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Meng Wang

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preprint2026arXiv

Building Digital Twins of Different Human Organs for Personalized Healthcare

Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins but also outlines the promising, albeit ambitious, future of interconnected multi-organ digital twins for whole-body precision healthcare.

preprint2026arXiv

Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion

Deep Unfolding Network-based methods have emerged as effective solutions for multi-source image fusion by combining model-driven iterative optimization with data-driven deep learning. However, most existing deep unfolding image fusion methods are derived from alternating minimization, which updates the features of different modalities separately. This design introduces considerable computational and memory overhead, limiting deployment on resource-constrained edge devices. To address this issue, we propose CDNet, a lightweight Combined Dictionary Unfolding Network for multi-source image fusion. Rather than introducing a new sparse coding prior or empirically compressing an existing fusion network, CDNet translates the unique-common decomposition prior of coupled dictionary learning into a structurally constrained joint unfolding architecture. The resulting CDBlock follows a block-sparse interaction topology and performs a model-derived joint update of common and modality-specific representations, thereby streamlining feature learning and improving efficiency.In addition, we design a compact High- and Low-frequency Image Fidelity loss for unsupervised training without ground-truth images. We evaluate CDNet on four tasks, including multi-exposure image fusion, infrared and visible image fusion, medical image fusion, and infrared and visible image fusion for semantic segmentation. Experimental results show that CDNet achieves competitive or superior fusion performance with high efficiency. For infrared and visible image fusion, CDNet outperforms competing methods on four of six metrics on the TNO dataset and five of six metrics on the RoadScene dataset. In particular, it surpasses the second-best method by 1.23 dB and 1.59 dB in PSNR on TNO and RoadScene, respectively.

preprint2026arXiv

Electronic Nematicity Revealed by Polarized Ultrafast Spectroscopy in Bilayer La$_3$Ni$_2$O$_7$

We report a polarized ultrafast pump-probe study of the normal-state electronic dynamics in bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$ single crystals at ambient pressure. While both nickelates exhibit density-wave (DW) transitions accompanied by the opening of a quasiparticle relaxation bottleneck, their electronic responses display strikingly different symmetry properties. La$_4$Ni$_3$O$_{10}$ maintains an isotropic optical response across the entire temperature range. In contrast, La$_3$Ni$_2$O$_7$ exhibits a pronounced twofold ($C_2$) anisotropy in its low-temperature electronic dynamics. This electronic nematicity, evident in both the relaxation dynamics and the effective gap scales, competes with a secondary isotropic order emerging below 115 K. The presence of macroscopic electronic anisotropy in the bilayer system, and its absence in the trilayer system, suggests an intimate relation between electronic nematic fluctuations and superconducting pairing in La$_3$Ni$_2$O$_7$ that worth for deeper explorations.

preprint2026arXiv

Electronic structures and superconductivity in Nd-doped La$_3$Ni$_2$O$_7$

The recent discovery of high-$T_c$ superconductivity in Ruddlesden-Popper (RP) nickelates has motivated extensive efforts to explore higher $T_c$ superconductors. Here, we systematically investigate Nd-doped La$_3$Ni$_2$O$_7$ using density functional theory (DFT) and renormalized mean-field theory (RMFT). DFT calculations reveal that both the lattice constants and interlayer spacing decrease upon Nd substitution, similar to the effect of physical pressure. However, the in-plane Ni-O-Ni bond angle evolves non-monotonically with doping, increasing to a maximum at 70% ($\sim$ 2/3) Nd doping level and then falling sharply at 80%, which leads to a reduction in orbital overlap. Moreover, Nd doping has a more pronounced effect on the Ni-$d{_{z^2}}$ orbital, demonstrating an orbital-dependent effect of rare-earth substitution. Through the bilayer two-orbital $t-J$ model, RMFT analysis further shows an $s\pm$-wave pairing symmetry, with $T_c$ rising to a maximum at about 70% Nd substitution before declining, in agreement with the transport measurements. The variation in $T_c$ can be traced to the competition between continuously enhanced interlayer superexchange coupling $J_\perp^z$ and a gradual decrease in particle density. These results highlight the delicate interplay among structural tuning, orbital hybridization, and superconductivity, providing important clues to design higher-$T_c$ RP nickelate superconductors.

preprint2026arXiv

Face Normal Estimation from Rags to Riches

Although recent approaches to face normal estimation have achieved promising results, their effectiveness heavily depends on large-scale paired data for training. This paper concentrates on relieving this requirement via developing a coarse-to-fine normal estimator. Concretely, our method first trains a neat model from a small dataset to produce coarse face normals that perform as guidance (called exemplars) for the following refinement. A self-attention mechanism is employed to capture long-range dependencies, thus remedying severe local artifacts left in estimated coarse facial normals. Then, a refinement network is customized for the sake of mapping input face images together with corresponding exemplars to fine-grained high-quality facial normals. Such a logical function split can significantly cut the requirement of massive paired data and computational resource. Extensive experiments and ablation studies are conducted to demonstrate the efficacy of our design and reveal its superiority over state-of-the-art methods in terms of both training expense as well as estimation quality. Our code and models are open-sourced at: https://github.com/AutoHDR/FNR2R.git.

preprint2026arXiv

FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture

Realizing large-scale quantum advantage is expected to require quantum error correction (QEC), making the compilation and optimization of logical operations a critical area of research. Logical computation imposes distinct constraints and operational paradigms that differ from those of the Noisy Intermediate-Scale Quantum (NISQ) regime, motivating the continued evolution of compilation tools. Given the complexity of this emerging stack, where factors such as gate decomposition precision and computational models must be co-designed, standardized benchmarks and toolkits are valuable for evaluating progress. To support this need, we introduce FTCircuitBench, which serves as: (1) a benchmark suite of impactful quantum algorithms, featuring pre-compiled instances in both Clifford+T and Pauli Based Computation models; (2) a modular end-to-end pipeline allowing users to compile and decompose algorithms for various fault-tolerant architectures, supporting both prebuilt and custom optimization passes; and (3) a toolkit for evaluating the impact of algorithms and optimization across the full compilation stack, providing detailed numerical analysis at each stage. FTCircuitBench is fully open-sourced and maintained on Github.

preprint2026arXiv

GPS-Synchronized Monitoring of Core-collapse Supernova Bursts with PandaX-4T via Coherent Elastic Neutrino Nuclear Scattering

The landmark detection of neutrinos from SN1987A marked the dawn of neutrino astrophysics. The neutrino burst provided essential insights into fundamental properties of neutrinos, and served as key probes of stellar evolution and supernova dynamics. The recent advancement in coherent elastic neutrino-nucleus scattering enables the detection of core-collapse supernova burst neutrinos using tonne-scale liquid xenon detectors originally designed for dark matter direct detection. Leveraging this capability, we developed and deployed an online supernova monitoring system for the PandaX-4T experiment. This system features a GPS module with millisecond-level timing precision, a low false-alarm rate, and high sensitivity to galactic core-collapse supernova explosion events. The methodology is robust, directly scalable, and planned for implementation in the next-generation PandaX-20T experiment.

preprint2026arXiv

Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias

Popularity bias is a common challenge in recommender systems. It often causes unbalanced item recommendation performance and intensifies the Matthew effect. Due to limited user-item interactions, unpopular items are frequently constrained to the embedding neighborhoods of only a few users, leading to representation collapse and weakening the model's generalization. Although existing supervised alignment and reweighting methods can help mitigate this problem, they still face two major limitations: (1) they overlook the inherent variability among different Graph Convolutional Networks (GCNs) layers, which can result in negative gains in deeper layers; (2) they rely heavily on fixed hyperparameters to balance popular and unpopular items, limiting adaptability to diverse data distributions and increasing model complexity. To address these challenges, we propose Graph-Structured Dual Adaptation Framework (GSDA), a dual adaptive framework for mitigating popularity bias in recommendation. Our theoretical analysis shows that supervised alignment in GCNs is hindered by the over-smoothing effect, where the distinction between popular and unpopular items diminishes as layers deepen, reducing the effectiveness of alignment at deeper levels. To overcome this limitation, GSDA integrates a hierarchical adaptive alignment mechanism that counteracts entropy decay across layers together with a distribution-aware contrastive weighting strategy based on the Gini coefficient, enabling the model to adapt its debiasing strength dynamically without relying on fixed hyperparameters. Extensive experiments on three benchmark datasets demonstrate that GSDA effectively alleviates popularity bias while consistently outperforming state-of-the-art methods in recommendation performance.

preprint2026arXiv

Lenses for Partially-Specified States (Extended Version)

A bidirectional transformation is a pair of transformations satisfying certain well-behavedness properties: one maps source data into view data, and the other translates changes on the view back to the source. However, when multiple views share a source, an update on one view may affect the others, making it hard to maintain correspondence while preserving the user's update, especially when multiple views are changed at once. Ensuring these properties within a compositional framework is even more challenging. In this paper, we propose partial-state lenses, which allow source and view states to be partially specified to precisely represent the user's update intentions. These intentions are partially ordered, providing clear semantics for merging intentions of updates coming from multiple views and a refined notion of update preservation compatible with this merging. We formalize partial-state lenses, together with partial-specifiedness-aware well-behavedness that supports compositional reasoning and ensures update preservation. In addition, we demonstrate the utility of the proposed system through examples.

preprint2026arXiv

MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization

The swift advancement in photo-realistic face generation technology has sparked considerable concerns across society and academia, emphasizing the requirement of generalizable face forgery detection and localization methods. Prior works tend to capture face forgery patterns across multiple domains using image modality, other modalities like fine-grained texts are not comprehensively investigated, which restricts the generalization capability of models. Besides, they usually analyze facial images created by GAN, but struggle to identify and localize those synthesized by diffusion. To solve the problems, in this paper, we devise a novel multi-domain fine-grained vision-language reconstruction (MFVLR) model, which explores comprehensive and diverse visual forgery traces via language-guided face forgery representation learning, to achieve generalizable diffusion-synthesized face forgery detection and localization (DFFDL). Specifically, we devise a fine-grained language transformer that studies general fine-grained language embeddings using language reconstruction. We propose a multi-domain vision encoder to capture general and complementary visual forgery patterns across the image and residual domains. A vision decoder is designed to reconstruct image appearance and achieve forgery localization. Besides, we propose an innovative plug-and-play vision injection module to enhance the interaction between the vision and language embeddings. Extensive experiments and visualizations demonstrate that our network outperforms the state of the art on different settings like cross-generator, cross-forgery, and cross-dataset evaluations.

preprint2026arXiv

Post-hoc Provider Fairness Adaptation via Hierarchical Exposure Alignment

Provider exposure fairness is crucial for sustaining a healthy content ecosystem and preventing monopolization in recommender systems. Yet, most existing methods either incorporate fairness constraints during model training, requiring expensive retraining when fairness objectives change, or rely on post-hoc reranking with fixed criteria, which lacks adaptability to diverse fairness requirements. To overcome these limitations, we propose Post-hoc Fairness Adaptation (PFA), a lightweight framework that equips a frozen recommender with a fairness adapter, enabling flexible fairness control without retraining the backbone model. Specifically, the fairness adapter learns personalized additive score adjustments from user-item embeddings, which are injected into the original ranking scores to steer provider exposure toward fairness. To train the adapter, we minimize the KL divergence between the actual and the target fair exposure distributions. However, this global objective implicitly treats all providers equally, ignoring structural disparities such as imbalanced provider group sizes and heterogeneous exposure within groups. Consequently, fairness may appear satisfied at an aggregate level while severe inter-group and intra-group exposure imbalances persist, undermining practical fairness. To address this, we design Hierarchical Exposure Fairness Alignment (HEFA), which explicitly balances inter- and intra-group provider exposure disparities, enabling flexible adaptation to diverse fairness requirements. To mitigate potential accuracy degradation, PFA jointly optimizes HEFA with a differentiable NDCG loss, enabling end-to-end fairness optimization while preserving ranking quality. Extensive experiments on three public datasets demonstrate that PFA achieves substantial fairness gains with negligible accuracy loss, consistently outperforming strong baselines.

preprint2026arXiv

Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine

Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing confounding bias often suppresses clinically informative heterogeneity, degrading patient-specific predictions. Here, we identify this tension as a bias-precision paradox in causal representation learning and introduce sampling-based maximum mean discrepancy (sMMD), a stochastic alignment strategy that replaces global adversarial balancing with subset-level matching. We instantiate this approach in a framework for counterfactual outcome prediction with attribution-grounded interpretability. Across two large-scale ICU cohorts (n = 27,783), our framework improves accuracy under distribution shift, reducing error by up to 11.5% and substantially increasing recall in high-risk tasks. Mechanistic analyses show that sMMD selectively preserves clinically decisive variables. In human-AI evaluation, our method outperforms clinicians-in-training and large language models, and improves clinician accuracy by 14.7% while reducing decision time, enabling interpretable, real-time clinical decision support.

preprint2026arXiv

Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling

Recent advances in generative video models are increasingly driven by post-training and test-time scaling, both of which critically depend on the quality of video reward models (RMs). An ideal reward model should predict accurate rewards that align with human preferences across diverse scenarios. However, existing paradigms face a fundamental dilemma: \textit{Discriminative RMs} regress rewards directly on features extracted by multimodal large language models (MLLMs) without explicit reasoning, making them prone to shortcut learning and heavily reliant on massive data scaling for generalization. In contrast, \textit{Generative RMs} with Chain-of-Thought (CoT) reasoning exhibit superior interpretability and generalization potential, as they leverage fine-grained semantic supervision to internalize the rationales behind human preferences. However, they suffer from inherent optimization bottlenecks due to the coupling of reasoning and scoring within a single autoregressive inference chain. To harness the generalization benefits of CoT reasoning while mitigating the training instability of coupled reasoning and scoring, we introduce DeScore, a training-efficient and generalizable video reward model. DeScore employs a decoupled ``think-then-score'' paradigm: an MLLM first generates an explicit CoT, followed by a dedicated discriminative scoring module consisting of a learnable query token and a regression head that predicts the final reward. DeScore is optimized via a two-stage framework: (1) a discriminative cold start incorporating a random mask mechanism to ensure robust scoring capabilities, and (2) a dual-objective reinforcement learning stage that independently refines CoT reasoning quality and calibrates the final reward, ensuring that higher-quality reasoning directly translates to superior model performance.

preprint2025arXiv

DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing

Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where models fail to use gold evidence even after it is retrieved, due to context blindness in noisy environments. To bridge this gap, we propose DeepResearch-Slice, a simple yet effective neuro-symbolic framework. Unlike implicit attention, our approach predicts precise span indices to perform a deterministic hard filter before reasoning. Extensive evaluations across six benchmarks show substantial robustness gains. Applying our method to frozen backbones yields a 73 percent relative improvement, from 19.1 percent to 33.0 percent, effectively mitigating noise without requiring parameter updates to the reasoning model. These results highlight the need for explicit grounding mechanisms in open-ended research.

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

Maximum principle for recursive optimal control problem of stochastic delay evolution equations

For a class of stochastic delay evolution equations driven by cylindrical $Q$-Wiener process, we study the Pontryagin's maximum principle for the stochastic recursive optimal control problem. The delays are given as moving averages with respect to general finite measures and appear in all the coefficients of the control system. In particular, the final cost can contain the state delay. To derive the main result, we introduce a new form of anticipated backward stochastic evolution equations with terminals acting on an interval as the adjoint equations of the delayed state equations and deploy a proper dual analysis between them. Under certain convex assumption on the coefficient function and the Hamiltonian, we also show sufficiency of the maximum principle.

preprint2024arXiv

Proposal of detecting topological transition of quantum braiding in three-fold degenerate eigen subspace

The braiding operations of quantum states have attracted substantial attention due to their great potential for realizing topological quantum computations. In this paper, we show that a three-fold degenerate eigen subspace can be obtained in a four-level Hamiltonian which is the minimal physical system. Braiding operations are proposed to apply to dressed states in the subspace. The topology of the braiding diagram can be characterized through physical methods once that the sequential braiding pulses are adopted. We establish an equivalent relationship function between the permutation group and the output states where different output states correspond to different values of the function. The topological transition of the braiding happens when two operations overlap, which is detectable through the measurement of the function. Combined with the phase variation method, we can analyze the wringing pattern of the braiding. Therefore, the experimentally-feasible system provides a platform to investigate braiding dynamics, the SU(3) physics and the qutrit gates.

preprint2024arXiv

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg.

preprint2023arXiv

A First Search for Solar $^8$B Neutrino in the PandaX-4T Experiment using Neutrino-Nucleus Coherent Scattering

A search for interactions from solar $^8$B neutrinos elastically scattering off xenon nuclei using PandaX-4T commissioning data is reported. The energy threshold of this search is further lowered compared with the previous search for dark matter, with various techniques utilized to suppress the background that emerges from data with the lowered threshold. A blind analysis is performed on the data with an effective exposure of 0.48 tonne$\cdot$year, and no significant excess of events is observed. Among results obtained using the neutrino-nucleus coherent scattering, our results give the best constraint on the solar $^8$B neutrino flux. We further provide a more stringent limit on the cross section between dark matter and nucleon in the mass range from 3 to 9 GeV/c$^2$.

preprint2023arXiv

Coexistence of zigzag antiferromagnetic order and superconductivity in compressed NiPSe3

NiPSe3 is regarded as a bandwidth-controlled Mott insulator, distinct from the widely studied Mott insulating magnetic graphene MPSe3 (M = Mn and Fe) family. By employing high-pressure synchrotron X-ray diffraction, we observe two structural transitions as a function of pressure. With the help of first-principles calculations, we discover the antiferromagnetic (AFM) moment directions of NiPSe3 switch from out-of-plane to in-plane and the honeycomb layers slide relative to each other at the first structural transition. The in-plane AFM order persists until the second structural transition, whereupon the two-dimensional (2D) structure assumes a more three-dimensional (3D) character. A bandwidth-controlled Mott insulator-metal transition (IMT) occurs between the two structural transitions at 8.0 GPa, concomitant with the emergence of superconductivity with 4.8 K. The superconductivity in NiPSe3 emerging in the 2D monoclinic phase coexists with the in-plane AFM order and continues into the 3D trigonal phase. Our electronic structure calculations reveal that the Mott IMT and superconductivity in NiPSe3 are both closely related to the enhanced Se2- 4p and Ni2+ 3d electronic hybridizations under pressure. From these results, we construct the temperature-pressure electronic phase diagram of NiPSe3, revealing rich physics and many similarities with copper oxide and iron-based superconductors.

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

Vocabulary-informed Zero-shot and Open-set Learning

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

preprint2022arXiv

A Review-aware Graph Contrastive Learning Framework for Recommendation

Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.

preprint2022arXiv

A Search for the Cosmic Ray Boosted Sub-GeV Dark Matter at the PandaX-II Experiment

We report a novel search for the cosmic ray boosted dark matter using the 100~tonne$\cdot$day full data set of the PandaX-II detector located at the China Jinping Underground Laboratory. With the extra energy gained from the cosmic rays, sub-GeV dark matter particles can produce visible recoil signals in the detector. The diurnal modulations in rate and energy spectrum are utilized to further enhance the signal sensitivity. Our result excludes the dark matter-nucleon elastic scattering cross section between 10$^{-31}$cm$^{2}$ and 10$^{-28}$cm$^{2}$ for a dark matter masses from 0.1 MeV/$c^2$ to 0.1 GeV/$c^2$, with a large parameter space previously unexplored by experimental collaborations.

preprint2022arXiv

A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment

In the two-component Majorana dark matter model, one dark matter particle can scatter off the target nuclei, and turn into a slightly heavier component. In the framework of a simplified model with a vector boson mediator, both the tree-level and loop-level processes contribute to the signal in direct detection experiment. In this paper, we report the search results for such dark matter from PandaX-II experiment, using total data of the full 100.7 tonne$\cdot$day exposure. No significant excess is observed, so strong constraints on the combined parameter space of mediator mass and dark matter mass are derived. With the complementary search results from collider experiments, a large range of parameter space can be excluded.

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

Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy

Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities. Previous studies tended to adopt a pipeline training strategy, which uses well-trained visual and acoustic encoders to extract high-level representations (embeddings) first, then utilizes them to train the audio-visual classifier. In this way, the extracted embeddings are well suited for uni-modal classifiers, but not necessarily suited for multi-modal ones. In this paper, we propose a joint training framework, using the acoustic features and raw images directly as inputs for the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image models as visual encoder, and jointly optimize the scene classifier and 1D-CNN based acoustic encoder during training. We evaluate the approach on the development dataset of TAU Urban Audio-Visual Scenes 2021. The experimental results show that our proposed approach achieves significant improvement over the conventional pipeline training strategy. Moreover, our best single system outperforms previous state-of-the-art methods, yielding a log loss of 0.1517 and accuracy of 94.59% on the official test fold.

preprint2022arXiv

Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach

In this study, we propose a rotation-based connected automated vehicle (CAV) distributed cooperative control strategy for an on-ramp merging scenario. By assuming the mainline and ramp line are straight, we firstly design a virtual rotation approach that transfers the merging problem to a virtual car following (CF) problem to reduce the complexity and dimension of the cooperative CAVs merging control. Based on this concept, a multiple-predecessor virtual CF model and a unidirectional multi-leader communication topology are developed to determine the longitudinal behavior of each CAV. Specifically, we exploit a distributed feedback and feedforward longitudinal controller in preparation for actively generating gaps for merging CAVs, reducing the voids caused by merging, and ensuring safety and traffic efficiency during the process. To ensure the disturbance attenuation property of this system, practical string stability is mathematically proved for the virtual CF controllers to prohibit the traffic oscillation amplification through the traffic stream. Moreover, as a provision for extending the virtual CF application scenarios of any curvy ramp geometry, we utilize a curvilinear coordinate to model the two-dimensional merging control, and further design a local lateral controller based on an extended linear-quadratic regulator to regulate the position deviation and angular deviation of the lane centerlines. For the purpose of systematically evaluating the control performance of the proposed methods, numerical simulation experiments are conducted. As the results indicate, the proposed controllers can actively reduce the void and meanwhile guarantee the damping of traffic oscillations in the merging control area.

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

Decomposition Without Regret

Programming languages are embracing both functional and object-oriented paradigms. A key difference between the two paradigms is the way of achieving data abstraction. That is, how to organize data with associated operations. There are important tradeoffs between functional and object-oriented decomposition in terms of extensibility and expressiveness. Unfortunately, programmers are usually forced to select a particular decomposition style in the early stage of programming. Once the wrong design decision has been made, the price for switching to the other decomposition style could be rather high since pervasive manual refactoring is often needed. To address this issue, this paper presents a bidirectional transformation system between functional and object-oriented decomposition. We formalize the core of the system in the FOOD calculus, which captures the essence of functional and object-oriented decomposition. We prove that the transformation preserves the type and semantics of the original program. We further implement FOOD in Scala as a translation tool called Cook and conduct several case studies to demonstrate the applicability and effectiveness of Cook.

preprint2022arXiv

Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion

Uncalibrated photometric stereo is proposed to estimate the detailed surface normal from images under varying and unknown lightings. Recently, deep learning brings powerful data priors to this underdetermined problem. This paper presents a new method for deep uncalibrated photometric stereo, which efficiently utilizes the inter-image representation to guide the normal estimation. Previous methods use optimization-based neural inverse rendering or a single size-independent pooling layer to deal with multiple inputs, which are inefficient for utilizing information among input images. Given multi-images under different lighting, we consider the intra-image and inter-image variations highly correlated. Motivated by the correlated variations, we designed an inter-intra image feature fusion module to introduce the inter-image representation into the per-image feature extraction. The extra representation is used to guide the per-image feature extraction and eliminate the ambiguity in normal estimation. We demonstrate the effect of our design on a wide range of samples, especially on dark materials. Our method produces significantly better results than the state-of-the-art methods on both synthetic and real data.

preprint2022arXiv

Downwash-aware Control Allocation for Over-actuated UAV Platforms

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.

preprint2022arXiv

DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://github.com/tsinghua-fib-lab/WTG-DVR.

preprint2022arXiv

Effect of iron vacancies on the magnetic order and spin dynamics of the spin ladder BaFe$_{2-δ}$S$_{1.5}$Se$_{1.5}$

Quasi-one-dimensional iron chalcogenides possess various magnetic states depending on the lattice distortion, electronic correlations, and presence of defects. We present neutron diffraction and inelastic neutron scattering experiments on the spin ladder compound BaFe$_{2-δ}$S$_{1.5}$Se$_{1.5}$ with $\sim$6% iron vacancies. The data reveal that long-range magnetic order is absent, while the characteristic magnetic excitations that correspond to both the stripe- and block-type antiferromagnetic correlations are observed. First-principles calculations support the existence of both stripe and block-type antiferromagnetic short-range order in the experimental sample. The disappearance of long-range magnetic order may be due to the competition between these two magnetic orders, which is greatly enhanced for a certain concentration of iron vacancies, which we calculate to be about 6%, consistent with the measured iron vacancy concentration. Our results highlight how iron vacancies in the iron-based spin ladder system strongly influence the magnetic ground state.

preprint2022arXiv

Efficiently Embedding Dynamic Knowledge Graphs

Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be retrained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE.

preprint2022arXiv

Exchange field enhanced upper critical field of the superconductivity in compressed antiferromagnetic EuTe2

We report high pressure studies on the C-type antiferromagnetic semiconductor EuTe2 up to 36.0 GPa. A structural transition from the I4/mcm to C2/m space group is identified at ~16 GPa. Superconductivity is discovered above ~5 GPa in both the I4/mcm and C2/m space groups. In the low-pressure phase (< 16 GPa), the antiferromagnetic transition temperature is enhanced with increasing pressure due to the enhanced magnetic exchange interactions. Magnetoresistance measurements indicate an interplay between the local moments of Eu2+ and the conduction electrons of Te 5p orbits. The upper critical field of the superconductivity is well above the Pauli limit. Across the structural transition to the high-pressure phase (> 16 GPa), EuTe2 becomes nonmagnetic and the superconducting transition temperature evolves smoothly with the upper critical field below the Pauli limit. Therefore, the high upper critical field of EuTe2 in the low-pressure phase is due to the exchange field compensation effect of the Eu magnetic order and the superconductivity in both structures may arise in the framework of the BCS theory.

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

HoSIM: Higher-order Structural Importance based Method for Multiple Local Community Detection

Local community detection has attracted much research attention recently, and many methods have been proposed for the single local community detection that finds a community containing the given set of query nodes. However, nodes may belong to several communities in the network, and detecting all the communities for the query node set, termed as the multiple local community detection (MLCD), is more important as it could uncover more potential information. MLCD is also more challenging because when a query node belongs to multiple communities, it always locates in the complicated overlapping region and the marginal region of communities. Accordingly, detecting multiple communities for such nodes by applying seed expansion methods is insufficient. In this work, we address the MLCD based on higher-order structural importance (HoSI). First, to effectively estimate the influence of higher-order structures, we propose a new variant of random walk called Active Random Walk to measure the HoSI score between nodes. Then, we propose two new metrics to evaluate the HoSI score of a subgraph to a node and the HoSI score of a node, respectively. Based on the proposed metrics, we present a novel algorithm called HoSIM to detect multiple local communities for a single query node. HoSIM enforces a three-stage processing, namely subgraph sampling, core member identification, and local community detection. The key idea is utilizing HoSI to find and identify the core members of communities relevant to the query node and optimize the generated communities. Extensive experiments illustrate the effectiveness of HoSIM.

preprint2022arXiv

How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes, its theoretical characterization remains elusive. To the best of our knowledge, this work establishes the first theoretical analysis for the known iterative self-training paradigm and proves the benefits of unlabeled data in both training convergence and generalization ability. To make our theoretical analysis feasible, we focus on the case of one-hidden-layer neural networks. However, theoretical understanding of iterative self-training is non-trivial even for a shallow neural network. One of the key challenges is that existing neural network landscape analysis built upon supervised learning no longer holds in the (semi-supervised) self-training paradigm. We address this challenge and prove that iterative self-training converges linearly with both convergence rate and generalization accuracy improved in the order of $1/\sqrt{M}$, where $M$ is the number of unlabeled samples. Experiments from shallow neural networks to deep neural networks are also provided to justify the correctness of our established theoretical insights on self-training.

preprint2022arXiv

How hidden 3D structure within crack fronts reveals energy balance

Griffith's energetic criterion, or `energy balance', has for a century formed the basis for fracture mechanics; the energy flowing into a crack front is precisely balanced by the dissipation (fracture energy) at the front. If the crack front structure is not properly accounted for, energy balance will either appear to fail or lead to unrealistic results. Here, we study the influence of the secondary structure of low-speed crack propagation in hydrogels under tensile loading conditions. We first show that these cracks are bistable; either simple (cracks having no secondary structure) or faceted crack states (formed by steps propagating along crack fronts) can be generated under identical loading conditions. The selection of either crack state is determined by the form of the initial `seed' crack; perfect seed cracks generate simple cracks while a small local mode~III component generates crack fronts having multiple steps. Step coarsening eventually leads to single steps that propagate along crack fronts. As they evolve, steps locally change the instantaneous structure and motion of the crack front, breaking transverse translational invariance. In contrast to simple cracks, faceted cracks can, therefore, no longer be considered as existing in a quasi-2D system. For both simple and faceted cracks we simultaneously measure the energy flux and local dissipation along these crack fronts over velocities, $v$, spanning $0<v<0.2c_R$ ($c_R$ is the Rayleigh wave speed). We find that, in the presence of secondary structure within the crack front, the implementation of energy balance must be generalized for 3D systems; faceted cracks reveal energy balance, only when we account for the local dynamic dissipation at each point along the crack front.

preprint2022arXiv

Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis

In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilizing different modalities and data sets. In our work, different kinds of multimodal features are extracted, including acoustic, visual, text and biological features. These features are fused by TEMMA and GRU with self-attention mechanism frameworks. In this paper, 1) several new audio features, facial expression features and paragraph-level text embeddings are extracted for accuracy improvement. 2) we substantially improve the accuracy and reliability of multimodal sentiment prediction by mining and blending the multimodal features. 3) effective data augmentation strategies are applied in model training to alleviate the problem of sample imbalance and prevent the model from learning biased subject characters. For the MuSe-Humor sub-challenge, our model obtains the AUC score of 0.8932. For the MuSe-Reaction sub-challenge, the Pearson's Correlations Coefficient of our approach on the test set is 0.3879, which outperforms all other participants. For the MuSe-Stress sub-challenge, our approach outperforms the baseline in both arousal and valence on the test dataset, reaching a final combined result of 0.5151.

preprint2022arXiv

KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences

Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images. It still poses several challenges due to practical scenarios where real-world scenes are complex and only partial annotations are available, leading to incompelete or false estimations. In this work, we present a novel framework to detect the densepose of multiple people in an image. The proposed method, which we refer to Knowledge Transfer Network (KTN), tackles two main problems: 1) how to refine image representation for alleviating incomplete estimations, and 2) how to reduce false estimation caused by the low-quality training labels (i.e., limited annotations and class-imbalance labels). Unlike existing works directly propagating the pyramidal features of regions for densepose estimation, the KTN uses a refinement of pyramidal representation, where it simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in a substantial increase in accuracy. Moreover, the KTN enhances the ability of 3D based body parsing with external knowledges, where it casts 2D based body parsers trained from sufficient annotations as a 3D based body parser through a structural body knowledge graph. In this way, it significantly reduces the adverse effects caused by the low-quality annotations. The effectiveness of KTN is demonstrated by its superior performance to the state-of-the-art methods on DensePose-COCO dataset. Extensive ablation studies and experimental results on representative tasks (e.g., human body segmentation, human part segmentation and keypoints detection) and two popular densepose estimation pipelines (i.e., RCNN and fully-convolutional frameworks), further indicate the generalizability of the proposed method.

preprint2022arXiv

Low Radioactive Material Screening and Background Control for the PandaX-4T Experiment

PandaX-4T is a ton-scale dark matter direct detection experiment using a dual-phase TPC technique at the China Jinping Underground Laboratory. Various ultra-low background technologies have been developed and applied to material screening for PandaX-4T, including HPGe gamma spectroscopy, ICP-MS, NAA, radon emanation measurement system, krypton assay station, and alpha detection system. Low background materials were selected to assemble the detector. Surface treatment procedures were investigated to further suppress radioactive background. Combining measured results and Monte Carlo simulation, the total material background rates of PandaX-4T in the energy region of 1-25 keV$\rm{}_{ee}$ are estimated to be (9.9 $\pm$ 1.9) $\times \ 10^{-3}$ mDRU for electron recoil and (2.8 $\pm$ 0.6) $\times \ 10^{-4}$ mDRU for nuclear recoil. In addition, $^{nat}$Kr in the detector is estimated to be <8 ppt.

preprint2022arXiv

Mass Testing and Characterization of 20-inch PMTs for JUNO

Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5,000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

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

Multi-modal Contrastive Representation Learning for Entity Alignment

Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities. Most previous works focus on how to utilize and encode information from different modalities, while it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. In this paper, we propose MCLEA, a Multi-modal Contrastive Learning based Entity Alignment model, to obtain effective joint representations for multi-modal entity alignment. Different from previous works, MCLEA considers task-oriented modality and models the inter-modal relationships for each entity representation. In particular, MCLEA firstly learns multiple individual representations from multiple modalities, and then performs contrastive learning to jointly model intra-modal and inter-modal interactions. Extensive experimental results show that MCLEA outperforms state-of-the-art baselines on public datasets under both supervised and unsupervised settings.

preprint2022arXiv

Multi-modal Emotion Estimation for in-the-wild Videos

In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition. Our method utilizes the multi-modal information, i.e., the visual and audio information, and employs a temporal encoder to model the temporal context in the videos. Besides, a smooth processor is applied to get more reasonable predictions, and a model ensemble strategy is used to improve the performance of our proposed method. The experiment results show that our method achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed method.

preprint2022arXiv

Nematic fluctuations in the non-superconducting iron pnictide BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$

The main driven force of the electronic nematic phase in iron-based superconductors is still under debate. Here, we report a comprehensive study on the nematic fluctuations in a non-superconducting iron pnictide system BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$ by electronic transport, angle-resolved photoemission spectroscopy (ARPES) and inelastic neutron scattering (INS) measurements. Previous neutron diffraction and transport measurements suggested that the collinear antiferromagnetism persists to $x=0.8$, with similar Néel temperature $T_N$ and structural transition temperature $T_s$ around 32 K, but the charge carriers change from electron type to hole type around $x=$ 0.5. In this study, we have found that the in-plane resistivity anisotropy also highly depends on the Cr dopings and the type of charge carriers. While ARPES measurements suggest possibly weak orbital anisotropy onset near $T_s$ for both $x=0.05$ and $x=0.5$ compounds, INS experiments reveal clearly different onset temperatures of low-energy spin excitation anisotropy, which is likely related to the energy scale of spin nematicity. These results suggest that the interplay between the local spins on Fe atoms and the itinerant electrons on Fermi surfaces is crucial to the nematic fluctuations of iron pnictides, where the orbital degree of freedom may behave differently from the spin degree of freedom, and the transport properties are intimately related to the spin dynamics.

preprint2022arXiv

Nested Bayesian Optimization for Computer Experiments

Computer experiments can emulate the physical systems, help computational investigations, and yield analytic solutions. They have been widely employed with many engineering applications (e.g., aerospace, automotive, energy systems. Conventional Bayesian optimization did not incorporate the nested structures in computer experiments. This paper proposes a novel nested Bayesian optimization for complex computer experiments with multi-step or hierarchical characteristics. We prove the theoretical properties of nested outputs given two cases: Gaussian or non-Gaussian. The closed forms of nested expected improvement are derived. We also propose the computational algorithms for nested Bayesian optimization. Three numerical studies show that the proposed nested Bayesian optimization outperforms the five benchmark Bayesian optimization methods ignoring the intermediate outputs of the inner computer code. The case study shows that the nested Bayesian optimization can efficiently minimize the residual stress during composite structures assembly and avoid convergence to the local optimum.

preprint2022arXiv

Neutron-induced nuclear recoil background in the PandaX-4T experiment

Neutron-induced nuclear recoil background is critical to the dark matter searches in the PandaX-4T liquid xenon experiment. This paper studies the feature of neutron background in liquid xenon and evaluates their contribution in the single scattering nuclear recoil events through three methods. The first method is fully Monte Carlo simulation based. The last two are data-driven methods that also use the multiple scattering signals and high energy signals in the data, respectively. In the PandaX-4T commissioning data with an exposure of 0.63 tonne-year, all these methods give a consistent result that there are $1.15\pm0.57$ neutron-induced background in dark matter signal region within an approximated nuclear recoil energy window between 5 and 100 keV.

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 mean-field control problems for backward doubly stochastic systems

This article is concerned with stochastic control problems for backward doubly stochastic differential equations of mean-field type, where the coefficient functions depend on the joint distribution of the state process and the control process. We obtain the stochastic maximum principle which serves as a necessary condition for an optimal control, and we also prove its sufficiency under proper conditions. As a byproduct, we prove the well-posedness for a type of mean-field fully coupled forward-backward doubly stochastic differential equation arising naturally from the control problem, which is of interest in its own right. Some examples are provided to illustrate the applications of our results to control problems in the types of scalar interaction and first order interaction.

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

Radiation hardness study on a CMOS pixel sensor for charged particle tracking

A CMOS pixel sensor, named Supix-1, is developed for a pixelated silicon tracker for the Circular Electron-Positron Collider (CEPC) project. The sensor, consisted of nine sectors varying in pixel sizes, diode sizes and geometries, is fabricated with a 180 nm CMOS Image Sensor (CIS) process to study the particle detection performance of enlarged pixels. In this work, the radiation-induced effects on the charge collection of the sensor under the fluence of 1 $\times$ 10^13 1 MeV neq/cm^2 are studied by the measurements with the radioactive source of Fe-55 and the Technology Computer Aided Design (TCAD) simulations, since the radiation hardness of 6.8 $\times$ 10^12 1 MeV neq/cm^2 per year for Non-Ionizing Energy Loss (NIEL) effects is required. In measurements, the sensor gain has been calibrated using the k-$α$ peak of Fe-55 before and after irradiation. The pixel-wise equivalent noise charge (ENC), charge collection efficiency (CCE) and signal-to-noise ratio (SNR) were evaluated. The radiation-induced effects on cluster properties are studied through a self-developed reconstruction algorithm. In TCAD simulations, charge collections in 5 $\times$ 5 pixel matrixes for two typical impinging cases of incident particles were simulated with and without irradiation. Both measurements and simulations indicate that enlarged pixels with area of 21 $μ$m $\times$ 84 $μ$m, though suffering greater loss on sensor performance than small pixels do, still have satisfactory noise and charge collection performance after irradiation for particle tracking in the upcoming collider detectors.

preprint2022arXiv

Readout electronics and data acquisition system of PandaX-4T experiment

PandaX-4T is a dark matter direct detection experiment located in China jinping underground laboratory. The central apparatus is a dual-phase xenon detector containing 4 ton liquid xenon in the sensitive volume, with about 500 photomultipliers instrumented in the top and the bottom of the detector. In this paper we present a completely new system of readout electronics and data acquisition in the PandaX-4T experiment. Compared to the one used in the previous PandaX dark matter experiments, the new system features triggerless readout and higher bandwidth. With triggerless readout, dark matter searches are not affected by the efficiency loss of external triggers. The system records single photelectron signals of the dominant PMTs with an average efficiency of 96\%, and achieves the bandwidth of more than 450 MB/s. The system has been used to successfully acquire data during the commissioning runs of PandaX-4T.

preprint2022arXiv

Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768x1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters.

preprint2022arXiv

Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation

The rapid deployment of renewable generations such as photovoltaic (PV) generations brings great challenges to the resiliency of existing power systems. Because PV generations are volatile and typically invisible to the power system operator, estimating the generation and characterizing the uncertainty are in urgent need for operators to make insightful decisions. This paper summarizes our recent results on energy disaggregation at the substation level with Behind-the-Meter solar generation. We formulate the so-called ``partial label'' problem for energy disaggregation at substations, where the aggregate measurements contain the total consumption of multiple loads, and the existence of some loads is unknown. We develop two model-free disaggregation approaches based on deterministic dictionary learning and Bayesian dictionary learning, respectively. Unlike conventional methods which require fully annotated training data of individual loads, our approaches can extract load patterns given partially labeled aggregate data. Therefore, our partial label formulation is more applicable in the real world. Compared with deterministic dictionary learning, the Bayesian dictionary learning-based approach provides the uncertainty measure for the disaggregation results, at the cost of increased computational complexity. All the methods are validated by numerical experiments.

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

Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022

Automatic snake species recognition is important because it has vast potential to help lower deaths and disabilities caused by snakebites. We introduce our solution in SnakeCLEF 2022 for fine-grained snake species recognition on a heavy long-tailed class distribution. First, a network architecture is designed to extract and fuse features from multiple modalities, i.e. photograph from visual modality and geographic locality information from language modality. Then, logit adjustment based methods are studied to relieve the impact caused by the severe class imbalance. Next, a combination of supervised and self-supervised learning method is proposed to make full use of the dataset, including both labeled training data and unlabeled testing data. Finally, post processing strategies, such as multi-scale and multi-crop test-time-augmentation, location filtering and model ensemble, are employed for better performance. With an ensemble of several different models, a private score 82.65%, ranking the 3rd, is achieved on the final leaderboard.

preprint2022arXiv

Study of background from accidental coincidence signals in the PandaX-II experiment

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved.

preprint2022arXiv

Superconducting dome associated with the suppression and re-emergence of charge density wave states upon sulfur substitution in CuIr2Te4 chalcogenides

We report the path from the charge density wave (CDW)-bearing superconductor CuIr2Te4 to the metal insulator transition (MIT)-bearing compound CuIr2S4 by chemical alloying with the gradual substitution of S for Te. The evolution of structural and physical properties of the CuIr2Te4-xSx polycrystalline system with the doping range from 0 to 4 is systemically examined. The X-ray diffraction (XRD) results imply CuIr2Te4-xSx in the range between 0 and 0.5 crystallizes in a NiAs defected trigonal structure, whereas it adapts to the cubic spinel structure in the doping range from 3.6 to 4 and it is a mixed phase in the doping range from 0.5 to 3.6. Unexpectedly, the resistivity and magnetization measurements reveal that small-concentration S substitution for Te can suppress the CDW transition, but it reappears around x = 0.2, and the CDW transition temperature enhances clearly as x augments in the range from 0.2 to 0.5. Besides, the superconducting critical temperature (Tc) first increases with S doping content and then decreases after reaching a maximum Tc = 2.82 K for CuIr2Te3.85S0.15. MIT order has been observed in the spinel region from 3.6 to 4 associated with TMI increasing with x increasing. Finally, the rich electronic phase diagram of temperature versus x for this CuIr2Te4-xSx system is assembled, where the superconducting dome is associated with the suppression and re-emergence of CDW as well as MIT states at the end upon sulfur substitution in the CuIr2Te4-xSx chalcogenides.

preprint2022arXiv

Switchable Online Knowledge Distillation

Online Knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap between them -- e.g., Why and when does a large gap harm the performance, especially for student? How to quantify the gap between teacher and student? -- have received limited formal study. In this paper, we propose Switchable Online Knowledge Distillation (SwitOKD), to answer these questions. Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher). To possess an appropriate distillation gap, we further devise an adaptive switching threshold, which provides a formal criterion as to when to switch to learning mode or expert mode, and thus improves the student's performance. Meanwhile, the teacher benefits from our adaptive switching threshold and keeps basically on a par with other online arts. We further extend SwitOKD to multiple networks with two basis topologies. Finally, extensive experiments and analysis validate the merits of SwitOKD for classification over the state-of-the-arts. Our code is available at https://github.com/hfutqian/SwitOKD.

preprint2022arXiv

TBraTS: Trusted Brain Tumor Segmentation

Despite recent improvements in the accuracy of brain tumor segmentation, the results still exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way to change this situation, as it provides a measure of confidence in the segmentation results. In this paper, we propose a trusted brain tumor segmentation network which can generate robust segmentation results and reliable uncertainty estimations without excessive computational burden and modification of the backbone network. In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution. Meanwhile, the trusted segmentation framework learns the function that gathers reliable evidence from the feature leading to the final segmentation results. Overall, our unified trusted segmentation framework endows the model with reliability and robustness to out-of-distribution samples. To evaluate the effectiveness of our model in robustness and reliability, qualitative and quantitative experiments are conducted on the BraTS 2019 dataset.

preprint2022arXiv

Temporal Action Localization with Multi-temporal Scales

Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions on a feature space of a single-temporal scale. However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries. To address this issue, we propose to predict actions on a feature space of multi-temporal scales. Specifically, we use refined feature pyramids of different scales to pass semantics from high-level scales to low-level scales. Besides, to establish the long temporal scale of the entire video, we use a spatial-temporal transformer encoder to capture the long-range dependencies of video frames. Then the refined features with long-range dependencies are fed into a classifier for the coarse action prediction. Finally, to further improve the prediction accuracy, we propose to use a frame-level self attention module to refine the classification and boundaries of each action instance. Extensive experiments show that the proposed method can outperform state-of-the-art approaches on the THUMOS14 dataset and achieves comparable performance on the ActivityNet1.3 dataset. Compared with A2Net (TIP20, Avg\{0.3:0.7\}), Sub-Action (CSVT2022, Avg\{0.1:0.5\}), and AFSD (CVPR21, Avg\{0.3:0.7\}) on the THUMOS14 dataset, the proposed method can achieve improvements of 12.6\%, 17.4\% and 2.2\%, respectively

preprint2022arXiv

Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction

Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at https://github.com/lixiaojieff/HGTAN.

preprint2022arXiv

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) is an important molecule family in the field of human immunity, which recognizes foreign threats and triggers immune responses by presenting peptides to T cells. In recent years, the synthesis of tumor vaccines to induce specific immune responses has become the forefront of cancer treatment. Computationally modeling the binding patterns between peptide and HLA can greatly accelerate the development of tumor vaccines. However, most of the prediction methods performance is very limited and they cannot fully take advantage of the analysis of existing biological knowledge as the basis of modeling. In this paper, we propose TripHLApan, a novel pan-specific prediction model, for HLA molecular peptide binding prediction. TripHLApan exhibits powerful prediction ability by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. The comprehensive evaluations demonstrate the effectiveness of TripHLApan in predicting HLA-I and HLA-II peptide binding in different test environments. The predictive power of HLA-I is further demonstrated in the latest data set. In addition, we show that TripHLApan has strong binding reconstitution ability in the samples of a melanoma patient. In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.

preprint2021arXiv

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.

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

Dark Matter Search Results from the PandaX-4T Commissioning Run

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$.

preprint2021arXiv

Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical information. However, most existing networks simply stacks the convolutional layers and hence failing to fully discover local and global feature information among layers. In this paper, we mainly explore how to enhance the local and global dense feature flow by exploiting hierarchical features fully from all the convolution layers. Technically, we propose an efficient and effective CNN framework, i.e., Fast Dense Residual Network (FDRN), for text recognition. To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time. After fully learning local residual dense features, we utilize the sum operation and several f-RDBs to define a new block termed global dense block (GDB) by imitating the construction of dense blocks to learn global dense residual features adaptively in a holistic way. Finally, we use two convolution layers to construct a down-sampling block to reduce the global feature size and extract deeper features. Extensive simulations show that FDRN obtains the enhanced recognition results, compared with other related models.

preprint2021arXiv

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user's first-order social neighbors' interests for better user modeling and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user. However, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the users' latent collaborative interests in the user-item interest network. In this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting these two network information for user embedding learning at the same time. This is achieved by iteratively aggregating each user's embedding from three aspects: the user's previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.

preprint2021arXiv

DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs). Many popular methods impose deep supervision to perform side-output predictions that are linearly aggregated for final saliency prediction. In this paper, we theoretically and experimentally demonstrate that linear aggregation of side-output predictions is suboptimal, and it only makes limited use of the side-output information obtained by deep supervision. To solve this problem, we propose Deeply-supervised Nonlinear Aggregation (DNA) for better leveraging the complementary information of various side-outputs. Compared with existing methods, it i) aggregates side-output features rather than predictions, and ii) adopts nonlinear instead of linear transformations. Experiments demonstrate that DNA can successfully break through the bottleneck of current linear approaches. Specifically, the proposed saliency detector, a modified U-Net architecture with DNA, performs favorably against state-of-the-art methods on various datasets and evaluation metrics without bells and whistles.

preprint2021arXiv

Dual Encoding for Video Retrieval by Text

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method.

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

Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer

Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e.g., sharing discrepant label granularity) without extensive re-training. Learning a single universal parsing model by unifying label annotations from different domains or at various levels of granularity is a crucial but rarely addressed topic. This poses many fundamental learning challenges, e.g., discovering underlying semantic structures among different label granularity or mining label correlation across relevant tasks. To address these challenges, we propose a graph reasoning and transfer learning framework, named "Graphonomy", which incorporates human knowledge and label taxonomy into the intermediate graph representation learning beyond local convolutions. In particular, Graphonomy learns the global and structured semantic coherency in multiple domains via semantic-aware graph reasoning and transfer, enforcing the mutual benefits of the parsing across domains (e.g., different datasets or co-related tasks). The Graphonomy includes two iterated modules: Intra-Graph Reasoning and Inter-Graph Transfer modules. The former extracts the semantic graph in each domain to improve the feature representation learning by propagating information with the graph; the latter exploits the dependencies among the graphs from different domains for bidirectional knowledge transfer. We apply Graphonomy to two relevant but different image understanding research topics: human parsing and panoptic segmentation, and show Graphonomy can handle both of them well via a standard pipeline against current state-of-the-art approaches. Moreover, some extra benefit of our framework is demonstrated, e.g., generating the human parsing at various levels of granularity by unifying annotations across different datasets.

preprint2021arXiv

Internal Calibration of the PandaX-II Detector with Radon Gaseous Sources

We have developed a low-energy electron recoil (ER) calibration method with $^{220}$Rn for the PandaX-II detector. $^{220}$Rn, emanated from natural thorium compounds, was fed into the detector through the xenon purification system. From 2017 to 2019, we performed three dedicated calibration campaigns with different radon sources. We studied the detector response to $α$, $β$, and $γ$ particles with focus on low energy ER events. During the runs in 2017 and 2018, the amount of radioactivity of $^{222}$Rn were on the order of 1\% of that of $^{220}$Rn and thorium particulate contamination was negligible, especially in 2018. We also measured the background contribution from $^{214}$Pb for the first time in PandaX-II with the help from a $^{222}$Rn injection. Calibration strategy with $^{220}$Rn and $^{222}$Rn will be implemented in the upcoming PandaX-4T experiment and can be useful for other xenon-based detectors as well.

preprint2021arXiv

JUNO Physics and Detector

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. With 6 years of data, the neutrino mass ordering can be determined at 3-4 sigma and three oscillation parameters can be measured to a precision of 0.6% or better by detecting reactor antineutrinos. With 10 years of data, DSNB could be observed at 3-sigma; a lower limit of the proton lifetime of 8.34e33 years (90% C.L.) can be set by searching for p->nu_bar K^+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A core-collapse supernova at 10 kpc would lead to ~5000 IBD and ~2000 (300) all-flavor neutrino-proton (electron) scattering events. Geo-neutrinos can be detected with a rate of ~400 events/year. We also summarize the final design of the JUNO detector and the key R&D achievements. All 20-inch PMTs have been tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the >20 m attenuation length of LS, we expect a yield of 1345 p.e. per MeV and an effective energy resolution of 3.02%/\sqrt{E (MeV)}$ in simulations. The underwater electronics is designed to have a loss rate <0.5% in 6 years. With degassing membranes and a micro-bubble system, the radon concentration in the 35-kton water pool could be lowered to <10 mBq/m^3. Acrylic panels of radiopurity <0.5 ppt U/Th are produced. The 20-kton LS will be purified onsite. Singles in the fiducial volume can be controlled to ~10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a LS testing facility OSIRIS, and a near detector TAO.

preprint2021arXiv

LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRrNet to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research.

preprint2021arXiv

Light yield and field dependence measurement in PandaX-II dual-phase xenon detector

The dual-phase xenon time projection chamber (TPC) is one of the most sensitive detector technology for dark matter direct search, where the energy deposition of incoming particle can be converted into photons and electrons through xenon excitation and ionization. The detector response to signal energy deposition varies significantly with the electric field in liquid xenon. We study the detector's light yield and its dependence on the electric field in the PandaX-II dual-phase detector containing 580~kg liquid xenon in the sensitive volume. From our measurements, the light yield at electric fields from 0~V/cm to 317~V/cm is obtained for energy depositions up to 236~keV.

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

Non-Thermal Emergence of an Orbital-Selective Mott Phase in FeTe$_{1-x}$Se$_x$

Electronic correlation is of fundamental importance to high temperature superconductivity. Iron-based superconductors are believed to possess moderate correlation strength, which combined with their multi-orbital nature makes them a fascinating platform for the emergence of exotic phenomena. A particularly striking form is the emergence of an orbital selective Mott phase, where the localization of a subset of orbitals leads to a drastically reconstructed Fermi surface. Here, we report spectroscopic evidence of the reorganization of the Fermi surface from FeSe to FeTe as Se is substituted by Te. We uncover a particularly transparent way to visualize the localization of the $d_{xy}$ electron orbital through the suppression of its hybridization with the more coherent $d$ electron orbitals, which leads to a redistribution of the orbital-dependent spectral weight near the Fermi level. These noteworthy features of the Fermi surface are accompanied by a divergent behavior of a band renormalization in the $d_{xy}$ orbital. All of our observations are further supported by our theoretical calculations to be salient spectroscopic signatures of such a non-thermal evolution from a strongly correlated metallic phase towards an orbital-selective Mott phase in FeTe$_{1-x}$Se$_x$ as Se concentration is reduced.

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

Observation of an Unusual Colossal Anisotropic Magnetoresistance Effect in an Antiferromagnetic Semiconductor

Searching for novel antiferromagnetic materials with large magnetotransport response is highly demanded for constructing future spintronic devices with high stability, fast switching speed, and high density. Here we report a colossal anisotropic magnetoresistance effect in an antiferromagnetic binary compound with layered structure rare-earth dichalcogenide EuTe2. The AMR reaches 40000%, which is 4 orders of magnitude larger than that in conventional antiferromagnetic alloys. Combined magnetization, resistivity, and theoretical analysis reveal that the colossal AMR effect is attributed to a novel mechanism of vector-field tunable band structure, rather than the conventional spin-orbit coupling mechanism. Moreover, it is revealed that the strong hybridization between orbitals of Eu-layer with localized spin and Te-layer with itinerant carriers is extremely important for the large AMR effect. Our results suggest a new direction towards exploring AFM materials with prominent magnetotransport properties, which creates an unprecedented opportunity for AFM spintronics applications.

preprint2021arXiv

On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization} of model parameters (that we call meta-model) to rapidly adapt to new tasks using a small amount of labeled training data. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. In addition to generalization, robustness is also desired for a meta-model to defend adversarial examples (attacks). Toward promoting adversarial robustness in MAML, we first study WHEN a robustness-promoting regularization should be incorporated, given the fact that MAML adopts a bi-level (fine-tuning vs. meta-update) learning procedure. We show that robustifying the meta-update stage is sufficient to make robustness adapted to the task-specific fine-tuning stage even if the latter uses a standard training protocol. We also make additional justification on the acquired robustness adaptation by peering into the interpretability of neurons' activation maps. Furthermore, we investigate HOW robust regularization can efficiently be designed in MAML. We propose a general but easily-optimized robustness-regularized meta-learning framework, which allows the use of unlabeled data augmentation, fast adversarial attack generation, and computationally-light fine-tuning. In particular, we for the first time show that the auxiliary contrastive learning task can enhance the adversarial robustness of MAML. Finally, extensive experiments are conducted to demonstrate the effectiveness of our proposed methods in robust few-shot learning.

preprint2021arXiv

Results of Dark Matter Search using the Full PandaX-II Exposure

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

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

Superconductivity and Charge Density Wave of CuIr2Te4 by Iodine Doping

Here we report a systematic investigation on the evolution of the structural and physical properties, including the charge density wave and superconductivity of the polycrystalline CuIr2Te4-xIx. X-ray diffraction results indicate that both of a and c lattice parameters increase linearly. The resistivity measurements indicate that the charge density wave is destabilized with slight x but reappears when x is large than 0.9. Meanwhile, the superconducting transition temperature enhances as x raises and reaches a maximum value of around 2.95 K for the optimal composition CuIr2Te3.9I0.1 followed by a slight decrease with higher iodine doping content. The specific heat jump for the optimal composition CuIr2Te3.9I0.1 is approximately 1.46, which is close to the Bardeen Cooper Schrieffer value which is 1.43, indicating it is a bulk superconductor. The results of thermodynamic heat capacity measurements under different magnetic fields, magnetization and magneto-transport measurements further suggest that CuIr2Te4-xIx bulks are type II superconductors. Finally, an electronic phase diagram for this CuIr2Te4-xIx system has been constructed. The present study provides a suitable material platform for further investigation of the interplay of the CDW and superconductivity.

preprint2021arXiv

Superconductivity in CuIr2-xAlxTe4 telluride chalcogenides

The relationship between charge-density-wave (CDW) and superconductivity (SC), two vital physical phases in condensed matter physics, has always been the focus of scientists' research over the past decades. Motivated by this research hotspot, we systematically studied the physical properties of the layered telluride chalcogenide superconductors CuIr2-xAlxTe4. Through the resistance and magnetization measurements, we found that the CDW order was destroyed by a small amount of Al doping. Meanwhile, the superconducting transition temperature (Tc) kept changing with the change of doping amount and rose towards the maximum value of 2.75 K when x = 0.075. The value of normalized specific heat jump for the highest Tc sample CuIr1.925Al0.075Te4 was 1.53, which was larger than the BCS value of 1.43 and showed that bulk superconducting nature. In order to clearly show the relationship between SC and CDW states, we propose a phase diagram of Tc vs. doping content.

preprint2021arXiv

Temperature-Dependent Group Delay of Photonic-Bandgap Hollow-Core Fiber Tuned by Surface-Mode Coupling

Surface modes (SM) are highly spatially localized modes existing at the core-cladding interface of photonic-bandgap hollow-core fiber (PBG-HCF). When coupling with SM, the air modes (AM) in the core would suffer a higher loss despite being spectrally within the cladding photonic bandgap, and would be highly dispersive around the avoided crossing (anti-crossing) wavelength. In this paper, we numerically demonstrate that such avoided crossings can play an important role in the tuning of the temperature dependence of group delay of AM of PBG-HCF. At higher temperatures, both the thermal-optic effect and thermal expansion contribute to the redshift of avoided crossing wavelength, giving rise to a temperature dependence of the AM dispersion. Numerical simulations show that the redshift of avoided crossing can significantly tune the thermal coefficient of delay (TCD) of PBG-HCF from -400 ps/km/K to 400 ps/km/K, approximately -120 ppm/K to 120 ppm/K. In comparison with the known tuning mechanism by the thermal-induced redshift of photonic bandgap [Fokoua et al., Optica 4, 659, 2017], the tuning of TCD by SM coupling presents a much broader tuning range and higher efficiency. Our finding would provide a new route to design PBG-HCF for propagation time sensitive applications.

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 $A_{\rm CP}^Λ = (-3.7\pm11.7\pm9.0)\times10^{-3}$.

Meng Wang

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274 published item(s)

Building Digital Twins of Different Human Organs for Personalized Healthcare

Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion

Electronic Nematicity Revealed by Polarized Ultrafast Spectroscopy in Bilayer La$_3$Ni$_2$O$_7$

Electronic structures and superconductivity in Nd-doped La$_3$Ni$_2$O$_7$

Face Normal Estimation from Rags to Riches

FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture

GPS-Synchronized Monitoring of Core-collapse Supernova Bursts with PandaX-4T via Coherent Elastic Neutrino Nuclear Scattering

Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias

Lenses for Partially-Specified States (Extended Version)

MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization

Post-hoc Provider Fairness Adaptation via Hierarchical Exposure Alignment

Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine

Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling

DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing

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

Maximum principle for recursive optimal control problem of stochastic delay evolution equations

Proposal of detecting topological transition of quantum braiding in three-fold degenerate eigen subspace

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

A First Search for Solar $^8$B Neutrino in the PandaX-4T Experiment using Neutrino-Nucleus Coherent Scattering

Coexistence of zigzag antiferromagnetic order and superconductivity in compressed NiPSe3

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

Vocabulary-informed Zero-shot and Open-set Learning

A Review-aware Graph Contrastive Learning Framework for Recommendation

A Search for the Cosmic Ray Boosted Sub-GeV Dark Matter at the PandaX-II Experiment

A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment

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

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

Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy

Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach

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

Decomposition Without Regret

Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion

Downwash-aware Control Allocation for Over-actuated UAV Platforms

DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

Effect of iron vacancies on the magnetic order and spin dynamics of the spin ladder BaFe$_{2-δ}$S$_{1.5}$Se$_{1.5}$

Efficiently Embedding Dynamic Knowledge Graphs

Exchange field enhanced upper critical field of the superconductivity in compressed antiferromagnetic EuTe2

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

HoSIM: Higher-order Structural Importance based Method for Multiple Local Community Detection

How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

How hidden 3D structure within crack fronts reveals energy balance

Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis

KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences

Low Radioactive Material Screening and Background Control for the PandaX-4T Experiment

Mass Testing and Characterization of 20-inch PMTs for JUNO

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

Multi-modal Contrastive Representation Learning for Entity Alignment

Multi-modal Emotion Estimation for in-the-wild Videos

Nematic fluctuations in the non-superconducting iron pnictide BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$

Nested Bayesian Optimization for Computer Experiments

Neutron-induced nuclear recoil background in the PandaX-4T experiment

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 $η'\to e^+e^-e^+e^-$

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 mean-field control problems for backward doubly stochastic systems

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

Radiation hardness study on a CMOS pixel sensor for charged particle tracking

Readout electronics and data acquisition system of PandaX-4T experiment

Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation

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

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

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