in the long time tends to blow up to infinity. This phenomenon may gain deeper understanding to some physical systems like the Poisson-Boltzmann system, and may help to understand the properties of some particle based variational inference sampling methods.

preprint2022arXiv

KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models

Previous works show the great potential of pre-trained language models (PLMs) for storing a large amount of factual knowledge. However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs. Firstly, knowledge memorization and identification abilities: traditional KBs can store various types of entities and relationships; do PLMs have a high knowledge capacity to store different types of knowledge? Secondly, reasoning ability: a qualified knowledge source should not only provide a collection of facts, but support a symbolic reasoner. Can PLMs derive new knowledge based on the correlations between facts? To evaluate these features of PLMs, we propose a benchmark, named Knowledge Memorization, Identification, and Reasoning test (KMIR). KMIR covers 3 types of knowledge, including general knowledge, domain-specific knowledge, and commonsense, and provides 184,348 well-designed questions. Preliminary experiments with various representative pre-training language models on KMIR reveal many interesting phenomenons: 1) The memorization ability of PLMs depends more on the number of parameters than training schemes. 2) Current PLMs are struggling to robustly remember the facts. 3) Model compression technology retains the amount of knowledge well, but hurts the identification and reasoning abilities. We hope KMIR can facilitate the design of PLMs as better knowledge sources.

preprint2022arXiv

Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets

Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint? It is a challenging task with long horizon and sparse reward -- the robot has to figure out physically stable design schemes and feasible actions to manipulate and transport blocks. Due to diverse block sizes, the state space and action trajectories are vast to explore. In this paper, we propose a hierarchical approach for this problem. It consists of a reinforcement-learning designer to propose high-level building instructions and a motion-planning-based action generator to manipulate blocks at the low level. For high-level learning, we develop a novel technique, prioritized memory resetting (PMR) to improve exploration. PMR adaptively resets the state to those most critical configurations from a replay buffer so that the robot can resume training on partial architectures instead of from scratch. Furthermore, we augment PMR with auxiliary training objectives and fine-tune the designer with the locomotion generator. Our experiments in simulation and on a real deployed robotic system demonstrate that it is able to effectively construct bridges with blocks of varying sizes at a high success rate. Demos can be found at https://sites.google.com/view/bridge-pmr.

preprint2022arXiv

Learning When to Translate for Streaming Speech

How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet effective method for translating streaming speech content. Given a usually long speech sequence, we develop an efficient monotonic segmentation module inside an encoder-decoder model to accumulate acoustic information incrementally and detect proper speech unit boundaries for the input in speech translation task. Experiments on multiple translation directions of the MuST-C dataset show that MoSST outperforms existing methods and achieves the best trade-off between translation quality (BLEU) and latency. Our code is available at https://github.com/dqqcasia/mosst.

preprint2022arXiv

LightSeq2: Accelerated Training for Transformer-based Models on GPUs

Transformer-based neural models are used in many AI applications. Training these models is expensive, as it takes huge GPU resources and long duration. It is challenging because typical data like sentences have variable lengths, and Transformer's computation patterns are more complex than convolutional neural networks. Existing systems either only focus on model inference or optimization for only BERT-like encoder models. In this paper, we present LightSeq2, a system to accelerate training for a general family of Transformer models on GPUs. We propose a series of GPU optimization techniques tailored to the specific computation flow and memory access patterns of Transformer models. LightSeq2 supports many model architectures, including BERT (encoder-only), GPT (decoder-only), Transformer (encoder-decoder), and vision Transformer. Our experiments for a variety of models and benchmarks show that LightSeq2 is consistently faster (1.4-3.5x) than previous systems on different GPUs. In particular, it gains 308% training speedup compared with existing systems on a large public machine translation benchmark (WMT14 English-German).

preprint2022arXiv

Measurement of $e^{+}e^{-} \to K^{+}K^{-}π^{0}$ cross section and observation of a resonant structure

Based on $e^{+}e^{-}$ collision data collected by the BESIII detector at the BEPCII collider at center-of-mass energies from 2.000 to 3.080 GeV, a partial-wave analysis is performed for the process $e^{+}e^{-} \to K^{+}K^{-}π^{0}$. The Born cross section of the process $e^{+}e^{-} \to K^{+}K^{-}π^{0}$ and its subprocesses $e^{+}e^{-} \to ϕπ^{0}$, $K^{*}(892)K$ and $K^{*}_{2}(1430)K$ are measured. The results for $e^{+}e^{-} \to K^{+}K^{-}π^{0}$ and $ϕπ^{0}$ are consistent with the BaBar measurements and with improved precision. By analyzing the cross section, of the subprocesses $e^{+}e^{-} \to$ $K^{*}(892)K$ and $K^{*}_{2}(1430)K$, a structure with mass $M_R$ = (2208 $\pm$ 19 $\pm$ 24) MeV/$c^{2}$ and width $Γ_R$ = (168 $\pm$ 24 $\pm$ 39) MeV is observed with a combined statistical significance of 7.6$σ$. The measured resonance parameters suggest it can be identified as the $ϕ(2170)$, thus the results provide valuable input to understand the internal nature of this state.

preprint2022arXiv

Measurement of $Λ$ baryon polarization in $e^+e^-\rightarrowΛ\barΛ$ at $\sqrt{s} = 3.773$ GeV

Using a data sample of $ψ(3770)$ events collected with the BESIII detector at BEPCII corresponding to an integrated luminosity of 2.9 fb$^{-1}$, we report a measurement of $Λ$ spin polarization in $e^+e^-\rightarrowΛ\barΛ$ at $\sqrt{s} = 3.773$ GeV. The significance of polarization is found to be 2$σ$ including the systematic uncertainty, which implies a zero phase between the transition amplitudes of the $Λ\barΛ$ helicity states. This phase can be interpreted in terms of psionic form factors, and is determined to be $ΔΦ^Ψ$ = $Φ^Ψ_{E} - Φ^Ψ_{M}$ = $(71^{+66}_{-46}$ $\pm$ 5)$^{\circ}$. Similarly, the ratio between the form factors is found to be $R^ψ$ = $|G^Ψ_{E}/G^Ψ_{M}|$ = $0.48^{+0.12}_{-0.07}$ $\pm$ 0.04. The first uncertainties are statistical and the second systematic.

preprint2022arXiv

Measurement of the $D \to K^-π^+π^+π^-$ and $D \to K^-π^+π^0$ coherence factors and average strong-phase differences in quantum-correlated ${D\bar{D}}$ decays

The decays $D\to K^-π^+π^+π^-$ and $D \to K^-π^+π^0$ are studied in a sample of quantum-correlated $D\bar{D}$ pairs produced through the process $e^+e^- \to ψ(3770) \to D\bar{D}$, exploiting a data set collected by the BESIII experiment that corresponds to an integrated luminosity of 2.93 fb$^{-1}$. Here $D$ indicates a quantum superposition of a $D^0$ and a $\bar{D}^0$ meson. By reconstructing one neutral charm meson in a signal decay, and the other in the same or a different final state, observables are measured that contain information on the coherence factors and average strong-phase differences of each of the signal modes. These parameters are critical inputs in the measurement of the angle $γ$ of the Unitarity Triangle in $B^- \to DK^-$ decays at the LHCb and Belle II experiments. The coherence factors are determined to be $R_{K3π}=0.52^{+0.12}_{-0.10}$ and $R_{Kππ^0}=0.78 \pm 0.04$, with values for the average strong-phase differences that are $δ_D^{K3π}=\left(167^{+31}_{-19}\right)^\circ$ and $δ_D^{Kππ^0}=\left(196^{+14}_{-15}\right)^\circ$, where the uncertainties include both statistical and systematic contributions. The analysis is re-performed in four bins of the phase-space of the $D \to K^-π^+π^+π^-$ to yield results that will allow for a more sensitive measurement of $γ$ with this mode, to which the BESIII inputs will contribute an uncertainty of around 6$^\circ$.

preprint2022arXiv

Measurement of the branching fraction and decay asymmetry of $Λ\to nγ$

The radiative hyperon decay $Λ\to nγ$ is studied using $(10087\pm44)\times 10^6$ $J/ψ$ events collected with the BESIII detector operating at BEPCII. The absolute branching fraction of the decay $Λ\to nγ$ is determined with a significance of 5.6$σ$ to be $[0.832\pm0.038(\rm stat.)\pm0.054(\rm syst.)]\times10^{-3}$, which lies significantly below the current PDG value. By analyzing the joint angular distribution of the decay products, the first determination of the decay asymmetry $α_γ$ is reported with a value of $-0.16\pm0.10(\rm stat.)\pm0.05(\rm syst.)$.

preprint2022arXiv

Measurement of the branching fraction for $ψ(3686)\to ωK^0_SK^0_S$

Analyzing $(448.1\pm2.9)\times10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the $ψ(3686)\to ωK_{S}^{0}K_{S}^{0}$ decay is observed for the first time. The branching fraction for this decay is determined to be $\mathcal{B}_{ψ(3686)\to ωK_{S}^{0}K^{0}_{S}}$=$(7.04\pm0.39\pm0.36)$$\times10^{-5}$, where the first uncertainty is statistical and the second is systematic.

preprint2022arXiv

Measurement of the branching fraction of the doubly Cabibbo-suppressed decay $D^0\to K^+π^-π^0$ and search for $D^0\to K^+π^-π^0π^0$

Using $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773\,GeV with the BESIII detector, we present a measurement of the branching fraction of the doubly Cabibbo-suppressed (DCS) decay $D^0\to K^+π^-π^0$ and a search for the DCS decay $D^0\to K^+π^-π^0π^0$. The branching fraction of $D^0\to K^+π^-π^0$ is determined to be $[3.13^{+0.60}_{-0.56}({\rm stat}) \pm 0.09({\rm syst})] \times 10^{-4}$. No signal is observed for $D^0\to K^+π^-π^0π^0$ and an upper limit of $3.6 \times 10^{-4}$ is set on the branching fraction at the 90\% C.L. We combine these results with the world-average branching fractions of their counterpart Cabibbo-favored decays to determine the ratios of the doubly Cabibbo-suppressed over the Cabibbo-favored branching fractions, ${\mathcal B}(D^0\to K^+π^-π^0)/{\mathcal B}(D^0\to K^-π^+π^0)=(0.22\pm 0.04)\%$~and ${\mathcal B}(D^0\to K^+π^-π^0π^0)/{\mathcal B}(D^0\to K^-π^+π^0π^0)<0.40\%$ at the 90\% C.L., which correspond to $(0.75\pm 0.14)\tan^{4} θ_C$~and $1.37\times \tan^{4} θ_C$, respectively, where $θ_C$ is the Cabibbo angle.

preprint2022arXiv

Measurement of the Cross Section for $e^{+}e^{-}\to$ hadrons at Energies from 2.2324 to 3.6710 GeV

Based on electron-positron collision data collected with the BESIII detector operating at the Beijing Electron Positron Collider II storage rings, the value of $R\equivσ(e^{+}e^{-}\to$hadrons)/$σ(e^{+}e^{-}\toμ^{+}μ^{-})$ is measured at 14 center-of-mass energies from 2.2324 to 3.6710 GeV. The resulting uncertainties are less than $3.0\%$, and are dominated by systematic uncertainties.

preprint2022arXiv

Measurement of the cross section of $e^{+}e^{-}\toηπ^{+}π^{-}$ at center-of-mass energies from 3.872 GeV to 4.700 GeV

Using data samples with an integrated luminosity of 19 fb$^{-1}$ at twenty-eight center-of-mass energies from 3.872 GeV to 4.700 GeV collected with the BESIII detector at the BEPCII electron--positron collider, the process $e^{+}e^{-}\toηπ^{+}π^{-}$ and the intermediate process $e^{+}e^{-}\toηρ^{0}$ are studied for the first time. The Born cross sections are measured. No significant resonance structure is observed in the cross section lineshape.

preprint2022arXiv

Measurement of the total and leptonic decay widths of the $J/ψ$ resonance with an energy scan method at BESIII

Using $e^+e^-$ annihilation data sets collected with the BESIII detector, we measure the cross sections of the processes $e^+e^- \to e^+e^-$ and $e^+e^- \to μ^+μ^-$ at fifteen center-of-mass energy points in the vicinity of the $J/ψ$ resonance. By a simultaneous fit to the measured, center-of-mass energy dependent cross sections of the two processes, the combined quantities $Γ_{ee} Γ_{ee} / Γ_{\rm tot}$ and $Γ_{ee} Γ_{μμ} / Γ_{\rm tot}$ are determined to be ($0.346 \pm 0.009$) and ($0.335 \pm 0.006$) keV, respectively, where $Γ_{ee}$, $Γ_{μμ}$, and $Γ_{\rm tot}$ are the electronic, muonic, and total decay widths of the $J/ψ$ resonance, respectively. Using the resultant $Γ_{ee} Γ_{μμ} / Γ_{\rm tot}$ and $Γ_{ee} Γ_{ee} / Γ_{\rm tot}$, the ratio $Γ_{ee} / Γ_{μμ}$ is calculated to be $1.031 \pm 0.015$, which is consistent with the expectation of lepton universality within about two standard deviations. Assuming lepton universality and using the branching fraction of the $J/ψ$ leptonic decay measured by BESIII in 2013, $Γ_{\rm tot}$ and $Γ_{ll}$ are determined to be ($93.0 \pm 2.1$) and ($5.56 \pm 0.11$) keV, respectively, where $Γ_{ll}$ is the average leptonic decay width of the $J/ψ$ resonance.

preprint2022arXiv

Measurements of Absolute Branching Fractions of $D^0\to K_L^0ϕ$, $K_L^0η$, $K_L^0ω$, and $K_L^0η^{\prime}$

We report the first measurements of the absolute branching fractions of $D^0\to K_L^0ϕ$, $D^0\to K_L^0η$, $D^0\to K_L^0ω$, and $D^0\to K_L^0η^{\prime}$, obtained by analyzing $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. Taking the world averages of the branching fractions of $D^0\to K_S^0ϕ$, $D^0\to K_S^0η$, $D^0\to K_S^0ω$, and $D^0\to K_S^0η^{\prime}$, the $K_S^0$-$K_L^0$ asymmetry $\mathcal{R}(D^0)$ in these decay modes are obtained. The CP asymmetries in these decays are also determined. No significant $CP$ violation is observed.

preprint2022arXiv

Measurements of the absolute branching fractions of hadronic $D$-meson decays involving kaons and pions

By analyzing an electron-positron collision data sample corresponding to an integrated luminosity of $2.93\,\rm fb^{-1}$ taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, we obtain for the first time the absolute branching fractions for seven $D^0$ and $D^+$ hadronic decay modes and search for the hadronic decay $D^0\to K^0_S K^0_Sπ^0$ with much improved sensitivity. The results are ${\mathcal B}(D^0\to K^0_Sπ^0π^0π^0 )=( 7.64\pm 0.30\pm 0.29)\times 10^{-3}$, ${\mathcal B}(D^0\to K^-π^+π^0π^0π^0 )=( 9.54\pm 0.30\pm 0.31)\times 10^{-3}$, ${\mathcal B}(D^0\to K^0_Sπ^+π^-π^0π^0)=(12.66\pm 0.45\pm 0.43)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^0π^0 )=(29.04\pm 0.62\pm 0.87)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^+π^-π^0)=(15.28\pm 0.57\pm 0.60)\times 10^{-3}$, ${\mathcal B}(D^+\to K^0_Sπ^+π^0π^0π^0)=( 5.54\pm 0.44\pm 0.32)\times 10^{-3}$, ${\mathcal B}(D^+\to K^-π^+π^+π^0π^0 )=( 4.95\pm 0.26\pm 0.19)\times 10^{-3}$, ${\mathcal B}({D^0\to K^0_S K^0_Sπ^0}) < 1.57 \times 10^{-4}$ at the 90\% confidence level. Here the first uncertainties are statistical and the second ones systematic. The newly studied decays greatly enrich the knowledge of the $D\to \bar Kπππ$ and $D\to \bar Kππππ$ hadronic decays, and open a bridge to access more two-body hadronic $D$ decays containing scalar, vector, axial and tensor mesons in the charm sector.

preprint2022arXiv

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.

preprint2022arXiv

MTG: A Benchmark Suite for Multilingual Text Generation

We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at \url{https://github.com/zide05/MTG}.

preprint2022arXiv

Multi-Modality Cardiac Image Computing: A Survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, \textit{either combining information from different modalities or transferring information across modalities}. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

preprint2022arXiv

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

preprint2022arXiv

Observation of $a_0(1710)^+ \to K_S^0K^+$ in study of the $D_s^+\to K_S^0K^+π^0$ decay

Using $e^+e^-$ annihilation data corresponding to an integrated luminosity of 6.32 fb$^{-1}$ collected at center-of-mass energies between 4.178 GeV and 4.226 GeV with the BESIII detector, we perform the first amplitude analysis of the decay $D_s^+\to K_S^0K^+π^0$ and determine the relative branching fractions and phases for intermediate processes. We observe the $a_0(1710)^+$, the isovector partner of the $f_0(1710)$ and $f_0(1770)$ mesons, in its decay to $K_S^0K^+$ for the first time. In addition, we measure the ratio $\frac{\mathcal{B}(D_{s}^{+} \to \bar{K}^{*}(892)^{0}K^{+})}{\mathcal{B}(D_{s}^{+} \to \bar{K}^{0}K^{*}(892)^{+})}$ to be $2.35^{+0.42}_{-0.23\text{stat.}}\pm 0.10_{\rm syst.}$. Finally, we provide a precision measurement of the absolute branching fraction $\mathcal{B}(D_s^+\to K_S^0K^+π^0) = (1.46\pm 0.06_{\text{stat.}}\pm 0.05_{\text{syst.}})\%$.

preprint2022arXiv

Observation of $η_c(2S) \to 3(π^+π^-)$ and measurements of $χ_{cJ} \to 3(π^+π^-)$ in $ψ(3686)$ radiative transitions

The hadronic decay $η_c(2S) \to 3(π^+π^-)$ is observed with a statistical significance of 9.3 standard deviations using $(448.1\pm2.9)\times10^6$ $ψ(3686)$ events collected by the BESIII detector at the BEPCII collider. The measured mass and width of $η_c(2S)$ are $(3643.4 \pm 2.3 (\rm stat.) \pm 4.4 (\rm syst.))$ MeV/$c^2$ and $(19.8 \pm 3.9 (\rm stat.) \pm 3.1 (\rm syst.))$ MeV, respectively, which are consistent with the world average values within two standard deviations. The product branching fraction $\mathcal{B}[ψ(3686)\to γη_c(2S)]\times\mathcal{B}[η_c(2S)\to3(π^+π^-)]$ is measured to be $(9.2 \pm 1.0 (\rm stat.) \pm 0.9 (\rm syst.))\times10^{-6}$. Using $\mathcal{B}[ψ(3686)\to γη_c(2S)]=(7.0^{+3.4}_{-2.5})\times10^{-4}$, we obtain $\mathcal{B}[η_c(2S) \to 3(π^+π^-)] = (1.31 \pm 0.15 (\rm stat.) \pm 0.13 (\rm syst.)(^{+0.64}_{-0.47}) (\rm extr))\times10^{-2}$, where the third uncertainty is from $\mathcal{B}[ψ(3686) \to γη_c(2S)]$. We also measure the $χ_{cJ} \to 3(π^+π^-)$ ($J=0, 1, 2$) decays via $ψ(3686) \to γχ_{cJ}$ transitions. The branching fractions are $\mathcal{B}[χ_{c0} \to 3(π^+π^-)] = (2.080\pm0.006 (\rm stat.)\pm0.068 (\rm syst.))\times10^{-2}$, $\mathcal{B}[χ_{c1} \to 3(π^+π^-)] = (1.092\pm0.004 (\rm stat.)\pm0.035 (\rm syst.))\times10^{-2}$, and $\mathcal{B}[χ_{c2} \to 3(π^+π^-)] = (1.565\pm0.005 (\rm stat.)\pm0.048 (\rm syst.))\times10^{-2}$.

preprint2022arXiv

Observation of resonance structures in $e^+e^-\to π^+π^-ψ_2(3823)$ and mass measurement of $ψ_2(3823)$

Using a data sample corresponding to an integrated luminosity of 11.3 $\rm fb^{-1}$ collected at center-of-mass energies from $4.23$ to $4.70$ GeV with the BESIII detector, we measure the product of the $e^+e^-\to π^+π^-ψ_2(3823)$ cross section and the branching fraction $\mathcal{B}[ψ_2(3823)\to γχ_{c1}]$. For the first time, resonance structure is observed in the cross section line shape of $e^+e^-\to π^+π^-ψ_2(3823)$ with significances exceeding $5σ$. A fit to data with two coherent Breit-Wigner resonances modeling the $\sqrt{s}$-dependent cross section yields $M(R_1)=4406.9\pm 17.2\pm 4.5$ MeV/$c^2$, $Γ(R_1)=128.1\pm 37.2\pm 2.3$ MeV, and $M(R_2)=4647.9\pm 8.6\pm 0.8$ MeV/$c^2$, $Γ(R_2)=33.1\pm 18.6\pm 4.1$ MeV. Though weakly disfavored by the data, a single resonance with $M(R)=4417.5\pm26.2\pm3.5$ MeV/$c^2$, $Γ(R)=245\pm48\pm13$ MeV is also possible to interpret data. This observation deepens our understanding of the nature of the vector charmoniumlike states. The mass of the $ψ_2(3823)$ state is measured as $(3823.12\pm 0.43\pm 0.13)$ MeV/$c^2$, which is the most precise measurement to date.

preprint2022arXiv

Observation of the double Dalitz decay $η'\to e^+e^-e^+e^-$

Based on $(10087 \pm 44)\times10^6$ $J/ψ$ events collected with the BESIII detector at BEPCII, the double Dalitz decay $η'\to e^+e^-e^+e^-$ is observed for the first time via the $J/ψ\toγη'$ decay process. The significance is found to be 5.7$σ$ with systematic uncertainties taken into consideration. Its branching fraction is determined to be $\mathcal{B}(η'\to e^+ e^- e^+ e^-) =(4.5\pm1.0(\mathrm{stat.})\pm0.5(\mathrm{sys.})) \times 10^{-6}$.

preprint2022arXiv

Observation of the electromagnetic Dalitz decay $D^{\ast 0}\to D^{0}e^{+}e^{-}$

Based on 3.19 fb$^{-1}$ of $e^+e^-$ collision data accumulated at the center-of-mass energy 4.178 GeV with the BESIII detector operating at the BEPCII collider, the electromagnetic Dalitz decay $D^{\ast 0}\to D^{0}e^{+}e^{-}$ is observed for the first time with a statistical significance of $13.2σ$. The ratio of the branching fraction of $D^{\ast 0}\to D^{0}e^{+}e^{-}$ to that of $D^{\ast 0}\to D^{0} γ$ is measured to be $(11.08\pm0.76\pm0.49)\times 10^{-3}$. By using the world average value of the branching fraction of $D^{\ast 0}\to D^{0} γ$, the branching fraction of $D^{\ast 0}\to D^{0}e^{+}e^{-}$ is determined to be $(3.91\pm0.27\pm0.17\pm0.10)\times 10^{-3}$, where the first uncertainty is statistical, the second systematic and the third external branching fractions.

preprint2022arXiv

Observation of the Singly Cabibbo-Suppressed Decay $Λ_{c}^{+} \to nπ^{+}$

The singly Cabibbo-suppressed decay $Λ_{c}^{+} \to nπ^{+}$ is observed for the first time with a statistical significance of $7.3σ$ by using 3.9 $\mathrm{fb}^{-1}$ of $e^{+}e^{-}$ collision data collected at center-of-mass energies between 4.612 and 4.699 GeV with the BESIII detector at BEPCII. The branching fraction of $Λ_{c}^{+} \to nπ^{+}$ is measured to be $(6.6\pm1.2_{\rm stat}\pm0.4_{\rm syst})\times 10^{-4}$. By taking the upper limit of branching fractions of $Λ_{c}^{+} \to pπ^0$ from the Belle experiment, the ratio of branching fractions between $Λ_{c}^{+} \to nπ^{+}$ and $Λ_{c}^{+} \to pπ^0$ is calculated to be larger than 7.2 at the 90% confidence level, which disagrees with the current predictions of available phenomenological models. In addition, the branching fractions of the Cabibbo-favored decays $Λ_{c}^{+} \to Λπ^{+}$ and $Λ_{c}^{+} \to Σ^{0}π^{+}$ are measured to be $(1.31\pm0.08_{\rm stat}\pm0.05_{\rm syst})\times 10^{-2}$ and $(1.22\pm0.08_{\rm stat}\pm0.07_{\rm syst})\times 10^{-2}$, respectively, which are consistent with previous results.

preprint2022arXiv

On the Impact of Noises in Crowd-Sourced Data for Speech Translation

Training speech translation (ST) models requires large and high-quality datasets. MuST-C is one of the most widely used ST benchmark datasets. It contains around 400 hours of speech-transcript-translation data for each of the eight translation directions. This dataset passes several quality-control filters during creation. However, we find that MuST-C still suffers from three major quality issues: audio-text misalignment, inaccurate translation, and unnecessary speaker's name. What are the impacts of these data quality issues for model development and evaluation? In this paper, we propose an automatic method to fix or filter the above quality issues, using English-German (En-De) translation as an example. Our experiments show that ST models perform better on clean test sets, and the rank of proposed models remains consistent across different test sets. Besides, simply removing misaligned data points from the training set does not lead to a better ST model.

preprint2022arXiv

On uniform-in-time diffusion approximation for stochastic gradient descent

The diffusion approximation of stochastic gradient descent (SGD) in current literature is only valid on a finite time interval. In this paper, we establish the uniform-in-time diffusion approximation of SGD, by only assuming that the expected loss is strongly convex and some other mild conditions, without assuming the convexity of each random loss function. The main technique is to establish the exponential decay rates of the derivatives of the solution to the backward Kolmogorov equation. The uniform-in-time approximation allows us to study asymptotic behaviors of SGD via the continuous stochastic differential equation (SDE) even when the random objective function $f(\cdot;ξ)$ is not strongly convex.

preprint2022arXiv

Partial wave analysis of $J/ψ\to γη^{\prime} η^{\prime}$

Using a sample of $(10.09~\pm~0.04)\times10^{9} ~J/ψ$ events collected with the BESIII detector, a partial wave analysis of $J/ψ\toγη^{\prime}η^{\prime}$ is performed. The masses and widths of the observed resonances and their branching fractions are reported. The main contribution is from $J/ψ\rightarrowγf_0(2020)$ with $f_0(2020)\rightarrowη^{\prime}η^{\prime}$, which is found with a significance of greater than 25$σ$. The product branching fraction ${\cal B}\left(J/ψ\rightarrowγf_0(2020)\right)\cdot{\cal B}\left(f_0(2020)\rightarrowη^{\prime}η^{\prime}\right)$ is measured to be $(2.63\pm0.06({\rm stat.})^{+0.31}_{-0.46}({\rm syst.}))\times10^{-4}$.

preprint2022arXiv

Phase control of localization in the nonlinear two-mode system from harmonic mixing driving: Perturbative analysis and symmetry consideration

In this paper, we present a rigorous analysis of symmetry and underlying physics of the nonlinear two-mode system driven by a harmonic mixing field, by means of multiple scale asymptotic analysis method. The effective description in the framework of the second-order perturbative theory provides an accurate picture for understanding the Floquet eigenspectrum and dynamical features of the nonlinear two-mode system, showing full agreement with the prediction of symmetry considerations. We find that two types of symmetries play significant role in the dynamical features of this model, the mechanism behind which can be interpreted in terms of the effective description. The results are of relevance for the phase control of the atomic localization in Bose-Einstein condensates or switch of the optical signals in nonlinear mediums.

preprint2022arXiv

Photometric properties and stellar parameters of the rapidly rotating magnetic early-B star HD 345439

We first present the multicolor photometry results of the rapidly rotating magnetic star HD 345439 using the Nanshan One-meter Wide-field Telescope. From the photometric observations, we derive a rotational period of 0.7699\pm0.0014 day. The light curves of HD 345439 are dominated by the double asymmetric S-wave feature that arises from the magnetic clouds. Pulsating behaviors are not observed in Sector 41 of the Transiting Exoplanet Survey Satellite. No evidence is found of the occurrence of centrifugal breakout events neither in the residual flux nor in the systematic variations at the extremum of the light curve. Based on the hypothesis of the Rigidly Rotating Magnetosphere model, we restrict the magnetic obliquity angle {$β$} and the rotational inclination angle $i$ so that they satisfy the approximate relation {$β+ i \approx 105^{\circ}$}. The colour excess, extinction, and luminosity are determined to be $E_{(B-V)}=0.745\pm0.016\,$mag, $A_{V}=2.31\pm0.05\,$mag, and $\rm log\,(L/L_{\odot})=3.82\pm0.1 $dex, respectively. Furthermore, we derive the effective temperature as $T$$\rm _{eff}=22\pm1 $kK and the surface gravity as log$g=4.00\pm0.22$. The mass$ M=7.24_{-1.24}^{+1.75}\rm M_{\odot}$, radius$ R=4.44_{-1.93}^{+2.68}\rm R_{\odot}$, and age$\rm τ_{age}=23.62\,_{-21.97}^{+4.24} $Myr are estimated from the Hertzsprung--Russell Diagram

preprint2022arXiv

Provably Confidential Language Modelling

Large language models are shown to memorize privacy information such as social security numbers in training data. Given the sheer scale of the training corpus, it is challenging to screen and filter these privacy data, either manually or automatically. In this paper, we propose Confidentially Redacted Training (CRT), a method to train language generation models while protecting the confidential segments. We borrow ideas from differential privacy (which solves a related but distinct problem) and show that our method is able to provably prevent unintended memorization by randomizing parts of the training process. Moreover, we show that redaction with an approximately correct screening policy amplifies the confidentiality guarantee. We implement the method for both LSTM and GPT language models. Our experimental results show that the models trained by CRT obtain almost the same perplexity while preserving strong confidentiality.

preprint2022arXiv

Rethinking Document-level Neural Machine Translation

This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words. We evaluate this model and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. Experiments show that document-level Transformer models outperforms sentence-level ones and many previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.

preprint2022arXiv

Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification

Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally transferred from other fields like CV or NLP, whose underlying working mechanism remains under-explored. In this work, we first deeply probe the working mechanism of GCL in SSNC, and find that the promotion brought by GCL is severely unevenly distributed: the improvement mainly comes from subgraphs with less annotated information, which is fundamentally different from contrastive learning in other fields. However, existing GCL methods generally ignore this uneven distribution of annotated information and apply GCL evenly to the whole graph. To remedy this issue and further improve GCL in SSNC, we propose the Topology InFormation gain-Aware Graph Contrastive Learning (TIFA-GCL) framework that considers the annotated information distribution across graph in GCL. Extensive experiments on six benchmark graph datasets, including the enormous OGB-Products graph, show that TIFA-GCL can bring a larger improvement than existing GCL methods in both transductive and inductive settings. Further experiments demonstrate the generalizability and interpretability of TIFA-GCL.

preprint2022arXiv

Search for $X(3872)\toπ^0χ_{c0}$ and $X(3872)\toππχ_{c0}$ at BESIII

Using 9.9 fb$^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector at center-of-mass energies between 4.15 and 4.30 GeV, we search for the processes $e^+e^-\toγX(3872)$ with $X(3872)\rightarrowπ^0χ_{c0}$ and $X(3872)\rightarrowππχ_{c0}$. Depending on the fitting model, the statistical significance for $X(3872)\toπ^0χ_{c0}$ ranges from 1.3$σ$ to 2.8$σ$. We set upper limits (at 90\% C.L.) of $\frac{\mathcal{B}(X(3872)\rightarrowπ^0χ_{c0})}{\mathcal{B}(X(3872)\toπ^+π^-J/ψ)}<3.6$, $\frac{\mathcal{B}(X(3872)\rightarrowπ^+π^-χ_{c0})}{\mathcal{B}(X(3872)\toπ^+π^-J/ψ)}<0.68$, and $\frac{\mathcal{B}(X(3872)\rightarrowπ^0π^0χ_{c0})}{\mathcal{B}(X(3872)\toπ^+π^-J/ψ)}<1.7$. Combined with the BESIII measurement of $X(3872)\toπ^0χ_{c1}$, we also set an upper limit of $\frac{\mathcal{B}(X(3872)\rightarrowπ^0χ_{c0})}{\mathcal{B}(X(3872)\toπ^0χ_{c1})}<4.4$.

preprint2022arXiv

Search for baryon and lepton number violating decays $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$

Using an electron-positron collision data sample corresponding to an integrated luminosity of 2.93~fb$^{-1}$ collected with the BESIII detector at a center-of-mass energy of 3.773 GeV, we search for the baryon and lepton number violating decays $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$. No obvious signals are found with the current statistics. The upper limits on the branching fractions for $D^{0}\to \bar{p}e^{+}$ and $D^{0}\to pe^{-}$ are set to be $1.2\times 10^{-6}$ and $2.2\times 10^{-6}$ at 90\% confidence level, respectively.

preprint2022arXiv

Search for baryon and lepton number violation decay $D^{\pm}\to n(\bar{n})e^{\pm}$

Using a data set of electron-positron collisions corresponding to an integrated luminosity of ${\rm 2.93~fb^{-1}}$ taken with the BESIII detector at a center-of-mass energy of 3.773 GeV, a search for the baryon ($B$) and lepton ($L$) number violating decays $D^{\pm}\to n(\bar{n})e^{\pm}$ is performed. No signal is observed and the upper limits on the branching fractions at the $90\%$ confidence level are set to be $1.43\times10^{-5}$ for the decays $D^{+(-)}\to \bar{n}(n)e^{+(-)}$ with $Δ|B-L|=0$, and $2.91\times10^{-5}$ for the decays $D^{+(-)}\to n(\bar{n})e^{+(-)}$ with $Δ|B-L|=2$ , where $Δ|B-L|$ denotes the change in the difference between baryon and lepton numbers.

preprint2022arXiv

Search for invisible decays of the $Λ$ baryon

A search for invisible decays of the $Λ$ baryon is carried out in the process $J/ψ\toΛ\barΛ$ based on $(1.0087\pm0.0044)\times10^{10}$ $J/ψ$ events collected with the BESIII detector located at the BEPCII storage ring. No signals are found for the invisible decays of $Λ$ baryon, and the upper limit of the branching fraction is determined to be $7.4 \times 10^{-5}$ at the 90% confidence level. This is the first search for invisible decays of baryons; such searches will play an important role in constraining dark sector models related to the baryon asymmetry.

preprint2022arXiv

Search for new hadronic decays of $h_{c}$ and observation of $h_{c}\to p\bar{p}η$

A search for the hadronic decays of the $h_{c}$ meson to the final states $p\bar{p}π^{+}π^{-}π^{0}$, $p\bar{p}η$, and $p\bar{p}π^0$ via the process $ψ(3686) \to π^{0}{h_c}$ is performed using $(4.48\pm0.03)\times10^{8}$ $ψ(3686)$ events collected with the BESIII detector. The decay channel $h_{c}\to p\bar{p}η$ is observed for the first time with a significance greater than $5σ$ and a branching fraction of $\left( {6.41 \pm 1.74 \pm 0.53 \pm 1.00} \right) \times {10^{ -4}}$, where the uncertainties are statistical, systematic, and that from the branching fraction of $ψ(3686)\toπ^{0}h_{c}$. Strong evidence for the decay ${h_c} \to p\bar{p}{π^+}{π^-}{π^0}$ is found with a significance of $4.9σ$ and a branching fraction of $\left( {3.84 \pm 0.83 \pm0.69} \pm 0.58 \right) \times {10^{ - 3}}$. The significances include systematic uncertainties. No clear signal of the decay $h_c\to p\bar{p}π^{0}$ is found, and an upper limit of $6.59\times 10^{-4}$ on its branching fraction is set at the 90% confidence level.

preprint2022arXiv

Search for the decay $D^{0} \to π^{0} ν\barν$

We present the first experimental search for the rare charm decay $D^{0} \to π^{0} ν\barν$. It is based on an $e^+e^-$ collision sample consisting of $10.6\times10^{6}$ pairs of $D^0\bar{D}^0$ mesons collected by the BESIII detector at $\sqrt{s}$=3.773 GeV, corresponding to an integrated luminosity of 2.93~fb$^{-1}$. A data-driven method is used to ensure the reliability of the background modeling. No significant $D^{0} \to π^{0} ν\barν$ signal is observed in data and an upper limit of the branching fraction is set to be $2.1\times 10^{-4}$ at the 90$\%$ confidence level. This is the first experimental constraint on charmed-hadron decays into dineutrino final states.

preprint2022arXiv

Search for the decay $h_c\rightarrowπ^0J/ψ$

A search for the decay $h_c\rightarrowπ^0J/ψ$ is performed using a sample of $h_c$ produced in the reaction $e^+e^-\rightarrowπ^+π^-h_c$. The data samples were collected with the BESIII detector at center-of-mass energies between 4.189 and 4.437 GeV, corresponding to a total integrated luminosity of 11 fb$^{-1}$. No significant signal is observed. Upper limits on the branching ratio $\mathcal{B}(h_c\rightarrowπ^0J/ψ)/\mathcal{B}(h_c\rightarrowγη_c\rightarrowγK^+K^-π^0)$ and on the branching fraction $\mathcal{B}(h_c\rightarrowπ^0J/ψ)$ are determined to be $7.5\times10^{-2}$ and $4.7\times10^{-4}$ at $90\%$ confidence level, respectively. The latter is derived from the former using the measured branching fraction of the normalization channel. This is the first determination of the upper limit of the decay $h_c\rightarrowπ^0J/ψ$.

preprint2022arXiv

Some random batch particle methods for the Poisson-Nernst-Planck and Poisson-Boltzmann equations

We consider in this paper random batch interacting particle methods for solving the Poisson-Nernst-Planck (PNP) equations, and thus the Poisson-Boltzmann (PB) equation as the equilibrium, in the external unbounded domain. To justify the simulation in a truncated domain, an error estimate of the truncation is proved in the symmetric cases for the PB equation. Then, the random batch interacting particle methods are introduced which are $O(N)$ per time step. The particle methods can not only be considered as a numerical method for solving the PNP and PB equations, but also can be used as a direct simulation approach for the dynamics of the charged particles in solution. The particle methods are preferable due to their simplicity and adaptivity to complicated geometry, and may be interesting in describing the dynamics of the physical process. Moreover, it is feasible to incorporate more physical effects and interactions in the particle methods and to describe phenomena beyond the scope of the mean-field equations.

preprint2022arXiv

Spin Josephson effects of spin-orbit-coupled Bose-Einstein condensates in a non-Hermitian double well

In this paper, we investigate the spin and tunneling dynamics of a spin-orbit-coupled noninteracting Bose-Einstein condensate in a periodically driven non-Hermitian double-well potential. Under high-frequency driving, we obtain the effective time-averaged Hamiltonian by using the standard time-averaging method, and analytically calculate the Floquet quasienergies, revealing that the parity-time (PT)-breaking phase transition appears even for arbitrarily small non-Hermitian parameters when the spin-orbit coupling strength takes half-integer value, irrespective of the values of other parameters used. When the system is PT-symmetric with balanced gain and loss, we find numerically and analytically that in the broken PT-symmetric regions, there will exist the net spin current together with a vanishing atomic current, if we drop the contribution of the exponential growth of the norm to the current behaviors. When the system is non-PT-symmetric, though the quasienergies are partial complex, a stable net spin current can be generated by controlling the periodic driving field, which is accompanied by a spatial localization of the condensate in the well with gain. The results deepen the understanding of non-Hermitian physics and could be useful for engineering a variety of devices for spintronics.

preprint2022arXiv

STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation

How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Existing techniques often attempt to transfer powerful machine translation (MT) capabilities to ST, but neglect the representation discrepancy across modalities. In this paper, we propose the Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy. Specifically, we mix up the representation sequences of different modalities, and take both unimodal speech sequences and multimodal mixed sequences as input to the translation model in parallel, and regularize their output predictions with a self-learning framework. Experiments on MuST-C speech translation benchmark and further analysis show that our method effectively alleviates the cross-modal representation discrepancy, and achieves significant improvements over a strong baseline on eight translation directions.

preprint2022arXiv

Symmetry-breaking-induced multifunctionalities of two-dimensional chromium-based materials for nanoelectronics and clean energy conversion

Structural symmetry-breaking that could lead to exotic physical properties plays a crucial role in determining the functions of a system, especially for two-dimensional (2D) materials. Here we demonstrate that multiple functionalities of 2D chromium-based materials could be achieved by breaking inversion symmetry via replacing Y atoms in one face of pristine CrY (Y=P, As, Sb) monolayers with N atoms, i.e., forming Janus Cr2NY monolayers. The functionalities include spin-gapless, very low work function, inducing carrier doping and catalytic activity, which are predominately ascribed to the large intrinsic dipole of Janus Cr2NY monolayers, making them having great potentials in various applications. Specifically, Cr2NSb is found to be a spin-gapless semiconductor, Cr2NP and Cr2NHPF could simultaneously induce n- and p-type carrier doping for two graphene sheets with different concentrations (forming intrinsic p-n vertical junction), and Cr2NY exhibits excellent electrocatalytic hydrogen evolution activity, even superior to benchmark Pt. The results confirm that breaking symmetry is a promising approach for the rational design of multifunctional 2D materials.

preprint2022arXiv

Towards Making the Most of BERT in Neural Machine Translation

GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various natural language processing tasks. However, LM fine-tuning often suffers from catastrophic forgetting when applied to resource-rich tasks. In this work, we introduce a concerted training framework (CTNMT) that is the key to integrate the pre-trained LMs to neural machine translation (NMT). Our proposed CTNMT consists of three techniques: a) asymptotic distillation to ensure that the NMT model can retain the previous pre-trained knowledge; b) a dynamic switching gate to avoid catastrophic forgetting of pre-trained knowledge; and c) a strategy to adjust the learning paces according to a scheduled policy. Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1.4 BLEU score. While for the large WMT14 English-French task with 40 millions of sentence-pairs, our base model still significantly improves upon the state-of-the-art Transformer big model by more than 1 BLEU score. The code and model can be downloaded from https://github.com/bytedance/neurst/ tree/master/examples/ctnmt.

preprint2022arXiv

Ultrafast switching dynamics of the ferroelectric order in stacking-engineered ferroelectrics

The recently discovered ferroelectricity of van der Waals bilayers offers an unconventional route to improve the performance of devices. Key parameters such as switching field and speed depend on the static and dynamic properties of domain walls (DWs). Here we theoretically explore the properties of textures in stacking-engineered ferroelectrics from first principles. Employing a machine-learning potential model, we present results of large-scale atomistic simulations of stacking DWs and Moiré structure of boron nitride bilayers. We predict that the competition between the switching barrier of stable ferroelectric states and the in-plane lattice distortion leads to a DW width of the order of ten nanometers. DWs motion reduces the critical ferroelectric switching field of a monodomain by two orders of magnitude, while high domain-wall velocities allow domain switching on a picosecond-timescale. The superior performance compared to conventional ferroelectrics (or ferromagnets) may enable ultrafast and power-saving non-volatile memories. By twisting the bilayer into a stacking Moiré structure, the ferroelectric transforms into a super-paraelectric since DWs move under ultralow electric fields.

preprint2022arXiv

Weak precompactness in Banach lattices

We show that the solid hull of every weakly precompact set of a Banach lattice $E$ is weakly precompact if and only if every order interval in $E$ is weakly precompact, or equivalently, if and only if every disjoint weakly compact set is weakly precompact. Some results on the domination property for weakly precompact positive operators are obtained. Among other things, we show that, for a pair of Banach lattices $E$ and $F$ with $E$ $σ$-Dedekind complete, every positive operator from $E$ to $F$ dominated by a weakly precompact operator is weakly precompact if and only if either the norm of $E^{\prime}$ is order continuous or else every order interval in $F$ is weakly precompact.

preprint2022arXiv

WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration

In this work, we present a novel method called WSDesc to learn 3D local descriptors in a weakly supervised manner for robust point cloud registration. Our work builds upon recent 3D CNN-based descriptor extractors, which leverage a voxel-based representation to parameterize local geometry of 3D points. Instead of using a predefined fixed-size local support in voxelization, we propose to learn the optimal support in a data-driven manner. To this end, we design a novel differentiable voxelization layer that can back-propagate the gradient to the support size optimization. To train the extracted descriptors, we propose a novel registration loss based on the deviation from rigidity of 3D transformations, and the loss is weakly supervised by the prior knowledge that the input point clouds have partial overlap, without requiring ground-truth alignment information. Through extensive experiments, we show that our learned descriptors yield superior performance on existing geometric registration benchmarks.

preprint2021arXiv

Cross section measurements of the $e^+e^-\to D^{+}D^{-}$ and $e^+e^-\to D^{*+}D^{-}$ processes at center-of-mass energies from 4.085 to 4.600 GeV

The Born cross sections of the $e^+e^-\to D^{*+}D^{*-}$ and $e^+e^-\to D^{*+}D^{-}$ processes are measured using $e^+e^-$ collision data collected with the BESIII experiment at center-of-mass energies from 4.085 to 4.600 GeV, corresponding to an integrated luminosity of $15.7~{\rm fb}^{-1}$. The results are consistent with and more precise than the previous measurements by the Belle, Babar and CLEO collaborations. The measurements are essential for understanding the nature of vector charmonium and charmonium-like states.

preprint2021arXiv

Cross sections for the reactions $e^+e^-\rightarrow K^+K^-π^+π^-(π^0)$, $K^+K^-K^+K^-(π^0)$, $π^+π^-π^+π^-(π^0)$, $p\bar{p}π^+π^-(π^0)$ in the energy region between 3.773 and 4.600 GeV

Using the data samples collected in the energy range from 3.773 to 4.600 GeV with the BESIII detector at the BEPCII collider, we measure the dressed cross sections as a function of center-of-mass energy for $e^+e^-\rightarrow K^+K^-π^+π^-(π^0)$, $K^+K^-K^+K^-(π^0)$, $π^+π^-π^+π^-(π^0)$, and $p\bar{p}π^+π^-(π^0)$. The cross sections for $e^+e^-\rightarrow K^+K^-K^+K^-π^0$, $p\bar{p}π^+π^-(π^0)$ are the first measurements. Cross sections for the other five channels are much more precise than previous results in this energy region. We also search for charmonium and charmonium-like resonances, such as the $Y(4230)$, decaying into the same final states. We find evidence of the $ψ(4040)$ decaying to $π^+π^-π^+π^-π^0$ with a statistical significance of $3.6σ$. Upper limits are provided for other decays since no clear signals are observed.

preprint2021arXiv

Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state

We study $e^{+}e^{-}$ collisions with a $π^{+}π^{-}π^{0}η_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4.226$, $4.258$, $4.358$, $4.416$, and $4.600$ GeV. Evidence for the decay $\zcpm\to\rhopm\etac$ is reported with a statistical significance of $3.9σ$ with various systematic uncertainties taken into account at $\sqrt{s} = 4.226$ GeV, and the Born cross section times branching fraction $σ^{B}(\EE\to \pimp\zcpm)\times \BR(\zcpm\to\rhopm\etac)$ is measured to be $(48 \pm 11 \pm 11)\,\rm{pb}$. The $\zcpm\to \rhopm\etac$ signal is not significant at the other center-of-mass energies and the corresponding upper limits are determined. In addition, no significant signal is observed in a search for $\zcppm\to ρ^{\pm}\etac$ with the same data samples. The ratios $R_{\zc}=\BR(\zcpm\to ρ^{\pm} \etac)/\BR(\zcpm\to π^{\pm} \jpsi)$ and $R_{\zcp}=\BR(\zcppm\to ρ^{\pm} \etac)/\BR(\zcppm\to π^{\pm} \hc)$ are obtained and used to discriminate between different theoretical interpretations of the $\zcpm$ and $\zcppm$.

preprint2021arXiv

Isostructural Metal-Insulator Transition Driven by Dimensional-Crossover in SrIrO3 Heterostructures

Dimensionality reduction induced metal-insulator transitions in oxide heterostructures are usually coupled with structural and magnetic phase transitions, which complicate the interpretation of the underlying physics. Therefore, achieving isostructural MIT is of great importance for fundamental physics and even more for applications. Here, we report an isostructural metal-insulator transition driven by dimensional-crossover in spin-orbital coupled SrIrO3 films. By using in-situ pulsed laser deposition and angle-resolved photoemission spectroscopy, we synthesized and investigated the electronic structure of SrIrO3 ultrathin films with atomic-layer precision. Through inserting orthorhombic CaTiO3 buffer layers, we demonstrate that the crystal structure of SrIrO3 films remains bulk-like with similar oxygen octahedra rotation and tilting when approaching the ultrathin limit. We observe that a dimensional-crossover metal-insulator transition occurs in isostructural SrIrO3 films. Intriguingly, we find the bandwidth of Jeff=3/2 states reduces with lowering the dimensionality and drives the metal-insulator transition. Our results establish a bandwidth controlled metal-insulator transition in the isostructural SrIrO3 thin films.

preprint2021arXiv

Measurement of Branching Fractions of $J/ψ$ and $ψ(3686)$ decays to $Σ^{+}$ and $\overlineΣ^-$

Using $1310.6\times10^{6}$ $J/ψ$ and $448.1\times10^{6}$ $ψ(3686)$ events collected with the BESIII detector, the branching fractions of $J/ψ$ and $ψ(3686)$ decays to $Σ^{+}\overlineΣ^{-}$ are measured to be $(10.61 \pm 0.04 \pm 0.36) \times 10^{-4}$ and $(2.52 \pm 0.04 \pm 0.09) \times 10^{-4}$, respectively. In addition, the ratio of $\mathcal{B}(ψ(3686) \rightarrow Σ^{+}\overlineΣ^{-})/\mathcal{B}(J/ψ\rightarrow Σ^{+}\overlineΣ^{-})$ is determined to be $(23.8 \pm 1.1)\%$ which violates the "$12\%$ rule".

preprint2021arXiv

Measurement of cross-section for $e^+e^-\toΞ^-\barΞ^+$ near threshold at BESIII

The Born cross-sections and effective form factors for process $e^+e^-\toΞ^-\barΞ^+$ are measured at eight center-of-mass energies between 2.644 and 3.080 GeV, using a total integrated luminosity of 363.9 pb$^{-1}$ $e^+e^-$ collision data collected with the BESIII detector at BEPCII. After performing a fit to the Born cross-section of $e^+e^-\toΞ^-\barΞ^+$, no significant threshold effect is observed.

preprint2021arXiv

Measurement of the $e^{+}e^{-}\toΣ^{0}\barΣ^{0}$ cross sections at center-of-mass energies from $2.3864$ to $3.0200$ GeV

The Born cross sections of $e^{+}e^{-}\to Σ^{0}\barΣ^{0}$ are measured at center-of-mass energies from $2.3864$ to $3.0200$ GeV using data samples with an integrated luminosity of $328.5$ pb$^{-1}$ collected with the BESIII detector operating at the BEPCII collider. The analysis makes use of a novel reconstruction method for energies near production threshold, while a single-tag method is employed at other center-of-mass energies. The measured cross sections are consistent with earlier results from BaBar, with a substantially improved precision. The cross-section lineshape can be well described by a perturbative QCD-driven energy function. In addition, the effective form factors of the $Σ^{0}$ baryon are determined. The results provide precise experimental input for testing various theoretical predictions.

preprint2021arXiv

Measurements of $e^+e^-\rightarrow η_{\rm c}π^+ π^-π^0$, $η_{\rm c}π^+ π^-$ and $η_{\rm c}π^0γ$ at $\sqrt{s}$ from 4.18 to 4.60\,GeV, and search for a $Z_{\rm c}$ state close to the $D\bar{D}$ threshold decaying to $η_{\rm c}π$ at $\sqrt{s}$ = 4.23 GeV

We study $η_{\rm c}$ production at center-of-mass energies $\sqrt{s}$ from 4.18 to 4.60 GeV in $e^+e^-$ annihilation data collected with the BESIII detector operating at the BEPCII storage ring, corresponding to 7.3 fb$^{-1}$ of integrated luminosity. We measure the cross sections of the three different exclusive reactions $e^+e^-\rightarrow η_{\rm c}π^+ π^-π^0$, $e^+e^- \rightarrow η_{\rm c}π^+ π^-$, and $e^+e^- \rightarrow η_{\rm c}π^0γ$. We find significant $η_{\rm c}$ production in $e^+e^-\rightarrow η_{\rm c}π^+ π^-π^0$ at $\sqrt{s}$ of 4.23 GeV and 4.26 GeV and observe a significant energy-dependent Born cross section that we measure to be consistent with the production via the intermediate $Y(4260)$ resonance. In addition, we perform a search for a charmonium-like $Z_{\rm c}$ state close to the $D\bar{D}$ threshold that decays to $η_{\rm c}π$, involving ground state charmonium, and observe no signal. Corresponding upper limits on the cross section of $η_{\rm c}$ and $Z_{\rm c}$ production are provided, where the yields are not found to be significant.

preprint2021arXiv

Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$

We present an analysis of the process $ψ(3686) \to Ω^- \barΩ^+$ ($Ω^-\to K^-Λ$, $\barΩ^+\to K^+\barΛ$, $Λ\to pπ^-$, $\barΛ\to \bar{p}π^+$) based on a data set of $448\times 10^6$ $ψ(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider. The helicity amplitudes for the process $ψ(3686) \to Ω^- \barΩ^+$ and the decay parameters of the subsequent decay $Ω^-\to K^-Λ$ $(\barΩ^+\to K^+\barΛ)$ are measured for the first time by a fit to the angular distribution of the complete decay chain. The branching fraction of $ψ(3686) \to Ω^- \barΩ^+$ is measured to be $(5.82\pm 0.12\pm 0.24)\times 10^{-5}$, with an improved precision compared to previous measurements.

preprint2021arXiv

Observation of $D^{0(+)}\to K^0_Sπ^{0(+)}ω$ and improved measurement of $D^0\to K^-π^+ω$

By analyzing an $e^+e^-$ annihilation data sample with an integrated luminosity of $2.93\ \rm fb^{-1}$ taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, we determine the absolute branching fractions of the hadronic decays $D^0\to K^-π^+ω$, $D^0\to K^0_Sπ^0ω$, and $D^+\to K^0_Sπ^+ω$ to be $(3.392 \pm 0.044_{\rm stat} \pm 0.085_{\rm syst})\%$, $(0.848 \pm 0.046_{\rm stat} \pm 0.031_{\rm syst})\%$, and $(0.707 \pm 0.041_{\rm stat} \pm 0.029_{\rm syst})\%$, respectively. The accuracy of the branching fraction measurement of the decay $D^0\to K^-π^+ω$ is improved by a factor of seven compared to the world average value. The $D^{0}\to K^0_Sπ^{0}ω$ and $D^{+}\to K^0_Sπ^{+}ω$ decays are observed for the first time.

preprint2021arXiv

Observation of $e^{+}e^{-}\rightarrowηψ(2S)$ at center-of-mass energies from 4.236 to 4.600 GeV

Using a total of $5.25~{\rm fb}^{-1}$ of $e^{+}e^{-}$ collision data with center-of-mass energies from 4.236 to 4.600 GeV, we report the first observation of the process $e^{+}e^{-}\to ηψ(2S)$ with a statistical significance of $5σ$. The data sets were collected by the BESIII detector operating at the BEPCII storage ring. We measure the yield of events integrated over center-of-mass energies and also present the energy dependence of the measured cross section.

preprint2021arXiv

PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors.

preprint2021arXiv

Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information

We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs? We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model. Our key idea in mRASP is its novel technique of random aligned substitution, which brings words and phrases with similar meanings across multiple languages closer in the representation space. We pre-train a mRASP model on 32 language pairs jointly with only public datasets. The model is then fine-tuned on downstream language pairs to obtain specialized MT models. We carry out extensive experiments on 42 translation directions across a diverse settings, including low, medium, rich resource, and as well as transferring to exotic language pairs. Experimental results demonstrate that mRASP achieves significant performance improvement compared to directly training on those target pairs. It is the first time to verify that multiple low-resource language pairs can be utilized to improve rich resource MT. Surprisingly, mRASP is even able to improve the translation quality on exotic languages that never occur in the pre-training corpus. Code, data, and pre-trained models are available at https://github.com/linzehui/mRASP.

preprint2021arXiv

Search for the $X(2370)$ and observation of $η_{c}\toηηη^\prime$ in $J/ψ\toγηηη^{\prime}$

Using a sample of $1.31\times10^{9} ~J/ψ$ events collected with the BESIII detector, we perform a study of $J/ψ\toγηηη^{\prime}$ to search for the $X(2370)$ and $η_{c}$ in the $ηηη^{\prime}$ invariant mass distribution. No significant signal for the $X(2370)$ is observed, and we set an upper limit for the product branching fraction of ${\cal B}(J/ψ\toγX(2370)\cdot{\cal B}(X(2370)\toηηη^{\prime}) < 9.2\times10^{-6}$ at the 90% confidence level. A clear $η_{c}$ signal is observed for the first time, yielding a product branching fraction of ${\cal B}(J/ψ\to γη_{c})\cdot{\cal B}(η_{c}\to ηηη^{\prime}) = (4.86\pm0.62~({\rm stat.})\pm0.45~({\rm sys.}))\times10^{-5}$.

preprint2021arXiv

Superscalability of the random batch Ewald method

Coulomb interaction, following an inverse-square force-law, quantifies the amount of force between two stationary and electrically charged particles. The long-range nature of Coulomb interactions poses a major challenge to molecular dynamics simulations which are major tools for problems at the nano-/micro- scale. Various algorithms are developed to calculate the pairwise Coulomb interactions to a linear scaling but the poor scalability limits the size of simulated systems. Here, we conduct an efficient molecular dynamics algorithm with the random batch Ewald method on all-atom systems where the complete Fourier components in the Coulomb interaction are replaced by randomly selected mini-batches. By simulating the $N$-body systems up to 100 million particles using $10$ thousand CPU cores, we show that this algorithm furnishes $O(N)$ complexity, almost perfect scalability and an order of magnitude faster computational speed when compared to the existing state-of-the-art algorithms. Further examinations of our algorithm on distinct systems, including pure water, micro-phase-separated electrolyte and protein solution demonstrate that the spatiotemporal information on all time and length scales investigated and thermodynamic quantities derived from our algorithm are in perfect agreement with those obtained from the existing algorithms. Therefore, our algorithm provides a breakthrough solution on scalability of computing the Coulomb interaction. It is particularly useful and cost-effective to simulate ultra-large systems, which was either impossible or very costing to conduct using existing algorithms, thus would benefit the broad community of sciences.

preprint2021arXiv

Taxonomy Completion via Triplet Matching Network

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, "taxonomy completion", by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate <hypernym, hyponym> pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on <query, hypernym, hyponym> triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.

preprint2021arXiv

Tight upper bound on the quantum value of Svetlichny operators under local filtering and hidden genuine nonlocality

Nonlocal quantum correlations among the quantum subsystems play essential roles in quantum science. The violation of the Svetlichny inequality provides sufficient conditions of genuine tripartite nonlocality. We provide tight upper bounds on the maximal quantum value of the Svetlichny operators under local filtering operations, and present a qualitative analytical analysis on the hidden genuine nonlocality for three-qubit systems. We investigate in detail two classes of three-qubit states whose hidden genuine nonlocalities can be revealed by local filtering.

preprint2021arXiv

Triangular Bidword Generation for Sponsored Search Auction

Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in <query, bidword> and <advertisement, bidword> pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired <query, advertisement> as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.

preprint2021arXiv

Weak phases and CP-symmetry tests in sequential decays of entangled double-strange baryons

Using a sample of $1.31\times10^9$ $J/ψ$ events collected with the BESIII detector at the electron-positron collider BEPCII, we analyse the full $J/ψ\to$ $Ξ^-\overlineΞ^+$, $Ξ^-\to Λπ^-$, $Λ\to pπ^-$, $\overlineΞ^+\to\overlineΛπ^+$, $\overlineΛ\to\overline{p}π^+$ decay chain. A new method, exploiting the fact that the $Ξ^-\overlineΞ^+$ pair is entangled and sequentially decaying, and where the complete decay chains are reconstructed, is applied for the first time. This enables precision measurements of the decay parameters for the $Ξ^-\toΛπ^-$ decay ($α_Ξ$, $ϕ_Ξ$) as well as the $\overlineΞ^+\to\overlineΛπ^+$ decay ($\overlineα_Ξ$, $\overlineϕ_Ξ$). From the decay parameters, two independent CP tests were performed, quantified by the observables $A_{\rm CP}^Ξ$ and $Δϕ_Ξ$. Our results, $A_{\rm CP}^Ξ$ = $(6.0\pm13.4\pm5.6)\times10^{-3}$ and $Δϕ_Ξ= (-4.8\pm13.7\pm2.9)\times10^{-3}~{\rm rad}$, are consistent with CP symmetry. Furthermore, our method enables a separation of strong and weak $Ξ\toΛπ$ decay amplitudes. This results in the first direct measurement of the weak phase difference for any baryon decay. The result is found to be $(ξ_{P} - ξ_{S}) = (1.2\pm3.4\pm0.8)\times10^{-2}$ rad and is one of the most precise tests of CP symmetry for strange baryons. The strong phase difference is measured to be $(δ_P - δ_S) = (-4.0\pm3.3\pm1.7)\times10^{-2}$ rad. In addition, we provide an independent measurement of the recently debated $Λ$ decay parameter, $α_Λ = 0.757 \pm 0.011 \pm 0.008 $. The $Λ\overlineΛ$ asymmetry is measured to be $A_{\rm CP}^Λ = (-3.7\pm11.7\pm9.0)\times10^{-3}$.

preprint2020arXiv

$Σ^{+}$ and $\barΣ^-$ polarization in the $J/ψ$ and $ψ(3686)$ decays

From $1310.6\times10^{6}$ $J/ψ$ and $448.1\times10^{6}$ $ψ(3686)$ events collected with the BESIII experiment, we report the first observation of $Σ^{+}$ and $\barΣ^{-}$ spin polarization in $e^+e^-\rightarrow J/ψ(ψ(3686)) \rightarrow Σ^{+} \barΣ^{-}$ decays. The relative phases of the form factors $ΔΦ$ have been measured to be $(-15.5\pm0.7\pm0.5)^{\circ}$ and $(21.7\pm4.0\pm0.8)^{\circ}$ with $J/ψ$ and $ψ(3686)$ data, respectively. The non-zero value of $ΔΦ$ allows for a direct and simultaneous measurement of the decay asymmetry parameters of $Σ^{+}\rightarrow p π^{0}~(α_0 = -0.998\pm0.037\pm0.009)$ and $\barΣ^{-}\rightarrow \bar{p} π^{0}~(\barα_0 = 0.990\pm0.037\pm0.011)$, the latter value being determined for the first time. The average decay asymmetry, $(α_{0} - \barα_{0})/2$, is calculated to be $-0.994\pm0.004\pm0.002$. The CP asymmetry $A_{\rm CP,Σ} = (α_0 + \barα_0)/(α_0 - \barα_0) = -0.004\pm0.037\pm0.010$ is extracted for the first time, and is found to be consistent with CP conservation.

preprint2020arXiv

3rd Place Solution to "Google Landmark Retrieval 2020"

Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the exploration of data cleaning and models with metric learning. We use a data cleaning strategy based on embedding clustering. Besides, we employ a data augmentation method called Corner-Cutmix, which improves the model's ability to recognize multi-scale and occluded landmark images. We show in detail the ablation experiments and results of our method.

preprint2020arXiv

A consensus-based global optimization method for high dimensional machine learning problems

We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl. Sci., 27(01):183--204, 2017], which is a gradient-free optimization method for general non-convex functions. We first replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted average which the individual particles tend to relax toward. For its mean-field limit--a nonlinear Fokker-Planck equation--we prove, in both time continuous and semi-discrete settings, that the convergence of the method, which is exponential in time, is guaranteed with parameter constraints {\it independent} of the dimensionality. We also conduct numerical tests to high dimensional problems to check the success rate of the method.

preprint2020arXiv

A viral propagation model with nonlinear infection rate and free boundaries

In this paper we put forward a viral propagation model with nonlinear infection rate and free boundaries and investigate the dynamical properties. This model is composed of two ordinary differential equations and one partial differential equation, in which the spatial range of the first equation is the whole space $\mathbb{R}$, and the last two equations have free boundaries. As a new mathematical model, we prove the existence, uniqueness and uniform estimates of global solution, and provide the criteria for spreading and vanishing, and long time behavior of the solution components $u,v,w$. Comparing with the corresponding ordinary differential systems, the {\it Basic Reproduction Number} ${\cal R}_0$ plays a different role. We find that when ${\cal R}_0\le 1$, the virus cannot spread successfully; when ${\cal R}_0>1$, the successful spread of virus depends on the initial value and varying parameters.

preprint2020arXiv

ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization

Due to its simplicity and outstanding ability to generalize, stochastic gradient descent (SGD) is still the most widely used optimization method despite its slow convergence. Meanwhile, adaptive methods have attracted rising attention of optimization and machine learning communities, both for the leverage of life-long information and for the profound and fundamental mathematical theory. Taking the best of both worlds is the most exciting and challenging question in the field of optimization for machine learning. Along this line, we revisited existing adaptive gradient methods from a novel perspective, refreshing understanding of second moments. Our new perspective empowers us to attach the properties of second moments to the first moment iteration, and to propose a novel first moment optimizer, \emph{Angle-Calibrated Moment method} (\method). Our theoretical results show that \method is able to achieve the same convergence rate as mainstream adaptive methods. Furthermore, extensive experiments on CV and NLP tasks demonstrate that \method has a comparable convergence to SOTA Adam-type optimizers, and gains a better generalization performance in most cases.

preprint2020arXiv

Adaptive Gradient Methods Can Be Provably Faster than SGD after Finite Epochs

Adaptive gradient methods have attracted much attention of machine learning communities due to the high efficiency. However their acceleration effect in practice, especially in neural network training, is hard to analyze, theoretically. The huge gap between theoretical convergence results and practical performances prevents further understanding of existing optimizers and the development of more advanced optimization methods. In this paper, we provide adaptive gradient methods a novel analysis with an additional mild assumption, and revise AdaGrad to \radagrad for matching a better provable convergence rate. To find an $ε$-approximate first-order stationary point in non-convex objectives, we prove random shuffling \radagrad achieves a $\tilde{O}(T^{-1/2})$ convergence rate, which is significantly improved by factors $\tilde{O}(T^{-1/4})$ and $\tilde{O}(T^{-1/6})$ compared with existing adaptive gradient methods and random shuffling SGD, respectively. To the best of our knowledge, it is the first time to demonstrate that adaptive gradient methods can deterministically be faster than SGD after finite epochs. Furthermore, we conduct comprehensive experiments to validate the additional mild assumption and the acceleration effect benefited from second moments and random shuffling.

preprint2020arXiv

Analysis of the decay $D^0\rightarrow K_{S}^{0} K^{+} K^{-}$

Using a data sample of $2.93~fb^{-1}$ of $e^+e^-$ collisions collected at $\sqrt{s}=3.773 GeV$ in the BESIII experiment, we perform an analysis of the decay $D^0\rightarrow K_{S}^{0} K^{+} K^{-}$. The Dalitz plot is analyzed using $1856\pm 45$ flavor-tagged signal decays. We find that the Dalitz plot is well described by a set of six resonances: $a_0(980)^0$, $a_0(980)^+$, $ϕ(1020)$, $a_2(1320)^+$, $a_2(1320)^-$ and $a_0(1450)^-$. Their magnitudes, phases and fit fractions are determined as well as the coupling of $a_0(980)$ to $K\bar{K}$, $g_{K\bar{K}}=3.77\pm 0.24\text{(stat.)}\pm0.35\text{(sys.)} GeV$. The branching fraction of the decay $D^0\rightarrow K_{S}^{0} K^{+} K^{-}$ is measured using $11660\pm 118$ untagged signal decays to be $(4.51\pm 0.05\text{(stat.)}\pm 0.16\text{(sys.)})10^{-3}$. Both measurements are limited by their systematic uncertainties.

preprint2020arXiv

Coherent control of dissipative dynamics in a periodically driven lattice array

We find a different mechanism for suppression of decay in an open one-dimensional lattice system, which originates from a dark Floquet state, a sink state to which the system is asymptotically driven, whose overall probability is determined only by the parameters of the periodic driving field. The zero-quasienergy of dark Floquet state has been shown to be not a real zero, but a vanishingly small negative imaginary number which will cause undesirable physical effect in long-time evolution of quantum states, which is extremely different from the conservative counterpart. Another important finding is that the value of the system's effective decay, determined by the size of the non-zero imaginary part of the dark-Floquet-state-related quasienergy, depends not on how many localized lossy sites there are but on which one of the lossy sites is nearest to the driven site. Thus, for specially designed local dissipation, by controlling the driving parameters, it is possible for us to drive the system to a dark Floquet state with a much lower level of overall probability loss as compared to the undriven case and with good stability over enough longer evolution time. These results are applicable to the multisite lattice system with an odd number of sites and may be significant for long-time control of decay in a vast family of multistate physical systems with localized dissipation.

preprint2020arXiv

Complete monotonicity-preserving numerical methods for time fractional ODEs

The time fractional ODEs are equivalent to convolutional Volterra integral equations with completely monotone kernels. We therefore introduce the concept of complete monotonicity-preserving ($\mathcal{CM}$-preserving) numerical methods for fractional ODEs, in which the discrete convolutional kernels inherit the $\mathcal{CM}$ property as the continuous equations. We prove that $\mathcal{CM}$-preserving schemes are at least $A(π/2)$ stable and can preserve the monotonicity of solutions to scalar nonlinear autonomous fractional ODEs, both of which are novel. Significantly, by improving a result of Li and Liu (Quart. Appl. Math., 76(1):189-198, 2018), we show that the $\mathcal{L}$1 scheme is $\mathcal{CM}$-preserving, so that the $\mathcal{L}$1 scheme is at least $A(π/2)$ stable, which is an improvement on stability analysis for $\mathcal{L}$1 scheme given in Jin, Lazarov and Zhou (IMA J. Numer. Analy. 36:197-221, 2016). The good signs of the coefficients for such class of schemes ensure the discrete fractional comparison principles, and allow us to establish the convergence in a unified framework when applied to time fractional sub-diffusion equations and fractional ODEs. The main tools in the analysis are a characterization of convolution inverses for completely monotone sequences and a characterization of completely monotone sequences using Pick functions due to Liu and Pego (Trans. Amer. Math. Soc. 368(12):8499-8518, 2016). The results for fractional ODEs are extended to $\mathcal{CM}$-preserving numerical methods for Volterra integral equations with general completely monotone kernels. Numerical examples are presented to illustrate the main theoretical results.

preprint2020arXiv

Convergence of Random Batch Method for interacting particles with disparate species and weights

We consider in this work the convergence of Random Batch Method proposed in our previous work [Jin et al., J. Comput. Phys., 400(1), 2020] for interacting particles to the case of disparate species and weights. We show that the strong error is of $O(\sqrtτ)$ while the weak error is of $O(τ)$ where $τ$ is the time step between two random divisions of batches. Both types of convergence are uniform in $N$, the number of particles. The proof of strong convergence follows closely the proof in [Jin et al., J. Comput. Phys., 400(1), 2020] for indistinguishable particles, but there are still some differences: since there is no exchangeability now, we have to use a certain weighted average of the errors; some refined auxiliary lemmas have to be proved compared with our previous work. To show that the weak convergence of empirical measure is uniform in $N$, certain sharp estimates for the derivatives of the backward equations have been used. The weak convergence analysis is also illustrating for the convergence of Random Batch Method for $N$-body Liouville equations.

preprint2020arXiv

Cross section measurement of $e^+e^- \rightarrow η'J/ψ$ from $\sqrt{s} = 4.178$ to $4.600$ GeV

The cross section of the process $e^+e^- \rightarrow η'J/ψ$ is measured at center-of-mass energies from $\sqrt{s} =$ 4.178 to 4.600 GeV using data samples corresponding to a total integrated luminosity of 11 fb$^{-1}$ collected with the BESIII detector operating at the BEPCII storage ring. The dependence of the cross section on $\sqrt{s}$ shows an enhancement around $4.2$ GeV. While the shape of the cross section cannot be fully explained with a single $ψ(4160)$ or $ψ(4260)$ state, a coherent sum of the two states does provide a reasonable description of the data.

preprint2020arXiv

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation.

preprint2020arXiv

Determination of strong-phase parameters in $D\rightarrow K^0_{S,L}π^+π^-$

We report the most precise measurements to date of the strong-phase parameters between $D^0$ and $\bar{D}^0$ decays to $K^0_{S,L}π^+π^-$ using a sample of 2.93 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. Our results provide the key inputs for a binned model-independent determination of the Cabibbo-Kobayashi-Maskawa angle $γ/ϕ_3$ with $B$ decays. Using our results, the decay model sensitivity to the $γ/ϕ_3$ measurement is expected to be between 0.7$^{\circ}$ and 1.2$^{\circ}$, approximately a factor of three smaller than that achievable with previous measurements. The improved precision of this work ensures that measurements of $γ/ϕ_3$ will not be limited by knowledge of strong phases for the next decade. Furthermore, our results provide critical input for other flavor-physics investigations, including charm mixing, other measurements of $CP$ violation, and the measurement of strong-phase parameters for other $D$-decay modes.

preprint2020arXiv

Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data. In this paper, we generalize the practice and introduce DEM-VAE, a class of models for text generation using VAEs with a mixture distribution of exponential family. Unfortunately, a standard variational training algorithm fails due to the mode-collapse problem. We theoretically identify the root cause of the problem and propose an effective algorithm to train DEM-VAE. Our method penalizes the training with an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a meaningful space, and it outperforms strong baselines in text generation benchmarks. The code is available at https://github.com/wenxianxian/demvae.

preprint2020arXiv

Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods

It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimation problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach. Our code is available at this url.

preprint2020arXiv

End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds

In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-crafted viewpoints for rendering in a preprocessing stage, which is detached from the subsequent descriptor learning stage. In our framework, we integrate the multi-view rendering into neural networks by using a differentiable renderer, which allows the viewpoints to be optimizable parameters for capturing more informative local context of interest points. To obtain discriminative descriptors, we also design a soft-view pooling module to attentively fuse convolutional features across views. Extensive experiments on existing 3D registration benchmarks show that our method outperforms existing local descriptors both quantitatively and qualitatively.

preprint2020arXiv

Energy and quadratic invariants preserving methods for Hamiltonian systems with holonomic constraints

We introduce a new class of parametricization structure-preserving partitioned Runge-Kutta ($α$-PRK) methods for Hamiltonian systems with holonomic constraints. When the scalar parameter $α=0$, the methods are reduced to the usual symplectic PRK methods like Shake-Rattle method or PRK schemes based on Lobatto IIIA-IIIB pairs, which can preserve all the quadratic invariants and the constraints. When $α\neq 0$, the methods are also shown to preserve all the quadratic invariants and the constraints manifold exactly. At the same time, for any given consistent initial values $(p_{0}, q_0)$ and small step size $h>0$, it is proved that there exists $α^*=α(h, p_0, q_0)$ such that the Hamiltonian energy can also be exactly preserved at each step. We provide a new variational formulation for symplectic PRK schemes and use it to prove that the parametrized PRK methods can preserve the quadratic invariants for Hamiltonian systems subject to holonomic constraints. The parametric $α$-PRK methods are shown to have the same convergence rate as the usual PRK methods and perform very well in various numerical experiments.

preprint2020arXiv

Erratum to "Measurement of the $e^+e^-\toπ^+π^-$ cross section between 600 and 900 MeV using initial state radiation"

In Phys. Lett. B 753, 629-638 (2016) [arXiv:1507.08188] the BESIII collaboration published a cross section measurement of the process $e^+e^-\to π^+ π^-$ in the energy range between 600 and 900 MeV. In this erratum we report a corrected evaluation of the statistical errors in terms of a fully propagated covariance matrix. The correction also yields a reduced statistical uncertainty for the hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon, which now reads as $a_μ^{ππ\mathrm{, LO}}(600 - 900\,\mathrm{MeV}) = (368.2 \pm 1.5_{\rm stat} \pm 3.3_{\rm syst})\times 10^{-10}$. The central values of the cross section measurement and of $a_μ^{ππ\mathrm{, LO}}$, as well as the systematic uncertainties remain unchanged.

preprint2020arXiv

First Measurements of $χ_{cJ}\rightarrow Σ^{-} \barΣ^{+} (J = 0, 1, 2)$ Decays

We measured the branching fractions of the decays $χ_{cJ}\toΣ^{-}\barΣ^{+}$ for the first time using the final states $n\bar{n}π^{+}π^{-}$. The data sample exploited here is $448.1\times10^{6}$ $ψ(3686)$ events collected with BESIII. We find $\mathcal{B}(χ_{cJ}\rightarrowΣ^{-}\barΣ^{+}) = (51.3\pm2.4\pm4.1)\times10^{-5},\, (5.7\pm1.4\pm0.6)\times10^{-5},\, \rm{and}~ (4.4\pm1.7\pm0.5)\times10^{-5}$, for $J=0,1,2$, respectively, where the first uncertainties are statistical and the second systematic.

preprint2020arXiv

FoveaBox: Beyond Anchor-based Object Detector

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox .

preprint2020arXiv

Future Physics Programme of BESIII

There has recently been a dramatic renewal of interest in the subjects of hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like $XYZ$ states at BESIII and $B$ factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related $X(1835)$ meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII over the remaining lifetime of BEPCII operation. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.

preprint2020arXiv

Generating Fluent Adversarial Examples for Natural Languages

Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients. Secondly, the fluency of the generated examples cannot be guaranteed. In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients. Experiments on IMDB and SNLI show that our proposed MHA outperforms the baseline model on attacking capability. Adversarial training with MAH also leads to better robustness and performance.

preprint2020arXiv

Generative Adversarial Network-Based Sinogram Super-Resolution for Computed Tomography Imaging

Compared with the conventional 1*1 acquisition mode of projection in computed tomography (CT) image reconstruction, the 2*2 acquisition mode improves the collection efficiency of the projection and reduces the X-ray exposure time. However, the collected projection based on the 2*2 acquisition mode has low resolution (LR) and the reconstructed image quality is poor, thus limiting the use of this mode in CT imaging systems. In this study, a novel sinogram-super-resolution generative adversarial network (SSR-GAN) model is proposed to obtain high-resolution (HR) sinograms from LR sinograms, thereby improving the reconstruction image quality under the 2*2 acquisition mode. The proposed generator is based on the residual network for LR sinogram feature extraction and super-resolution (SR) sinogram generation. A relativistic discriminator is designed to render the network capable of obtaining more realistic SR sinograms. Moreover, we combine the cycle consistency loss, sinogram domain loss, and reconstruction image domain loss in the total loss function to supervise SR sinogram generation. Then, a trained model can be obtained by inputting the paired LR/HR sinograms into the network. Finally, the classic FBP reconstruction algorithm is used for CT image reconstruction based on the generated SR sinogram. The qualitative and quantitative results of evaluations on digital and real data illustrate that the proposed model not only obtains clean SR sinograms from noisy LR sinograms but also outperforms its counterparts.

preprint2020arXiv

Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation

Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the number of training samples for Maximum Likelihood Estimation is usually limited and the input data distributions are different at training and inference stages. Many method shave been proposed to solve the above problem (Yu et al., 2017; Lu et al., 2018), which relies on sampling from the non-stationary model distribution and suffers from high variance or biased estimations. In this paper, we proposeψ-MLE, a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation. We derive our algorithm from a new perspective of self-augmentation and introduce bias correction with density ratio estimation. Extensive experimental results on synthetic data and real-world text generation tasks demonstrate that our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.

preprint2020arXiv

In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes

Various Seq2Seq learning models designed for machine translation were applied for abstractive summarization task recently. Despite these models provide high ROUGE scores, they are limited to generate comprehensive summaries with a high level of abstraction due to its degenerated attention distribution. We introduce Diverse Convolutional Seq2Seq Model(DivCNN Seq2Seq) using Determinantal Point Processes methods(Micro DPPs and Macro DPPs) to produce attention distribution considering both quality and diversity. Without breaking the end to end architecture, DivCNN Seq2Seq achieves a higher level of comprehensiveness compared to vanilla models and strong baselines. All the reproducible codes and datasets are available online.

preprint2020arXiv

Inclusive charged and neutral particle multiplicity distributions in $χ_{cJ}$ and $J/ψ$ decays

Using a sample of 106 million $ψ(3686)$ decays, $ψ(3686) \to γχ_{cJ} (J = 0, 1, 2)$ and $ψ(3686) \to γχ_{cJ}, χ_{cJ} \to γJ/ψ$ $(J = 1, 2)$ events are utilized to study inclusive $χ_{cJ} \to$ anything, $χ_{cJ} \to$ hadrons, and $J/ψ\to$ anything distributions, including distributions of the number of charged tracks, electromagnetic calorimeter showers, and $π^0$s, and to compare them with distributions obtained from the BESIII Monte Carlo simulation. Information from each Monte Carlo simulated decay event is used to construct matrices connecting the detected distributions to the input predetection "produced" distributions. Assuming these matrices also apply to data, they are used to predict the analogous produced distributions of the decay events. Using these, the charged particle multiplicities are compared with results from MARK I. Further, comparison of the distributions of the number of photons in data with those in Monte Carlo simulation indicates that G-parity conservation should be taken into consideration in the simulation.

preprint2020arXiv

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars. The framework incorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods. To fully utilize the inherent spatial relationship between LA and LA scars, we further propose a shape attention (SA) mechanism through an explicit surface projection to build an end-to-end-trainable model. Specifically, the SA scheme is embedded into a two-task network to perform the joint LA segmentation and scar quantification. Moreover, the proposed method can alleviate the severe class-imbalance problem when detecting small and discrete targets like scars. We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge. For LA segmentation, the proposed method reduced the mean Hausdorff distance from 36.4 mm to 20.0 mm compared to the 3D basic U-Net using the binary cross-entropy loss. For scar quantification, the method was compared with the results or algorithms reported in the literature and demonstrated better performance.

preprint2020arXiv

Measurement of {\boldmath $J/ψ\toΞ(1530)^{-}\barΞ^{+}$} and evidence for the radiative decay {\boldmath $Ξ(1530)^{-}\toγΞ^-$}

The SU(3)-flavor violating decay $J/ψ\toΞ(1530)^{-}\barΞ^{+}+c.c.$ is studied using $(1310.6\pm7.0)\times 10^{6} ~J/ψ$ events collected with the BESIII detector at BEPCII and the branching fraction is measured to be ${\cal{B}}(J/ψ\toΞ(1530)^{-}\barΞ^{+}+c.c.)=(3.17\pm0.02_{\rm stat.}\pm0.08_{\rm syst.})\times10^{-4}$. This is consistent with previous measurements with an improved precision. The angular parameter for this decay is measured for the first time and is found to be $α=-0.21\pm0.04_{\rm stat.}\pm0.06_{\rm syst.}$. In addition, we report evidence for the radiative decay $Ξ(1530)^{-}\toγΞ^- $ with a significance of 3.9$σ$, including the systematic uncertainties. The 90\% confidence level upper limit on the branching fraction is determined to be $\mathcal{B}(Ξ(1530)^{-}\toγΞ^- )\leq3.7$\%.

preprint2020arXiv

Measurement of proton electromagnetic form factors in $e^+e^- \to p\bar{p}$ in the energy region 2.00-3.08 GeV

The process of $e^+e^- \rightarrow p\bar{p}$ is studied at 22 center-of-mass energy points ($\sqrt{s}$) from 2.00 to 3.08 GeV, exploiting 688.5~pb$^{-1}$ of data collected with the BESIII detector operating at the BEPCII collider. The Born cross section~($σ_{p\bar{p}}$) of $e^+e^- \rightarrow p\bar{p}$ is measured with the energy-scan technique and it is found to be consistent with previously published data, but with much improved accuracy. In addition, the electromagnetic form-factor ratio ($|G_{E}/G_{M}|$) and the value of the effective ($|G_{\rm{eff}}|$), electric ($|G_E|$) and magnetic ($|G_M|$) form factors are measured by studying the helicity angle of the proton at 16 center-of-mass energy points. $|G_{E}/G_{M}|$ and $|G_M|$ are determined with high accuracy, providing uncertainties comparable to data in the space-like region, and $|G_E|$ is measured for the first time. We reach unprecedented accuracy, and precision results in the time-like region provide information to improve our understanding of the proton inner structure and to test theoretical models which depend on non-perturbative Quantum Chromodynamics.

preprint2020arXiv

Measurement of Singly Cabibbo-Suppressed Decays $D \to ωππ$

Using 2.93 fb$^{-1}$ of $e^{+}e^{-}$ collision data taken at a center-of-mass energy of 3.773 GeV by the BESIII detector at the BEPCII, we measure the branching fractions of the singly Cabibbo-suppressed decays $D \to ωππ$ to be $\mathcal{B}(D^0 \to ωπ^+π^-) = (1.33 \pm 0.16 \pm 0.12)\times 10^{-3}$ and $\mathcal{B}(D^+ \to ωπ^+π^0) =(3.87 \pm 0.83 \pm 0.25)\times 10^{-3}$, where the first uncertainties are statistical and the second ones systematic. The statistical significances are $12.9σ$ and $7.7 σ$, respectively. The precision of $\mathcal{B}(D^0 \to ωπ^+π^-)$ is improved by a factor of 2.1 over the CLEO measurement, and $\mathcal{B}(D^+ \to ωπ^+π^0)$ is measured for the first time. No significant signal of $\mathcal{B}(D^0 \to ωπ^0π^0)$ is observed, and the upper limit on the branching fraction is $\mathcal{B}(D^0 \to ωπ^0π^0) < 1.10 \times 10^{-3}$ at the $90\%$ confidence level. The branching fractions of $D\to ηππ$ are also measured and consistent with existing results.

preprint2020arXiv

Measurement of the Born Cross Sections for $e^+e^-\to D_s^+ D_{s1}(2460)^- +c.c.$ and $e^+e^-\to D_s^{\ast +} D_{s1}(2460)^- +c.c.$

The processes $e^+e^-\to D_s^+ D_{s1}(2460)^- +c.c.$ and $e^+e^-\to D_s^{\ast +} D_{s1}(2460)^- +c.c.$ are studied for the first time using data samples collected with the BESIII detector at the BEPCII collider. The Born cross sections of $e^+e^-\to D_s^+ D_{s1}(2460)^- +c.c.$ at nine center-of-mass energies between 4.467\,GeV and 4.600\,GeV and those of $e^+e^-\to D_s^{\ast +} D_{s1}(2460)^- +c.c.$ at ${\sqrt s}=$ 4.590\,GeV and 4.600\,GeV are measured. No obvious charmonium or charmonium-like structure is seen in the measured cross sections.

preprint2020arXiv

Measurement of the cross section for $e^{+}e^{-}\rightarrowΞ^{-}\barΞ^{+}$ and observation of an excited $Ξ$ baryon

Using a total of 11.0 fb$^{-1}$ of $e^{+}e^{-}$ collision data with center-of-mass energies between 4.009 GeV and 4.6 GeV and collected with the BESIII detector at BEPCII, we measure fifteen exclusive cross sections and effective form factors for the process $e^{+}e^{-}\rightarrowΞ^{-}\barΞ^{+}$ by means of a single baryon-tag method. After performing a fit to the dressed cross section of $e^{+}e^{-}\rightarrowΞ^{-}\barΞ^{+}$, no significant $ψ(4230)$ or $ψ(4260)$ resonance is observed in the $Ξ^{-}\barΞ^{+}$ final states, and upper limits at the 90\% confidence level on $Γ_{ee}\mathcal{B}$ for the processes $ψ(4230)$/$ψ(4260)\rightarrowΞ^{-}\barΞ^{+}$ are determined. In addition, an excited $Ξ$ baryon at 1820 MeV/$c^{2}$ is observed with a statistical significance of 6.2 $\sim$ 6.5$σ$ by including the systematic uncertainty, and the mass and width are measured to be $M = (1825.5 \pm 4.7 \pm 4.7)$~MeV/$c^{2}$ and $Γ= (17.0 \pm 15.0 \pm 7.9)$~MeV, which confirms the existence of the $J^{P}=\frac{3}{2}^{-}$ state $Ξ(1820)$.

preprint2020arXiv

Model-independent determination of the relative strong-phase difference between $D^0$ and $\bar{D}^0\rightarrow K^0_{S,L}π^+π^-$ and its impact on the measurement of the CKM angle $γ/ϕ_3$

Crucial inputs for a variety of $CP$-violation studies can be determined through the analysis of pairs of quantum-entangled neutral $D$ mesons, which are produced in the decay of the $ψ(3770)$ resonance. The relative strong-phase parameters between $D^0$ and $\bar{D}^0$ in the decays $D^0\rightarrow K^0_{S,L}π^+π^-$ are studied using 2.93~${\rm fb}^{-1}$ of $e^+e^-$ annihilation data delivered by the BEPCII collider and collected by the BESIII detector at a center-of-mass energy of 3.773 GeV. Results are presented in regions of the phase space of the decay. These are the most precise measurements to date of the strong-phase parameters in $D \to K_{S,L}^0π^+π^-$ decays. Using these parameters, the associated uncertainty on the Cabibbo-Kobayashi-Maskawa angle $γ/ϕ_3$ is expected to be between $0.7^\circ$ and $1.2^\circ$, for an analysis using the decay $B^{\pm}\rightarrow DK^{\pm}$, $D\rightarrow K^0_Sπ^+π^-$, where $D$ represents a superposition of $D^0$ and $\bar{D^0}$ states. This is a factor of three smaller than that achievable with previous measurements. Furthermore, these results provide valuable input for charm-mixing studies, other measurements of $CP$ violation, and the measurement of strong-phase parameters for other $D$-decay modes.

preprint2020arXiv

Nucleons pair shell model in M-scheme

The nucleon pair shell model (NPSM) is casted into the so-called M-scheme for the cases with isospin symmetry and without isospin symmetry. The odd system and even system are treated on the same foot. The uncoupled commutators for nucleon-pairs, which are suitable for M-scheme, are given. Explicit formula of matrix elements in M-scheme for overlap, one-body operators, two-body operators are obtained. It is found that the $cpu$ time used in calculating the matrix elements in M-scheme is much shorter than that in the J-scheme of NPSM.

preprint2020arXiv

Observation of a cross-section enhancement near mass threshold in $e^{+}e^{-}\rightarrowΛ\barΛ$

The process $e^{+}e^{-}\rightarrowΛ\barΛ$ is studied using data samples at $\sqrt{s}=2.2324$, 2.400, 2.800 and 3.080 GeV collected with the BESIII detector operating at the BEPCII collider. The Born cross section is measured at $\sqrt{s}$=2.2324 GeV, which is 1.0 MeV above the $Λ\barΛ$ mass threshold, to be $305\pm45^{+66}_{-36}$ pb, where the first uncertainty is statistical and the second systematic. The substantial cross section near threshold is significantly larger than that expected from theory, which predicts the cross section to vanish at threshold. The Born cross sections at $\sqrt{s}$=2.400, 2.800 and 3.080 GeV are measured and found to be consistent with previous experimental results, but with improved precision. Finally, the corresponding effective electromagnetic form factors of $Λ$ are deduced.

preprint2020arXiv

Observation of a resonant structure in $e^{+}e^{-} \to ωη$ and another in $e^{+}e^{-} \to ωπ^{0}$ at center-of-mass energies between 2.00 and 3.08 GeV

Born cross sections for the processes $e^+e^- \to ωη$ and $e^+e^- \to ωπ^{0}$ have been determined for center-of-mass energies between 2.00 and 3.08 GeV with the BESIII detector at the BEPCII collider. The results obtained in this work are consistent with previous measurements but with improved precision. Two resonant structures are observed. In the $e^{+}e^{-} \to ωη$ cross sections, a resonance with a mass of $(2179 \pm 21 \pm 3)\text{MeV}/c^2$ and a width of $(89 \pm 28 \pm 5)\text{MeV}$ is observed with a significance of 6.1$σ$. Its properties are consistent with the $ϕ(2170)$. In the $e^{+}e^{-} \toωπ^{0}$ cross sections, a resonance denoted $Y(2040)$ is observed with a significance of more than 10$σ$. Its mass and width are determined to be $(2034 \pm 13 \pm 9)\text{MeV}/c^2$ and $(234 \pm 30 \pm 25)\text{MeV}$, respectively, where the first uncertainties are statistical and the second ones are systematic.

preprint2020arXiv

Observation of a structure in $e^+e^- \to ϕη^{\prime}$ at $\sqrt{s}$ from 2.05 to 3.08 GeV

The process $e^{+}e^{-} \to ϕη^{\prime}$ has been studied for the first time in detail using data sample collected with the BESIII detector at the BEPCII collider at center of mass energies from 2.05 to 3.08 GeV. A resonance with quantum numbers $J^{PC}=1^{--}$ is observed with mass $M$ = (2177.5 $\pm$ 4.8 (stat) $\pm$ 19.5 (syst)) MeV/${ \it{c}^{\mathrm{2}}}$ and width $Γ$ = (149.0 $\pm$ 15.6 (stat) $\pm$ 8.9 (syst)) MeV with a statistical significance larger than 10$σ$. The observed structure could be identified with the $ϕ(2170)$, then the ratio of partial width between the $ϕη^{\prime}$ by BESIII and $ϕη$ by BABAR is ($\mathcal{B}^{R}_{ϕη}Γ^{R}_{ee})/{(\mathcal{B}^{R}_{ϕη^{\prime}}Γ^{R}_{ee})}$ = 0.23 $\pm$ 0.10 (stat) $\pm$ 0.18 (syst), which is smaller than the prediction of the $s\bar{s}g$ hybrid models by several orders of magnitude.

preprint2020arXiv

Observation of the $Y(4220)$ and $Y(4360)$ in the process $e^{+}e^{-} \to ηJ/ψ$

The cross sections of the process $e^{+}e^{-} \to ηJ/ψ$ at center-of-mass energies ($\sqrt{s}$) between 3.81 and 4.60 GeV are measured with high precision by using data samples collected with the BESIII detector operating at the BEPCII storage ring. Three structures are observed by analyzing the lineshape of the measured cross sections, and a maximum-likelihood fit including three resonances is performed by assuming the lowest lying structure is the $ψ(4040)$. For the other resonances, we obtain masses of $(4218.7 \pm 4.0 \pm 2.5)$ and $(4380.4 \pm 14.2 \pm 1.8)$ MeV/c$^{2}$ with corresponding widths of $(82.5 \pm 5.9 \pm 0.5)$ and $(147.0 \pm 63.0 \pm 25.8)$ MeV, respectively, where the first uncertainties are statistical and the second ones systematic. The measured resonant parameters are consistent with those of the $Y(4220)$ and $Y(4360)$ from pr evious measurements of different final states. For the first time, we observe the decays of the $Y(4220)$ and $Y(4360)$ into $ηJ/ψ$ final states.

preprint2020arXiv

Partial wave analysis of $ψ(3686)\rightarrow K^{+}K^{-}η$

Using a sample of $(448.1\pm2.9)\times10^6$ $ψ(3686)$ events collected with the BESIII detector, we perform the first partial wave analysis of $ψ(3686)\rightarrow K^+K^-η$. In addition to the well established states, $ϕ(1020)$, $ϕ(1680)$, and $K_3^*(1780)$, contributions from $X(1750)$, $ρ(2150)$, $ρ_3(2250)$, and $K^*_2(1980)$ are also observed. The $X(1750)$ state is determined to be a $1^{--}$ resonance. The simultaneous observation of the $ϕ(1680)$ and $X(1750)$ indicates that the $X(1750)$, with previous observations in photoproduction, is distinct from the $ϕ(1680)$. The masses, widths, branching fractions of $ψ(3686)\rightarrow K^+K^-η$ and the intermediate resonances are also measured.

preprint2020arXiv

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations. In this work, we present a random style transfer network to tackle the domain generalization problem for multi-vendor and center cardiac image segmentation. Style transfer is used to generate training data with a wider distribution/ heterogeneity, namely domain augmentation. As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains. The model can be trained in a semi-supervised manner by simultaneously optimizing a supervised segmentation and an unsupervised style translation objective. Besides, the framework incorporates the spatial information and shape prior of the target by introducing two regularization terms. We evaluated the proposed framework on 40 subjects from the M\&Ms challenge2020, and obtained promising performance in the segmentation for data from unknown vendors and centers.

preprint2020arXiv

Search for baryon and lepton number violating decays $D^+\to\barΛ(\barΣ^0)e^+$ and $D^+\toΛ(Σ^0)e^+$

Using a 2.93 fb$^{-1}$ data sample of electron-positron collisions taken with the BESIII detector at a center-of-mass energy of 3.773 GeV, which corresponds to $(8296\pm31\pm64)\times10^3 D^+D^-$ pairs, we search for the baryon and lepton number violating decays $D^+\to\barΛ(\barΣ^0)e^+$ and $D^+\toΛ(Σ^0)e^+$. No obvious signals are found with the current statistics and upper limits on the branching fractions of these four decays are set at the level of $10^{-6}$ at 90% confidence level.

preprint2020arXiv

Search for New Hadronic Decays of $h_c$ and Observation of $h_c\rightarrow K^{+}K^{-}π^{+}π^{-}π^{0}$

Ten hadronic final states of the $h_c$ decays are investigated via the process $ψ(3686)\rightarrow π^0 h_c$, using a data sample of $(448.1 \pm 2.9) \times 10^6$ $ψ(3686)$ events collected with the BESIII detector. The decay channel $h_c\rightarrow K^{+}K^{-}π^{+}π^{-}π^{0}$ is observed for the first time with a significance of $6.0 σ$. The corresponding branching fraction is determined to be $\mathcal{B}(h_c\rightarrow K^{+}K^{-}π^{+}π^{-}π^{0}) =(3.3 \pm 0.6 \pm 0.6)\times 10^{-3}$ (the first uncertainty is statistical and the second systematical). Evidence for the decays $h_c\rightarrow π^{+} π^{-} π^{0} η$ and $h_c\rightarrow K^{0}_{S}K^{\pm}π^{\mp}π^{+}π^{-}$ is found with a significance of $3.6 σ$ and $3.8 σ$, respectively. The corresponding branching fractions (and upper limits) are obtained to be $\mathcal{B}(h_c\rightarrow π^{+} π^{-} π^{0} η) =(7.2 \pm 1.8 \pm 1.3)\times 10^{-3}$ $(< 1.8 \times 10^{-2})$ and $\mathcal{B}(h_c\rightarrow K^{0}_{S}K^{\pm}π^{\mp}π^{+}π^{-}) =(2.8 \pm 0.9 \pm 0.5)\times 10^{-3}$ $(<4.7\times 10^{-3})$. Upper limits on the branching fractions for the final states $h_c \rightarrow K^{+}K^{-}π^{0}$, $K^{+}K^{-}η$, $K^{+}K^{-}π^{+}π^{-}η$, $2(K^{+}K^{-})π^{0}$, $K^{+}K^{-}π^{0}η$, $K^{0}_{S}K^{\pm}π^{\mp}$, and $p\bar{p}π^{0}π^{0}$ are determined at a confidence level of 90\%.

preprint2020arXiv

Search for the decay $J/ψ\toγ+ \rm {invisible}$

We search for $J/ψ$ radiative decays into a weakly interacting neutral particle, namely an invisible particle, using the $J/ψ$ produced through the process $ψ(3686)\toπ^+π^-J/ψ$ in a data sample of $(448.1\pm2.9)\times 10^6$ $ψ(3686)$ decays collected by the BESIII detector at BEPCII. No significant signal is observed. Using a modified frequentist method, upper limits on the branching fractions are set under different assumptions of invisible particle masses up to 1.2 $\mathrm{\ Ge\kern -0.1em V}/c^2$. The upper limit corresponding to an invisible particle with zero mass is 7.0$\times 10^{-7}$ at the 90\% confidence level.

preprint2020arXiv

Search for the semileptonic decay $D^{0(+)}\to b_1(1235)^{-(0)} e^+ν_e$

Using $2.93~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at a center-of-mass energy $\sqrt{s}=3.773$ GeV with the BESIII detector operating at the BEPCII collider, we search for the semileptonic $D^{0(+)}$ decays into a $b_1(1235)^{-(0)}$ axial-vector meson for the first time. No significant signal is observed for either charge combination. The upper limits on the product branching fractions are ${\mathcal B}_{D^0\to b_1(1235)^- e^+ν_e}\cdot {\mathcal B}_{b_1(1235)^-\to ωπ^-}<1.12\times 10^{-4}$ and ${\mathcal B}_{D^+\to b_1(1235)^0 e^+ν_e}\cdot {\mathcal B}_{b_1(1235)^0\to ωπ^0}<1.75\times 10^{-4}$ at the 90\% confidence level.

preprint2020arXiv

SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence

In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multi-scale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets.

preprint2020arXiv

SOLO: Segmenting Objects by Locations

We present a new, embarrassingly simple approach to instance segmentation in images. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that have made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the 'detect-thensegment' strategy as used by Mask R-CNN, or predict category masks first then use clustering techniques to group pixels into individual instances. We view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size, thus nicely converting instance mask segmentation into a classification-solvable problem. Now instance segmentation is decomposed into two classification tasks. We demonstrate a much simpler and flexible instance segmentation framework with strong performance, achieving on par accuracy with Mask R-CNN and outperforming recent singleshot instance segmenters in accuracy. We hope that this very simple and strong framework can serve as a baseline for many instance-level recognition tasks besides instance segmentation.

preprint2020arXiv

SPAN: A Stochastic Projected Approximate Newton Method

Second-order optimization methods have desirable convergence properties. However, the exact Newton method requires expensive computation for the Hessian and its inverse. In this paper, we propose SPAN, a novel approximate and fast Newton method. SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products. Our experiments on multiple benchmark datasets demonstrate that SPAN outperforms existing first-order and second-order optimization methods in terms of the convergence wall-clock time. Furthermore, we provide a theoretical analysis of the per-iteration complexity, the approximation error, and the convergence rate. Both the theoretical analysis and experimental results show that our proposed method achieves a better trade-off between the convergence rate and the per-iteration efficiency.

preprint2020arXiv

Study of $e^{+}e^{-} \to D^{+} D^{-} π^{+} π^{-} $ at center-of-mass energies from 4.36 to 4.60 GeV

We report a study of the $e^{+}e^{-} \to D^{+} D^{-} π^{+} π^{-}$ process using $e^{+}e^{-}$ collision data samples with an integrated luminosity of $2.5\,\rm{fb}^{-1}$ at center-of-mass energies from 4.36 to $4.60 \rm{GeV}$, collected with the BESIII detector at the BEPCII storage ring. The $D_{1}(2420)^+$ is observed in the $D^{+} π^{+} π^{-}$ mass spectrum. The mass and width of the $D_{1}(2420)^+$ are measured to be $(2427.2\pm 1.0_{\rm stat.}\pm 1.2_{\rm syst.}) \rm{MeV}/c^2$ and $(23.2\pm 2.3_{\rm stat.} \pm2.3_{\rm syst.}) \rm{MeV}$, respectively. The first errors are statistical and the second ones are systematic. In addition, the Born cross sections of the $e^{+}e^{-} \to D_{1}(2420)^+D^- + c.c. \to D^{+} D^{-} π^{+} π^{-}$ and $e^{+}e^{-} \to ψ(3770) π^{+} π^{-} \to D^{+} D^{-} π^{+} π^{-}$ processes are measured as a function of the center-of-mass energy.

preprint2020arXiv

Study of BESIII Trigger Efficiencies with the 2018 $J/ψ$ Data

Using a dedicated data sample taken in 2018 on the $J/ψ$ peak, we perform a detailed study of the trigger efficiencies of the BESIII detector. The efficiencies are determined from three representative physics processes, namely Bhabha-scattering, dimuon production and generic hadronic events with charged particles. The combined efficiency of all active triggers approaches $100\%$ in most cases with uncertainties small enough as not to affect most physics analyses.

preprint2020arXiv

Study of open-charm decays and radiative transitions of the X(3872)

The processes $X(3872)\to D^{*0}\bar{D^{0}}+c.c.,~γJ/ψ,~γψ(2S),$ and $γD^{+}D^{-}$ are searched for in a $9.0~\rm fb^{-1}$ data sample collected at center-of-mass energies between $4.178$ and $4.278$ GeV with the BESIII detector. We observe $X(3872)\to D^{*0}\bar{D^{0}}+c.c.$ and find evidence for $X(3872)\toγJ/ψ$ with statistical significances of $7.4σ$ and $3.5σ$, respectively. No evident signals for $X(3872)\toγψ(2S)$ and $γD^{+}D^{-}$ are found, and upper limit on the relative branching ratio $R_{γψ} \equiv\frac{\mathcal{B}(X(3872)\toγψ(2S))}{\mathcal{B}(X(3872)\toγJ/ψ)}<0.59$ is set at 90$\%$ confidence level. Measurements of branching ratios relative to decay $X(3872)\toπ^+π^- J/ψ$ are also reported for decays $X(3872)\to D^{*0}\bar{D^{0}}+c.c., ~γψ(2S),~γJ/ψ$, $γD^{+}D^{-}$, as well as the non-$D^{*0}\bar{D}^{0}$ three-body decays $π^0 D^{0}\bar{D}^{0}$ and $γD^{0}\bar{D}^{0}$.

preprint2020arXiv

The reduction of the number of incoherent Kraus operations for qutrit systems

Quantum coherence is a fundamental property that can emerge within any quantum system. Incoherent operations, defined in terms of the Kraus decomposition, take an important role in state transformation. The maximum number of incoherent Kraus operators has been presented in [A. Streltsov, S. Rana, P. Boes, J. Eisert, Phys. Rev. Lett. 119. 140402 (2017)]. In this work, we show that the number of incoherent Kraus operators for a single qubit can be reduced from 5 to 4 by constructing a proper unitary matrix. For qutrit systems we further obtain 32 incoherent Kraus operators, while the upper bound in the research of Sterltsov gives 39 Kraus operators. Besides, we reduce the number of strictly incoherent Kraus operators from more than 15 to 13. And we consider the state transformation problem for these two types of operations in single qutrit systems.

preprint2020arXiv

Variational Quantum Algorithms for Dimensionality Reduction and Classification

In this work, we present a quantum neighborhood preserving embedding and a quantum local discriminant embedding for dimensionality reduction and classification. We demonstrate that these two algorithms have an exponential speedup over their respectively classical counterparts. Along the way, we propose a variational quantum generalized eigenvalue solver that finds the generalized eigenvalues and eigenstates of a matrix pencil $(\mathcal{G},\mathcal{S})$. As a proof-of-principle, we implement our algorithm to solve $2^5\times2^5$ generalized eigenvalue problems. Finally, our results offer two optional outputs with quantum or classical form, which can be directly applied in another quantum or classical machine learning process.

preprint2020arXiv

Variational Template Machine for Data-to-Text Generation

How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohibitive since it often requires a large paired <table, description> corpus, which is seldom available. This paper explores the problem of automatically learning reusable "templates" from paired and non-paired data. We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables. Our contributions include: a) we carefully devise a specific model architecture and losses to explicitly disentangle text template and semantic content information, in the latent spaces, and b)we utilize both small parallel data and large raw text without aligned tables to enrich the template learning. Experiments on datasets from a variety of different domains show that VTM is able to generate more diversely while keeping a good fluency and quality.

preprint2020arXiv

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context. Extensive experiments on the VATEX dataset show that, first, the unified multilingual model can not only produce both English and Chinese descriptions for a video more efficiently, but also offer improved performance over the monolingual models. Furthermore, we demonstrate that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. In the end, we discuss the potentials of using VATEX for other video-and-language research.

preprint2020arXiv

Xiaomingbot: A Multilingual Robot News Reporter

This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation. Its system summarizes Chinese news that it automatically generates from data tables. Next, it translates the summary or the full article into multiple languages, and reads the multilingual rendition through synthesized speech. Notably, Xiaomingbot utilizes a voice cloning technology to synthesize the speech trained from a real person's voice data in one input language. The proposed system enjoys several merits: it has an animated avatar, and is able to generate and read multilingual news. Since it was put into practice, Xiaomingbot has written over 600,000 articles, and gained over 150,000 followers on social media platforms.

preprint2020arXiv

XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference

Automatic identification of mentioned entities in social media posts facilitates quick digestion of trending topics and popular opinions. Nonetheless, this remains a challenging task due to limited context and diverse name variations. In this paper, we study the problem of entity linking for Chinese news comments given mentions' spans. We hypothesize that comments often refer to entities in the corresponding news article, as well as topics involving the entities. We therefore propose a novel model, XREF, that leverages attention mechanisms to (1) pinpoint relevant context within comments, and (2) detect supporting entities from the news article. To improve training, we make two contributions: (a) we propose a supervised attention loss in addition to the standard cross entropy, and (b) we develop a weakly supervised training scheme to utilize the large-scale unlabeled corpus. Two new datasets in entertainment and product domains are collected and annotated for experiments. Our proposed method outperforms previous methods on both datasets.

preprint2019arXiv

A stochastic version of Stein Variational Gradient Descent for efficient sampling

We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful for Bayesian inference. The method is to apply the Random Batch Method (RBM) for interacting particle systems proposed by Jin et al to the interacting particle systems in SVGD. While keeping the behaviors of SVGD, it reduces the computational cost, especially when the interacting kernel has long range. Numerical examples verify the efficiency of this new version of SVGD.

preprint2019arXiv

Observation of the decays $χ_{cJ} \to ϕϕη$

Using a data sample of $(448.1\pm2.9)\times10^{6}$ $ψ(3686)$ decays collected by the BESIII detector at the Beijing Electron Positron Collider (BEPCII), we observe the decays $χ_{cJ}\to ϕϕη~(J=0,~1,~2)$, where the $χ_{cJ}$ are produced via the radiative processes $ψ(3686)\toγχ_{cJ}$. The branching fractions are measured to be $\mathcal B(χ_{c0}\toϕϕη)=(8.41\pm0.74\pm0.62)\times10^{-4}$, $\mathcal B(χ_{c1}\toϕϕη)=(2.96\pm0.43\pm0.22)\times 10^{-4}$, and $\mathcal B(χ_{c2} \to ϕϕη)=(5.33\pm0.52\pm0.39) \times 10^{-4}$, where the first uncertainties are statistical and the second are systematic. We also search for intermediate states in the $ϕϕ$ or $ηϕ$ combinations, but no significant structure is seen due to the limited statistics.

preprint2019arXiv

On mean field limit for Brownian particles with Coulomb interaction in 3D

In this paper, we consider the mean field limit of Brownian particles with Coulomb interaction in 3D space. In particular, using a symmetrization technique, we show that the limit measure almost surely is a weak solution to the limiting nonlinear Fokker-Planck equation. By proving that the energy almost surely is bounded by the initial energy, we improve the regularity of the weak solutions. Moreover, by a natural assumption, we establish the weak strong uniqueness principle, which is closely related to the propagation of chaos.

preprint2019arXiv

Period doubling eigenstates in a fiber laser mode-locked by nonlinear polarization rotation

Due to the weak birefringence of single mode fibers, solitons generated in fiber lasers are indeed vector pulses and exhibit periodic parameter change including polarization evolution even when there is a polarizer inside the cavity. Period doubling eigenstates of solitons generated in a fiber laser mode-locked by the nonlinear polarization rotation, i.e., period doubling of polarization components of the soliton, are numerically explored in detail. We found that, apart from the synchronous evolution between the two polarization components, there exists asynchronous development depending on the detailed operation conditions. In addition, period doubling of one polarization component together with period-one of another polarization component can be achieved. When the period tripling window is obtained, much complexed dynamics on the two polarization components could be observed.

preprint2019arXiv

Period-doubling bifurcation of dissipative-soliton-resonance pulses in a passively mode-locked fiber laser

We report on the experimental observation of period-doubling bifurcation of dissipative-soliton-resonance (DSR) pulses in a fiber laser passively mode-locked by using the nonlinear optical loop mirror. Increasing the pump power of the fiber laser, we show that temporally a stable, uniform DSR pulse train could be transformed into a period-doubling state, exhibiting two sets of pulse parameters between the adjacent cavity roundtrip. It is found that DSR pulses in the period-doubling state could maintain the typical feature of DSR pulse: fixed pulse peak power and linear variation in pulse width with respect to the pump power change. The mechanism for achieving period-doubling of DSR pulses is discussed.

preprint2019arXiv

Search for the rare decay $η'\rightarrowπ^{0}π^{0}π^{0}π^{0}$ at BESIII

Based on a sample of 1.31 billion $J/ψ$ events collected with the BESIII detector, we perform a search for the rare decay $η'\rightarrow 4π^{0}$ via $J/ψ\rightarrowγη'$. No significant $η'$ signal is observed in the invariant mass spectrum of 4$π^{0}$. With a Bayesian approach, the upper limit on the branching fraction of $η'\rightarrow 4π^{0}$ is determined to be $\mathcal{B}(η'\rightarrow 4π^{0})$ $< 4.94\times10^{-5}$ at the 90\% confidence level, which is a factor of six smaller than the previous experimental limit.

preprint2017arXiv

Observation of the decay $Λ_c^+\rightarrow Σ^- π^+π^+π^0$

We report the first observation of the decay $Λ^+_{c}\rightarrow Σ^- π^+π^+π^0$, based on data obtained in $e^+e^-$ annihilations with an integrated luminosity of 567~pb$^{-1}$ at $\sqrt{s}=4.6$~GeV. The data were collected with the BESIII detector at the BEPCII storage rings. The absolute branching fraction $\mathcal{B}(Λ^+_{c}\rightarrowΣ^-π^+π^+π^0)$ is determined to be $(2.11\pm0.33({\rm stat.})\pm0.14({\rm syst.}))\%$. In addition, an improved measurement of $\mathcal{B}(Λ^+_{c}\rightarrowΣ^-π^+π^+)$ is determined as $(1.81\pm0.17({\rm stat.})\pm0.09({\rm syst.}))\%$.

preprint2015arXiv

Measurement of the $\mathrm e^+\mathrm e^-\rightarrow\mathrmπ^+\mathrmπ^-$ Cross Section between 600 and 900 MeV Using Initial State Radiation

We extract the $e^+e^-\rightarrow π^+π^-$ cross section in the energy range between 600 and 900 MeV, exploiting the method of initial state radiation. A data set with an integrated luminosity of 2.93 fb$^{-1}$ taken at a center-of-mass energy of 3.773 GeV with the BESIII detector at the BEPCII collider is used. The cross section is measured with a systematic uncertainty of 0.9%. We extract the pion form factor $|F_π|^2$ as well as the contribution of the measured cross section to the leading order hadronic vacuum polarization contribution to $(g-2)_μ$. We find this value to be $a_μ^{ππ,\rm LO}(600-900\;\rm MeV) = (368.2 \pm 2.5_{\rm stat} \pm 3.3_{\rm sys})\cdot 10^{-10}$.

Lei Li

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174 published item(s)

ChipLingo: A Systematic Training Framework for Large Language Models in EDA

MiMo-Audio: Audio Language Models are Few-Shot Learners

Training Report of TeleChat3-MoE

A multi-objective optimization framework for terrain modification based on a combined hydrological and earthwork cost-benefit

Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design

Investigation of the $ΔI = 1/2$ rule and test of CP violation through the measurement of decay asymmetry parameters in $Ξ^-$ decays

Some Grönwall inequalities for a class of discretizations of time fractional equations on nonuniform meshes

BuildSeg: A General Framework for the Segmentation of Buildings

Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior

Personalized Prompt Learning for Explainable Recommendation

Search for hidden-charm tetraquark with strangeness in $e^{+}e^{-}\rightarrow K^+ D_{s}^{*-} D^{*0}+c.c.$

Self-Supervised Speech Denoising Using Only Noisy Audio Signals

Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area

$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation

3D Textured Shape Recovery with Learned Geometric Priors

A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation

A projection--less approach to Rickart Jordan structures

A splitting Hamiltonian Monte Carlo method for efficient sampling

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

Amplitude analysis and branching fraction measurement of the decay $D_{s}^{+} \to K^+π^+π^-$

Amplitude analysis and branching-fraction measurement of $D_{s}^{+} \to π^{+}π^{0}η^{\prime}$

Analytical results for the superflow of spin-orbit-coupled Bose-Einstein condensates in optical lattices

AutoCellLibX: Automated Standard Cell Library Extension Based on Pattern Mining

Automatic Fatou Property of Law-invariant Risk Measures

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

Completely Spin-Decoupled Geometric Phase of Metasurface

Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation

Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

Consecutive Decoding for Speech-to-text Translation

Contextual Representation Learning beyond Masked Language Modeling

Cross section measurements of the processes $e^+e^- \rightarrow ωπ^{0}$ and $ωη$ at center-of-mass energies between 3.773 and 4.701 GeV

Cross-modal Contrastive Learning for Speech Translation

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

Deep Computational Model for the Inference of Ventricular Activation Properties

Deepfake Network Architecture Attribution

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

Enhancing Cross-lingual Transfer by Manifold Mixup

Ergodicity and long-time behavior of the Random Batch Method for interacting particle systems

First Observation of the Semileptonic Decay $Λ_c^+\rightarrow pK^- e^+ν_e$

Fluctuation suppression and enhancement in interacting particle systems

KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models

Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets

Learning When to Translate for Streaming Speech

LightSeq2: Accelerated Training for Transformer-based Models on GPUs

Measurement of $e^{+}e^{-} \to K^{+}K^{-}π^{0}$ cross section and observation of a resonant structure

Measurement of $Λ$ baryon polarization in $e^+e^-\rightarrowΛ\barΛ$ at $\sqrt{s} = 3.773$ GeV

Measurement of the $D \to K^-π^+π^+π^-$ and $D \to K^-π^+π^0$ coherence factors and average strong-phase differences in quantum-correlated ${D\bar{D}}$ decays

Measurement of the branching fraction and decay asymmetry of $Λ\to nγ$

Measurement of the branching fraction for $ψ(3686)\to ωK^0_SK^0_S$

Measurement of the branching fraction of the doubly Cabibbo-suppressed decay $D^0\to K^+π^-π^0$ and search for $D^0\to K^+π^-π^0π^0$

Measurement of the Cross Section for $e^{+}e^{-}\to$ hadrons at Energies from 2.2324 to 3.6710 GeV

Measurement of the cross section of $e^{+}e^{-}\toηπ^{+}π^{-}$ at center-of-mass energies from 3.872 GeV to 4.700 GeV

Measurement of the total and leptonic decay widths of the $J/ψ$ resonance with an energy scan method at BESIII

Measurements of Absolute Branching Fractions of $D^0\to K_L^0ϕ$, $K_L^0η$, $K_L^0ω$, and $K_L^0η^{\prime}$

Measurements of the absolute branching fractions of hadronic $D$-meson decays involving kaons and pions

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

MTG: A Benchmark Suite for Multilingual Text Generation

Multi-Modality Cardiac Image Computing: A Survey

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Observation of $a_0(1710)^+ \to K_S^0K^+$ in study of the $D_s^+\to K_S^0K^+π^0$ decay

Observation of $η_c(2S) \to 3(π^+π^-)$ and measurements of $χ_{cJ} \to 3(π^+π^-)$ in $ψ(3686)$ radiative transitions

Observation of resonance structures in $e^+e^-\to π^+π^-ψ_2(3823)$ and mass measurement of $ψ_2(3823)$

Observation of the double Dalitz decay $η&#39;\to e^+e^-e^+e^-$

Observation of the electromagnetic Dalitz decay $D^{\ast 0}\to D^{0}e^{+}e^{-}$

Observation of the Singly Cabibbo-Suppressed Decay $Λ_{c}^{+} \to nπ^{+}$

On the Impact of Noises in Crowd-Sourced Data for Speech Translation

On uniform-in-time diffusion approximation for stochastic gradient descent

Partial wave analysis of $J/ψ\to γη^{\prime} η^{\prime}$

Phase control of localization in the nonlinear two-mode system from harmonic mixing driving: Perturbative analysis and symmetry consideration

Search for hidden-charm tetraquark with strangeness in $e^{+}e^{-}\rightarrow K^+ D_{s}^{-} D^{0}+c.c.$

Observation of the double Dalitz decay $η'\to e^+e^-e^+e^-$

Cross section measurements of the $e^+e^-\to D^{+}D^{-}$ and $e^+e^-\to D^{*+}D^{-}$ processes at center-of-mass energies from 4.085 to 4.600 GeV

3rd Place Solution to "Google Landmark Retrieval 2020"

Cross section measurement of $e^+e^- \rightarrow η'J/ψ$ from $\sqrt{s} = 4.178$ to $4.600$ GeV

Erratum to "Measurement of the $e^+e^-\toπ^+π^-$ cross section between 600 and 900 MeV using initial state radiation"