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Yi Wang

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hep-th astro-ph.CO gr-qc hep-ph Computer Vision math.AP math.CO Machine Learning physics.ins-det cond-mat.mtrl-sci hep-ex math.DG math.DS eess.IV math-ph math.MP Artificial Intelligence astro-ph.SR Information Theory math.IT math.OC physics.optics Information Retrieval astro-ph.HE eess.SP physics.acc-ph astro-ph Computation and Language cond-mat.mes-hall eess.SY Systems and Control cond-mat.soft cond-mat.stat-mech cs.CY Data Structures and Algorithms Distributed, Parallel, and Cluster Computing eess.AS math.CA math.FA math.GT math.OA math.PR math.ST Multimedia Networking and Internet Architecture Neural and Evolutionary Computing Populations and Evolution quant-ph Robotics Sound Statistics Theory Applications cond-mat.dis-nn Databases Genomics Human-Computer Interaction math.AT math.GR math.SG Methodology Numerical Analysis physics.app-ph physics.atm-clus physics.data-an physics.flu-dyn Quantitative Methods Social and Information Networks Tissues and Organs

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preprint2026arXiv

AcademiClaw: When Students Set Challenges for AI Agents

Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad-level mathematics and linguistics problems to GPU-intensive reinforcement learning and full-stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi-dimensional rubrics combining six complementary techniques, with an independent five-category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55\% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine-grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real-world academic demands. All data and code are available at https://github.com/GAIR-NLP/AcademiClaw.

preprint2025arXiv

Prospect for measurement of CP-violating parameters of $B_s^0 \to ϕγ$ at the Tera Z factory

$b \to sγ$ transition is a critical flavor-changing neutral current (FCNC) process that could be used to probe CP violation (CPV) and new physics (NP). We quantify the anticipated precision for measuring $B_s^0 \to ϕγ$ at the CEPC Z pole operation, showing that the relative statistical uncertainty could be as low as 0.16\%, improved by approximately two orders of magnitude compared to existing measurements. Additionally, we perform a time-dependent analysis of the $B_s^0 \to ϕγ$ decay, accounting for $B_s^0/\bar{B}_s^0$ mixing extract the mixing-induced and CP-violating parameters $\boldsymbol{\mathcal{A}_{ϕγ}^Δ}$, $\boldsymbol{C_{ϕγ}}$ and $\boldsymbol{S_{ϕγ}}$. Using central value from LHCb measurement as input, we evaluate the anticipated accuracy of measurements of these parameters. The projected statistical uncertainties are $σ_{A_{ϕγ}^Δ{}^{\text{stat}}} = 0.021$, $σ_C^{\text{stat}} = 0.0092$ and $σ_S^{\text{stat}} = 0.0096$, and the systematic uncertainties are $σ_{A_{ϕγ}^Δ{}^{\text{syst}}} = 0.035$, $σ_C^{\text{syst}} = 0.0027$ and $σ_S^{\text{syst}} = 0.0064$. Furthermore, the 1$σ$ sensitivity boundaries for NP in this study are found to be $\mathcal{A}_{ϕγ}^Δ< -0.05$ or $\mathcal{A}_{ϕγ}^Δ> 0.15$, $\mathcal{C}_{ϕγ} < -0.02$ or $\mathcal{C}_{ϕγ} > 0.04$, and $\mathcal{S}_{ϕγ} < -0.04$ or $\mathcal{S}_{ϕγ} > 0.04$. We also conduct a relevant detector optimization study by establishing the correlation between the anticipated precision and the intrinsic resolution of the ECAL, as well as the performance of the PID system.

preprint2024arXiv

InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.

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

VideoChat: Chat-Centric Video Understanding

In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we build a video-centric instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and captures causal relationships, providing a valuable asset for training our chat-centric video understanding system. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications, which could serve as a simple prototype system for future research on chat-centric video understanding. Access our code and data at https://github.com/OpenGVLab/Ask-Anything

preprint2023arXiv

Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis

With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals. Based on the cases of sewage sampling and testing in some regions such as Hong Kong, Brazil, and the United States, the feasibility of detecting the novel coronavirus in sewage is extremely high. This study re-views domestic and international achievements in detecting SARS-CoV-2 through WBE and summarizes four aspects of COVID-19, including sampling methods, virus decay rate cal-culation, standardized population coverage of the watershed, algorithm prediction, and provides ideas for combining field modeling with epidemic prevention and control. Moreover, we highlighted some diagnostic techniques for detection of the virus from sew-age sample. Our review is a new approach in identification of the research gaps in waste water-based epidemiology and diagnosis and we also predict the future prospect of our analysis.

preprint2023arXiv

Microlensing effect of charged spherically symmetric wormhole

We systematically investigate the microlensing effect of charged spherically symmetric wormhole, where the light source is remote from the throat. Remarkably, there will be at most three images by considering the charge part. We study all situations including three images, two images, and one image, respectively. The numerical result shows that the range of total magnification is from $10^5$ to $10^{-2}$ depending on various metrics. In the case of three images, there will be two maximal values of magnification (a peak, and a gentle peak) when the contribution via mass is much less than that of charge. However, we cannot distinguish the case that forms three images or only one image as the total magnification is of order $10^5$. Finally, our theoretical investigation could shed new light on exploring the wormhole with the microlensing effect.

preprint2023arXiv

Positivity problem of three-term recurrence sequences

We present some necessary and/or sufficient conditions for the positivity problem of three-term recurrence sequences. As applications we show the positivity of diagonal Taylor coefficients of some rational functions in a unified approach. We also establish a criterion for the positivity and log-convexity of such sequences.

preprint2022arXiv

1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)

This report presents the methods of the winning entry of the RxR-Habitat Competition in CVPR 2022. The competition addresses the problem of Vision-and-Language Navigation in Continuous Environments (VLN-CE), which requires an agent to follow step-by-step natural language instructions to reach a target. We present a modular plan-and-control approach for the task. Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller. In each decision loop, CWP first predicts a set of candidate waypoints based on depth observations from multiple views. It can reduce the complexity of the action space and facilitate planning. Then, a history-enhanced planner is adopted to select one of the candidate waypoints as the subgoal. The planner additionally encodes historical memory to track the navigation progress, which is especially effective for long-horizon navigation. Finally, we propose a non-parametric heuristic controller named tryout to execute low-level actions to reach the planned subgoal. It is based on the trial-and-error mechanism which can help the agent to avoid obstacles and escape from getting stuck. All three modules work hierarchically until the agent stops. We further take several recent advances of Vision-and-Language Navigation (VLN) to improve the performance such as pretraining based on large-scale synthetic in-domain dataset, environment-level data augmentation and snapshot model ensemble. Our model won the RxR-Habitat Competition 2022, with 48% and 90% relative improvements over existing methods on NDTW and SR metrics respectively.

preprint2022arXiv

A Comprehensive Review of Deep Learning-based Single Image Super-resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

preprint2022arXiv

A Global Modeling Approach for Load Forecasting in Distribution Networks

Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations. Because distribution networks include a large amount of different loads at various aggregation levels, such as individual consumers, transformer stations and feeders loads, it is impractical to develop individual (or so-called local) forecasting models for each load separately. Furthermore, such local models ignore the strong dependencies between different loads that might be present due to their spatial proximity and the characteristics of the distribution network. To address these issues, this paper proposes a global modeling approach based on deep learning for efficient forecasting of a large number of loads in distribution networks. In this way, the computational burden of training a large amount of local forecasting models can be largely reduced, and the cross-series information shared among different loads can be utilized. Additionally, an unsupervised localization mechanism and optimal ensemble construction strategy are also proposed to localize/personalize the forecasting model to different groups of loads and to improve the forecasting accuracy further. Comprehensive experiments are conducted on real-world smart meter data to demonstrate the superiority of the proposed approach compared to competing methods.

preprint2022arXiv

A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans

This is the technical report of the 9th place in the final result of PARSE2022 Challenge. We solve the segmentation problem of the pulmonary artery by using a two-stage method based on a 3D CNN network. The coarse model is used to locate the ROI, and the fine model is used to refine the segmentation result. In addition, in order to improve the segmentation performance, we adopt multi-view and multi-window level method, at the same time we employ a fine-tune strategy to mitigate the impact of inconsistent labeling.

preprint2022arXiv

A Solution to the Temperature Evolution of Multi-well Free-energy Landscape

It has been a grand challenge to resolve the temperature evolution of multi-well free-energy landscape which is fundamentally relevant to phase transitions and associated critical phenomena as listed by Ginzburg [Rev. Mod. Phys. 76 (2004) 981]. To address this challenge, here we provide a simple solution based on a priori concept of Boltzmann thermal mixing among multiple parabolic potentials. The success and the impact of the present approach have been extensively demonstrated using a variety of materials, including Nb, YBa2Cu3O(7-x), Ca3Ru2O7, Ni, BiFeO3, and PbTiO3.

preprint2022arXiv

Beam Training and Tracking in MmWave Communication: A Survey

Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

preprint2022arXiv

Calibration-Free Travel Time After Photobleaching Velocimetry

In interfacial science, there is an increasing need to measure flow velocity fields at interfaces with ultrahigh spatial and temporal resolution to study transport phenomena. Although laser-induced fluorescence photobleaching anemometry (LIFPA) has achieved nanoscopic resolution for flow measurement, it requires pre-calibration, which is unavailable for unknown flows. We present a novel, calibration-free travel time after photobleaching velocimeter (TTAPV) which can both measure fluid flow velocity and satisfy the long-anticipated need of calibration for LIFPA.

preprint2022arXiv

Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection. The baseline Conformer system trained with speed perturbation and SpecAugment based data augmentation is significantly improved by incorporating a set of purposefully designed modeling features, including neural architecture search based auto-configuration of domain-specific Conformer hyper-parameters in addition to parameter fine-tuning; fine-grained elderly speaker adaptation using learning hidden unit contributions (LHUC); and two-pass cross-system rescoring based combination with hybrid TDNN systems. An overall word error rate (WER) reduction of 13.6% absolute (34.8% relative) was obtained on the evaluation data of 48 elderly speakers. Using the final systems' recognition outputs to extract textual features, the best-published speech recognition based AD detection accuracy of 91.7% was obtained.

preprint2022arXiv

Cutting Rule for Cosmological Collider Signals: A Bulk Evolution Perspective

We show that the evolution of interacting massive particles in the de Sitter bulk can be understood at leading order as a series of resonant decay and production events. From this perspective, we classify the cosmological collider signals into local and nonlocal categories with drastically different physical origins. This further allows us to derive a cutting rule for efficiently extracting these cosmological collider signals in an analytical fashion. Our cutting rule is a practical way for extracting cosmological collider signals in model building, and can be readily implemented as symbolic computational packages in the future.

preprint2022arXiv

Deformation retraction of the group of strict contactomorphisms of the three-sphere to the unitary group

We prove that the group of strict contactomorphisms of the standard tight contact structure on the three-sphere deformation retracts to its unitary subgroup U(2).

preprint2022arXiv

Exploring linguistic feature and model combination for speech recognition based automatic AD detection

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of such specialist data leads to uncertainty in both model selection and feature learning when developing such systems. To this end, this paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders on limited data, before the resulting embedding features being fed into an ensemble of backend classifiers to produce the final AD detection decision via majority voting. Experiments conducted on the ADReSS20 Challenge dataset suggest consistent performance improvements were obtained using model and feature combination in system development. State-of-the-art AD detection accuracies of 91.67 percent and 93.75 percent were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.

preprint2022arXiv

First report of a solar energetic particle event observed by China's Tianwen-1 mission in transit to Mars

Solar energetic particles (SEPs) associated with flares and/or coronal mass ejection (CME)-driven shocks can impose acute radiation hazards to space explorations. To measure energetic particles in near-Mars space, the Mars Energetic Particle Analyzer (MEPA) instrument onboard China's Tianwen-1 (TW-1) mission was designed. Here, we report the first MEPA measurements of the widespread SEP event occurring on 29 November 2020 when TW-1 was in transit to Mars. This event occurred when TW-1 and Earth were magnetically well connected, known as the Hohmann-Parker effect, thus offering a rare opportunity to understand the underlying particle acceleration and transport process. Measurements from TW-1 and near-Earth spacecraft show similar double-power-law spectra and a radial dependence of the SEP peak intensities. Moreover, the decay phases of the time-intensity profiles at different locations clearly show the reservoir effect. We conclude that the double-power-law spectrum is likely generated at the acceleration site, and that a small but finite cross-field diffusion is crucial to understand the formation of the SEP reservoir phenomenon. These results provide insight into particle acceleration and transport associated with CME-driven shocks, which may contribute to the improvement of relevant physical models.

preprint2022arXiv

Homologous Coronal Mass Ejections Caused by Recurring Formation and Disruption of Current Sheet within a Sheared Magnetic Arcade

The Sun often produces coronal mass ejections with similar structure repeatedly from the same source region, and how these homologous eruptions are initiated remains an open question. Here, by using a new magnetohydrodynamic simulation, we show that homologous solar eruptions can be efficiently produced by recurring formation and disruption of coronal current sheet as driven by continuously shearing of the same polarity inversion line within a single bipolar configuration. These eruptions are initiated by the same mechanism, in which an internal current sheet forms slowly in a gradually sheared bipolar field and reconnection of the current sheet triggers and drives the eruption. Each of the eruptions does not release all the free energy but with a large amount left in the post-flare arcade below the erupting flux rope. Thus, a new current sheet can be more easily formed by further shearing of the post-flare arcade than by shearing a potential field arcade, and this is favorable for producing the next eruption. Furthermore, it is found that the new eruption is stronger since the newly formed current sheet has a larger current density and a lower height. In addition, our results also indicate the existence of a magnetic energy threshold for a given flux distribution, and eruption occurs once this threshold is approached.

preprint2022arXiv

Long-run User Value Optimization in Recommender Systems through Content Creation Modeling

Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophisticated solutions than off-the-shelf simple recommender algorithms. In this paper we lay out such a solution to optimize \textit{long-run} user value through discounted utility maximization and a machine learning method we have developed for estimating it. Our method estimates which content producers are most likely to create the highest long-run user value if their content is shown more to users who enjoy it in the present. We do this estimation with the help of an A/B test and heterogeneous effects machine learning model. We have used such models in Facebook's feed ranking system, and such a model can be used in other recommender systems.

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

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel transformer-based model for large hole inpainting, which unifies the merits of transformers and convolutions to efficiently process high-resolution images. We carefully design each component of our framework to guarantee the high fidelity and diversity of recovered images. Specifically, we customize an inpainting-oriented transformer block, where the attention module aggregates non-local information only from partial valid tokens, indicated by a dynamic mask. Extensive experiments demonstrate the state-of-the-art performance of the new model on multiple benchmark datasets. Code is released at https://github.com/fenglinglwb/MAT.

preprint2022arXiv

Monitoring Urban Forests from Auto-Generated Segmentation Maps

We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for semantic segmentation typically depends on the availability of high-quality labels. We evaluate the benefit of high-resolution, three-dimensional point cloud data (LiDAR) as source of noisy labels in order to train models for the localization of trees in orthophotos. As proof of concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New York City (NYC) and reference it to less impacted urban space in Brooklyn, NYC.

preprint2022arXiv

Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification

Re-ranking utilizes contextual information to optimize the initial ranking list of person or vehicle re-identification (re-ID), which boosts the retrieval performance at post-processing steps. This paper proposes a re-ranking network to predict the correlations between the probe and top-ranked neighbor samples. Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serving as discriminative combination weights. The combination process is equivalent to moving independent embeddings toward the identity centers, improving cluster compactness. For correlation prediction, we first aggregate the contextual information for probe's k-nearest neighbors via the Transformer encoder. Then, we distill and refine the probe-related features into the Contextual Memory cell via attention mechanism. Like humans that retrieve images by not only considering probe images but also memorizing the retrieved ones, the Contextual Memory produces multi-view descriptions for each instance. Finally, the neighbors are reconstructed with features fetched from the Contextual Memory, and a binary classifier predicts their correlations with the probe. Experiments on six widely-used person and vehicle re-ID benchmarks demonstrate the effectiveness of the proposed method. Especially, our method surpasses the state-of-the-art re-ranking approaches on large-scale datasets by a significant margin, i.e., with an average 4.83% CMC@1 and 14.83% mAP improvements on VERI-Wild, MSMT17, and VehicleID datasets.

preprint2022arXiv

Nonlinear stability of planar viscous shock wave to three-dimensional compressible Navier-Stokes equations

We prove the nonlinear stability of the planar viscous shock up to a time-dependent shift for the three-dimensional (3D) compressible Navier-Stokes equations under the generic perturbations, in particular, without zero mass conditions. Moreover, the time-dependent shift function keeps the shock profile shape time-asymptotically. Our stability result is unconditional for the weak planar Navier-Stokes shock. Our proof is motivated by the $a$-contraction method (a kind of weighted $L^2$-relative entropy method) with time-dependent shift introduced in [10,11,13] for the stability of viscous shock in one-dimensional (1D) case. Instead of the classical anti-derivative techniques, we perform the stability analysis of planar Navier-Stokes shock in original $H^2$-perturbation framework and therefore zero mass conditions are not necessarily needed, which, in turn, brings out the essential difficulties due to the compressibility of viscous shock. Furthermore, compared with 1D case, there are additional difficulties coming from the wave propagation along the multi-dimensional transverse directions and their interactions with the viscous shock. To overcome these difficulties, a multi-dimensional version sharp weighted Poincar${\rm \acute{e}}$ inequality (see Lemma 3.1), $a$-contraction techniques with time-dependent shift, and some essential physical structures of the multi-dimensional Navier-Stokes system are fully used.

preprint2022arXiv

Numerical prescriptions of early-time divergences of the in-in formalism

In quantum field theory, the in and out states can be related to the full Hamiltonian by the $iε$ prescription. A Wick rotation can further bring the correlation functions to Euclidean spacetime where the integrals are better defined. This setup is convenient for analytical calculations. However, for numerical calculations, an infinitesimal $ε$ or a Wick rotation of numerical functions are difficult to implement. We propose two new numerical methods to solve this problem, namely an Integral Basis method based on linear regression and a Beta Regulator method based on Cesàro/Riesz summation. Another class of partition-extrapolation methods previously used in electromagnetic engineering is also introduced. We benchmark these methods with existing methods using in-in formalism integrals, indicating advantages of these new methods over the existing methods in computation time and accuracy.

preprint2022arXiv

Numerical Simulation of Solar Magnetic Flux Emergence Using the AMR--CESE--MHD Code

Magnetic flux emergence from the solar interior to the atmosphere is believed to be a key process of formation of solar active regions and driving solar eruptions. Due to the limited capability of observation, the flux emergence process is commonly studied using numerical simulations. In this paper, we developed a numerical model to simulate the emergence of a twisted magnetic flux tube from the convection zone to the corona using the AMR--CESE--MHD code, which is based on the conservation-element solution-element method with adaptive mesh refinement. The result of our simulation agrees with that of many previous ones with similar initial conditions but using different numerical codes. In the early stage, the flux tube rises from the convection zone as driven by the magnetic buoyancy until it reaches close to the photosphere. The emergence is decelerated there and with piling-up of the magnetic flux, the magnetic buoyancy instability is triggered, which allows the magnetic field to partially enter into the atmosphere. Meanwhile, two gradually separated polarity concentration zones appear in the photospheric layer, transporting the magnetic field and energy into the atmosphere through their vortical and shearing motions. Correspondingly, the coronal magnetic field has also been reshaped to a sigmoid configuration containing a thin current layer, which resembles the typical pre-eruptive magnetic configuration of an active region. Such a numerical framework of magnetic flux emergence as established will be applied in future investigations of how solar eruptions are initiated in flux emergence active regions.

preprint2022arXiv

On the $p$-essential normality of principal submodules of the Bergman module on strongly pseudoconvex domains

In this paper, we show that under a mild condition, a principal submodule of the Bergman module on a bounded strongly pseudoconvex domain with smooth boundary in $\mathbb{C}^n$ is $p$-essentially normal for all $p>n$. This improves a previous result by the first author and K. Wang, in which it was shown that any polynomial-generated principal submodule of the Bergman module on the unit ball $\mathbb{B}_n$ is $p$-essentially normal for all $p>n$. As a consequence, we show that the submodule of $L_a^2(\mathbb{B}_n)$ consisting of functions vanishing on an analytic subset of pure codimension $1$ is $p$-essentially normal for all $p>n$.

preprint2022arXiv

Prevalent behavior and almost sure Poincare-Bendixson Theorem for smooth flows with invariant k-cones

We investigate the global dynamics from a measure-theoretic perspective for smooth flows with invariant cones of rank k. For such systems, it is shown that prevalent (or equivalently, almost all) orbits will be pseudo-ordered or convergent to equilibria. This reduces to Hirsch's prevalent convergence Theorem if the rank k=1; and implies an almost-sure Poincare-Bendixson Theorem for the case k=2. These results are then applied to obtain an almost sure Poincare-Bendixson theorem for high-dimensional differential equations.

preprint2022arXiv

Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction

Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate. In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality. For each one-shot segment in a video, the proposed method firstly extracts spatial, temporal and pre-coding features by an ultra fast pre-process. Based on these features, a RF parameter is predicted by a deep neural network. Video encoder uses the RF to compress segment as the first encoding pass. Then VMAF quality of the first pass encoding is measured. If the quality doesn't meet target, a second pass RF prediction and encoding will be performed. With the help of first pass predicted RF and corresponding actual quality as feedback, the second pass prediction will be highly accurate. Experiments show the proposed method requires only 1.55 times encoding complexity on average, meanwhile the accuracy, that the compressed video's actual VMAF is within $\pm1$ around the target VMAF, reaches 98.88%.

preprint2022arXiv

Quantitative Quermassintegral Inequalities for Nearly Spherical Sets

In this paper, we establish quantitative Alexandrov-Fenchel inequalities for quermassintegrals on nearly spherical sets. In particular, we bound the $(k,m)$-isoperimetric deficit from below by the Frankael asymmetry. We also find a lower bound on the $(k,m)$-isoperimetric deficit using the spherical deviation.

preprint2022arXiv

Robust Anomaly Detection for Time-series Data

Time-series anomaly detection plays a vital role in monitoring complex operation conditions. However, the detection accuracy of existing approaches is heavily influenced by pattern distribution, existence of multiple normal patterns, dynamical features representation, and parameter settings. For the purpose of improving the robustness and guaranteeing the accuracy, this research combined the strengths of negative selection, unthresholded recurrence plots, and an extreme learning machine autoencoder and then proposed robust anomaly detection for time-series data (RADTD), which can automatically learn dynamical features in time series and recognize anomalies with low label dependency and high robustness. Yahoo benchmark datasets and three tunneling engineering simulation experiments were used to evaluate the performance of RADTD. The experiments showed that in benchmark datasets RADTD possessed higher accuracy and robustness than recurrence qualification analysis and extreme learning machine autoencoder, respectively, and that RADTD accurately detected the occurrence of tunneling settlement accidents, indicating its remarkable performance in accuracy and robustness.

preprint2022arXiv

Scale-invariant enhancement of gravitational waves during inflation

The inflationary 1-loop tensor power spectrum from an excited spectator scalar field is calculated. Recent studies on primordial black holes suggest that the inflationary curvature perturbation may be huge on small scales. An enhanced curvature perturbation may arise from a drastic enhancement of spectator scalar field fluctuations. In this letter, using the in-in formalism, we calculate 1-loop quantum corrections to primordial gravitational waves by such an excited spectator field with a sharp peak in momentum space. We find scale-invariant loop corrections in this full quantum setup, in contrast to the sharply peaked corrections in the previously calculated scalar-induced tensor modes. Especially, on super Hubble scales, the primordial gravitational waves are also amplified, which can be understood as a Bogolyubov transformation of the vacuum due to the excited scalar field. This mechanism allows us to probe the scalar field properties on extremely short-distance scales with the current and future cosmic microwave background and gravitational wave experiments, opening a novel window for inflationary cosmology.

preprint2022arXiv

Self-supervised Learning in Remote Sensing: A Review

In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.

preprint2022arXiv

Self-supervised Vision Transformers for Joint SAR-optical Representation Learning

Self-supervised learning (SSL) has attracted much interest in remote sensing and earth observation due to its ability to learn task-agnostic representations without human annotation. While most of the existing SSL works in remote sensing utilize ConvNet backbones and focus on a single modality, we explore the potential of vision transformers (ViTs) for joint SAR-optical representation learning. Based on DINO, a state-of-the-art SSL algorithm that distills knowledge from two augmented views of an input image, we combine SAR and optical imagery by concatenating all channels to a unified input. Subsequently, we randomly mask out channels of one modality as a data augmentation strategy. While training, the model gets fed optical-only, SAR-only, and SAR-optical image pairs learning both inner- and intra-modality representations. Experimental results employing the BigEarthNet-MM dataset demonstrate the benefits of both, the ViT backbones and the proposed multimodal SSL algorithm DINO-MM.

preprint2022arXiv

Spectra of weighted uniform hypertrees

Let $T$ be a $k$-tree equipped with a weighting function $\w: V(T)\cup E(T)\rightarrow \C$, where $k \geq 3$. The weighted matching polynomial of the weighted $k$-tree $(T,\w)$ is defined to be $$ μ(T,\w,x)= \sum_{M \in \mathcal{M}(T)}(-1)^{|M|}\prod_{e \in E(M)}\mathbf{w}(e)^k \prod_{v \in V(T)\backslash V(M)}(x-\w(v)), $$ where $\mathcal{M}(T)$ denotes the set of matchings (including empty set) of $T$. In this paper, we investigate the eigenvalues of the adjacency tensor $\A(T,\w)$ of the weighted $k$-tree $(T,\w)$. The main result provides that $\w(v)$ is an eigenvalue of $\A(T,\w)$ for every $v\in V(T)$, and if $λ\neq \w(v)$ for every $v\in V(T)$, then $λ$ is an eigenvalue of $\A(T,\w)$ if and only if there exists a subtree $T'$ of $T$ such that $λ$ is a root of $μ(T',\w,x)$. Moreover, the spectral radius of $\A(T,\w)$ is equal to the largest root of $μ(T,\w,x)$ when $\w$ is real and nonnegative. The result extends a work by Clark and Cooper ({\em On the adjacency spectra of hypertrees, Electron. J. Combin., 25 (2)(2018) $\#$P2.48}) to weighted $k$-trees. As applications, two analogues of the above work for the Laplacian and the signless Laplacian tensors of $k$-trees are obtained.

preprint2022arXiv

Stability of quermassintegral inequalities along inverse curvature flows

In this paper, we consider the stability of quermassintegral inequalities along a inverse curvature flow. We choose a special rescaling of the flow such that the $k$-th quermassintegral is decreasing and the $k-1$-th quermassintegral is preserved. Along this rescaled flow, we prove that the decreasing rate of the $k$-th quermassintegral is faster than the Fraenkel asymmetry of the domain when approaching to the sphere. This leads to the stability inequality of quermassintegral inequalities for nearly spherical sets using the flow method.

preprint2022arXiv

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

In this paper, we propose a three-stream adaptive fusion network named TAFNet, which uses paired RGB and thermal images for crowd counting. Specifically, TAFNet is divided into one main stream and two auxiliary streams. We combine a pair of RGB and thermal images to constitute the input of main stream. Two auxiliary streams respectively exploit RGB image and thermal image to extract modality-specific features. Besides, we propose an Information Improvement Module (IIM) to fuse the modality-specific features into the main stream adaptively. Experiment results on RGBT-CC dataset show that our method achieves more than 20% improvement on mean average error and root mean squared error compared with state-of-the-art method. The source code will be publicly available at https://github.com/TANGHAIHAN/TAFNet.

preprint2022arXiv

Termination of Superradiance from a Binary Companion

We study the impact of a binary companion on black hole superradiance at orbital frequencies away from the gravitational-collider-physics (GCP) resonance bands. A superradiant state can couple to a strongly absorptive state via the tidal perturbation of the companion, thereby acquiring a suppressed superradiance rate. Below a critical binary separation, this superradiance rate becomes negative, and the boson cloud gets absorbed by the black hole. This critical binary separation leads to tight constraints on GCP. Especially, a companion with mass ratio $q>10^{-3}$ invalidates all GCP fine structure transitions, as well as almost all Bohr transitions except those from the $|ψ_{211}\rangle$ state. Meanwhile, the backreaction on the companion manifests itself as a torque acting on the binary, producing floating/sinking orbits that can be verified via pulsar timing. In addition, the possible termination of cloud growth may help to alleviate the current bounds on the ultralight boson mass from various null detections.

preprint2022arXiv

The Yamabe flow on asymptotically Euclidean manifolds with nonpositive Yamabe constant

We study the Yamabe flow on asymptotically flat manifolds with non-positive Yamabe constant $Y\leq 0$. Previous work by the second and third named authors \cite{ChenWang} showed that while the Yamabe flow always converges in a global weighted sense when $Y>0$, the flow must diverge when $Y\leq 0$. We show here in the $Y\leq 0$ case however that after suitable rescalings, the Yamabe flow starting from any asymptotically flat manifold must converge to the unique positive function which solves the Yamabe problem on a compactification of the original manifold.

preprint2022arXiv

Thermodynamic modeling with uncertainty quantification using the modified quasichemical model in quadruplet approximation: Implementation into PyCalphad and ESPEI

The modified quasichemical model in the quadruplet approximation (MQMQA) considers the first- and the second-nearest-neighbor coordination and interactions, particularly useful in describing short-range ordering in complex liquids such as molten salts, slag in metal processing, and electrolytic solutions. The present work implements the MQMQA into the Python based open-source software PyCalphad for thermodynamic calculations. This endeavor facilitates the development of MQMQA-based thermodynamic database with uncertainty quantification (UQ) using the open-source software ESPEI. A new database structure based on Extensible Markup Language (XML) is proposed for ESPEI evaluation of MQMQA model parameters. Using the KF-NiF2 system as an example, we demonstrate the successful implementation of MQMQA in PyCalphad through thermodynamic calculations of Gibbs energy, equilibrium quadruplet fractions, and phase diagram, as well as database development with UQ using ESPEI. The present implementation offers an open-source capability for performing CALPHAD modeling for complex liquids with short-range ordering using MQMQA.

preprint2022arXiv

Towards Implicit Text-Guided 3D Shape Generation

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the given text description. This work has several technical contributions. First, we decouple the shape and color predictions for learning features in both texts and shapes, and propose the word-level spatial transformer to correlate word features from text with spatial features from shape. Also, we design a cyclic loss to encourage consistency between text and shape, and introduce the shape IMLE to diversify the generated shapes. Further, we extend the framework to enable text-guided shape manipulation. Extensive experiments on the largest existing text-shape benchmark manifest the superiority of this work. The code and the models are available at https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation.

preprint2022arXiv

Ultrahigh-energy Gamma Rays and Gravitational Waves from Primordial Exotic Stellar Bubbles

We put forward a novel class of exotic celestial objects that can be produced through phase transitions occurred in the primordial Universe. These objects appear as bubbles of stellar sizes and can be dominated by primordial black holes (PBHs). We report that, due to the processes of Hawking radiation and binary evolution of PBHs inside these stellar bubbles, both electromagnetic and gravitational radiations can be emitted that are featured on the gamma-ray spectra and stochastic gravitational waves (GWs). Our results reveal that, depending on the mass distribution, the exotic stellar bubbles consisting of PBHs provide not only a decent fit for the ultrahigh-energy gamma-ray spectrum reported by the recent LHAASO experiment, but also predict GW signals that are expected to be tested by the forthcoming GW surveys.

preprint2022arXiv

UMSNet: An Universal Multi-sensor Network for Human Activity Recognition

Human activity recognition (HAR) based on multimodal sensors has become a rapidly growing branch of biometric recognition and artificial intelligence. However, how to fully mine multimodal time series data and effectively learn accurate behavioral features has always been a hot topic in this field. Practical applications also require a well-generalized framework that can quickly process a variety of raw sensor data and learn better feature representations. This paper proposes a universal multi-sensor network (UMSNet) for human activity recognition. In particular, we propose a new lightweight sensor residual block (called LSR block), which improves the performance by reducing the number of activation function and normalization layers, and adding inverted bottleneck structure and grouping convolution. Then, the Transformer is used to extract the relationship of series features to realize the classification and recognition of human activities. Our framework has a clear structure and can be directly applied to various types of multi-modal Time Series Classification (TSC) tasks after simple specialization. Extensive experiments show that the proposed UMSNet outperforms other state-of-the-art methods on two popular multi-sensor human activity recognition datasets (i.e. HHAR dataset and MHEALTH dataset).

preprint2022arXiv

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

Characterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the heterogeneous nature of uncertainty or inadequate to perform well with unknown mathematical models. In this paper, we propose using multi-layer convolutional neural networks for empirical uncertainty estimation and feature extraction of nuclear pulse signals. This method is based on deep learning, a recent development of machine learning techniques, which learns the desired mapping function from training data and generalizes to unseen test data. Furthermore, ensemble learning is utilized to estimate the uncertainty originating from trainable parameters of the network and to improve the robustness of the whole model. To evaluate the performance of the proposed method, simulation studies, in comparison with curve fitting, investigate extensive conditions and show its universal applicability. Finally, a case study is made using data from a NICA-MPD electromagnetic calorimeter module exposed to a test beam at DESY, Germany. The uncertainty estimation method successfully detected out-of-distribution samples and also achieved good accuracy in time and energy measurements.

preprint2022arXiv

WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis

Recently, GAN-based neural vocoders such as Parallel WaveGAN, MelGAN, HiFiGAN, and UnivNet have become popular due to their lightweight and parallel structure, resulting in a real-time synthesized waveform with high fidelity, even on a CPU. HiFiGAN and UnivNet are two SOTA vocoders. Despite their high quality, there is still room for improvement. In this paper, motivated by the structure of Vision Outlooker from computer vision, we adopt a similar idea and propose an effective and lightweight neural vocoder called WOLONet. In this network, we develop a novel lightweight block that uses a location-variable, channel-independent, and depthwise dynamic convolutional kernel with sinusoidally activated dynamic kernel weights. To demonstrate the effectiveness and generalizability of our method, we perform an ablation study to verify our novel design and make a subjective and objective comparison with typical GAN-based vocoders. The results show that our WOLONet achieves the best generation quality while requiring fewer parameters than the two neural SOTA vocoders, HiFiGAN and UnivNet.

preprint2021arXiv

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

In this paper, we propose a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations (i.e., objects are labeled with points) to estimate both the center points and sizes of crowded objects. Specifically, during training, we utilize the available point annotations to supervise the estimation of the center points of objects directly. Based on a locally-uniform distribution assumption, we initialize pseudo object sizes from the point-level supervisory information, which are then leveraged to guide the regression of object sizes via a crowdedness-aware loss. Meanwhile, we propose a confidence and order-aware refinement scheme to continuously refine the initial pseudo object sizes such that the ability of the detector is increasingly boosted to detect and count objects in crowds simultaneously. Moreover, to address extremely crowded scenes, we propose an effective decoding method to improve the detector's representation ability. Experimental results on the WiderFace benchmark show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks, i.e., our method improves the average precision by more than 10% and reduces the counting error by 31.2%. Besides, our method obtains the best results on the crowd counting and localization datasets (i.e., ShanghaiTech and NWPU-Crowd) and vehicle counting datasets (i.e., CARPK and PUCPR+) compared with state-of-the-art counting-by-detection methods. The code will be publicly available at https://github.com/WangyiNTU/Point-supervised-crowd-detection.

preprint2021arXiv

Almost automorphy of minimal sets for $C^1$-smooth strongly monotone skew-product semiflows on Banach spaces

We focus on the presence of almost automorphy in strongly monotone skew-product semiflows on Banach spaces. Under the $C^1$-smoothness assumption, it is shown that any linearly stable minimal set must be almost automorphic. This extends the celebrated result of Shen and Yi [Mem. Amer. Math. Soc. 136(1998), No. 647] for the classical $C^{1,α}$-smooth systems. Based on this, one can reduce the regularity of the almost periodically forced differential equations and obtain the almost automorphic phenomena in a wider range.

preprint2021arXiv

Directional Design of Materials Based on the Multi-Objective Optimization: A Case Study of Two-Dimensional Thermoelectric SnSe

Directional design of functional materials with multi-objective constraints is a big challenge, whose performance and stability are determined by different physics factors entangled with each other complicatedly. In this work, we apply the multi-objective optimization based on the Pareto Efficiency and Particle-Swarm Optimization methods to design new functional materials directionally. As a demonstration, we achieve the thermoelectric design of 2D SnSe materials through the methods. We identify several novel metastable 2D SnSe structures with simultaneously lower free energy and better thermoelectric performance over the experimentally-reported monolayer structures. We hope our results about the multi-objective Pareto Optimization method can make a step towards the integrative design of multi-objective and multi-functional materials in the future.

preprint2021arXiv

Gravitational Collider Physics via Pulsar-Black Hole Binaries II: Fine and Hyperfine Structures are Favored

A rotating black hole can be clouded by light bosons via superradiance, and thus acquire an atom-like structure. If such a gravitational atom system is companioned with a pulsar, the pulsar can trigger transitions between energy levels of the gravitational atom, and these transitions can be detected by pulsar timing. We show that in such pulsar-black hole systems, fine and hyperfine structure transitions are more likely to be probed than the Bohr transition. Also, the calculation of these fine and hyperfine structure transitions are under better analytic control. Thus, these fine and hyperfine structure transitions are more ideal probes in the search for gravitational collider signals in pulsar-black hole systems.

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

NeRD: Neural Representation of Distribution for Medical Image Segmentation

We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution. Using NeRD, we propose an end-to-end deep learning model for medical image segmentation that can compensate the negative impact of feature distribution shifting issue caused by commonly used network operations such as padding and pooling. An implicit function is used to represent the parameter space of the feature distribution by querying the image coordinate. With NeRD, the impact of issues such as over-segmenting and missing have been reduced, and experimental results on the challenging white matter lesion segmentation and left atrial segmentation verify the effectiveness of the proposed method. The code is available via https://github.com/tinymilky/NeRD.

preprint2021arXiv

Numerical Simulation of a Fundamental Mechanism of Solar Eruption with Different Magnetic Flux Distributions

Solar eruptions are explosive release of coronal magnetic field energy as manifested in solar flares and coronal mass ejection. Observations have shown that the core of eruption-productive regions are often a sheared magnetic arcade, i.e., a single bipolar configuration, and, particularly, the corresponding magnetic polarities at the photosphere are elongated along a strong-gradient polarity inversion line (PIL). It remains unclear what mechanism triggers the eruption in a single bipolar field and why the one with a strong PIL is eruption-productive. Recently, using high accuracy simulations, we have established a fundamental mechanism of solar eruption initiation that a bipolar field as driven by quasi-static shearing motion at the photosphere can form an internal current sheet, and then fast magnetic reconnection triggers and drives the eruption. Here we investigate the behavior of the fundamental mechanism with different photospheric magnetic flux distributions, i.e., magnetograms, by combining theoretical analysis and numerical simulation. Our study shows that the bipolar fields of different magnetograms, as sheared continually, all exhibit similar evolutions from the slow storage to fast release of magnetic energy in accordance with the fundamental mechanism, which demonstrates the robustness of the mechanism. We further found that the magnetograms with stronger PIL produce larger eruptions, and the key reason is that the sheared bipolar fields with stronger PIL can achieve more non-potentiality, and their internal current sheet can form at a lower height and with a larger current density, by which the reconnection can be more efficient. This also provides a viable trigger mechanism for the observed eruptions in active region with strong PIL.

preprint2021arXiv

Prevalent Behavior of Smooth Strongly Monotone Discrete-Time Dynamical Systems

For C1-smooth strongly monotone discrete-time dynamical systems, it is shown that ``convergence to linearly stable cycles" is a prevalent asymptotic behavior in the measuretheoretic sense. The results are then applied to classes of time-periodic parabolic equations and give new results on prevalence of convergence to periodic solutions. In particular, for equations with Neumann boundary conditions on convex domains, we show the prevalence of the set of initial conditions corresponding to the solutions that converge to spatiallyhomogeneous periodic solutions. While, for equations on radially symmetric domains, we obtain the prevalence of the set of initial values corresponding to solutions that are asymptotic to radially symmetric periodic solutions.

preprint2021arXiv

Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.

preprint2021arXiv

Pulse shape study of the fast scintillation light emitted from xenon-doped liquid argon using silicon photomultipliers

Xenon-doped liquid argon has been proposed as a good alternative to pure liquid argon in scintillation detectors. In this study, we report on the measurement of the time profile of scintillation light emitted from xenon-doped liquid argon with molar concentrations up to 1600 ppm. A compact setup has been developed for this study, with silicon photomultiplier (SiPM) as the photosensor and $^{210}\mathrm{Po}$ and $^{90}\mathrm{Sr}$ as scintillation sources. An effective model based on the de-excitation processes has been developed to describe the data. The results show that xenon-doped liquid argon is a good fast scintillator and can be used in lieu of tetraphenyl butadiene (TPB) in a way that preserves its capability for particle identification via pulse shape discrimination (PSD).

preprint2021arXiv

Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art

For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

preprint2021arXiv

The Yamabe flow on asymptotically flat manifolds

We study the Yamabe flow starting from an asymptotically flat manifold $(M^n,g_0)$. We show that the flow converges to an asymptotically flat, scalar flat metric in a weighted global sense if $Y(M,[g_0])>0$, and show that the flow does not converge otherwise. If the scalar curvature is nonnegative and integrable, then the ADM mass at time infinity drops by the limit of the total scalar curvature along the flow.

preprint2021arXiv

Vanishing dissipation limit to the planar rarefaction wave for the three-dimensional compressible Navier-Stokes-Fourier equations

We study the vanishing dissipation limit of the three-dimensional (3D) compressible Navier-Stokes-Fourier equations to the corresponding 3D full Euler equations. Our results are twofold. First, we prove that the 3D compressible Navier-Stokes-Fourier equations admit a family of smooth solutions that converge to the planar rarefaction wave solution of the 3D compressible Euler equations with arbitrary strength. Second, we obtain a uniform convergence rate in terms of the viscosity and heat-conductivity coefficients. For this multi-dimensional problem, we first need to introduce the hyperbolic wave to recover the physical dissipations of the inviscid rarefaction wave profile as in our previous work [29] on the two-dimensional (2D) case. However, due to the 3D setting that makes the analysis significantly more challenging than the 2D problem, the hyperbolic scaled variables for the space and time could not be used to normalize the dissipation coefficients as in the 2D case. Instead, the analysis of the 3D case is carried out in the original non-scaled variables, and consequently the dissipation terms are more singular compared with the 2D scaled case. Novel ideas and techniques are developed to establish the uniform estimates. In particular, more accurate {\it a priori} assumptions with respect to the dissipation coefficients are crucially needed for the stability analysis, and some new observations on the cancellations of the physical structures for the flux terms are essentially used to justify the 3D limit. Moreover, we find that the decay rate with respect to the dissipation coefficients is determined by the nonlinear flux terms in the original variables for the 3D limit in this paper, but fully determined by the error terms in the scaled variables for the 2D case in [29].

preprint2020arXiv

A Fast Radio Burst discovered in FAST drift scan survey

We report the discovery of a highly dispersed fast radio burst, FRB~181123, from an analysis of $\sim$1500~hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0--1.5~GHz observing band. We measure the peak flux density to be $>0.065$~Jy and the corresponding fluence $>0.2$~Jy~ms. Based on the observed dispersion measure of 1812~cm$^{-3}$~pc, we infer a redshift of $\sim 1.9$. From this, we estimate the peak luminosity and isotropic energy to be $\lesssim 2\times10^{43}$~erg~s$^{-1}$ and $\lesssim 2\times10^{40}$~erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95\% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences $>0.025$~Jy~ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs.

preprint2020arXiv

A3: An Automatic Topology-Aware Malfunction Detection and Fixation System in Data Center Networks

Link failures and cable miswirings are not uncommon in building data center networks, which prevents the existing automatic address configuration methods from functioning correctly. However, accurately detecting such malfunctions is not an easy task because there could be no observable node degree changes. Fixing or correcting such malfunctions is even harder as almost no work can provide accurate fixation suggestions now. To solve the problems, we design and implement A3, an automatic topology-aware malfunction detection and fixation system. A3 innovatively formulates the problem of finding minimal fixation to the problem of computing minimum graph difference (NP-hard) and solves it in O(k^6) and O(k^3) for any less than k/2 and k/4 undirected link malfunctions for FatTree, respectively. Our evaluation demonstrates that for less than k/2 undirected link malfunctions, A3 is 100% accurate for malfunction detection and provides the minimum fixation result. For greater or equal to k/2 undirected link malfunctions, A3 still has accuracy of about 100% and provides the near optimal fixation result.

preprint2020arXiv

Almost automorphically forced flows on $S^1$ or $\mathbb{R}$ in one-dimensional almost periodic semilinear heat equations

In this paper, we consider the asymptotic dynamics of the skew-product semiflow generated by the following time almost-periodically forced scalar reaction-diffusion equation \begin{equation}\label{eq0} u_{t}=u_{xx}+f(t,u,u_{x}),\,\,t>0,\, 0<x<L \end{equation} with periodic boundary condition \begin{equation} \label{bdc1} u(t,0)=u(t,L),\quad u_x(t,0)=u_x(t,L), \end{equation} where $f$ is uniformly almost periodic in $t$. In particular, we study the topological structure of the limit sets of the skew-product semiflow. It is proved that any compact minimal invariant set (throughout this paper, we refer to it as a minimal set) can be residually embedded into an invariant set of some almost automorphically-forced flow on a circle $S^1=\mathbb{R}/L\mathbb{Z}$. Particularly, if $f(t,u,p)=f(t,u,-p)$, then the flow on a minimal set topologically conjugates to an almost periodically-forced minimal flow on $\mathbb{R}$. Moreover, it is proved that the $ω$-limit set of any bounded orbit contains at most two minimal sets that cannot be obtained from each other by phase translation. In addition, we further consider the asymptotic dynamics of the skew-product semiflow generated by \eqref{eq0} with Neumann boundary condition \begin{equation*} \label{bcd2} u_x(t,0)=u_x(t,L)=0, \end{equation*} or Dirichlet boundary condition \begin{equation*}\label{bdc3} u(t,0)=u(t,L)=0. \end{equation*} Under certain direct assumptions on $f$, it is proved in this paper that the flow on any minimal set of \eqref{eq0}, with Neumann boundary condition or Dirichlet boundary condition, topologically conjugates to an almost periodically-forced minimal flow on $\mathbb{R}$. Finally, a counterexample is given to show that even for quasi-periodic equations, the results we obtain here cannot be further improved in general.

preprint2020arXiv

An Inflationary Probe of Cosmic Higgs Switching

A scalar Higgs field can be repeatedly switched on and off when it couples to a classically oscillating scalar modulus field. The modulus flips the Higgs mass term between stable and tachyonic values. We study a cosmological scenario in which such repeated phase transitions occur during inflation. An irrelevant operator coupling the Higgs field to the inflaton can then imprint the pattern of phase transitions in the correlation functions of the inflaton. Using both numerical and analytic studies, we show that the inflaton 2-point function carries characteristic imprints of the modulus oscillation and its effect on the Higgs boson. We briefly remark on the potential observability of such patterns and how they might be distinguished from other dynamics in the early universe.

preprint2020arXiv

Attentive Normalization for Conditional Image Generation

Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN), which is an extension to traditional instance normalization. Specifically, the input feature map is softly divided into several regions based on its internal semantic similarity, which are respectively normalized. It enhances consistency between distant regions with semantic correspondence. Compared with self-attention GAN, our attentive normalization does not need to measure the correlation of all locations, and thus can be directly applied to large-size feature maps without much computational burden. Extensive experiments on class-conditional image generation and semantic inpainting verify the efficacy of our proposed module.

preprint2020arXiv

Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation. A deep convolutional neural network (CNN) is used to represent the multivariate Gaussian distribution as the approximated posterior distribution of susceptibility given the input measured field. In PDI, such CNN is firstly trained on healthy subjects dataset with labels by maximizing the posterior Gaussian distribution loss function as used in Bayesian deep learning. When tested on new dataset without any label, PDI updates the pre-trained network in an unsupervised fashion by minimizing the KL divergence between the approximated posterior distribution represented by CNN and the true posterior distribution given the likelihood distribution from known physical model and prior distribution. Based on our experiments, PDI provides additional uncertainty estimation compared to the conventional MAP approach, meanwhile addressing the potential discrepancy issue of CNN when test data deviates from training dataset.

preprint2020arXiv

Cache-enabling UAV Communications: Network Deployment and Resource Allocation

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

preprint2020arXiv

Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network

Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720.

preprint2020arXiv

Continuous Null-Point Magnetic Reconnection Builds Up a Torus Unstable Magnetic Flux Rope Triggering the X9.3 Flare in Solar Active Region~12673

Two X-class solar flares occurred on 2017 September 6 from active region NOAA 12673: the first one is a confined X2.2 flare, and it is followed only $\sim 3$ hours later by the second one, which is the strongest flare in solar cycle 24, reaching X9.3 class and accompanied with a coronal mass ejection. Why these two X-class flares occurred in the same position with similar magnetic configurations, but one is eruptive while the other is not? Here we track the coronal magnetic field evolution via nonlinear force-free field extrapolations from a time sequence of vector magnetograms with high cadence. A detailed analysis of the magnetic field shows that a magnetic flux rope (MFR) forms and grows gradually before the first flare, and shortly afterwards, the MFR's growth is significantly enhanced with a much faster rise in height, from far below the threshold of torus instability to above it, while the magnetic twist only increases mildly. Combining EUV observations and the magnetic field extrapolation, we found that overlying the MFR is a null-point magnetic topology, where recurrent brightening is seen after the first flare. We thus suggest a scenario to interpret the occurrence of the two flares. The first flare occurred since the MFR reached a high enough height to activate the null point, and its continuous expansion forces the null-point reconnection recurrently. Such reconnection weakens the overlying field, allowing the MFR to rise faster, which eventually crosses the threshold of torus instability and triggers the second, eruptive flare.

preprint2020arXiv

Controlled release of entrapped nanoparticles from thermoresponsive hydrogels with tunable network characteristics

Thermoresponsive hydrogels have been studied intensively for creating smart drug carriers and controlled drug delivery. Understanding the drug release kinetics and corresponding transport mechanisms of nanoparticles (NPs) in a thermoresponsive hydrogel network is the key to the successful design of a smart drug delivery system. We construct a mesoscopic model of rigid NPs entrapped in a hydrogel network in an aqueous solution, where the hydrogel network is formed by cross-linked semiflexible polymers of PNIPAM. By varying the environmental temperature crossing the lower critical solution temperature of PNIPAM we can significantly change the hydrogel network characteristics. We systematically investigate how the matrix porosity and the nanoparticle size affect the NPs' transport kinetics at different temperatures. Quantitative results on the mean-squared displacement and the van Hove displacement distributions of NPs show that all NPs entrapped in the smart hydrogels undergo subdiffusion at both low and high temperatures. For a coil state, the subdiffusive exponent and the diffusion coefficient of NPs increase due to the increased kinetic energy and the decreased confinement on NPs, while the transport of NPs in the hydrogels can be also enhanced by decreasing the matrix porosity and NPs' size. However, when the solution temperature is increased above the critical temperature, the hydrogel network collapses following the coil-to-globule transition, with the NPs tightly trapped in some local regions inside the hydrogels. Consequently, the NP diffusion coefficient can be reduced by two orders of magnitude, or the diffusion processes can even be completely stopped. These findings provide new insights for designing controlled drug release from stimuli-responsive hydrogels, including autonomously switch on/off drug release to respond to the changes of the local environment.

preprint2020arXiv

Convolutional Neural Networks with Dynamic Regularization

Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training. That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures. In this paper, we propose a dynamic regularization method for CNNs. Specifically, we model the regularization strength as a function of the training loss. According to the change of the training loss, our method can dynamically adjust the regularization strength in the training procedure, thereby balancing the underfitting and overfitting of CNNs. With dynamic regularization, a large-scale model is automatically regularized by the strong perturbation, and vice versa. Experimental results show that the proposed method can improve the generalization capability on off-the-shelf network architectures and outperform state-of-the-art regularization methods.

preprint2020arXiv

Cosmological Collider Signatures of Massive Vectors from Non-Gaussian Gravitational Waves

The cosmological collider provides a model-independent probe of particle physics during inflation. We extend the study of cosmological collider physics to much smaller scales through gravitational wave (GW) probes. With a Chern-Simons interaction, a massive vector field can obtain a chemical potential and its particle production can cause significant non-Gaussian GW signals. We calculate the mass and spin dependences of the induced GW 3-point correlation function in the squeezed limit, and estimate its amplitude. Such signals may be detectable in the current and upcoming GW interferometer experiments.

preprint2020arXiv

Cosmological Signatures of Superheavy Dark Matter

We discuss two possible scenarios, namely the curvaton mechanism and the dark matter density modulation, where non-Gaussianity signals of superheavy dark matter produced by gravity can be enhanced and observed. In both scenarios, superheavy dark matter couples to an additional light field as a mediator. In the case of derivative coupling, the resulting non-Gaussianities induced by the light field can be large, which can provide inflationary evidences for these superheavy dark matter scenarios.

preprint2020arXiv

Data-driven MHD Simulation of the Formation and Initiation of a Large-scale Pre-flare Magnetic Flux Rope in Solar Active Region 12371

Solar eruptions are the most powerful drivers of space weather. To understand their cause and nature, it is crucial to know how the coronal magnetic field evolves before eruption. Here we study the formation process of a relatively large-scale magnetic flux rope (MFR) in active region NOAA~12371 that erupts with a major flare and coronal mass ejection on 2015 June 21. A data-driven numerical magnetohydrodynamic model is employed to simulate three-dimensional coronal magnetic field evolution of one-day duration before the eruption. Comparison between the observed features and our modeled magnetic field discloses how the pre-eruption MFR forms. Initially, the magnetic field lines were weakly twisted as being simple sheared arcades. Then a long MFR was formed along the polarity inversion line due to the complex photospheric motion, which is mainly shearing rather than twisting. The presence of the MFR is evidenced by a coherent set of magnetic field lines with twist number above unity. Below the MFR a current sheet is shown in the model, suggesting that tether-cutting reconnection plays a key role in the MFR formation. The MFR's flux grows as more and more field lines are twisted due to continuous injection of magnetic helicity by the photospheric motions. Meanwhile, the height of the MFR's axis increases monotonely from its formation. By an analysis of the decay index of its overlying field, we suggest that it is because the MFR runs into the torus instability regime and becomes unstable that finally triggers the eruption.

preprint2020arXiv

Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI

The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-space sampling, rather than approximate stochastic k-space sampling of LOUPE during training, was applied together with a straight-through (ST) estimator to estimate the gradient of the threshold operation in a neural network; thirdly, modified unrolled optimization network, rather than modified U-Net in LOUPE, was used as the reconstruction network in order to reconstruct multi-coil data properly and reduce the dependency on training data. Experimental results show that when dealing with the in-vivo k-space data, unrolled optimization network with binary under-sampling block and ST estimator had better reconstruction performance compared to the ones with either U-Net reconstruction network or approximate sampling pattern optimization network, and once trained, the learned optimal sampling pattern worked better than the hand-crafted variable density sampling pattern when deployed with other conventional reconstruction methods.

preprint2020arXiv

Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid scintillator detectors. In this paper we present a comprehensive assessment of JUNO's potential for detecting $^8$B solar neutrinos via the neutrino-electron elastic scattering process. A reduced 2~MeV threshold on the recoil electron energy is found to be achievable assuming the intrinsic radioactive background $^{238}$U and $^{232}$Th in the liquid scintillator can be controlled to 10$^{-17}$~g/g. With ten years of data taking, about 60,000 signal and 30,000 background events are expected. This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter, which will shed new light on the tension between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework. If $Δm^{2}_{21}=4.8\times10^{-5}~(7.5\times10^{-5})$~eV$^{2}$, JUNO can provide evidence of neutrino oscillation in the Earth at the about 3$σ$~(2$σ$) level by measuring the non-zero signal rate variation with respect to the solar zenith angle. Moveover, JUNO can simultaneously measure $Δm^2_{21}$ using $^8$B solar neutrinos to a precision of 20\% or better depending on the central value and to sub-percent precision using reactor antineutrinos. A comparison of these two measurements from the same detector will help elucidate the current tension between the value of $Δm^2_{21}$ reported by solar neutrino experiments and the KamLAND experiment.

preprint2020arXiv

Gravitational Collider Physics via Pulsar-Black Hole Binaries

We propose to use pulsar-black hole binaries as a probe of gravitational collider physics. Induced by the gravitation of the pulsar, the atomic transitions of the boson cloud around the black hole back-react on the orbital motion. This leads to the deviation of binary period decrease from that predicted by general relativity, which can be directly probed by the Rømer delay of pulsar time-of-arrivals. The sensitivity and accuracy of this approach is estimated for two typical atomic transitions. It is shown that once the transitions happen within the observable window, the pulsar-timing accuracy is almost always sufficient to capture the resonance phenomenon.

preprint2020arXiv

Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes

Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes. Methods: The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. With little computation overhead, HAS proves to be effective in addressing boundary ambiguity and deficiency. To enhance the spatial consistency in segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by revisiting context in the prediction of predecessors. Results: Validated on a large dataset collected from 100 healthy volunteers, our method presents superior segmentation performance (DSC (Dice Similarity Coefficient), 96.05%), remarkable agreements with experts. With another 156 volumes collected from 52 volunteers, we ahieve high reproducibilities (mean standard deviation 11.524 mL) against scan variations. Conclusion: This is the first investigation about whole fetal head segmentation in 3D US. Our method is promising to be a feasible solution in assisting the volumetric US-based prenatal studies.

preprint2020arXiv

Improving the time resolution of the MRPC detector using deep-learning algorithms

The multi-gap resistive plate chambers (MRPCs) will be used as the Time-of-Flight (ToF) system in the Solenoidal Large Intensity Device (SoLID). The time resolution required by the experiment for the MRPC system is 20 ps in order to make a 3 $σ$ separation of the $π/K$ created in the collisions. To achieve this goal, the whole system including the MRPC detector, the front-end electronics and the readout system will be upgraded. Based on the new system, a time reconstruction algorithm using a combined LSTM (ComLSTM) neural network is proposed. The best time resolution achieved with this algorithm in a cosmic ray test is 16.8 ps, which largely improves the timing ability of the MRPC detector and well satisfies the requirement of the SoLID.

preprint2020arXiv

Inelastic Electron Tunneling Spectroscopy at High-Temperatures

Ion conducting materials are critical components of batteries, fuel cells, and devices such as memristive switches. Analytical tools are therefore sought that allow the behavior of ions in solids to be monitored and analyzed with high spatial resolution and in real time. In principle, inelastic tunneling spectroscopy offers these capabilities. However, as its spectral resolution is limited by thermal softening of the Fermi-Dirac distribution, tunneling spectroscopy is usually constrained to cryogenic temperatures. This constraint would seem to render tunneling spectroscopy useless for studying ions in motion. We report here the first inelastic tunneling spectroscopy studies above room temperature. For these measurements, we have developed high-temperature-stable tunnel junctions that incorporate within the tunnel barrier ultrathin layers for efficient proton conduction. By analyzing the vibrational modes of O-H bonds in BaZrO3-based heterostructures, we demonstrate the detection of protons with a spectral resolution of 20 meV at 400 K (FWHM). Overturning the hitherto existing prediction for the spectral resolution limit of 186 meV (5.4 kBT at 400 K), this resolution enables high-temperature tunneling spectroscopy of ion conductors. With these advances, inelastic tunneling spectroscopy constitutes a novel, valuable analytical tool for solid-state ionics.

preprint2020arXiv

Inertia indices and eigenvalue inequalities for Hermitian matrices

We present a characterization of eigenvalue inequalities between two Hermitian matrices by means of inertia indices. As applications, we deal with some classical eigenvalue inequalities for Hermitian matrices, including the Cauchy interlacing theorem and the Weyl inequality, in a simple and unified approach. We also give a common generalization of eigenvalue inequalities for (Hermitian) normalized Laplacian matrices of simple (signed, weighted, directed) graphs. Our approach is also suitable for Hermitian matrices of the second kind of digraphs recently introduced by Mohar.

preprint2020arXiv

mTOF performance during mCBM beam time at GSI

The future Facility for Anti-proton and Ion Research (FAIR), currently in construction in Darmstadt, Germany, is one of the largest research projects worldwide. The Compressed Baryonic Matter (CBM) experiment is one of the main pillars at FAIR, studying the quantum chromodynamics (QCD) phase diagram at high baryon densities with unprecedented interaction rate in heavy ion collisions up to 10 MHz. This requires new data-driven readout chain, new data analysis methods and high-rate capable detector systems. The task of the CBM Time of Flight wall (CBM-TOF) is the charged particle identification. Multi-gap Resistive Plate Chambers (MRPCs) with different rate capabilities will be used at CBM-TOF corresponding regions. To reduce the commissioning time for CBM, a CBM full system test-setup called mini-CBM (mCBM) had been installed and tested with beams at GSI SIS18 facility in 2019. The high-rate MRPC prototypes developed at Tsinghua University, called MRPC2, were selected to be implemented in mTOF modules for mCBM. Additional thin float glass MRPCs from USTC called MRPC3, foreseen for the CBM lower rate region, were also tested in the mCBM experiment. Performance results of the two kinds of MRPCs analyzed by the so called tracking method will be shown.

preprint2020arXiv

Non-wandering points for autonomous/periodic parabolic equations on the circle

We study the properties of non-wandering points of the following scalar reaction-diffusion equation on the circle $S^1$, \begin{equation*} u_{t}=u_{xx}+f(t,u,u_{x}),\,\,t>0,\,x\in S^{1}=\mathbb{R}/2π\mathbb{Z}, \end{equation*} where $f$ is independent of $t$ or $T$-periodic in $t$. Assume that the equation admits a compact global attractor. It is proved that, any non-wandering point is a limit point of the system (that is, it is a point in some $ω$-limit set). More precisely, in the autonomous case, it is proved that any non-wandering point is either a fixed point or generates a rotating wave on the circle. In the periodic case, it is proved that any non-wandering point is a periodic point or generates a rotating wave on a torus. In particular, if $f(t,u,-u_x)=f(t,u,u_x)$, then any non-wandering point is a fixed point in the autonomous case, and is a periodic point in the periodic case.

preprint2020arXiv

Privacy-Preserving Distributed Clustering for Electrical Load Profiling

Electrical load profiling supports retailers and distribution network operators in having a better understanding of the consumption behavior of consumers. However, traditional clustering methods for load profiling are centralized and require access to all the smart meter data, thus causing privacy issues for consumers and retailers. To tackle this issue, we propose a privacy-preserving distributed clustering framework for load profiling by developing a privacy-preserving accelerated average consensus (PP-AAC) algorithm with proven convergence. Using the proposed framework, we modify several commonly used clustering methods, including k-means, fuzzy C-means, and Gaussian mixture model, to provide privacy-preserving distributed clustering methods. In this way, load profiling can be performed only by local calculations and information sharing between neighboring data owners without sacrificing privacy. Meanwhile, compared to traditional centralized clustering methods, the computational time consumed by each data owner is significantly reduced. The privacy and complexity of the proposed privacy-preserving distributed clustering framework are analyzed. The correctness, efficiency, effectiveness, and privacy-preserving feature of the proposed framework and the proposed PP-AAC algorithm are verified using a real-world Irish residential dataset.

preprint2020arXiv

Privacy-preserving Distributed Probabilistic Load Flow

Probabilistic load flow (PLF) allows to evaluate uncertainties introduced by renewable energy sources on system operation. Ideally, the PLF calculation is implemented for an entire grid requiring all the parameters of the transmission lines and node load/generation to be available. However, in a multi-regional interconnected grid, the independent system operators (ISOs) across regions may not share the parameters of their respective areas with other ISOs. Consequently, the challenge is how to identify the functional relationship between the flows in the regional grid and the uncertain power injections of renewable generation sources across regions without full information about the entire grid. To overcome this challenge, we first propose a privacy-preserving distributed accelerated projection-based consensus algorithm for each ISO to calculate the corresponding coefficient matrix of the desired functional relationship. Then, we leverage a privacy-preserving accelerated average consensus algorithm to allow each ISO to obtain the corresponding constant vector of the same relationship. Using the two algorithms, we finally derive a privacy-preserving distributed PLF method for each ISO to analytically obtain its regional joint PLF in a fully distributed manner without revealing its parameters to other ISOs. The correctness, effectiveness, and efficiency of the proposed method are verified through a case study on the IEEE 118-bus system.

preprint2020arXiv

Probing P and CP Violations on the Cosmological Collider

In direct analogy to the 4-body decay of a heavy scalar particle, the 4-point correlation function of primordial fluctuations carries P and CP information. The CP violation appears as a P-odd angular dependence in the imaginary part of the trispectrum in momentum space. We construct a model with axion-like couplings which leads to observably large CP-violating trispectrum for future surveys. Furthermore, we show the importance of on-shell particle production in observing P- and CP-violating signals. It is impossible to observe these signals from local 4-scalar EFT operators that respect dS isometries, and thus any such observation can rule out single-field EFT with sufficiently small slow-roll parameters. This calculation opens a new frontier of studying P and CP at very high energy scales.

preprint2020arXiv

Quantifying Low-Battery Anxiety of Mobile Users and Its Impacts on Video Watching Behavior

People nowadays are increasingly dependent on mobile phones for daily communication, study, and business. Along with this it incurs the low-battery anxiety (LBA). Although having been unveiled for a while, LBA has not been thoroughly investigated yet. Without a better understanding of LBA, it would be difficult to precisely validate energy saving and management techniques in terms of alleviating LBA and enhancing Quality of Experience (QoE) of mobile users. To fill the gap, we conduct an investigation over 2000+ mobile users, look into their feelings and reactions towards LBA, and quantify their anxiety degree during the draining of battery power. As a case study, we also investigate the impact of LBA on user's behavior at video watching, and with the massive collected answers we are able to quantify user's abandoning likelihood of attractive videos versus the battery status of mobile phone. The empirical findings and quantitative models obtained in this work not only disclose the characteristics of LBA among modern mobile users, but also provide valuable references for the design, evaluation, and improvement of QoE-aware mobile applications and services.

preprint2020arXiv

Quantum States of Higher-order Whispering gallery modes in a Silicon Micro-disk Resonator

The quantum states of light in an integrated photonics platform provide an important resource for quantum information processing and takes advantage of the scalability and practicality of silicon photonics. Integrated resonators have been well explored in classical and quantum optics. However, to encode multiple information through integrated quantum optics requires broader utilization of the available degrees of freedom on a chip. Here, we studied the quantum interference between photon pairs of the same higher order whispering gallery modes populated by spontaneous four-wave mixing in an integrated silicon micro-disk resonator. The quantum interference between the photon pairs of the first two quasi-TE0 and quasi-TE1 radial modes was measured to be Vnet ~ 98 + 0.8 % and Vnet ~ 94 + 2.6 %, respectively. The results are promising for achieving higher-dimensional quantum states using the higher-order radial modes of a micro-disk resonator coupled with an integrated waveguide.

preprint2020arXiv

Resonant Asymmetric All-Dielectric Metasurface for Boosting Third-Harmonic Generation

Resonant metasurfaces have received extensive attention due to their sharp spectral feature and extraordinary field enhancement. In this work, by breaking the in-plane symmetry of silicon nanopillars, we achieve a sharp Fano resonance. The far-field radiation and near-field distribution of metasurfaces are calculated and analyzed to further uncover the resonant performance of metasurfaces. Moreover, the theoretical derivation and simulation exhibit an inverse quadratic dependence of Q-factors on asymmetry parameters, revealing that the resonance is governed by the symmetry-protected bound states in the continuum. Finally we experimentally demonstrate the sharp resonance, and employ it to effciently boost the third-harmonic generation. This enhancement can be attributed to the strong optical intensity enhancement inside the metasurface.

preprint2020arXiv

Spin-orbit torque magnetization switching in MoTe2/permalloy heterostructures

The ability to switch magnetic elements by spin-orbit-induced torques has recently attracted much attention for a path towards high-performance, non-volatile memories with low power consumption. Realizing efficient spin-orbit-based switching requires harnessing both new materials and novel physics to obtain high charge-to-spin conversion efficiencies, thus making the choice of spin source crucial. Here we report the observation of spin-orbit torque switching in bilayers consisting of a semimetallic film of 1T'-MoTe2 adjacent to permalloy. Deterministic switching is achieved without external magnetic fields at room temperature, and the switching occurs with currents one order of magnitude smaller than those typical in devices using the best-performing heavy metals. The thickness dependence can be understood if the interfacial spin-orbit contribution is considered in addition to the bulk spin Hall effect. Further threefold reduction in the switching current is demonstrated with resort to dumbbell-shaped magnetic elements. These findings foretell exciting prospects of using MoTe2 for low-power semimetal material based spin devices.

preprint2020arXiv

SQLFlow: A Bridge between SQL and Machine Learning

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows efficiently in SQL. SQL enables developers to write short programs focusing on the purpose (what) and ignoring the procedure (how). Previous database systems extended their SQL dialect to support ML. SQLFlow (https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute, and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL syntax carefully to make the extension working with various SQL dialects. We implement the extension by inventing a collaborative parsing algorithm. SQLFlow is efficient and expressive to a wide variety of ML techniques -- supervised and unsupervised learning; deep networks and tree models; visual model explanation in addition to training and prediction; data processing and feature extraction in addition to ML. SQLFlow compiles a SQL program into a Kubernetes-native workflow for fault-tolerable execution and on-cloud deployment. Current industrial users include Ant Financial, DiDi, and Alibaba Group.

preprint2020arXiv

Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs

Ultrasound (US) is widely accepted in clinic for anatomical structure inspection. However, lacking in resources to practice US scan, novices often struggle to learn the operation skills. Also, in the deep learning era, automated US image analysis is limited by the lack of annotated samples. Efficiently synthesizing realistic, editable and high resolution US images can solve the problems. The task is challenging and previous methods can only partially complete it. In this paper, we devise a new framework for US image synthesis. Particularly, we firstly adopt a sketch generative adversarial networks (Sgan) to introduce background sketch upon object mask in a conditioned generative adversarial network. With enriched sketch cues, Sgan can generate realistic US images with editable and fine-grained structure details. Although effective, Sgan is hard to generate high resolution US images. To achieve this, we further implant the Sgan into a progressive growing scheme (PGSgan). By smoothly growing both generator and discriminator, PGSgan can gradually synthesize US images from low to high resolution. By synthesizing ovary and follicle US images, our extensive perceptual evaluation, user study and segmentation results prove the promising efficacy and efficiency of the proposed PGSgan.

preprint2020arXiv

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022.

preprint2020arXiv

The Dirichlet principle for the complex $k$-Hessian functional

We study the variational structure of the complex $k$-Hessian equation on bounded domain $X\subset \mathbb C^n$ with boundary $M=\partial X$. We prove that the Dirichlet problem $σ_k (\partial \bar{\partial} u) =0$ in $X$, and $u=f$ on $M$ is variational and we give an explicit construction of the associated functional $\mathcal{E}_k(u)$. Moreover we prove $\mathcal{E}_k(u)$ satisfies the Dirichlet principle. In a special case when $k=2$, our constructed functional $\mathcal{E}_2(u)$ involves the Hermitian mean curvature of the boundary, the notion first introduced and studied by X. Wang. Earlier work of J. Case and and the first author of this article introduced a boundary operator for the (real) $k$-Hessian functional which satisfies the Dirichlet principle. The present paper shows that there is a parallel picture in the complex setting.

preprint2020arXiv

VCNet: A Robust Approach to Blind Image Inpainting

Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image. Previous works assume missing region patterns are known, limiting its application scope. In this paper, we relax the assumption by defining a new blind inpainting setting, making training a blind inpainting neural system robust against various unknown missing region patterns. Specifically, we propose a two-stage visual consistency network (VCN), meant to estimate where to fill (via masks) and generate what to fill. In this procedure, the unavoidable potential mask prediction errors lead to severe artifacts in the subsequent repairing. To address it, our VCN predicts semantically inconsistent regions first, making mask prediction more tractable. Then it repairs these estimated missing regions using a new spatial normalization, enabling VCN to be robust to the mask prediction errors. In this way, semantically convincing and visually compelling content is thus generated. Extensive experiments are conducted, showing our method is effective and robust in blind image inpainting. And our VCN allows for a wide spectrum of applications.

preprint2020arXiv

When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks

Various architectures (such as GoogLeNets, ResNets, and DenseNets) have been proposed. However, the existing networks usually suffer from either redundancy of convolutional layers or insufficient utilization of parameters. To handle these challenging issues, we propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Specifically, residual learning aims to efficiently retrieve features from different convolutional blocks, while the micro-dense aggregation is able to enhance each block and avoid redundancy of convolutional layers by lessening residual aggregations. Moreover, the proposed micro-dense architecture has two characteristics: pyramidal multi-level feature learning which can widen the deeper layer in a block progressively, and dimension cardinality adaptive convolution which can balance each layer using linearly increasing dimension cardinality. The experimental results over three datasets (i.e., CIFAR-10, CIFAR-100, and ImageNet-1K) demonstrate that the proposed Micro-Dense Net with only 4M parameters can achieve higher classification accuracy than state-of-the-art networks, while being 12.1$\times$ smaller depends on the number of parameters. In addition, our micro-dense block can be integrated with neural architecture search based models to boost their performance, validating the advantage of our architecture. We believe our design and findings will be beneficial to the DNN community.

preprint2019arXiv

A Cosmological Higgs Collider

The quantum fluctuations of the Higgs field during inflation could be a source of primordial density perturbations through Higgs-dependent inflaton decay. By measuring primordial non-Gaussianities, this so-called Higgs-modulated reheating scenario provides us a unique chance to probe Higgs interactions at extremely high energy scale, which we call the Cosmological Higgs Collider (CHC). We realize CHC in a simple scenario where the inflaton decays into Higgs-portal scalars, taking account of the decay of the Higgs fluctuation amplitude after inflation. We also calculate the CHC signals of Standard Model particles, namely their imprints in the squeezed bispectrum, which can be naturally large. The concept of CHC can be straightforwardly generalized to cosmological isocurvature colliders with other types of isocurvature perturbations.

preprint2019arXiv

G-networks and the optimization of supply chains

Supply chains are fundamental to the economy of the world and many supply chains focus on perishable items, such as food, or even clothing that is subject to a limited shelf life due to fashion and seasonable effects. G-networks have not been previously applied to this important area. Thus in this paper, we apply G-networks to supply chain systems and investigate an optimal order allocation problem for a N-node supply chain with perishable products that share the same order source of fresh products. The objective is to find an optimal order allocation strategy to minimize the purchase price per object from the perspective of the customers. An analytical solution based on G-networks with batch removal, together with optimization methods are shown to produce the desired results. The results are illustrated by a numerical example with realistic parameters.

preprint2019arXiv

Quantum Creation of a Toy Universe without Inflation

We propose a toy model for the origin of the universe, where the scale-invariant fluctuations are generated together with the quantum creation process of the universe. The fluctuations arise inside an instanton in the Euclidean domain of time. In the Lorentzian point of view, the universe emerges with passive, coherent and scale-invariant fluctuations present from the beginning, without the need of inflation or a bounce. For this mechanism to work, we need anisotropic scaling in space and time, which is realized in a toy model of Horava-Lifshitz gravity with a Lifshitz scalar field.

preprint2019arXiv

Turaev Hyperbolicity of Classical and Virtual Knots

By work of W. Thurston, knots and links in the 3-sphere are known to either be torus links, or to contain an essential torus in their complement, or to be hyperbolic, in which case a unique hyperbolic volume can be calculated for their complement. We employ a construction of Turaev to associate a family of hyperbolic 3-manifolds of finite volume to any classical or virtual link, even if non-hyperbolic. These are in turn used to define the Turaev volume of a link, which is the minimal volume among all the hyperbolic 3-manifolds associated via this Turaev construction. In the case of a classical link, we can also define the classical Turaev volume, which is the minimal volume among all the hyperbolic 3-manifolds associated via this Turaev construction for the classical projections only. We then investigate these new invariants.

Yi Wang

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259 published item(s)

AcademiClaw: When Students Set Challenges for AI Agents

Prospect for measurement of CP-violating parameters of $B_s^0 \to ϕγ$ at the Tera Z factory

InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

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

VideoChat: Chat-Centric Video Understanding

Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis

Microlensing effect of charged spherically symmetric wormhole

Positivity problem of three-term recurrence sequences

1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)

A Comprehensive Review of Deep Learning-based Single Image Super-resolution

A Global Modeling Approach for Load Forecasting in Distribution Networks

A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans

A Solution to the Temperature Evolution of Multi-well Free-energy Landscape

Beam Training and Tracking in MmWave Communication: A Survey

Calibration-Free Travel Time After Photobleaching Velocimetry

Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection

Cutting Rule for Cosmological Collider Signals: A Bulk Evolution Perspective

Deformation retraction of the group of strict contactomorphisms of the three-sphere to the unitary group

Exploring linguistic feature and model combination for speech recognition based automatic AD detection

First report of a solar energetic particle event observed by China's Tianwen-1 mission in transit to Mars

Homologous Coronal Mass Ejections Caused by Recurring Formation and Disruption of Current Sheet within a Sheared Magnetic Arcade

Long-run User Value Optimization in Recommender Systems through Content Creation Modeling

Mass Testing and Characterization of 20-inch PMTs for JUNO

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Monitoring Urban Forests from Auto-Generated Segmentation Maps

Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification

Nonlinear stability of planar viscous shock wave to three-dimensional compressible Navier-Stokes equations

Numerical prescriptions of early-time divergences of the in-in formalism

Numerical Simulation of Solar Magnetic Flux Emergence Using the AMR--CESE--MHD Code

On the $p$-essential normality of principal submodules of the Bergman module on strongly pseudoconvex domains

Prevalent behavior and almost sure Poincare-Bendixson Theorem for smooth flows with invariant k-cones

Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction

Quantitative Quermassintegral Inequalities for Nearly Spherical Sets

Robust Anomaly Detection for Time-series Data

Scale-invariant enhancement of gravitational waves during inflation

Self-supervised Learning in Remote Sensing: A Review

Self-supervised Vision Transformers for Joint SAR-optical Representation Learning

Spectra of weighted uniform hypertrees

Stability of quermassintegral inequalities along inverse curvature flows

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

Termination of Superradiance from a Binary Companion

The Yamabe flow on asymptotically Euclidean manifolds with nonpositive Yamabe constant

Thermodynamic modeling with uncertainty quantification using the modified quasichemical model in quadruplet approximation: Implementation into PyCalphad and ESPEI

Towards Implicit Text-Guided 3D Shape Generation

Ultrahigh-energy Gamma Rays and Gravitational Waves from Primordial Exotic Stellar Bubbles

UMSNet: An Universal Multi-sensor Network for Human Activity Recognition

Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning

WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

Almost automorphy of minimal sets for $C^1$-smooth strongly monotone skew-product semiflows on Banach spaces

Directional Design of Materials Based on the Multi-Objective Optimization: A Case Study of Two-Dimensional Thermoelectric SnSe

Gravitational Collider Physics via Pulsar-Black Hole Binaries II: Fine and Hyperfine Structures are Favored

JUNO Physics and Detector

NeRD: Neural Representation of Distribution for Medical Image Segmentation

Numerical Simulation of a Fundamental Mechanism of Solar Eruption with Different Magnetic Flux Distributions

Prevalent Behavior of Smooth Strongly Monotone Discrete-Time Dynamical Systems

Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

Pulse shape study of the fast scintillation light emitted from xenon-doped liquid argon using silicon photomultipliers

Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art

The Yamabe flow on asymptotically flat manifolds

Vanishing dissipation limit to the planar rarefaction wave for the three-dimensional compressible Navier-Stokes-Fourier equations

A Fast Radio Burst discovered in FAST drift scan survey

A3: An Automatic Topology-Aware Malfunction Detection and Fixation System in Data Center Networks

Almost automorphically forced flows on $S^1$ or $\mathbb{R}$ in one-dimensional almost periodic semilinear heat equations

An Inflationary Probe of Cosmic Higgs Switching

Attentive Normalization for Conditional Image Generation

Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping

Cache-enabling UAV Communications: Network Deployment and Resource Allocation

Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network

Continuous Null-Point Magnetic Reconnection Builds Up a Torus Unstable Magnetic Flux Rope Triggering the X9.3 Flare in Solar Active Region~12673

Controlled release of entrapped nanoparticles from thermoresponsive hydrogels with tunable network characteristics

Convolutional Neural Networks with Dynamic Regularization

Cosmological Collider Signatures of Massive Vectors from Non-Gaussian Gravitational Waves

Cosmological Signatures of Superheavy Dark Matter