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Gang Chen

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preprint2026arXiv

A two-temperature gas-kinetic scheme for hypersonic nonequilibrium flow computations

Accurate aerodynamic and aerothermodynamic predictions are crucial for numerous hypersonic applications. This paper proposes a gas-kinetic scheme (GKS) coupled with a two-temperature kinetic model, which distinguishes between the translational-rotational and vibrational modes of temperature. Compared with one-temperature model and the translational-rotational multi-temperature model, the proposed model provides a more physically accurate simulation of real gas effects when vibrational energy modes of air are excited. On the other hand, it is computationally simpler than multi-temperature model with independent translational, rotational and vibrational modes. The scheme is implemented on both structured and unstructured grids. To further improve the robustness for strong shock and rarefaction waves, the discontinuity feedback factor is employed instead of traditional limiters. Numerical verifications are conducted on one-dimensional shock structure, two-dimensional (2D) hypersonic flow over a cylinder, 2D hypersonic flow over a wedge and 2D Edney Type IV shock/shock interaction. Compared with experimental data, the reference results from direct simulation Monte Carlo (DSMC) method and Navier--Stokes (NS) solvers, the present method demonstrates accurate prediction of the thermally non-equilibrium shock wave structures and hypersonic flow fields.

preprint2026arXiv

Adapting Depth Anything to Adverse Imaging Conditions with Events

Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great success in ideal scenes but remain challenging under adverse imaging conditions such as extreme illumination and motion blur. These degradations corrupt the visual signals of frame cameras, weakening the discriminative features of frame-based depths across the spatial and temporal dimensions. Typically, existing approaches incorporate event cameras to leverage their high dynamic range and temporal resolution, aiming to compensate for corrupted frame features. However, such specialized fusion models are predominantly trained from scratch on domain-specific datasets, thereby failing to inherit the open-world knowledge and robust generalization inherent to foundation models. In this work, we propose ADAE, an event-guided spatiotemporal fusion framework for Depth Anything in degraded scenes. Our design is guided by two key insights: 1) Entropy-Aware Spatial Fusion. We adaptively merge frame-based and event-based features using an information entropy strategy to indicate illumination-induced degradation. 2) Motion-Guided Temporal Correction. We resort to the event-based motion cue to recalibrate ambiguous features in blurred regions. Under our unified framework, the two components are complementary to each other and jointly enhance Depth Anything under adverse imaging conditions. Extensive experiments have been performed to verify the superiority of the proposed method. Our code will be released upon acceptance.

preprint2026arXiv

From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition

Managing extensive context remains a critical bottleneck for Large Language Models (LLMs), particularly in applications like long-document question answering and autonomous agents where lengthy inputs incur high computational costs and introduce noise. Existing compression techniques often disrupt local coherence through discrete token removal or rely on implicit latent encoding that suffers from positional bias and incompatibility with closed-source APIs. To address these limitations, we introduce the EDU-based Context Compressor, a novel explicit compression framework designed to preserve both global structure and fine-grained details. Our approach reformulates context compression as a structure-then-select process. First, our LingoEDU transforms linear text into a structural relation tree of Elementary Discourse Units (EDUs) which are anchored strictly to source indices to eliminate hallucination. Second, a lightweight ranking module selects query-relevant sub-trees for linearization. To rigorously evaluate structural understanding, we release StructBench, a manually annotated dataset of 248 diverse documents. Empirical results demonstrate that our method achieves state-of-the-art structural prediction accuracy and significantly outperforms frontier LLMs while reducing costs. Furthermore, our structure-aware compression substantially enhances performance across downstream tasks ranging from long-context tasks to complex Deep Search scenarios.

preprint2026arXiv

From Context to Skills: Can Language Models Learn from Context Skillfully?

Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the rules and procedures from context into natural-language skills. However, constructing such skills for context learning scenarios faces two challenges: the prohibitive cost of manual skill annotation for long, technically dense contexts, and the lack of external feedback for automated skill construction. In this paper, we propose Ctx2Skill, a self-evolving framework that autonomously discovers, refines, and selects context-specific skills without human supervision or external feedback. At its core, a multi-agent self-play loop has a Challenger that generates probing tasks and rubrics, a Reasoner that attempts to solve them guided by an evolving skill set, and a neutral Judge that provides binary feedback. Crucially, both the Challenger and the Reasoner evolve through accumulated skills: dedicated Proposer and Generator agents analyze failure cases and synthesize them into targeted skill updates for both sides, enabling automated skill discovery and refinement. To prevent adversarial collapse caused by increasingly extreme task generation and over-specialized skill accumulation, we further introduce a Cross-time Replay mechanism that identifies the skill set achieving the best balance across representative cases for the Reasoner side, ensuring robust and generalizable skill evolution. The resulting skills can be plugged into any language model to obtain better context learning capability. Evaluated on four context learning tasks from CL-bench, Ctx2Skill consistently improves solving rates across backbone models.

preprint2026arXiv

InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs

Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretability and failing to capture fine-grained error types. In this paper, we introduce InFi-Check, a framework for interpretable and fine-grained fact-checking of LLM outputs. Specifically, we first propose a controlled data synthesis pipeline that generates high-quality data featuring explicit evidence, fine-grained error type labels, justifications, and corrections. Based on this, we further construct large-scale training data and a manually verified benchmark InFi-Check-FG for fine-grained fact-checking of LLM outputs. Building on these high-quality training data, we further propose InFi-Checker, which can jointly provide supporting evidence, classify fine-grained error types, and produce justifications along with corrections. Experiments show that InFi-Checker achieves state-of-the-art performance on InFi-Check-FG and strong generalization across various downstream tasks, significantly improving the utility and trustworthiness of factuality evaluation.

preprint2026arXiv

OpenSGA: Efficient 3D Scene Graph Alignment in the Open World

Scene graph alignment establishes object correspondences between two 3D scene graphs constructed from partially overlapping observations. This enables efficient scene understanding and object-level relocalization when a robot revisits a place, as well as global map fusion across multiple agents. Such capabilities are essential for robots that require long-term memory for long-horizon tasks involving interactions with the environment. Existing approaches mainly focus on subscan-to-subscan (S2S) alignment and depend heavily on geometric point-cloud features, leaving frame-to-scan (F2S) alignment and open-set vision-language features underexplored. In addition, existing datasets for scene graph alignment remain small-scale with limited object diversity, constraining systematic training and evaluation. We present a unified and efficient scene graph alignment framework that predicts object correspondences by fusing vision-language, textual, and geometric features with spatial context. The framework comprises modules such as a distance-gated spatial attention encoder, a minimum-cost-flow-based allocator, and a global scene embedding generator to achieve accurate alignment even under large coordinate discrepancies. We further introduce ScanNet-SG, a large-scale dataset generated via an automated annotation pipeline with over 700k samples, covering 509 object categories from ScanNet labels and over 3k categories from GPT-4o-based tagging. Experiments show that our method achieves the best overall performance on both F2S and S2S tasks, substantially outperforming existing scene graph alignment methods. Our code and dataset are released at: https://autonomousrobots.nl/paper_websites/opensga.

preprint2026arXiv

SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses

Memory overload is a common form of resource exhaustion in cloud data warehouses. When database queries fail due to memory overload, it not only wastes critical resources such as CPU time but also disrupts the execution of core business processes, as memory-overloading (MO) queries are typically part of complex workflows. If such queries are identified in advance and scheduled to memory-rich serverless clusters, it can prevent resource wastage and query execution failure. Therefore, cloud data warehouses desire an admission control framework with high prediction precision, interpretability, efficiency, and adaptability to effectively identify MO queries. However, existing admission control frameworks primarily focus on scenarios like SLA satisfaction and resource isolation, with limited precision in identifying MO queries. Moreover, there is a lack of publicly available MO-labeled datasets with workloads for training and benchmarking. To tackle these challenges, we propose SafeLoad, the first query admission control framework specifically designed to identify MO queries. Alongside, we release SafeBench, an open-source, industrial-scale benchmark for this task, which includes 150 million real queries. SafeLoad first filters out memory-safe queries using the interpretable discriminative rule. It then applies a hybrid architecture that integrates both a global model and cluster-level models, supplemented by a misprediction correction module to identify MO queries. Additionally, a self-tuning quota management mechanism dynamically adjusts prediction quotas per cluster to improve precision. Experimental results show that SafeLoad achieves state-of-the-art prediction performance with low online and offline time overhead. Specifically, SafeLoad improves precision by up to 66% over the best baseline and reduces wasted CPU time by up to 8.09x compared to scenarios without SafeLoad.

preprint2026arXiv

SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search

Approximate Nearest Neighbor Search (ANNS) underpins modern applications such as information retrieval and recommendation. With the rapid growth of vector data, efficient indexing for real-time vector search has become rudimentary. Existing CPU-based solutions support updates but suffer from low throughput, while GPU-accelerated systems deliver high performance but face challenges with dynamic updates and limited GPU memory, resulting in a critical performance gap for continuous, large-scale vector search requiring both accuracy and speed. In this paper, we present SVFusion, a GPU-CPU-disk collaborative framework for real-time vector search that bridges sophisticated GPU computation with online updates. SVFusion leverages a hierarchical vector index architecture that employs CPU-GPU co-processing, along with a workload-aware vector caching mechanism to maximize the efficiency of limited GPU memory. It further enhances performance through real-time coordination with CUDA multi-stream optimization and adaptive resource management, along with concurrency control that ensures data consistency under interleaved queries and updates. Empirical results demonstrate that SVFusion achieves significant improvements in query latency and throughput, exhibiting a 20.9x higher throughput on average and 1.3x to 50.7x lower latency compared to baseline methods, while maintaining high recall for large-scale datasets under various streaming workloads.

preprint2026arXiv

Table as a Modality for Large Language Models

To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed tabular data. Despite that, in this work, by showing a probing experiment on our proposed StructQA benchmark, we postulate that even the most advanced LLMs (such as GPTs) may still fall short of coping with tabular data. More specifically, the current scheme often simply relies on serializing the tabular data, together with the meta information, then inputting them through the LLMs. We argue that the loss of structural information is the root of this shortcoming. In this work, we further propose TAMO, which bears an ideology to treat the tables as an independent modality integrated with the text tokens. The resulting model in TAMO is a multimodal framework consisting of a hypergraph neural network as the global table encoder seamlessly integrated with the mainstream LLM. Empirical results on various benchmarking datasets, including HiTab, WikiTQ, WikiSQL, FeTaQA, and StructQA, have demonstrated significant improvements on generalization with an average relative gain of 42.65%.

preprint2026arXiv

Token Economics for LLM Agents: A Dual-View Study from Computing and Economics

As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and security bottlenecks. Current surveys remain fragmented across system optimization, architecture design, and trust, lacking a unified framework to evaluate the fundamental trade-off between output quality and economic cost. To bridge this gap, this survey presents the first comprehensive survey of Token Economics. By unifying computer science and economics, we conceptualize tokens as production factors, exchange mediums, and units of account. We synthesize existing literature across a four-dimensional taxonomy: (1) Micro-level (Single Agent): Optimizing budget-constrained factor substitution via neoclassical firm theory. (2) Meso-level (Multi-Agent Systems): Minimizing collaboration friction using transaction cost and principal-agent theories. (3) Macro-level (Agent Ecosystems): Addressing congestion externalities and pricing via mechanism design. (4) Security: Internalizing adversarial threats as endogenous economic constraints. Finally, we outline frontier directions, including differentiable token budgets and dynamic markets, to lay the theoretical foundation for scalable next-generation agent systems.

preprint2024arXiv

Theoretical Study on Superradiant Raman Scattering with Rubidium Atoms in An Optical Cavity

Superradiant Raman scattering of Rubidium atoms has been explored in the experiment [Nature 484, 78 (2012)] to prove the concept of the superradiant laser, which attracts significant attentions in quantum metrology due to the expected ultra-narrow linewidth down to millihertz. To better understand the physics involved in this experiment, we have developed a quantum master equation theory by treating the Rubidium atoms as three-level systems, and coupling them with a dressed laser and an optical cavity. Our simulations show different superradiant Raman scattering pulses for the systems within the crossover and strong coupling regime, and the shifted and broader spectrum of the steady-state Raman scattering. Thus, our studies provide a unified view on the superradiant Raman scattering pulses, and an alternative explanation to the broad spectrum of the steady-state Raman scattering, as observed in the experiment. In future, our theory can be readily applied to study other interesting phenomena relying on the superradiant Raman scattering, such as magnetic field sensing, real-time tracking of quantum phase, Dicke phase transition of non-equilibrium dynamics and so on.

preprint2023arXiv

Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

Although sparse-view computed tomography (CT) has significantly reduced radiation dose, it also introduces severe artifacts which degrade the image quality. In recent years, deep learning-based methods for inverse problems have made remarkable progress and have become increasingly popular in CT reconstruction. However, most of these methods suffer several limitations: dependence on high-quality training data, weak interpretability, etc. In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations. DRP introduces a neural network as an implicit prior into the iterative method, thereby realizing cross-domain gradient feedback. During the reconstruction process, the neural network is progressively optimized in multiple stages to narrow the solution space in radon domain for the under-constrained imaging protocol, and the convergence of the proposed method has been discussed in this work. Compared with the popular pre-trained method, the proposed framework requires no dataset and exhibits superior interpretability and generalization ability. The experimental results demonstrate that the proposed method can generate detailed images while effectively suppressing image artifacts.Meanwhile, DRP achieves comparable or better performance than the supervised methods.

preprint2023arXiv

Stacking-induced magnetic frustration and spiral spin liquid

Like the twisting control in magic angle twisted bilayer graphenes, the stacking control is another mechanical approach to manipulate the fundamental properties of solids, especially the van der Waals materials. We explore the stacking-induced magnetic frustration and the spiral spin liquid on a multilayer triangular lattice antiferromagnet where the system is built from ABC stacking with competing intralayer and interlayers couplings. By combining the nematic bond theory and the self-consistent Gaussian approximation, we establish the phase diagram for this ABC-stacked multilayer magnet. It is shown that, the system supports a wide regime of spiral spin liquid with multiple degenerate spiral lines in the reciprocal space, separating the low-temperature spiral order and the high-temperature featureless paramagnet. The transition to the spiral order from the spiral spin liquid regime is first order. We further show that the spiral-spin-liquid behavior persists even with small perturbations such as further neighbor intralayer exchanges. The connection to the ABC-stacked magnets, the effects of Ising or planar spin anisotropy, and the outlook on the stacking-engineered quantum magnets are discussed.

preprint2022arXiv

A New Global Divergence Free and Pressure-Robust HDG Method for Tangential Boundary Control of Stokes Equations

In [ESAIM: M2AN, 54(2020), 2229-2264], we proposed an HDG method to approximate the solution of a tangential boundary control problem for the Stokes equations and obtained an optimal convergence rate for the optimal control {that reflects its global regularity}. However, the error estimates depend on the pressure, and the velocity is not divergence free. The importance of pressure-robust numerical methods for fluids was addressed by John et al. [SIAM Review, 59(2017), 492-544]. In this work, we devise a new HDG method to approximate the solution of the Stokes tangential boundary control problem; the HDG method is also of independent interest for solving the Stokes equations. This scheme yields a $\mathbf{H}(\mathrm{div})$ conforming, globally divergence free, and pressure-robust solution. To the best of our knowledge, this is the first time such a numerical scheme has been obtained for an optimal boundary control problem for the Stokes equations. We also provide numerical experiments to show the performance of the new HDG method and the advantage over the non pressure-robust scheme.

preprint2022arXiv

Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

The proposal of Pseudo-Lidar representation has significantly narrowed the gap between visual-based and active Lidar-based 3D object detection. However, current researches exclusively focus on pushing the accuracy improvement of Pseudo-Lidar by taking the advantage of complex and time-consuming neural networks. Seldom explore the profound characteristics of Pseudo-Lidar representation to obtain the promoting opportunities. In this paper, we dive deep into the pseudo Lidar representation and argue that the performance of 3D object detection is not fully dependent on the high precision stereo depth estimation. We demonstrate that even for the unreliable depth estimation, with proper data processing and refining, it can achieve comparable 3D object detection accuracy. With this finding, we further show the possibility that utilizing fast but inaccurate stereo matching algorithms in the Pseudo-Lidar system to achieve low latency responsiveness. In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy. The evaluation on the KITTI benchmark shows that the presented system achieves competitive accuracy to the state-of-the-art approaches with only 23 ms computing, showing it is a suitable candidate for deploying to real car-hold applications.

preprint2022arXiv

Analysis of a class of globally divergence-free HDG methods for stationary Navier-Stokes equations

This paper analyzes a class of globally divergence-free (and therefore pressure-robust) hybridizable discontinuous Galerkin (HDG) finite element methods for stationary Navier-Stokes equations. The methods use the $\mathcal{P}_{k}/\mathcal{P}_{k-1}$ $(k\geq1)$ discontinuous finite element combination for the velocity and pressure approximations in the interior of elements, and piecewise $\mathcal{P}_k/\mathcal{P}_{k}$ for the trace approximations of the velocity and pressure on the inter-element boundaries. It is shown that the uniqueness condition for the discrete solution is guaranteed by that for the continuous solution together with a sufficiently small mesh size. Based on the derived discrete HDG Sobolev embedding properties, optimal error estimates are obtained. Numerical experiments are performed to verify the theoretical analysis.

preprint2022arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VII. A catalogue of SiO clumps from ACA observations

To understand the nature of SiO emission, we conducted ACA observations of the SiO (2-1) lines toward 146 massive star-forming regions, as part of the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey. We detected SiO emission in 128 (87.7$\%$) sources and identified 171 SiO clumps, 105 of which are spatially separated from 3 mm continuum emission. A large amount of the SiO line profiles (60$\%$) are non-Gaussian. The velocity dispersion of the SiO lines ranges from 0.3 to 5.43 km s$^{-1}$. In 63 sources the SiO clumps are associated with H$_\rm{II}$ regions characterized by H40$α$ emission. We find that 68$\%$ (116) of the SiO clumps are associated with strong outflows. The median velocity dispersion of the SiO line for outflow sources and non-outflow sources is 1.91 km s$^{-1}$ and 0.99 km s$^{-1}$, respectively. These results indicate that outflow activities could be connected to strongly shocked gas. The velocity dispersion and [SiO]/[H$^{13}$CO$^+$] intensity ratio do not show any correlation with the dust temperature and particle number density of clumps. We find a positive correlation between the SiO line luminosity and the bolometric luminosity, implying stronger shock activities are associated with more luminous proto-clusters. The SiO clumps in associations with H$_\rm{II}$ regions were found to show a steeper feature in $L_\rm{sio}$/$L_\rm{bol}$. The SiO line luminosity and the fraction of shocked gas have no apparent evidence of correlation with the evolutionary stages traced by luminosity to mass ratio ($L_\rm{bol}/M$).

preprint2022arXiv

Centrality dependence and isospin effect on $\Wpm$ and $\Zn$ productions in nucleus-nucleus collisions at $\sNN=5.02$~TeV

In this paper, the centrality dependent $\Zn$ and $\Wpm$ production and the isospin effect in $\Wpm$ production are investigated with a parton and hadron cascade model PACIAE in Pb--Pb collisions at $\sNN=5.02$~TeV. ALICE data of $\Zn$ production in Pb--Pb collisions at $\sNN=5.02$~TeV are found to be reproduced fairly well. The prediction on $\Wpm$ production in the same collision system is given as well. An interesting isospin effect is observed in exploring the charge asymmetry between $\Wp$ and $\Wm$ as a function of the asymmetry between number of valence $u$- and $d$-quarks varied from small to large collision systems at center-of-mass energy 5.02 TeV. The results serve as a important benchmarks for understanding the initial conditions of heavy-ion collisions.

preprint2022arXiv

European Aerosol Phenomenology -- 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Organic aerosol (OA) is a key component to total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol enables the quantifications of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, a range of 43.7-100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.

preprint2022arXiv

Fractionalization on the Surface: Is Type-II Terminated $1T$-TaS$_2$ Surface an Anomalously Realized Spin Liquid?

The type-II terminated $1T$-TaS$_2$ surface of a three-dimensional $1T$-TaS$_2$ bulk material realizes the effective spin-$1/2$ degree of freedom on each David star cluster with ${\mathcal{T}^2=-1}$ such that the time-reversal symmetry is realized anomalously, despite the fact that bulk three-dimensional $1T$-TaS$_2$ material has an even number of electrons per unit cell with ${\mathcal{T}^2=+1}$. This surface is effectively viewed as a spin-$1/2$ triangular lattice magnet, except with a fully gapped topological bulk. We further propose this surface termination realizes a spinon Fermi surface spin liquid with the surface fractionalization but with a nonexotic three-dimensional bulk. We analyze possible experimental consequences, especially the surface spectroscopic measurements, of the type-II terminated surface spin liquid.

preprint2022arXiv

Inducing and tuning Kondo screening in a narrow-electronic-band system

Although the single-impurity Kondo physics has already been well understood, the understanding of the Kondo lattice problem where a dense array of local moments couples to the conduction electrons is still far from complete. The ability of creating and tuning the Kondo lattice in non-f-electron systems will be great helpful for further understanding the Kondo lattice behavior. Here we show that the Pb intercalation in the charge-density-wave-driven narrow-electronic-band system 1T-TaS2 induces a transition from the insulating gap to a sharp Kondo resonance in the scanning tunneling microscopy measurements. It results from the Kondo screening of the localized moment in the 13-site Star-of-David clusters of 1T-TaS2, and thus confirms the cluster Mott localization of the unpaired electrons and local moment formation in the 1T-TaS2 layer. As increasing the Pb concentration, the narrow electronic band derived from the localized electrons shifts away from the Fermi level and the Kondo resonance peak is gradually suppressed. Our results pave a way for creating and tuning many-body electronic states in layered narrow-electronic-band materials.

preprint2022arXiv

Interplay of itinerant electrons and Ising moments in a hybrid honeycomb quantum magnet TmNi$_3$Al$_9$

The interplay between itinerant electrons and local magnetic moments in quantum materials brings about rich and fascinating phenomena and stimulates various developments in the theoretical framework. In this work, thermodynamic, electric transport, and neutron diffraction measurements were performed on a newly synthesized honeycomb lattice magnet TmNi$_3$Al$_9$. Based on the experimental data, a magnetic field temperature phase diagram was constructed, exhibiting three essentially different magnetic regions. Below ${T_{\rm N}=2.97 \pm 0.02}\ \rm K$ Tm$^{3+}$ moments order antiferromagnetically in zero field. We found that the Tm$^{3+}$ ions form a pseudo-doublet ground state with the Ising-like moments lying normal to the two-dimensional honeycomb layers. Application of a magnetic field along the easy axis gradually suppresses the antiferromagnetic order in favor of an induced ferromagnetic state above the critical field ${B_c=0.92 \pm 0.05}\ \rm T$. In the vicinity of $B_c$, a strong enhancement of the quantum spin fluctuations was observed. The quantum Ising nature of the local moments and the coupling to itinerant electrons are discussed.

preprint2022arXiv

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretation of itself with formal language. Experiments with real datasets show the proposed neural network can obtain highly accurate fault diagnosis results with good computation efficiency. Additionally, the embedded formal language of the neuron network can provide explanations about the decision process, thus achieve interpretable fault diagnosis.

preprint2022arXiv

Intertwining SU($N$) symmetry and frustration on a honeycomb lattice

Large symmetry groups in quantum many-body systems could strongly enhance quantum fluctuations and thereby stabilize exotic quantum phases. Frustrated interactions were long known to have similar effects. Here we intertwine the large SU($N$) symmetry and the frustration in a $J_1$-$J_2$ SU($N$) Heisenberg model on a honeycomb lattice, where $J_1$ is the nearest-neighbor coupling and $J_2$ is the next-nearest-neighbor coupling. With a large-$N$ analysis, we obtain a rich phase diagram by varying both $N$ and the ratio $J_2/J_1$. The ground states include Dirac spin liquid, chiral spin liquid, valence cluster solids, flux ordered state, and stripe states. The physical properties of each phase are discussed.

preprint2022arXiv

Kinematic Hopf Algebra for BCJ Numerators in Heavy-Mass Effective Field Theory and Yang-Mills Theory

We present a closed formula for all Bern-Carrasco-Johansson (BCJ) numerators describing $D$-dimensional tree-level scattering amplitudes in a heavy-mass effective field theory with two massive particles and an arbitrary number of gluons. The corresponding gravitational amplitudes obtained via the double copy directly enter the computation of black-hole scattering and gravitational-wave emission. Our construction is based on finding a kinematic algebra for the numerators, which we relate to a quasi-shuffle Hopf algebra. The BCJ numerators thus obtained have a compact form and intriguing features: gauge invariance is manifest, locality is respected for massless exchange, and they contain poles corresponding to massive exchange. Counting the number of terms in a BCJ numerator for $n{-}2$ gluons gives the Fubini numbers $\mathsf{F}_{n-3}$, reflecting the underlying quasi-shuffle Hopf algebra structure. Finally, by considering an appropriate factorisation limit, the massive particles decouple, and we thus obtain a kinematic algebra and all tree-level BCJ numerators for $D$-dimensional pure Yang-Mills theory.

preprint2022arXiv

Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment

This paper proposes a perception-shared and swarm trajectory global optimal (STGO) algorithm fused UAVs formation motion planning framework aided by an active sensing system. First, the point cloud received by each UAV is fit by the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from the received GMM contributes to a global map, which is used as the foundation for consensus. Second, to improve flight safety, an active sensing system is designed to plan the observation angle of each UAV considering the unknown field, overlap of the field of view (FOV), velocity direction and smoothness of yaw rotation, and this planning problem is solved by the distributed particle swarm optimization (DPSO) algorithm. Last, for the formation motion planning, to ensure obstacle avoidance, the formation structure is allowed for affine transformation and is treated as the soft constraint on the control points of the B-spline. Besides, the STGO is introduced to avoid local minima. The combination of GMM communication and STGO guarantees a safe and strict consensus between UAVs. Tests on different formations in the simulation show that our algorithm can contribute to a strict consensus and has a success rate of at least 80% for obstacle avoidance in a dense environment. Besides, the active sensing system can increase the success rate of obstacle avoidance from 50% to 100% in some scenarios.

preprint2022arXiv

On the Molecular Picture and Interfacial Temperature Discontinuity During Evaporation and Condensation

Although it has been shown experimentally that a temperature discontinuity exists at the liquid-vapor interface during evaporation and condensation, quantitatively modeling this temperature jump has been difficult. The classical Schrage equation does not give enough information to determine the interfacial temperature jump. Starting from the Boltzmann transport equation, this paper establishes three interfacial boundary conditions to connect the temperature, density, and pressure jumps at the liquid-vapor interface to the interfacial mass and heat fluxes: one for the mass flux (the Schrage equation), one for the heat flux, and the third for the density discontinuities. These expressions can be readily coupled to heat and mass transport equations in the continuum of the liquid and the vapor phases, enabling one to determine the values of the interfacial temperature, density, and pressure jumps. Comparison with past experiments is favorable. A thermomolecular emission model, mimicking thermionic emission of electrons, is also presented to gain more molecular-level insights on the thermal evaporation processes.

preprint2022arXiv

Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning

One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computation lead to mode-based information, it is hindered by unknown defects in bulk region and at interfaces. Here we present a framework that can reveal microscopic phonon transport information in heterostructures, integrating state-of-the-art ultrafast electron diffraction (UED) with advanced scientific machine learning. Taking advantage of the dual temporal and reciprocal-space resolution in UED, we are able to reliably recover the frequency-dependent interfacial transmittance with possible extension to frequency-dependent relaxation times of the heterostructure. This enables a direct reconstruction of real-space, real-time, frequency-resolved phonon dynamics across an interface. Our work provides a new pathway to experimentally probe phonon transport mechanisms with unprecedented details.

preprint2022arXiv

Photomolecular Effect Leading to Water Evaporation Exceeding Thermal Limit

We report the discovery of photomolecular effect: cleavage of water clusters off surfaces by photons. This effect is demonstrated through surprising absorption of partially wetted hydrogel in the visible spectrum where both water and hydrogel materials' absorption are negligible. Illumination of hydrogel under solar or visible-spectrum light-emitting-diode leads to evaporation rates exceeding the thermal evaporation limit, even in hydrogels without additional absorbers. Measurements of temperature and transmission spectrum of vapor above evaporating surfaces show clear signatures of water clusters. The photomolecular effect happens at liquid-vapor interface due to large electrical field gradients and quadrupole force on molecular clusters. This photomolecular evaporation process might be happening widely in nature, potentially impacting climate and plants growth, and can be exploited for clean water and drying technologies.

preprint2022arXiv

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The detection module requires pre-training, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrary-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights in this map. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach to split from and merge to an arbitrary global trajectory, the planner can also be used in the tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.

preprint2022arXiv

Significant reduction in semiconductor interface resistance via interfacial atomic mixing

The contact resistance between two dissimilar semiconductors is determined by the carrier transmission through their interface. Despite the ubiquitous presence of interfaces, quantitative simulation of charge transport across such interfaces is difficult, limiting the understanding of interfacial charge transport. This work employs Green's functions to study the charge transport across representative Si/Ge interfaces. For perfect interfaces, it is found that the transmittance is small and the contact resistance is high, not only because the mismatch of carrier pockets makes it hard to meet the momentum conservation requirement, but also because of the incompatible symmetries of the Bloch wave functions of the two sides. In contrast, atomic mixing at the interface increases the carrier transmittance as the interface roughness opens many nonspecular transmission channels, which greatly reduces the contact resistance compared with the perfect interface. Specifically, we show that disordered interfaces with certain symmetries create more nonspecular transmission. The insights from our study will benefit the future design of high-performance heterostructures with low contact resistance.

preprint2022arXiv

Speed up the inference of diffusion models via shortcut MCMC sampling

Diffusion probabilistic models have generated high quality image synthesis recently. However, one pain point is the notorious inference to gradually obtain clear images with thousands of steps, which is time consuming compared to other generative models. In this paper, we present a shortcut MCMC sampling algorithm, which balances training and inference, while keeping the generated data's quality. In particular, we add the global fidelity constraint with shortcut MCMC sampling to combat the local fitting from diffusion models. We do some initial experiments and show very promising results. Our implementation is available at https://github.com//vividitytech/diffusion-mcmc.git.

preprint2022arXiv

Spiral-spin-liquid behaviors and persistent reciprocal kagomé structure in frustrated van der Waals magnets and beyond

We study classical $J_1$-$J_2$ models with distinct spin degrees of freedom on a honeycomb lattice. For the XY and Heisenberg spins, the system develops a spiral spin liquid (SSL) that is a thermal cooperative paramagnetic regime with spins fluctuating around the spiral contours in the momentum space, and at low temperatures supports a vector spin-chirality order despite the absence of long-range magnetic order. In a strong contrast, for the Ising moments, the low-temperature spin correlation forms a reciprocal "kagomé" structure in the momentum space that resembles the SSL behaviors and persists for a range of exchange couplings. The unexpected emergence and persistence of the reciprocal "kagomé" are attributed to the stiffness of the Ising moments and the frustration. At higher temperatures when the thermal fluctuations is strong and the spin correlation is not fully melted, the reciprocal structures evolve from the "kagomé" towards the ones demanded by the soft spin limit. This contrasts strongly with the behaviors of the spiral contours in the SSL regime for the continuous spins. We suggest various experimentally relevant systems including van der Waals magnets such as the transition metal phosphorus trichalcogenides TMPX$_3$, Cr$_2$Ge$_2$Te$_6$, the rare-earth chalcohalides (like HoOF, ErOF and DyOF) and other isostructural systems to realize the SSL-like behaviors and/or the reciprocal kagomé structure.

preprint2022arXiv

Study for (anti)hypertriton and light (anti)nuclei productions in high energy collisions at 200 GeV

We use the parton and hadron cascade (PACIAE) model and the dynamically constrained phase-space coalescence (DCPC) model to investigate the productions of (anti)hypertriton and light (anti)nuclei generated by 0-10% centrality $^{12}$C+$^{12}$C, $^{24}$Mg+$^{24}$Mg, $^{40}$Ca+$^{40}$Ca, and $^{64}$Cu+$^{64}$Cu collisions at $\sqrt{S_{NN}}$ = 200 GeV with $|y|$ $<$ 1.5 and $p_{T}$ $<$ 5. We study the yield ratios of the antiparticle to particle and the rapidity distributions of the different (anti)nuclei. We find that the amounts of antimatter produced are significantly lower than that of the corresponding particles, the results of theoretical models are well consistent with the PHOBOS data. The yield ratios of the particle to antiparticle in different transverse momentum regions are also given, and we find that the ratios are raise with the increase of the transverse momentum.

preprint2022arXiv

Thermodynamics of Hydrogels for Applications to Atmospheric Water Harvesting, Evaporation, and Desalination

Most thermodynamic modeling of hydrogels focused on predicting their final volumes in equilibrium with water, built on Flory's theories for the entropy of mixing and rubber elasticity, and Donnan's equilibrium conditions if polyelectrolyte polymer and mobile ions are involved. This work will focus on water and ions in and outside hydrogels, which are of interests in solar interfacial water evaporation for desalination and waste water treatment, atmospheric water harvesting, and forward osmosis. Via a reformulation of Flory's classical hydrogel thermodynamic model by considering a combined system consisting of the hydrogel and its ambient, some confusions in previous work will be clarified. The reformulated thermodynamic model shows clearly the high pressure state of water in hydrogels and the coupling between the osmotic pressure and the Donnan potential. The model is applied to study thermodynamic properties of both pure and salty water in non-electrolyte and electrolyte hydrogels such as (1) the latent heat of evaporation, (2) the ability of hydrogels to retain water and to absorb water from the atmosphere, (3) the use of hydrogels for desalination via solar or forward osmosis, (4) the antifouling characteristics of hydrogels, and (5) melting point suppression and boiling point elevation, and solubility of salts in hydrogels. The reformulated thermodynamic framework will also be useful for understanding polymer electrolytes and ion transport in electrochemical and biological systems.

preprint2022arXiv

Universal excitonic superexchange in spin-orbit-coupled Mott insulators

We point out the universal presence of the excitonic superexchange in spin-orbit-coupled Mott insulators. It is observed that, the restriction to the lowest spin-orbit-entangled "$J$" states may sometimes be insufficient to characterize the microscopic physics, and the virtual excitonic processes via the upper "$J$" states provide an important correction to the superexchange. We illustrate this excitonic superexchange from a two-dimensional $5d$ iridate Sr$_2$IrO$_4$ and explain its physical consequences such as the orbital-like coupling to the external magnetic flux and the nonlinear magnetic susceptibility. The universal presence of the excitonic superexchange in other spin-orbit-coupled Mott insulators such as $3d$ Co-based Kitaev magnets and even $f$ electron rare-earth magnets is further discussed.

preprint2021arXiv

3D hinge transport in acoustic higher-order topological insulators

The discovery of topologically protected boundary states in topological insulators opens a new avenue toward exploring novel transport phenomena. The one-way feature of boundary states against disorders and impurities prospects great potential in applications of electronic and classical wave devices. Particularly, for the 3D higher-order topological insulators, it can host hinge states, which allow the energy to transport along the hinge channels. However, the hinge states haveonly been observed along a single hinge, and a natural question arises: whether the hinge states can exist simultaneously on all the three independent directions of one sample? Here we theoretically predict and experimentally observe the hinge states on three different directions of a higher-order topological phononic crystal, and demonstrate their robust one-way transport from hinge to hinge. Therefore, 3D topological hinge transport is successfully achieved. The novel sound transport may serve as the basis for acoustic devices of unconventional functions.

preprint2021arXiv

A study of $Λ$ and $\barΛ$ polarization splitting by meson field in PICR hydrodynamic model

With the PICR hydrodynamic model, we study the polarization splitting between $Λ$ and $\barΛ$ at RHIC BES energy range, based on the meson field mechanism. Our results fit to the experimental data fairly well. Besides, two unexpected effect emerges: (1) the baryon density gradient has non-trivial and negative contribution to the polarization splitting; (2) for 7.7 GeV Au+Au collisions within the centrality range of 20\%-50\%, the polarization splitting surprisingly increases with the centrality decreases. The second effect might help to explain the significant signal of polarization splitting measured in STAR's Au+Au 7.7 Gev collisions.

preprint2021arXiv

An Active Sense and Avoid System for Flying Robots in Dynamic Environments

This paper investigates a novel active-sensing-based obstacle avoidance paradigm for flying robots in dynamic environments. Instead of fusing multiple sensors to enlarge the field of view (FOV), we introduce an alternative approach that utilizes a stereo camera with an independent rotational DOF to sense the obstacles actively. In particular, the sensing direction is planned heuristically by multiple objectives, including tracking dynamic obstacles, observing the heading direction, and exploring the previously unseen area. With the sensing result, a flight path is then planned based on real-time sampling and uncertainty-aware collision checking in the state space, which constitutes an active sense and avoid (ASAA) system. Experiments in both simulation and the real world demonstrate that this system can well cope with dynamic obstacles and abrupt goal direction changes. Since only one stereo camera is utilized, this system provides a low-cost and effective approach to overcome the FOV limitation in visual navigation.

preprint2021arXiv

Blockchains vs. Distributed Databases: Dichotomy and Fusion

Blockchain has come a long way: a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems. Existing works on this comparison focus on high-level properties, such as security and throughput. They stop short of showing how the underlying design choices contribute to the overall differences. Our work fills this important gap and provides a principled framework for analyzing the emerging trend of blockchain-database fusion. We perform a twin study of blockchains and distributed database systems as two types of transactional systems. We propose a taxonomy that illustrates the dichotomy across four dimensions, namely replication, concurrency, storage, and sharding. Within each dimension, we discuss how the design choices are driven by two goals: security for blockchains, and performance for distributed databases. To expose the impact of different design choices on the overall performance, we conduct an in-depth performance analysis of two blockchains, namely Quorum and Hyperledger Fabric, and two distributed databases, namely TiDB, and etcd. Lastly, we propose a framework for back-of-the-envelope performance forecast of blockchain-database hybrids.

preprint2021arXiv

C-for-Metal: High Performance SIMD Programming on Intel GPUs

The SIMT execution model is commonly used for general GPU development. CUDA and OpenCL developers write scalar code that is implicitly parallelized by compiler and hardware. On Intel GPUs, however, this abstraction has profound performance implications as the underlying ISA is SIMD and important hardware capabilities cannot be fully utilized. To close this performance gap we introduce C-For-Metal (CM), an explicit SIMD programming framework designed to deliver close-to-the-metal performance on Intel GPUs. The CM programming language and its vector/matrix types provide an intuitive interface to exploit the underlying hardware features, allowing fine-grained register management, SIMD size control and cross-lane data sharing. Experimental results show that CM applications from different domains outperform the best-known SIMT-based OpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.

preprint2021arXiv

Collision system size scan for light (anti-)nuclei and (anti-)hypertriton production in high energy nuclear collisions

The production of light (anti-)nuclei and (anti-)hypertriton in a recent collsion system size scan program proposed for the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) is investigated by using the dynamically constrained phase-space coalescence model and the parton and hadron cascade model. The collision system dependence of yield ratios for deuteron to proton, helium-3 to proton, and hypertriton to $Λ$-hyperon with the corresponding values for antiparticles is predicted. The work presents that for the yield ratios a significant difference exists between (hyper)nuclei and their anti-(hyper)nuclei. Besides, much more suppression for (anti-)hypernuclei than light (anti-)nuclei is present. We further investigate strangeness population factors $s_3$ as a function of atomic mass number $A$. Our present study can provide a reference for a upcoming collision system scan program at RHIC.

preprint2021arXiv

Fusion categories containing a fusion subcategory with maximal rank

In this paper, we study fusion categories which contain a proper fusion subcategory with maximal rank. They can be viewed as generalizations of near-group fusion categories. We first prove that they admit spherical structure. We then classify those which are non-degenerate or symmetric. Finally, we classify such fusion categories of rank 4.

preprint2021arXiv

Guided Interpolation for Adversarial Training

To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. However, as the training progresses, the training data becomes less and less attackable, undermining the robustness enhancement. A straightforward remedy is to incorporate more training data, but sometimes incurring an unaffordable cost. In this paper, to mitigate this issue, we propose the guided interpolation framework (GIF): in each epoch, the GIF employs the previous epoch's meta information to guide the data's interpolation. Compared with the vanilla mixup, the GIF can provide a higher ratio of attackable data, which is beneficial to the robustness enhancement; it meanwhile mitigates the model's linear behavior between classes, where the linear behavior is favorable to generalization but not to the robustness. As a result, the GIF encourages the model to predict invariantly in the cluster of each class. Experiments demonstrate that the GIF can indeed enhance adversarial robustness on various adversarial training methods and various datasets.

preprint2021arXiv

Higher-order topological semimetal in acoustic crystals

The notion of higher-order topological insulators has endowed materials with topological states beyond the first order. Particularly, a three-dimensional (3D) higher-order topological insulator can host topologically protected 1D hinge states, referred to as the second-order topological insulator, or 0D corner states, referred to as the third-order topological insulator. Similarly, a 3D higher-order topological semimetal can be envisaged if it hosts states on the 1D hinges. Here we report the realization of a second-order topological Weyl semimetal in a 3D-printed acoustic crystal, which possesses Weyl points in 3D momentum space, 2D Fermi arc states on surfaces and 1D gapless states on hinges. Like the arc surface states, the hinge states also connect the projections of the Weyl points. Our experimental results evidence the existence of the higher-order topological semimetal, which may pave the way towards innovative acoustic devices.

preprint2021arXiv

Light (anti-)nuclei and (anti-)hypertriton production in $pp$ collisions at $\sqrt{s} =0.90, 2.76$ and $7$ TeV

Production of light (anti-)nuclei and (anti-)hypertriton within midrapidity ($|y|<0.5$) and $p_T<3.0$ GeV/c in $pp$ interactions at $\sqrt{s}$ = 0.90, 2.76 and 7 TeV is investigated by the dynamically constrained phase space coalescence model (DCPC), combined with {\footnotesize{PACIAE}} model. The ALICE data for yields, ratios, as well as transverse momentum distributions of $\overline{d}$ and $d$ are well reproduced by the model simulations, meanwhile the three basic characters of $\rm^3{\overline{He}}$, $\rm^3{He}$, $\rm{{^3_{\overline Λ}\overline H}}$, and $\rm{^3_ΛH}$ are also predicted. Besides, we found the yields of light (anti-)nuclei produced are dependent upon their mass number $A$, namely, their yields sharply decrease with the increasing of $A$. The strangeness population factor $s_3=\rm{({^3_ΛH}/{^3{He}})/(Λ/p)}$ is found to be about 0.7 $\sim$ 0.8, and is comparable with the available experimental results.

preprint2021arXiv

Predictions for production of $\rm{^3_ΛH}$ and $\rm{{^3_{\overline Λ}\overline H}}$ in isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr collisions at $\sqrt{s_{\rm{NN}}}$ = 200 GeV

The production of $\rm{^3_ΛH}$ and $\rm{{^3_{\overline Λ}\overline H}}$, as well as $\rm{^3H}$, $\rm{^3\overline H}$, $\rm{^3He}$, and $\rm{^3\overline {He}}$ are studied in central collisions of isobars $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr at $\sqrt{s_{\rm{NN}}}=200$ GeV, using the dynamically constrained phase-space coalescence model and the {\footnotesize PACIAE} model with chiral magnetic effect. The yield, yield ratio, coalescence parameters, and strangeness population factor of (anti-)hypertriton and (anti-)nuclei produced in isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr collisions are predicted. The (anti-)hypertriton and (anti-)nuclei production is found to be insensitive to the chiral magnetic effects. Experimental data of Cu+Cu, Au+Au and Pb+Pb collisions from RHIC, LHC, and the results of {\footnotesize PACIAE+DCPC} model are presented in the results for comparison.

preprint2021arXiv

Semi-discrete and fully discrete HDG methods for Burgers' equation

This paper proposes semi-discrete and fully discrete hybridizable discontinuous Galerkin (HDG) methods for the Burgers' equation in two and three dimensions. In the spatial discretization, we use piecewise polynomials of degrees $ k \ (k \geq 1), k-1$ and $ l \ (l=k-1; k) $ to approximate the scalar function, flux variable and the interface trace of scalar function, respectively. In the full discretization method, we apply a backward Euler scheme for the temporal discretization. Optimal a priori error estimates are derived. Numerical experiments are presented to support the theoretical results.

preprint2021arXiv

The study of exotic state $Z_c^{\pm}(3900)$ decaying to $J/ψπ^{\pm}$ in the $pp$ collisions at $\sqrt{s}$ = 1.96, 7, and 13 TeV

A dynamically constrained phase-space coalescence model plus PACIAE model was used to predict the exotic resonant state $Z_c^{\pm}(3900)$ yield, transverse momentum distribution, and the rapidity distribution with $|y| < 6$ and $p_T < 10$ GeV/c in $pp$ collisions at $\sqrt{s} = 1.96, 7$ and 13 TeV, respectively. The yield of the $Z_c^{\pm}(3900)$ is estimated to be around $10^{-6}$ to $10^{-5}$. We also present the energy dependence of the transverse momentum distributions and rapidity distributions for ${Z_c^{+}(3900)}$ and ${Z_c^{-}(3900)}$. The production of ${Z_c^{+}(3900)}$ and its anti-particle ${Z_c^{-}(3900)}$ is found to be quite similar to each other.

preprint2020arXiv

$L^\infty$ norm error estimates for HDG methods applied to the Poisson equation with an application to the Dirichlet boundary control problem

We prove quasi-optimal $L^\infty$ norm error estimates (up to logarithmic factors) for the solution of Poisson's problem by the standard Hybridizable Discontinuous Galerkin (HDG) method. Although such estimates are available for conforming and mixed finite element methods, this is the first proof for HDG. The method of proof is motivated by known $L^\infty$ norm estimates for mixed finite elements. We show two applications: the first is to prove optimal convergence rates for boundary flux estimates, and the second is to prove that numerically observed convergence rates for the solution of a Dirichlet boundary control problem are to be expected theoretically. Numerical examples show that the predicted rates are seen in practice.

preprint2020arXiv

A characterization of exceptional pseudocyclic association schemes by multidimensional intersection numbers

Recent classification of $\frac{3}{2}$-transitive permutation groups leaves us with three infinite families of groups which are neither $2$-transitive, nor Frobenius, nor one-dimensional affine. The groups of the first two families correspond to special actions of ${\mathrm{PSL}}(2,q)$ and ${\mathrm{PΓL}}(2,q),$ whereas those of the third family are the affine solvable subgroups of ${\mathrm{AGL}}(2,q)$ found by D. Passman in 1967. The association schemes of the groups in each of these families are known to be pseudocyclic. It is proved that apart from three particular cases, each of these exceptional pseudocyclic schemes is characterized up to isomorphism by the tensor of its $3$-dimensional intersection numbers.

preprint2020arXiv

A Néel-type antiferromagnetic order in the spin 1/2 rare-earth honeycomb YbCl$_3$

Most of the searches for Kitaev materials deal with $4d/5d$ magnets with spin-orbit-coupled ${J=1/2}$ local moments such as iridates and $α$-RuCl$_3$. Here we propose the monoclinic YbCl$_3$ with a Yb$^{3+}$ honeycomb lattice for the exploration of Kiteav physics. We perform thermodynamic, $ac$ susceptibility, angle-dependent magnetic torque and neutron diffraction measurements on YbCl$_3$ single crystal. We find that the Yb$^{3+}$ ion exhibits a Kramers doublet ground state that gives rise to an effective spin ${J_{\text{eff}}=1/2}$ local moment. The compound exhibits short-range magnetic order below 1.20 K, followed by a long-range Néel-type antiferromagnetic order at 0.60 K, below which the ordered Yb$^{3+}$ spins lie in the $ac$ plane with an angle of 16(11)$^{\circ}$ away from the $a$ axis. These orders can be suppressed by in-plane and out-of-plane magnetic fields at around 6 and 10 T, respectively. Moreover, the Néel temperature varies non-monotonically under the out-of-plane magnetic fields. The in-plane magnetic anisotropy and the reduced order moment 0.8(1) $μ_B$ at 0.25 K indicate that YbCl$_3$ could be a two-dimensional spin system to proximate the Kitaev physics.

preprint2020arXiv

A new method for driven-dissipative problems: Keldysh-Heisenberg equations

Driven-dissipative systems have recently attracted great attention due to the existence of novel physical phenomena with no analog in the equilibrium case. The Keldysh path-integral theory is a powerful tool to investigate these systems. However, it has still been challenge to study strong nonlinear effects implemented by recent experiments, since in this case the photon number is few and quantum fluctuations play a crucial role in dynamics of system. Here we develop a new approach for deriving exact steady states of driven-dissipative systems by introducing the Keldysh partition function in the Fock-state basis and then mapping the standard saddle-point equations into KeldyshHeisenberg equations. We take the strong Kerr nonlinear resonators with/without the nonlinear driving as two examples to illustrate our method. It is found that in the absence of the nonlinear driving, the exact steady state obtained does not exhibit bistability and agree well with the complex P-representation solution. While in the presence of the nonlinear driving, the multiphoton resonance effects are revealed and are consistent with the qualitative analysis. Our method provides an intuitive way to explore a variety of driven-dissipative systems especially with strong correlations.

preprint2020arXiv

A Transactional Perspective on Execute-order-validate Blockchains

Smart contracts have enabled blockchain systems to evolve from simple cryptocurrency platforms, such as Bitcoin, to general transactional systems, such as Ethereum. Catering for emerging business requirements, a new architecture called execute-order-validate has been proposed in Hyperledger Fabric to support parallel transactions and improve the blockchain's throughput. However, this new architecture might render many invalid transactions when serializing them. This problem is further exaggerated as the block formation rate is inherently limited due to other factors beside data processing, such as cryptography and consensus. In this work, we propose a novel method to enhance the execute-order-validate architecture, by reducing invalid transactions to improve the throughput of blockchains. Our method is inspired by state-of-the-art optimistic concurrency control techniques in modern database systems. In contrast to existing blockchains that adopt database's preventive approaches which might abort serializable transactions, our method is theoretically more fine-grained. Specifically, unserializable transactions are aborted before ordering and the remaining transactions are guaranteed to be serializable. For evaluation, we implement our method in two blockchains respectively, FabricSharp on top of Hyperledger Fabric, and FastFabricSharp on top of FastFabric. We compare the performance of FabricSharp with vanilla Fabric and three related systems, two of which are respectively implemented with one standard and one state-of-the-art concurrency control techniques from databases. The results demonstrate that FabricSharp achieves 25% higher throughput compared to the other systems in nearly all experimental scenarios. Moreover, the FastFabricSharp's improvement over FastFabric is up to 66%.

preprint2020arXiv

Acoustic corner states in topological insulators with built-in Zeeman-like fields

The higher-order topological insulators (HOTIs), with such as the topological corner states, emerge as a thriving topic in the field of topological physics. But few connections have been found for the HOTIs with the well explored first-order topological insulators described by the Z_2 index. However, most recently, a proposal asserts that a significant bridge can be established between the HOTIs and the Z_2 topological insulators. When subject to an in-plane Zeeman field, the corner states, the signature of the HOTIs, can be induced in a Z_2 topological insulator. Such Zeeman field can be produced, for example, by the ferromagnetic proximity effect or magnetic atom doping, which obviously involves the drastically experimental complexity. Here we show that, a phononic crystal, designed as a bilayer of coupled acoustic cavities, hosts exactly the Kane-Mele model with built-in in-plane Zeeman fields. We observe that the helical edge states along the zigzag edges are gapped, and the corner states, localized spatially at the corners of the samples, appear in the gap, confirming the effect induced by the Zeeman field. We further demonstrate the intriguing contrast properties of the corner states at the outer and inner corners in a hexagonal ring-shaped sample.

preprint2020arXiv

Analysis of an interior penalty DG method for the quad-curl problem

The quad-curl term is an essential part of the resistive magnetohydrodynamic (MHD) equation and the fourth order inverse electromagnetic scattering problem, which are both of great significance in science and engineering. It is desirable to develop efficient and practical numerical methods for the quad-curl problem. In this paper, we first present some new regularity results for the quad-curl problem on Lipschitz polyhedron domains and then propose a mixed finite element method for solving the quad-curl problem. With a {\em novel} discrete Sobolev imbedding inequality for the piecewise polynomials, we obtain stability results and derive error estimates based on a relatively low regularity assumption of the exact solution.

preprint2020arXiv

Analysis of Indexing Structures for Immutable Data

In emerging applications such as blockchains and collaborative data analytics, there are strong demands for data immutability, multi-version accesses, and tamper-evident controls. This leads to three new index structures for immutable data, namely Merkle Patricia Trie (MPT), Merkle Bucket Tree (MBT), and Pattern-Oriented-Split Tree (POS-Tree). Although these structures have been adopted in real applications, there is no systematic evaluation of their pros and cons in the literature. This makes it difficult for practitioners to choose the right index structure for their applications, as there is only a limited understanding of the characteristics of each index. To alleviate the above deficiency, we present a comprehensive analysis of the existing index structures for immutable data, evaluating both their asymptotic and empirical performance. Specifically, we show that MPT, MBT, and POS-Tree are all instances of a recently proposed framework, dubbed \my{Structurally Invariant and Reusable Indexes (SIRI)}. We propose to evaluate the SIRI instances based on five essential metrics: their efficiency for four index operations (i.e., lookup, update, comparison, and merge), as well as their \my{deduplication ratios} (i.e., the size of the index with deduplication over the size without deduplication). We establish the worst-case guarantees of each index in terms of these five metrics, and we experimentally evaluate all indexes in a large variety of settings. Based on our theoretical and empirical analysis, we conclude that POS-Tree is a favorable choice for indexing immutable data.

preprint2020arXiv

Atomic self-organization emerging from tunable quadrature coupling

The recent experimental observation of dissipation-induced structural instability provides new opportunities for exploring the competition mechanism between stationary and nonstationary dynamics [Science 366, 1496 (2019)]. In that study, two orthogonal quadratures of cavity field are coupled to two different Zeeman states of a spinor Bose-Einstein condensate (BEC). Here we propose a novel scheme to couple two density-wave degrees of freedom of a BEC to two quadratures of the cavity field. Being drastically different from previous studies, the light-matter quadratures coupling in our model is endowed with a tunable coupling angle. Apart from the uniform and self-organized phases, we unravel a dynamically unstable state induced by the cavity dissipation. Interestingly, the dissipation defines a particular coupling angle, across which the instabilities disappear. Moreover, at this critical coupling angle, one of the two atomic density waves can be independently excited without affecting one another. It is also found that our system can be mapped into a reduced three-level model under the commonly used low-excitation-mode approximation. However, the effectiveness of this approximation is shown to be broken by the dissipation nature for some special system parameters, hinting that the low-excitation-mode approximation is insufficient in capturing some dissipation-sensitive physics. Our work enriches the quantum simulation toolbox in the cavity-quantum-electrodynamics system and broadens the frontiers of light-matter interaction.

preprint2020arXiv

Clusterization transition between cluster Mott insulators on a breathing Kagomé lattice

Motivated by recent experimental progress on various cluster Mott insulators, we study an extended Hubbard model on a breathing Kagomé lattice with a single electron orbital and $1/6$ electron filling. Two distinct types of cluster localization are found in the cluster Mott regime due to the presence of the electron repulsion between neighboring sites, rather than from the on-site Hubbard interaction in the conventional Mott insulators. We introduce a unified parton construction framework to accommodate both type of cluster Mott insulating phase as well as a trivial Ferm liquid metal and discuss the phase transitions in the phase diagram. It is shown that, in one of the cluster localization phases, the strong inter-site repulsion results into locally metallic behavior within one of two triangular clusters on the breathing Kagomé lattice. We further comment on experimental relevance to existing Mo-based cluster magnets.

preprint2020arXiv

Database Meets Deep Learning: Challenges and Opportunities

Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.

preprint2020arXiv

Decorrelated Double Q-learning

Q-learning with value function approximation may have the poor performance because of overestimation bias and imprecise estimate. Specifically, overestimation bias is from the maximum operator over noise estimate, which is exaggerated using the estimate of a subsequent state. Inspired by the recent advance of deep reinforcement learning and Double Q-learning, we introduce the decorrelated double Q-learning (D2Q). Specifically, we introduce the decorrelated regularization item to reduce the correlation between value function approximators, which can lead to less biased estimation and low variance. The experimental results on a suite of MuJoCo continuous control tasks demonstrate that our decorrelated double Q-learning can effectively improve the performance.

preprint2020arXiv

Detection of multipolar orders in the spin-orbit-entangled 5d Mott insulator Ba2MgReO6

In electronic solids with strong spin-orbit interactions (SOIs), the spin and orbital degrees of freedom of an electron are quantum mechanically entangled, which may result in an exotic multipolar order instead of a conventional dipolar order such as a magnetic order. Such a higher-degree order is called "hidden order" because of difficulties in experimental detection. Moreover, the number of candidate compounds is limited, especially rare in d electron systems, in which an interplay between SOIs and Coulomb interactions is expected to cause rich physics. Here, we employ state-of-the-art synchrotron X-ray diffraction techniques on a high-quality single crystal to probe subtle symmetry breaking induced by a multipolar order. We unequivocally demonstrate that the double-perovskite Ba2MgReO6 exhibits successive transitions to quadrupolar and then dipolar orders upon cooling, which is consistent with a theory considering SOIs. Our findings are a significant step towards understanding the intriguing physics of multipoles realized by spin-orbit-entangled 5d electrons.

preprint2020arXiv

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly found in black hole X-ray binaries, and their origin is still under debate. The properties of LFQPOs at high energies (above 30 keV) are closely related to the nature of the accretion flow in the innermost regions, and thus play a crucial role in critically testing various theoretical models. The Hard X-ray Modulation Telescope (Insight-HXMT) is capable of detecting emissions above 30 keV, and is therefore an ideal instrument to do so. Here we report the discovery of LFQPOs above 200 keV in the new black hole MAXI J1820+070 in the X-ray hard state, which allows us to understand the behaviours of LFQPOs at hundreds of kiloelectronvolts. The phase lag of the LFQPO is constant around zero below 30 keV, and becomes a soft lag (that is, the high-energy photons arrive first) above 30 keV. The soft lag gradually increases with energy and reaches ~0.9s in the 150-200 keV band. The detection at energies above 200 keV, the large soft lag and the energy-related behaviors of the LFQPO pose a great challenge for most currently existing models, but suggest that the LFQPO probably originates from the precession of a small-scale jet.

preprint2020arXiv

Dynamical Zeeman resonance in spin-orbit-coupled spin-1 Bose gases

We predict a dynamical resonant effect, which is driven by externally applied linear and quadratic Zeeman fields, in a spin-orbit-coupled spin-1 Bose-Einstein condensate. The Bose-Einstein condensate is assumed to be initialized in some superposed state of Zeeman sublevels and subject to a sudden shift of the trapping potential. It is shown that the time-averaged center-of-mass oscillation and the spin polarizations of the Bose-Einstein condensate exhibit remarkable resonant peaks when the Zeeman fields are tuned to certain strengths. The underlying physics behind this resonance can be traced back to the out-of-phase interference of the dynamical phases carried by different spinorbit states. By analyzing the single particle spectrum, the resonant condition is summarized as a simple algebraic relation, connecting the strengths of the linear and quadratic Zeeman fields. This property is potentially applicable in quantum information and quantum precision measurement.

preprint2020arXiv

Effective and Efficient Dropout for Deep Convolutional Neural Networks

Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitting, dropout has been widely used as an efficient regularization technique in practice. However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs. In this paper, we revisit this issue and examine various dropout variants in an attempt to improve existing dropout-based regularization techniques for CNNs. We attribute the failure of standard dropout to the conflict between the stochasticity of dropout and its following Batch Normalization (BN), and propose to reduce the conflict by placing dropout operations right before the convolutional operation instead of BN, or totally address this issue by replacing BN with Group Normalization (GN). We further introduce a structurally more suited dropout variant Drop-Conv2d, which provides more efficient and effective regularization for deep CNNs. These dropout variants can be readily integrated into the building blocks of CNNs and implemented in existing deep learning platforms. Extensive experiments on benchmark datasets including CIFAR, SVHN and ImageNet are conducted to compare the existing building blocks and the proposed ones with dropout training. Results show that our building blocks improve over state-of-the-art CNNs significantly, which is mainly due to the better regularization and implicit model ensemble effect.

preprint2020arXiv

Featureless quantum paramagnet with frustrated criticality and competing spiral magnetism on spin-1 honeycomb lattice magnet

We study the spin-1 honeycomb lattice magnets with frustrated exchange interactions. The proposed microscopic spin model contains first and second neighbor Heisenberg interactions as well as the single-ion anisotropy. We establish a rich phase diagram that includes a featureless quantum paramagnet and various spin spiral states induced by the mechanism of order by quantum disorder. Although the quantum paramagnet is dubbed featureless, it is shown that, the magnetic excitations develop a contour degeneracy in the reciprocal space at the band minima. These contour degenerate excitations are responsible for the frustrated criticality from the quantum paramagnet to the ordered phases. This work illustrates the effects of magnetic frustration on both magnetic orderings and the magnetic excitations. We discuss the experimental relevance to various Ni-based honeycomb lattice magnets.

preprint2020arXiv

ForkBase: Immutable, Tamper-evident Storage Substrate for Branchable Applications

Data collaboration activities typically require systematic or protocol-based coordination to be scalable. Git, an effective enabler for collaborative coding, has been attested for its success in countless projects around the world. Hence, applying the Git philosophy to general data collaboration beyond coding is motivating. We call it Git for data. However, the original Git design handles data at the file granule, which is considered too coarse-grained for many database applications. We argue that Git for data should be co-designed with database systems. To this end, we developed ForkBase to make Git for data practical. ForkBase is a distributed, immutable storage system designed for data version management and data collaborative operation. In this demonstration, we show how ForkBase can greatly facilitate collaborative data management and how its novel data deduplication technique can improve storage efficiency for archiving massive data versions.

preprint2020arXiv

HDG and CG methods for the Indefinite Time-Harmonic Maxwell's Equations under minimal regularity

We propose to use a hybridizable discontinuous Galerkin (HDG) method combined with the continuous Galerkin (CG) method to approximate Maxwell's equations. We make two contributions in this paper. First, even though there are many papers using HDG methods to approximate Maxwell's equations, to our knowledge they all assume that the coefficients are smooth (or constant). Here, we derive optimal convergence estimates for our HGD-CG approximation when the electromagnetic coefficients are {\em piecewise} smooth. This requires new techniques of analysis. Second, we use CG elements to approximate the Lagrange multiplier used to enforce the divergence condition and we obtain a discrete system in which we can decouple the discrete the Lagrange multiplier. Because we are using a continuous Lagrange multiplier space, the number of degrees of freedom devoted to this are less than for other HDG methods. We present numerical experiments to confirm our theoretical results.

preprint2020arXiv

Hybrid deep neural network based prediction method for unsteady flows with moving boundaries

A novel hybrid deep neural network architecture is designed to capture the spatial-temporal features of unsteady flows around moving boundaries directly from high-dimensional unsteady flow fields data. The hybrid deep neural network is constituted by the convolutional neural network (CNN), improved convolutional Long-Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). Flow fields at future time step can be predicted through flow fields by previous time steps and boundary positions at those steps by the novel hybrid deep neural network. Unsteady wake flows around a forced oscillation cylinder with various amplitudes are calculated to establish the datasets as training samples for training the hybrid deep neural networks. The trained hybrid deep neural networks are then tested by predicting the unsteady flow fields around a forced oscillation cylinder with new amplitude. The effect of neural network structure parameters on prediction accuracy was analyzed. The hybrid deep neural network, constituted by the best parameter combination, is used to predict the flow fields in the future time. The predicted flow fields are in good agreement with those calculated directly by computational fluid dynamic solver, which means that this kind of deep neural network can capture accurate spatial-temporal information from the spatial-temporal series of unsteady flows around moving boundaries. The result shows the potential capability of this kind novel hybrid deep neural network in flow control for vibrating cylinder, where the fast calculation of high-dimensional nonlinear unsteady flow around moving boundaries is needed.

preprint2020arXiv

Large nonreciprocal absorption and emission of radiation in type-I Weyl semimetals with time reversal symmetry breaking

The equality between the spectral, directional emittance and absorptance of an object under local thermal equilibrium is known as Kirchhoff's law of radiation. The breakdown of Kirchhoff's law of radiation is physically allowed by breaking time reversal symmetry and can open opportunities for nonreciprocal light emitters and absorbers. Large anomalous Hall conductivity and angle recently observed in topological Weyl semimetals, particularly type-I magnetic Weyl semimetals and type-II Weyl semimetals, are expected to create large nonreciprocal electromagnetic wave propagation. In this work, we focus on type-I magnetic Weyl semimetals and show via modeling and simulation that nonreciprocal surface plasmons polaritons can result in pronounced nonreciprocity without an external magnetic field. The modeling in this work begins with a single pair of Weyl nodes, followed by a more realistic model with multiple paired Weyl nodes. Fermi-arc surface states are also taken into account through the surface conductivity. This work points to the promising applicability of topological Weyl semimetals for magneto-optical and energy applications.

preprint2020arXiv

Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning

Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs). Previous research revealed that the performance of these PG algorithms depends heavily on the bias-variance tradeoffs involved in estimating and using PGs. A notable approach towards balancing this tradeoff is to merge both on-policy and off-policy gradient estimations. However existing PG merging methods can be sample inefficient and are not suitable to train deterministic policies directly. To address these issues, this paper introduces elite PGs and strengthens their variance reduction effect by adopting elitism and policy consolidation techniques to regularize policy training based on policy behavioral knowledge extracted from elite trajectories. Meanwhile, we propose a two-step method to merge elite PGs and conventional PGs as a new extension of the conventional interpolation merging method. At both the theoretical and experimental levels, we show that both two-step merging and interpolation merging can induce varied bias-variance tradeoffs during policy training. They enable us to effectively use elite PGs and mitigate their performance impact on trained policies. Our experiments also show that two-step merging can outperform interpolation merging and several state-of-the-art algorithms on six benchmark control tasks.

preprint2020arXiv

Neural Machine Translation: A Review of Methods, Resources, and Tools

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation. Then we summarize the resources and tools that are useful for researchers. Finally, we conclude with a discussion of possible future research directions.

preprint2020arXiv

Normal density and moment of inertia of a moving superfluid

In this work, the normal density $ρ_n$ and moment of inertia of a moving superfluid are investigated. We find that, even at zero temperature, there exists a finite normal density for the moving superfluid. When the velocity of superfluid reaches sound velocity, the normal density becomes total mass density $ρ$, which indicates that the system losses superfluidity. At the same time, the Landau's critical velocity also becomes zero. The existence of the non-zero normal density is attributed to the coupling between the motion of superflow and density fluctuation in transverse directions. With Josephson relation, the superfluid density $ρ_s$ is also calculated and the identity $ρ_s+ρ_n=ρ$ holds. Further more, we find that the finite normal density also results in a quantized moment of inertia in a moving superfluid trapped by a ring. The normal density and moment of inertia at zero temperature could be verified experimentally by measuring the angular momentum of a moving superfluid in a ring trap.

preprint2020arXiv

Planimation

Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL. We introduce a preliminary declarative PDDL-like animation profile specification, expressive enough to synthesise animations of arbitrary initial states and goals of a benchmark with just a single profile.

preprint2020arXiv

Privacy Preserving Vertical Federated Learning for Tree-based Models

Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies {\it vertical} federated learning, which tackles the scenarios where (i) collaborating organizations own data of the same set of users but with disjoint features, and (ii) only one organization holds the labels. We propose Pivot, a novel solution for privacy preserving vertical decision tree training and prediction, ensuring that no intermediate information is disclosed other than those the clients have agreed to release (i.e., the final tree model and the prediction output). Pivot does not rely on any trusted third party and provides protection against a semi-honest adversary that may compromise $m-1$ out of $m$ clients. We further identify two privacy leakages when the trained decision tree model is released in plaintext and propose an enhanced protocol to mitigate them. The proposed solution can also be extended to tree ensemble models, e.g., random forest (RF) and gradient boosting decision tree (GBDT) by treating single decision trees as building blocks. Theoretical and experimental analysis suggest that Pivot is efficient for the privacy achieved.

preprint2020arXiv

Progressive Neural Index Search for Database System

As a key ingredient of the DBMS, index plays an important role in the query optimization and processing. However, it is a non-trivial task to apply existing indexes or design new indexes for new applications, where both data distribution and query distribution are unknown. To address the issue, we propose a new indexing approach, NIS (Neural Index Search), which searches for the optimal index parameters and structures using a neural network. In particular, NIS is capable for building a tree-like index automatically for an arbitrary column that can be sorted/partitioned using a customized function. The contributions of NIS are twofold. First, NIS constructs a tree-like index in a layer-by-layer way via formalizing the index structure as abstract ordered and unordered blocks. Ordered blocks are implemented using B+-tree nodes or skip lists, while unordered blocks adopt hash functions with different configurations. Second, all parameters of the building blocks (e.g., fanout of B+-tree node, bucket number of hash function and etc.) are tuned by NIS automatically. We achieve the two goals for a given workload and dataset with one RNN-powered reinforcement learning model. Experiments show that the auto-tuned index built by NIS can achieve a better performance than the state-of-the-art index.

preprint2020arXiv

Pyrochlore U(1) spin liquid of mixed symmetry enrichments in magnetic fields

We point out the experimental relevance and the detection scheme of symmetry enriched U(1) quantum spin liquids (QSLs) outside the perturbative spin-ice regime. Recent experiments on Ce-based pyrochlore QSL materials suggest that the candidate QSL may not be proximate to the well-known spin ice regime, and thus differs fundamentally from other pyrochlore QSL materials. We consider the possibility of the $π$-flux U(1) QSL favored by frustrated transverse exchange interactions rather than the usual quantum spin ice. It was previously suggested that both dipolar U(1) QSL and octupolar U(1) QSL can be realized for the generic spin model for the dipole-octupole doublets of the Ce$^{3+}$ local moments on the pyrochlore magnets Ce$_2$Sn$_2$O$_7$ and Ce$_2$Zr$_2$O$_7$. We explain and predict the experimental signatures especially the magnetic field response of the octupolar $π$-flux U(1) QSL. Fundamentally, this remarkable state is a mixture of symmetry enrichments from point group symmetry and from translational symmetry. We discuss the relevant experiments for pyrochlore U(1) QSLs and further provide some insights to the pyrochlore Heisenberg model.

preprint2020arXiv

Quantum spiral spin-tensor magnetism

The characterization of quantum magnetism in a large spin ($\geq 1$) system naturally involves both spin-vectors and -tensors. While certain types of spin-vector (e.g., ferromagnetic, spiral) and spin-tensor (e.g., nematic in frustrated lattices) orders have been investigated separately, the coexistence and correlation between them have not been well explored. Here we propose a novel quantum spiral spin-tensor order on a spin-1 Heisenberg chain subject to a spiral spin-tensor Zeeman field, which can be experimentally realized using a Raman-dressed cold atom optical lattice. We develop a method to fully characterize quantum phases of such spiral tensor magnetism with the coexistence of spin-vector and spin-tensor orders as well as their correlations using eight geometric parameters. Our method provides a powerful tool for characterizing spin-1 quantum magnetism and opens an avenue for exploring novel magnetic orders and spin-tensor electronics/atomtronics in large-spin systems.

preprint2020arXiv

Robust Federated Recommendation System

Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning attacks that can degrade their performance. In this paper, we develop a novel federated recommendation technique that is robust against the poisoning attack where Byzantine clients prevail. We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients. We then propose a robust learning strategy where instead of using model parameters, the central server computes and utilizes the gradients to filter out Byzantine clients. Theoretically, we justify our robust learning strategy by our proposed definition of Byzantine resilience. Empirically, we confirm the efficacy of our robust learning strategy employing four datasets in a federated recommendation system.

preprint2020arXiv

Schur Rings over Z \times Z_3

For the direct product $\cZ\times \cZ_3$ of infinite cyclic group $\cZ$ and a cyclic group $\cZ_3$ of order $3$, the schur rings over it are classified. In particular, all the schur rings are proved to be traditional.

preprint2020arXiv

Second-order topological insulator in a coinless discrete-time quantum walk

Higher-order topological insulators not only exhibit exotic bulk-boundary correspondence principle, but also have an important application in quantum computing. However, they have never been achieved in quantum walk. In this paper, we construct a two-dimensional coinless discrete-time quantum walk to simulate second-order topological insulator with zero-dimensional corner states. We show that both of the corner and edge states can be observed through the probability distribution of the walker after multi-step discrete-time quantum walks. Furthermore, we demonstrate the robustness of the topological corner states by introducing the static disorder. Finally, we propose a possible experimental implementation to realize this discrete-time quantum walk in a three-dimensional integrated photonic circuits. Our work offers a new route to explore exotic higher-order topological matters using discrete-time quantum walks.

preprint2020arXiv

Superconvergent Interpolatory HDG methods for reaction diffusion equations II: HHO-inspired methods

In J. Sci. Comput., 81: 2188-2212, 2019, we considered a superconvergent hybridizable discontinuous Galerkin (HDG) method, defined on simplicial meshes, for scalar reaction diffusion equations and showed how to define an interpolatory version which maintained its convergence properties. The interpolatory approach uses a locally postprocessed approximate solution to evaluate the nonlinear term, and assembles all HDG matrices once before the time intergration leading to a reduction in computational cost. The resulting method displays a superconvergent rate for the solution for polynomial degree $k\ge 1$. In this work, we take advantage of the link found between the HDG and the hybrid high-order (HHO) methods, in ESAIM Math. Model. Numer. Anal., 50: 635-650, 2016 and extend this idea to the new, HHO-inspired HDG methods, defined on meshes made of general polyhedral elements, uncovered therein}. We prove that the resulting interpolatory HDG methods converge at the same rate as for the linear elliptic problems. Hence, we obtain superconvergent methods for $k\ge 0$ by some methods. We present numerical results to illustrate the convergence theory.

preprint2020arXiv

Synthetic gauge field and chiral physics on two-leg superconducting circuits

Gauge field is essential for exploring novel phenomena in modern physics. However, it has not been realized in the recent breakthrough experiment about two-leg superconducting circuits with transmon qubits [Phys. Rev. Lett. 123, 050502 (2019)]. Here we present an experimentally-feasible method to achieve the synthetic gauge field by introducing ac microwave driving in each qubit. In particular, the effective magnetic flux per plaquette achieved can be tuned independently by properly choosing the driving phases. Moreover, the ground-state chiral currents for the single- and two-qubit excitations are obtained and the Meissner-vortex phase transition is found. In the Meissner phase, the ground-state chiral current increases as the magnetic flux increases, while it decreases in the vortex phase. In addition, the chiral dynamics that depends crucially on the initial state of the system is also revealed. Finally, the possible experimental observations of the chiral current and dynamics are addressed. Therefore, our results provide a new route to explore novel many-body properties induced by the interplay of gauge field, two-leg hoppings and interaction of photons on superconducting circuits.

preprint2019arXiv

A new prediction method of unsteady wake flow by the hybrid deep neural network

The fast and accurate prediction of unsteady flow becomes a serious challenge in fluid dynamics, due to the high-dimensional and nonlinear characteristics. A novel hybrid deep neural network (DNN) architecture was designed to capture the unsteady flow spatio-temporal features directly from the high-dimensional unsteady flow fields. The hybrid deep neural network is constituted by the convolutional neural network (CNN), convolutional Long Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). The flow around a cylinder at various Reynolds numbers and the flow around an airfoil at higher Reynolds number are carried out to establish the datasets used to train the networks separately. The trained hybrid DNNs were then tested by the prediction of the flow fields at future occasions. The predicted flow fields using the trained hybrid DNNs are in good agreement with the flow fields calculated directly by the computational fluid dynamic solver.

preprint2019arXiv

BCJ Numerators from Differential Operator of Multidimensional Residue

In previous works, we devised a differential operator for evaluating typical integrals appearing in the Cachazo-He-Yuan (CHY) forms and in this paper we further streamline this method. We observe that at tree level, the number of free parameters controlling the differential operator depends solely on the number of external lines, after solving the constraints arising from the scattering equations. This allows us to construct a reduction matrix that relates the parameters of a higher-order differential operator to those of a lower-order one. The reduction matrix is theory-independent and can be obtained by solving a set of explicitly given linear conditions. The repeated application of such reduction matrices eventually transforms a given tree-level CHY-like integral to a prepared form. We also provide analytic expressions for the parameters associated with any such prepared form at tree level. We finally give a compact expression for the multidimensional residue for any CHY-like integral in terms of the reduction matrices. We adopt a dual basis projector which leads to the CHY-like representation for the non-local Bern-Carrasco-Johansson (BCJ) numerators at tree level in Yang-Mills theory. These BCJ numerators are efficiently computed by the improved method involving the reduction matrix.

preprint2019arXiv

Experimental identification of electric dipoles induced by magnetic monopoles in Tb2Ti2O7

The fundamental principles of electrodynamics allow an electron carrying both electric monopole (charge) and magnetic dipole (spin) but prohibit its magnetic counterpart. Recently it was predicted that the magnetic "monopoles" carrying emergent magnetic charges in spin ice systems can induce electric dipoles. The inspiring prediction offers a novel way to study magnetic monopole excitations and magnetoelectric coupling. However, no clear example has been identified up to now. Here, we report the experimental evidence for electric dipoles induced by magnetic monopoles in spin frustrated Tb2Ti2O7. The magnetic field applied to pyrochlore Tb2Ti2O7 along [111] direction, brings out a "3-in-1-out" magnetic monopole configuration, and then induces a subtle structural phase transition at Hc~2.3 T. The transition is evidenced by the non-linear phonon splitting under magnetic fields and the anomalous crystal-field excitations of Tb3+ ions. The observations consistently point to the displacement of the oxygen O" anions along [111] axis which gives rise to the formation of electric dipoles. The finding demonstrates that the scenario of magnetic monopole having both magnetic charge and electric dipole is realized in Tb2Ti2O7 and sheds light into the coupling between electricity and magnetism of magnetic monopoles in spin frustrated systems.

preprint2019arXiv

Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition

Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request. In this paper, we proposed novel memetic EDA-based approaches to tackle this problem. The proposed method investigates the effectiveness of several neighborhood structures of composite services by proposing domain-dependent local search operators. Apart from that, a joint strategy of the local search procedure is proposed to integrate with a modified EDA to reduce the overall computation time of our memetic approach. To better demonstrate the effectiveness and scalability of our approach, we create a more challenging, augmented version of the service composition benchmark based on WSC-08 \cite{bansal2008wsc} and WSC-09 \cite{kona2009wsc}. Experimental results on this benchmark show that one of our proposed memetic EDA-based approach (i.e., MEEDA-LOP) significantly outperforms existing state-of-the-art algorithms.

preprint2019arXiv

On the kinematic algebra for BCJ numerators beyond the MHV sector

The duality between color and kinematics present in scattering amplitudes of Yang-Mills theory strongly suggest the existence of a hidden kinematic Lie algebra that controls the gauge theory. While associated BCJ numerators are known on closed forms to any multiplicity at tree level, the kinematic algebra has only been partially explored for the simplest of four-dimensional amplitudes: up to the MHV sector. In this paper we introduce a framework that allows us to characterize the algebra beyond the MHV sector. This allows us to both constrain some of the ambiguities of the kinematic algebra, and better control the generalized gauge freedom that is associated with the BCJ numerators. Specifically, in this paper, we work in dimension-agnostic notation and determine the kinematic algebra valid up to certain ${\cal O}\big((\varepsilon_i \cdot \varepsilon_j)^2\big)$ terms that in four dimensions compute the next-to-MHV sector involving two scalars. The kinematic algebra in this sector is simple, given that we introduce tensor currents that generalize standard Yang-Mills vector currents. These tensor currents controls the generalized gauge freedom, allowing us to generate multiple different versions of BCJ numerators from the same kinematic algebra. The framework should generalize to other sectors in Yang-Mills theory.

preprint2019arXiv

Optical properties of cubic boron arsenide

The ultrahigh thermal conductivity of boron arsenide makes it a promising material for next-generation electronics and optoelectronics. In this work, we report measured optical properties of cubic boron arsenide crystals including the complex dielectric function, refractive index, and absorption coefficient in the ultraviolet, visible, and near-infrared wavelength range. The data were collected at room temperature using spectroscopic ellipsometry as well as transmission and reflection spectroscopy. We further calculate the optical response using density functional and many-body perturbation theory, considering quasiparticle and excitonic corrections. The computed values for the direct and indirect band gaps (4.25 eV and 2.07 eV) agree well with the measured results (4.12 eV and 2.02 eV). Our findings contribute to the effort of using boron arsenide in novel electronic and optoelectronic applications that take advantage of its demonstrated ultrahigh thermal conductivity and predicted high ambipolar carrier mobility.

preprint2019arXiv

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.

preprint2019arXiv

Superconvergent interpolatory HDG methods for reaction diffusion equations I: An HDG$_{k}$ method

In our earlier work [8], we approximated solutions of a general class of scalar parabolic semilinear PDEs by an interpolatory hybridizable discontinuous Galerkin (Interpolatory HDG) method. This method reduces the computational cost compared to standard HDG since the HDG matrices are assembled once before the time integration. Interpolatory HDG also achieves optimal convergence rates; however, we did not observe superconvergence after an element-by-element postprocessing. In this work, we revisit the Interpolatory HDG method for reaction diffusion problems, and use the postprocessed approximate solution to evaluate the nonlinear term. We prove this simple change restores the superconvergence and keeps the computational advantages of the Interpolatory HDG method. We present numerical results to illustrate the convergence theory and the performance of the method.

preprint2019arXiv

The evolution of Information entropy components in Relativistic Heavy-Ion Collisions

Shannon information entropy provides an effective tool to study the evolution process in relativistic heavy-ion collisions. The time evolution process of thermodynamic entropy $S_{\rm thermal}$, multiple entropy $S_{\rm mul}$, and configuration entropy $S_{\rm conf}$ at RHIC is studied using the AMPT model to generate central Au-Au collisions. By superimposing the three kinds of information entropy, we can get a more complete information entropy of the system to describe the physical information of the relativistic heavy ion collision. The results show that the four stages of the time evolution process of the system entropy $S$ seem to correspond to the four physical processes in the relativistic heavy ion collision, indicating that the total entropy of the system can reflect the physical information in the relativistic heavy ion collision more accurately.

preprint2019arXiv

Thermal Transport for Probing Quantum Materials

Thermal transport is less appreciated in probing quantum materials in comparison to electrical transport. This article aims to show the pivotal role that thermal transport may play in understanding quantum materials: the longitudinal thermal transport reflects the itinerant quasiparticles even in an electrical insulating phase, while the transverse thermal transport such as thermal Hall and Nernst effect are tightly linked to nontrivial topology. We discuss three types of examples: quantum spin liquids where thermal transport identifies its existence, superconductors where thermal transport reveals the superconducting gap structure, and topological Weyl semimetals where anomalous Nernst effect is a consequence of nontrivial Berry curvature. We conclude with an outlook of the unique insights thermal transport may offer to probe a much broader category of quantum phenomena.

preprint2019arXiv

Topological quantum walks in cavity-based quantum networks

We present a protocol to implement discrete-time quantum walks and simulate topological insulator phases in cavity-based quantum networks, where the single photon is the quantum walker and the cavity input-output process is employed to realize the state-dependent translation operation. Different topological phases can be simulated through tuning the single-photon polarization rotation angles. We show that both the topological boundary states and topological phase transitions can be directly observed via measuring the final photonic density distribution. Moreover, we also demonstrate that these topological signatures are quite robust to practical imperfections. Our work opens a new prospect using cavity-based quantum networks as quantum simulators to study discrete-time quantum walks and mimic condensed matter physics.

preprint2019arXiv

Topological thermal Hall effect for topological excitations in spin liquid: Emergent Lorentz force on the spinons

We study the origin of Lorentz force on the spinons in a U(1) spin liquid. We are inspired by the previous observation of gauge field correlation in the pairwise spin correlation using the neutron scattering measurement when the Dzyaloshinskii-Moriya interaction intertwines with the lattice geometry. We extend this observation to the Lorentz force that exerts on the (neutral) spinons. The external magnetic field, that polarizes the spins, effectively generates an internal U(1) gauge flux for the spinons and twists the spinon motion through the Dzyaloshinskii-Moriya interaction. Such a mechanism for the emergent Lorentz force differs fundamentally from the induction of the internal U(1) gauge flux in the weak Mott insulating regime from the charge fluctuations. We apply this understanding to the specific case of spinon metals on the kagome lattice. Our suggestion of emergent Lorentz force generation and the resulting topological thermal Hall effect may apply broadly to other non-centrosymmetric spin liquids with Dzyaloshinskii-Moriya interaction. We discuss the relevance with the thermal Hall transport in kagome materials volborthite and kapellasite.

preprint2019arXiv

Topological thermal Hall effect of "magnetic monopoles" in pyrochlore U(1) spin liquid

"Magnetic monopole" is an exotic quantum excitation in pyrochlore U(1) spin liquid, and its emergence is purely of quantum origin and has no classical analogue. We predict topological thermal Hall effect (TTHE) of "magnetic monopoles" and present this prediction through non-Kramers doublets. We observe that, when the external magnetic field polarizes the Ising component of the local moment, internally this corresponds to the induction of emergent dual U(1) gauge flux for the "magnetic monopoles". The motion of "magnetic monopoles" is then twisted by the induced dual gauge flux. This emergent Lorentz force on "magnetic monopoles" is the fundamental origin of TTHE. Therefore, TTHE would be a direct evidence of the "monopole"-gauge coupling and the emergent U(1) gauge structure in pyrochlore U(1) spin liquid. Our result does not depend strongly on our choice of non-Kramers doublets for our presentation, and can be well extended to Kramers doublets. Our prediction can be readily tested among the pyrochlore spin liquid candidate materials. We give a detailed discussion about the expectation for different pyrochlore magnets.

preprint2018arXiv

A Class of Embedded DG Methods for Dirichlet Boundary Control of Convection Diffusion PDEs

We investigated an hybridizable discontinuous Galerkin (HDG) method for a convection diffusion Dirichlet boundary control problem in our earlier work [SIAM J. Numer. Anal. 56 (2018) 2262-2287] and obtained an optimal convergence rate for the control under some assumptions on the desired state and the domain. In this work, we obtain the same convergence rate for the control using a class of embedded DG methods proposed by Nguyen, Peraire and Cockburn [J. Comput. Phys. vol. 302 (2015), pp. 674-692] for simulating fluid flows. Since the global system for embedded DG methods uses continuous elements, the number of degrees of freedom for the embedded DG methods are smaller than the HDG method, which uses discontinuous elements for the global system. Moreover, we introduce a new simpler numerical analysis technique to handle low regularity solutions of the boundary control problem. We present some numerical experiments to confirm our theoretical results.

preprint2018arXiv

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

preprint2018arXiv

An HDG Method for Dirichlet Boundary Control of Convection Dominated Diffusion PDE

We first propose a hybridizable discontinuous Galerkin (HDG) method to approximate the solution of a \emph{convection dominated} Dirichlet boundary control problem. Dirichlet boundary control problems and convection dominated problems are each very challenging numerically due to solutions with low regularity and sharp layers, respectively. Although there are some numerical analysis works in the literature on \emph{diffusion dominated} convection diffusion Dirichlet boundary control problems, we are not aware of any existing numerical analysis works for convection dominated boundary control problems. Moreover, the existing numerical analysis techniques for convection dominated PDEs are not directly applicable for the Dirichlet boundary control problem because of the low regularity solutions. In this work, we obtain an optimal a priori error estimate for the control under some conditions on the domain and the desired state. We also present some numerical experiments to illustrate the performance of the HDG method for convection dominated Dirichlet boundary control problems.

preprint2017arXiv

FMRI Clustering and False Positive Rates

Recently, Eklund et al. (2016) analyzed clustering methods in standard FMRI packages: AFNI (which we maintain), FSL, and SPM [1]. They claimed: 1) false positive rates (FPRs) in traditional approaches are greatly inflated, questioning the validity of "countless published fMRI studies"; 2) nonparametric methods produce valid, but slightly conservative, FPRs; 3) a common flawed assumption is that the spatial autocorrelation function (ACF) of FMRI noise is Gaussian-shaped; and 4) a 15-year-old bug in AFNI's 3dClustSim significantly contributed to producing "particularly high" FPRs compared to other software. We repeated simulations from [1] (Beijing-Zang data [2], see [3]), and comment on each point briefly.

Gang Chen

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225 published item(s)

A two-temperature gas-kinetic scheme for hypersonic nonequilibrium flow computations

Adapting Depth Anything to Adverse Imaging Conditions with Events

From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition

From Context to Skills: Can Language Models Learn from Context Skillfully?

InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs

OpenSGA: Efficient 3D Scene Graph Alignment in the Open World

SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses

SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search

Table as a Modality for Large Language Models

Token Economics for LLM Agents: A Dual-View Study from Computing and Economics

Theoretical Study on Superradiant Raman Scattering with Rubidium Atoms in An Optical Cavity

Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

Stacking-induced magnetic frustration and spiral spin liquid

A New Global Divergence Free and Pressure-Robust HDG Method for Tangential Boundary Control of Stokes Equations

Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

Analysis of a class of globally divergence-free HDG methods for stationary Navier-Stokes equations

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VII. A catalogue of SiO clumps from ACA observations

Centrality dependence and isospin effect on $\Wpm$ and $\Zn$ productions in nucleus-nucleus collisions at $\sNN=5.02$~TeV

European Aerosol Phenomenology -- 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Fractionalization on the Surface: Is Type-II Terminated $1T$-TaS$_2$ Surface an Anomalously Realized Spin Liquid?

Inducing and tuning Kondo screening in a narrow-electronic-band system

Interplay of itinerant electrons and Ising moments in a hybrid honeycomb quantum magnet TmNi$_3$Al$_9$

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Intertwining SU($N$) symmetry and frustration on a honeycomb lattice

Kinematic Hopf Algebra for BCJ Numerators in Heavy-Mass Effective Field Theory and Yang-Mills Theory

Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment

On the Molecular Picture and Interfacial Temperature Discontinuity During Evaporation and Condensation

Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning

Photomolecular Effect Leading to Water Evaporation Exceeding Thermal Limit

Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Significant reduction in semiconductor interface resistance via interfacial atomic mixing

Speed up the inference of diffusion models via shortcut MCMC sampling

Spiral-spin-liquid behaviors and persistent reciprocal kagomé structure in frustrated van der Waals magnets and beyond

Study for (anti)hypertriton and light (anti)nuclei productions in high energy collisions at 200 GeV

Thermodynamics of Hydrogels for Applications to Atmospheric Water Harvesting, Evaporation, and Desalination

Universal excitonic superexchange in spin-orbit-coupled Mott insulators

3D hinge transport in acoustic higher-order topological insulators

A study of $Λ$ and $\barΛ$ polarization splitting by meson field in PICR hydrodynamic model

An Active Sense and Avoid System for Flying Robots in Dynamic Environments

Blockchains vs. Distributed Databases: Dichotomy and Fusion

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Collision system size scan for light (anti-)nuclei and (anti-)hypertriton production in high energy nuclear collisions

Fusion categories containing a fusion subcategory with maximal rank

Guided Interpolation for Adversarial Training

Higher-order topological semimetal in acoustic crystals

Light (anti-)nuclei and (anti-)hypertriton production in $pp$ collisions at $\sqrt{s} =0.90, 2.76$ and $7$ TeV

Predictions for production of $\rm{^3_ΛH}$ and $\rm{{^3_{\overline Λ}\overline H}}$ in isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr collisions at $\sqrt{s_{\rm{NN}}}$ = 200 GeV

Semi-discrete and fully discrete HDG methods for Burgers' equation

The study of exotic state $Z_c^{\pm}(3900)$ decaying to $J/ψπ^{\pm}$ in the $pp$ collisions at $\sqrt{s}$ = 1.96, 7, and 13 TeV

$L^\infty$ norm error estimates for HDG methods applied to the Poisson equation with an application to the Dirichlet boundary control problem

A characterization of exceptional pseudocyclic association schemes by multidimensional intersection numbers

A Néel-type antiferromagnetic order in the spin 1/2 rare-earth honeycomb YbCl$_3$

A new method for driven-dissipative problems: Keldysh-Heisenberg equations

A Transactional Perspective on Execute-order-validate Blockchains

Acoustic corner states in topological insulators with built-in Zeeman-like fields

Analysis of an interior penalty DG method for the quad-curl problem

Analysis of Indexing Structures for Immutable Data

Atomic self-organization emerging from tunable quadrature coupling

Clusterization transition between cluster Mott insulators on a breathing Kagomé lattice

Database Meets Deep Learning: Challenges and Opportunities

Decorrelated Double Q-learning

Detection of multipolar orders in the spin-orbit-entangled 5d Mott insulator Ba2MgReO6

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Dynamical Zeeman resonance in spin-orbit-coupled spin-1 Bose gases

Effective and Efficient Dropout for Deep Convolutional Neural Networks

Featureless quantum paramagnet with frustrated criticality and competing spiral magnetism on spin-1 honeycomb lattice magnet

ForkBase: Immutable, Tamper-evident Storage Substrate for Branchable Applications

HDG and CG methods for the Indefinite Time-Harmonic Maxwell's Equations under minimal regularity

Hybrid deep neural network based prediction method for unsteady flows with moving boundaries

Large nonreciprocal absorption and emission of radiation in type-I Weyl semimetals with time reversal symmetry breaking

Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning

Neural Machine Translation: A Review of Methods, Resources, and Tools

Normal density and moment of inertia of a moving superfluid

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