Search

We study the nonleptonic decays of $B\to PP$ in the modified perturbative QCD approach, where $P$ stands for pseudoscalar mesons. Transverse momenta of partons and the Sudakov factor are included, which help to suppress the contributions of soft interactions. The wave function of the $B$ meson obtained from the relativistic potential model is used, and then the contributions in the infrared region can not be suppressed completely. So a soft cutoff scale and soft form factors are introduced. The contributions with the scale higher than the soft cutoff scale are calculated with perturbative QCD, while the contributions lower than the cutoff scale are replaced by the soft form factors. To explain experimental data, we find that contributions of color-octet operators for the quark-antiquarks in the mesons in the final state need to be considered. The contributions of the color-octet operators are parametrized by a few parameters with the help of SU(3) flavor symmetry and symmetry breaking. These parameters for color-octet contributions are universal for all the nonleptonic decay modes of the $B$ meson, where the mesons in the final state belong to the same flavor SU(3) nonet. Both the branching ratios and $CP$ violations are studied. We find that the theoretical calculation can well explain the experimental data of $B$ factories.

Dust attenuation in star-forming galaxies (SFGs), as parameterized by the infrared excess (IRX $\equiv L_{\rm IR}/L_{\rm UV}$), is found to be tightly correlated with star formation rate (SFR), metallicity and galaxy size, following a universal IRX relation up to $z=3$. This scaling relation can provide a fundamental constraint for theoretical models to reconcile galaxy star formation, chemical enrichment, and structural evolution across cosmic time. We attempt to reproduce the universal IRX relation over $0.1\leq z\leq 2.5$ using the EAGLE hydrodynamical simulations and examine sensitive parameters in determining galaxy dust attenuation. Our findings show that while the predicted universal IRX relation from EAGLE approximately aligns with observations at $z\leq 0.5$, noticeable disparities arise at different stellar masses and higher redshifts. Specifically, we investigate how modifying various galaxy parameters can affect the predicted universal IRX relation in comparison to the observed data. We demonstrate that the simulated gas-phase metallicity is the critical quantity for the shape of the predicted universal IRX relation. We find that the influence of the infrared luminosity and infrared excess is less important while galaxy size has virtually no significant effect. Overall, the EAGLE simulations are not able to replicate some of the observed characteristics between IRX and galaxy parameters of SFGs, emphasizing the need for further investigation and testing for our current state-of-the-art theoretical models., Comment: 19 pages, 15 figures, accepted for publication in MNRAS

Transposed Poisson $3$-Lie algebra is a dual notion of Nambu-Poisson algebra of order 3. In this paper, the classification of transposed Poisson $3$-Lie algebras of dimension $3$ is obtained by $\frac{1}{3}$-derivations and automorphisms of $3$-Lie algebras under some certain conditions.

Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics. However, the reliance on self-supervision introduces a strong static-scene assumption, thereby posing challenges in achieving optimal performance in dynamic scenes, which are prevalent in most real-world situations. To address these issues, we propose PPEA-Depth, a Progressive Parameter-Efficient Adaptation approach to transfer a pre-trained image model for self-supervised depth estimation. The training comprises two sequential stages: an initial phase trained on a dataset primarily composed of static scenes, succeeded by an expansion to more intricate datasets involving dynamic scenes. To facilitate this process, we design compact encoder and decoder adapters to enable parameter-efficient tuning, allowing the network to adapt effectively. They not only uphold generalized patterns from pre-trained image models but also retain knowledge gained from the preceding phase into the subsequent one. Extensive experiments demonstrate that PPEA-Depth achieves state-of-the-art performance on KITTI, CityScapes and DDAD datasets., Comment: Accepted by AAAI 2024 Project homepage: https://yuejiangdong.github.io/PPEADepth/

The Schr\"{o}dinger cat state produced differently in two directions is anticipated to be a critical quantum resource in quantum information technologies. By exploring the interplay between quantum nonreciprocity and topology in a one-dimensional microcavity array, we obtain the Schr\"{o}dinger cat state ({\it a pure quantum state}) in a chosen direction at the edge cavity, whereas a {\it classical state} in the other direction. This {\it nonreciprocal generation of the cat state} originates from the {\it topologically protected chirality-mode excitation} in the nontrivial phase, but in the trivial phase the {\it nonreciprocal generation of cat state} vanishes. Thus, our proposal is switchable by tuning the parameters so that a topological phase transition occurs. Moreover, the obtained cat state has nonreciprocal high fidelity, nonclassicality, and quantum coherence, which are sufficient to be used in various one-way quantum technologies, e.g., invisible quantum sensing, noise-tolerant quantum computing, and chiral quantum networks. Our work provides a general approach to control quantum nonreciprocities with the topological effect, which substantially broadens the fields of nonreciprocal photonics and topological physics., Comment: 8 pages, 4 figures, accepted by SCIENCE CHINA Physics, Mechanics & Astronomy

We study the time-inconsistent linear quadratic optimal control problem for forward-backward stochastic differential equations with potentially indefinite cost weighting matrices for both the state and the control variables. Our research makes two contributions. Firstly, we introduce a novel type of Riccati equation system with parameters and constraint conditions, known as the generalized equilibrium Riccati equation. This equation system offers a comprehensive solution for the closed-loop equilibrium strategy of the problem at hand. Secondly, we establish the well-posedness of the generalized equilibrium Riccati equation for the one-dimensional case, provided certain conditions are met.

We study the quantum metric in a driven Tavis-Cummings model, comprised of multiple qubits interacting with a quantized photonic field. The parametrical driving of the photonic field breaks the system's U(1) symmetry down to a ${\rm Z}_2$ symmetry, whose spontaneous breaking initiates a superradiant phase transition. We analytically solved the eigenenergies and eigenstates, and numerically simulated the system behaviors near the critical point. The critical behaviors near the superradiant phase transition are characterized by the quantum metric, defined in terms of the response of the quantum state to variation of the control parameter. In addition, a quantum metrological protocol based on the critical behaviors of the quantum metric near the superradiant phase transition is proposed, which enables greatly the achievable measurement precision., Comment: 13 pages, 5 figures

It is commonly believed that black holes are the smallest self-gravitating objects of the same mass in the Universe. Here, we demonstrate, in a subclass of higher-order pure gravities known as quasi-topological gravity, that by modifying general relativity (GR) to reduce the strength of gravity in strong-field regimes while keeping GR unchanged in weak-field regimes, it is possible for stars to collapse to radii less than $2M$ while still maintaining equilibrium between gravity and pressure gradients, leading to physically-reasonable neutron stars smaller in size than a black hole of the same mass. We present concrete solutions for such objects and discuss some of their observational consequences. These objects may furnish new avenues for understanding the nature of gravity in strong-field regimes and leave imprints on gravitational wave echoes from compact binary mergers. An observation of these imprints may constitute evidence for new physics beyond GR when effects of gravity in strong-field regimes are concerned., Comment: 15 pages, 3 figures, submitted in August

We investigate various cosmological aspects of a 4-Dimensional Gauss-Bonnet Lagrangian, which is integrated into the Einstein Lagrangian with an arbitrary sign, using the Friedman-Lema\^itre-Robertson-Walker (FLRW) metric. We consider a general potential term, $V(a)$, that depends on the scale factor $a$, and we analyze several scenarios by investigating the critical points of the dynamical equations and stability conditions to understand how the universe's behavior is affected by the Gauss-Bonnet term. Our research suggests that choosing the negative sign, this integration allows for the spontaneous breaking of time reflection symmetry. This can lead to the generation of a bounce universe even with a normal matter sector, marking a significant departure from traditional theories. Furthermore, we examine the possibility of a time-crystal universe, showing that under certain circumstances, the theory might give rise to cyclic universes., Comment: 24 pages, 8 figures, 2 tables

Inner edge state with spin and valley degrees of freedom is a promising candidate to design a dissipationless device due to the topological protection. The central challenge for the application of inner edge state is to generate and modulate the polarized currents. In this work, we discover a new mechanism to generate fully valley- and spin-valley-polarized current caused by the Bloch wavevector mismatch (BWM). Based on this mechanism, we design some serial-typed inner-edge filters. With once of the BWM, the coincident states could be divided into transmitted and reflected modes, which can serve as a valley or spin-valley filter. In particular, while with twice of the BWM, the incident current is absolutely reflected to support an off state with a specified valley and spin, which is different from the gap effect. These findings give rise to a new platform for designing valleytronics and spin-valleytronics., Comment: 20 pages, 8 figures

We present the interesting resonance of two kinds of geometric quantities, namely the Aharonov-Anandan (AA) phase and the time-energy uncertainty, and reveal the relation between resonance and the hidden symmetry in the asymmetric Rabi model by numerical and analytical methods. By combining the counter-rotating hybridized rotating-wave method with time-dependent perturbation theory, we solve systematically the time evolution operator and then obtain the geometric phase of the Rabi model. In comparison with the numerically exact solutions, we find that the analytical results accurately describe the geometric quantities in a wide parameter space. We unveil the effect of the bias on the resonance of geometric quantities, (1) the positions of all harmonic resonances stemming from the shift of the Rabi frequency at the presence of the bias; (2) the occurrence of even order harmonic resonance due to the bias. When the driving frequency is equal to the subharmonics of the bias, the odd higher-order harmonic resonances disappear. Finally, the hidden symmetry has a resemblance to that of the quantum Rabi model with bias, which indicates the quasienergy spectra are similar to the energy spectra of the latter., Comment: 17 pages, 5 figures

Graph research, the systematic study of interconnected data points represented as graphs, plays a vital role in capturing intricate relationships within networked systems. However, in the real world, as graphs scale up, concerns about data security among different data-owning agencies arise, hindering information sharing and, ultimately, the utilization of graph data. Therefore, establishing a mutual trust mechanism among graph agencies is crucial for unlocking the full potential of graphs. Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks. Essentially, this CNL framework unifies the local and global perspectives of GNN computing with distributed data for an agency by virtually connecting all participating agencies as a global graph without a fixed central coordinator. Inter-agency computing is protected by various technologies inherent in our framework, including homomorphic encryption and secure transmission. Moreover, each agency has a fair right to design or employ various graph learning models from its local or global perspective. Thus, CNL can collaboratively train GNN models based on decentralized graphs inferred from local and global graphs. Experiments on contagion dynamics prediction and traditional graph tasks (i.e., node classification and link prediction) demonstrate that our CNL architecture outperforms state-of-the-art GNNs developed at individual sites, revealing that CNL can provide a reliable, fair, secure, privacy-preserving, and global perspective to build effective and personalized models for network applications. We hope this framework will address privacy concerns in graph-related research and integrate decentralized graph data structures to benefit the network research community in cooperation and innovation.

Electrons are the carriers of heat and electricity in materials, and exhibit abundant transport phenomena such as ballistic, diffusive, and hydrodynamic behaviors in systems with different sizes. The electron Boltzmann transport equation (eBTE) is a reliable model for describing electron transport, but it is a challenging problem to efficiently obtain the numerical solutions of eBTE within one unified scheme involving ballistic, hydrodynamics and/or diffusive regimes. In this work, a discrete unified gas kinetic scheme (DUGKS) in finite-volume framework is developed based on the eBTE with the Callaway relaxation model for electron transport. By reconstructing the distribution function at the cell interface, the processes of electron drift and scattering are coupled together within a single time step. Numerical tests demonstrate that the DUGKS can be adaptively applied to multiscale electron transport, across different regimes.

In this paper, we explicitly determine all $\mathcal{O}$-operators with respect to the adjoint representation of 3-dimensional complex 3-Lie algebras. Furthermore, we provide the induced 3-Pre-Lie algebra structures and the corresponding solutions of the 3-Lie classical Yang-Baxter equation in the 6-dimensional 3-Lie algebras $A\ltimes_{\mathrm{ad}^*} A^*$.

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs.

Traditional methods plan feasible paths for multiple agents in the stochastic environment. However, the methods' iterations with the changes in the environment result in computation complexities, especially for the decentralized agents without a centralized planner. Although reinforcement learning provides a plausible solution because of the generalization for different environments, it struggles with enormous agent-environment interactions in training. Here, we propose a novel centralized training with decentralized execution method based on multi-agent reinforcement learning, which is improved based on the idea of model predictive control. In our approach, agents communicate only with the centralized planner to make decentralized decisions online in the stochastic environment. Furthermore, considering the communication constraint with the centralized planner, each agent plans feasible paths through the extended observation, which combines information on neighboring agents based on the distance-weighted mean field approach. Inspired by the rolling optimization approach of model predictive control, we conduct multi-step value convergence in multi-agent reinforcement learning to enhance the training efficiency, which reduces the expensive interactions in convergence. Experiment results in both comparison, ablation, and real-robot studies validate the effectiveness and generalization performance of our method.

The Lifshitz-Kosevich (LK) theory is the pillar of magnetic quantum oscillations, which have been extensively applied to characterize a wide range of metallic states. In this study, we focus on the Shubnikov-de Haas (SdH) effect observed in SmSb, a rare-earth monopnictide. We observed a significant departure from the expected LK theory near $T_N=2.4$~K: both a peak-like anomaly and an enhancement in the temperature dependence of quantum oscillation amplitude are seen in SmSb. Moreover, we discovered a remarkable sensitivity of the SdH amplitudes to sample purity. By adjusting the sample purity, we were able to tune the temperature dependence of the $\alpha$ band's SdH amplitudes from a peak-like anomalous behavior to an enhancement. Therefore, SdH oscillations from the $\alpha$ band connect the two well-known non-LK behaviours, controllable through varying the sample purity, paving the way for developing further understanding of the mechanism leading to the anomalous quantum oscillations., Comment: 4 figures

Edges, binding together nodes within networks, have the potential to induce dramatic transitions when specific collective failure behaviors emerge. These changes, initially unfolding covertly and then erupting abruptly, pose substantial, unforeseeable threats to networked systems, and are termed explosive vulnerability. Thus, identifying influential edges capable of triggering such drastic transitions, while minimizing cost, is of utmost significance. Here, we address this challenge by introducing edge collective influence (ECI), which builds upon the optimal percolation theory applied to line graphs. ECI embodies features of both optimal and explosive percolation, involving minimized removal costs and explosive dismantling tactic. Furthermore, we introduce two improved versions of ECI, namely IECI and IECIR, tailored for objectives of hidden and fast dismantling, respectively, with their superior performance validated in both synthetic and empirical networks. Finally, we present a dual competitive percolation (DCP) model, whose reverse process replicates the explosive dismantling process and the trajectory of the cost function of ECI, elucidating the microscopic mechanisms enabling ECI's optimization. ECI and the DCP model demonstrate the profound connection between optimal and explosive percolation. This work significantly deepens our comprehension of percolation and provides valuable insights into the explosive vulnerabilities arising from edge collective behaviors., Comment: 19 pages, 11 figures, 2 tables

Neutrino losses play a crucial role in the evolution of massive stars. We study the neutrino luminosity of stars ranging from 20 to 90 M_{\odot} from Zero Age Main Sequence (ZAMS) to Fe Core Collapse (FeCC) with different rotation and metallicity in a neutrino Hertzsprung-Russell diagram. In our simulations, we consider {\omega}/{\omega}crit = 0 and 0.7 to represent non-rotation and high rotation, respectively, and set the metallicities to 0.014, 0.001, and 0.0001. During hydrogen burning stages, neutrino luminosity primarily originates from CNO cycle, and increases with higher stellar mass while decreasing with increasing metallicity. For the high metallicity models (Z = 0.014) during the helium burning stage, the reduction of the hydrogen envelope caused by a larger mass loss rate leads to a gradual decrease in neutrino luminosity. The rapid rotation results in extra mixing inside massive stars, which increases the neutrino luminosity during main sequence (MS), while decreases the neutrino luminosity during helium burning phase. Simultaneously, the rapid rotation also increases CO core mass, which enhances the neutrino luminosity during C and O burning phase. We also investigate the effect of neutrino magnetic moment (NMM) on the massive stars. We find that the energy loss caused by the NMM does not have effects on the evolutionary destiny of massive stars, and it does not significant change the compactness at the time of Fe core collapse., Comment: This paper has been accepted by Monthly Notices of the Royal Astronomical Society on October 3, 2023

Despite being a layered, easily-exfoliated compound, copper monotelluride (CuTe) features an unusual quasi-one-dimensional charge density wave below $T_{\rm CDW}\approx335$ K. Within a CuTe layer, the electrical resistivity depends sensitively on the direction of the electrical current. Here, we use magnetotransport to probe the metallic state of CuTe with two distinct in-plane current directions. When the current flows along the $a$-axis ($I//a$), the magnetoresistance exhibits a downward curvature as the magnetic field increases. On the other hand, when the current is along the $b$-axis ($I//b$), the magnetoresistance shows the opposite curvature. Our analysis uncovers a violation of Kohler scaling, but only for $I//a$. Shubnikov-de Haas oscillations are detected at low temperatures. Our results shed light on the nature of the metallic state in CuTe with the development of the charge density wave., Comment: 6 pages, 4 figures

By using some deep tools from microlocal analysis, the authors of the papers (Ann. of Math., 165 (2007), 567--591, J. Amer. Math. Soc., 23 (2010), 655--691; Invent. Math., 178 (2009), 119--171; Duke Math. J., 158(2011), 83--120) have successfully established various Carleman estimates for elliptic operators that possess limiting Carleman weight. In this study, we revisit these problems and present a unified and fundamental approach for deriving these estimates. The main tool we employ is an elementary pointwise estimate for second-order elliptic operators.

From a complex network perspective, investigating the stock market holds paramount significance as it enables the systematic revelation of topological features inherent in the market. This approach is crucial in exploring market interconnectivity, systemic risks, portfolio management, and structural evolution. However, prevailing methodologies for constructing networks based on stock data rely on threshold filtering, often needing help to uncover intricate underlying associations among stocks. To address this, we introduce the Stock Network Inference Framework (SNIF), which leverages a self-encoding mechanism. Specifically, the Stock Network Inference Encoder (SNIE) facilitates network construction, while the Movement Prediction Decoder (MPD) enhances movement forecasting. This integrated process culminates in the inference of a stock network, exhibiting remarkable performance across applications such as market structure analysis, stock movement prediction, portfolio construction, and community evolution analysis. Our approach streamlines the automatic construction of stock networks, liberating the process from threshold dependencies and eliminating the need for additional financial indicators. Incorporating Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) models within the SNIF framework, we effectively unearth deep-seated associations among stocks, augmenting the toolset available for comprehensive financial market research. This integration empowers our methodology to automatically construct stock networks without threshold dependencies or reliance on additional economic indicators.

We investigate the CP violation in the decay process $\bar B_{s} \rightarrow \phi(\rho,\omega) P \rightarrow K^{+}K^{-}P$ by considering the interference effects of $\phi\rightarrow K^{+}K^{-}$, $\rho\rightarrow K^{+}K^{-}$ and $\omega\rightarrow K^{+}K^{-}$ within the framework of perturbative QCD method (P refers to $\pi$, K, $\eta$ and $\eta'$ pseudoscalar mesons, respectively). We analyse the mixings of $\phi-\rho^{0}$, $\phi-\omega$ and $\omega-\rho^{0}$ and provide the amplitudes of the quasi-two-body decay processes. The CP violation for $\bar B_{s} \rightarrow K^{+}K^{-} P$ decay process is obvious at the ranges of the three vector mesons interferences. Meanwhile, the localised CP violation can be found for comparing with the experiment results from three-body decay process at the LHC in the near future.

Black hole-neutron star (BH-NS) mergers are expected to emit gravitational-wave (GW) and electromagnetic (EM) counterparts when the NS is tidally disrupted or plunges into the BH. Recently, GW 200105 and GW200115 were claimed as originating in BH-NS mergers, even GW 200105 remains in debate. Several optical source candidates are reported to possible associate with the two GW events, but not confirmed yet. In this work, we assume that the BH is charged (the NS is naturally charged) and try to constrain the charge of the BH by using the possible associated EM emission from the charged BH and NS system working in the inspiral regime. We adopt electric and magnetic dipole radiations for the binaries which power a Poynting-flux-dominated outflow to accelerate electrons. Then, it produces the observed EM radiation via synchrotron radiation. We find that the conversion efficiency in the X-ray band is much higher than that of the ultraviolet (UV), near-infrared, and radio bands. The estimated maximum charge-to-mass ratio (the charge for unit mass) of the BH is $1.12\times 10^{-6}$ and $1.53\times 10^{-6}$ esu for the binary systems of GW200105 and GW200115, respectively, if magnetic field strength $B_{p}\lesssim ~10^{16}$ G and period $P>~1$ ms for the NS spin., Comment: 13 pages, 4 Tables, and 2 Figures. Accepted for publication in PRD

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs. Codes and datasets are available at https://github.com/Yiminghh/HiGCN.

We consider generalisations of the elliptic Calogero--Moser systems associated to complex crystallographic groups in accordance to \cite{EFMV11ecm}. In our previous work \cite{Argyres:2021iws}, we proposed these systems as candidates for Seiberg--Witten integrable systems of certain SCFTs. Here we examine that proposal for complex crystallographic groups of rank one. Geometrically, this means considering elliptic curves $T^2$ with $\Z_m$-symmetries, $m=2,3,4,6$, and Poisson deformations of the orbifolds $(T^2\times\mathbb{C})/\Z_m$. The $m=2$ case was studied in \cite{Argyres:2021iws}, while $m=3,4,6$ correspond to Seiberg--Witten integrable systems for the rank 1 Minahan--Nemeshansky SCFTs of type $E_{6,7,8}$. This allows us to describe the corresponding elliptic fibrations and the Seiberg--Witten differential in a compact elegant form. This approach also produces quantum spectral curves for these SCFTs, which are given by Fuchsian ODEs with special properties.

Stochastic parabolic equations are widely used to model many random phenomena in natural sciences, such as the temperature distribution in a noisy medium, the dynamics of a chemical reaction in a noisy environment, or the evolution of the density of bacteria population. In many cases, the equation may involve an unknown moving boundary which could represent a change of phase, a reaction front, or an unknown population. In this paper, we focus on an inverse problem where the goal is to determine an unknown moving boundary based on data observed in a specific interior subdomain for the stochastic parabolic equation and prove that the unknown boundary depends logarithmically on the interior measurement. This allows us, theoretically, to track and to monitor the behavior of unknown boundary from observation in an arbitrary interior domain. The stability estimate is based on a new Carleman estimate for stochastic parabolic equations. As a byproduct, we obtain a quantitative unique continuation property for stochastic parabolic equations.

The gravitational waves (GW) from core-collapse supernovae (CCSN) have been proposed as a probe to investigate physical properties inside of the supernova. However, how to search and extract the GW signals from core-collapse supernovae remains an open question due to its complicated time-frequency structure. In this paper, we apply the Ensemble Empirical Mode Decomposition (EEMD) method to decompose and reconstruct simulated GW data generated by magnetorotational mechanism and neutrino-driven mechanism within the advanced LIGO, using the match score as the criterion for assessing the quality of the reconstruction. The results indicate that by decomposing the data, the sum of the first six intrinsic mode functions (IMFs) can be used as the reconstructed waveform. To determine the probability that our reconstructed waveform corresponds to a real GW waveform, we calculate the false alarm probability of reconstruction (FAPR). By setting the threshold of the match score to be 0.75, we obtain FAPR of GW sources at a distance of 5 kpc and 10 kpc to be $6\times10^{-3}$ and $1\times10^{-2}$ respectively. If we normalize the maximum amplitude of the GW signal to $5\times10^{-21}$, the FAPR at this threshold is $4\times10^{-3}$. Furthermore, in our study, the reconstruction distance is not equivalent to the detection distance. When the strain of GW reaches $7 \times 10^{-21}$, and the match score threshold is set at 0.75, we can reconstruct GW waveform up to approximately 36 kpc., Comment: 9 pages, 6 figures. Accepted by MNRAS

We calculate the next-to-leading order QCD corrections to $B$ to scalar meson form factors from QCD light-cone sum rules with $B$ meson light-cone distribution amplitudes. We demonstrate that the $B$ meson-to-vacuum correlation functions can be factorized into the convolution of short-distance coefficients and light-cone distribution amplitudes at the one-loop level and find that only $\phi_B^+(\omega,\mu)$ contributes to the form factors. We then employ the $z$-parameterization combined with constraints from strong coupling constants to reconstruct the $q^2$ dependence of the form factors in the whole kinematic allowed regions. Due to the large cancellations between the hard functions and the jet functions, the next-to-leading order results show a modest increase of approximately 5\% compared to the leading order results. Based on the results of form factors, we predict the branching ratios of semi-leptonic $B\to S\ell\bar{\nu}_\ell$ and $B\to S\nu_\ell\bar{\nu}_\ell$ processes, as well as several angular observables, such as forward-backward asymmetries, "flat terms" and lepton polarization asymmetries. We compare these results with calculations from other methods. Experimental verification of these results is required in future experiments., Comment: 47 pages, 13 figures

Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works have researched various higher-order networks, but research for evaluating the effects of higher-order structures on network functions is still scarce. In this paper, we propose a framework to quantify the effects of higher-order structures (e.g., 2-simplex) and vital functions of complex networks by comparing the original network with its simplicial model. We provide a simplicial model that can regulate the quantity of 2-simplices and simultaneously fix the degree sequence. Although the algorithm is proposed to control the quantity of 2-simplices, results indicate it can also indirectly control simplexes more than 2-order. Experiments on spreading dynamics, pinning control, network robustness, and community detection have shown that regulating the quantity of 2-simplices changes network performance significantly. In conclusion, the proposed framework is a general and effective tool for linking higher-order structures with network functions. It can be regarded as a reference object in other applications and can deepen our understanding of the correlation between micro-level network structures and global network functions.

In this paper, we investigate an ill-posed Cauchy problem involving a stochastic parabolic equation. We first establish a Carleman estimate for this equation. Leveraging this estimate, we derive the conditional stability and convergence rate of the Tikhonov regularization method for the aforementioned ill-posed Cauchy problem. To complement our theoretical analysis, we employ kernel-based learning theory to implement the completed Tikhonov regularization method for several numerical examples.

We first classify all Nijenhuis operators on 2-dimensional complex pre-Lie algebras and 3-dimensional complex associative algebras. Moreover, applying the obtained classification, we provide solutions of classical Yang-Baxter equation of the semi-direct product sub-adjacent Lie algebras.

Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic reasoning, where perception models are facilitated with information inferred from a symbolic knowledge base through logical reasoning. Despite empirical evidence showing the ability of hybrid systems to learn accurate perception models, the theoretical understanding of learnability is still lacking. Hence, it remains unclear why a hybrid system succeeds for a specific task and when it may fail given a different knowledge base. In this paper, we introduce a novel way of characterising supervision signals from a knowledge base, and establish a criterion for determining the knowledge's efficacy in facilitating successful learning. This, for the first time, allows us to address the two questions above by inspecting the knowledge base under investigation. Our analysis suggests that many knowledge bases satisfy the criterion, thus enabling effective learning, while some fail to satisfy it, indicating potential failures. Comprehensive experiments confirm the utility of our criterion on benchmark tasks.

Recent experiments and calculations in topological semimetals have observed anomalously strong second-order optical nonlinearity, but yet whether the enhancement also occurs at surfaces of topological semimetals in general remains an open question. In this work, we tackle this problem by measuring polarization-dependent and rotational-anisotropy optical second harmonic generation (SHG) from centrosymmetric type-II Dirac semimetal PdTe$_2$. We found the SHG to follow C$_{3v}$ surface symmetry with a time-varying intensity dictated by the oxidation kinetics of the material after its surface cleavage, indicating the surface origin of SHG. Quantitative characterization of the surface nonlinear susceptibility indicates a large out-of-plane response of PdTe$_2$ with $|\chi_{ccc}^{(2)}|$ up to 25 $\times$ 10$^{-18}$ m$^2$/V. Our results support the topological surfaces/interfaces as a new route toward applications of nonlinear optical effects with released symmetry constraints, and demonstrate SHG as a viable means to in situ study of kinetics of topological surfaces.

LiDAR-based fully sparse architecture has garnered increasing attention. FSDv1 stands out as a representative work, achieving impressive efficacy and efficiency, albeit with intricate structures and handcrafted designs. In this paper, we present FSDv2, an evolution that aims to simplify the previous FSDv1 while eliminating the inductive bias introduced by its handcrafted instance-level representation, thus promoting better general applicability. To this end, we introduce the concept of \textbf{virtual voxels}, which takes over the clustering-based instance segmentation in FSDv1. Virtual voxels not only address the notorious issue of the Center Feature Missing problem in fully sparse detectors but also endow the framework with a more elegant and streamlined approach. Consequently, we develop a suite of components to complement the virtual voxel concept, including a virtual voxel encoder, a virtual voxel mixer, and a virtual voxel assignment strategy. Through empirical validation, we demonstrate that the virtual voxel mechanism is functionally similar to the handcrafted clustering in FSDv1 while being more general. We conduct experiments on three large-scale datasets: Waymo Open Dataset, Argoverse 2 dataset, and nuScenes dataset. Our results showcase state-of-the-art performance on all three datasets, highlighting the superiority of FSDv2 in long-range scenarios and its general applicability to achieve competitive performance across diverse scenarios. Moreover, we provide comprehensive experimental analysis to elucidate the workings of FSDv2. To foster reproducibility and further research, we have open-sourced FSDv2 at https://github.com/tusen-ai/SST.

In this work, we study the low-lying mass spectra for bottom-charmed baryons in a nonrelativistic quark model by solving the three-body Schr\"odinger equation. The lowest $\Xi_{bc}$, $\Xi_{bc}^\prime$, $\Omega_{bc}$, and $\Omega_{bc}^\prime$ states are predicted to be about 6979, 6953, 7109, and 7092 MeV, respectively. Also, the strong decays for the low-lying excited states are investigated. Our results indicate that some of $\lambda-$mode $P-$wave bottom-charmed baryons are relatively narrow, which can be searched for in future experiments. For the low-lying $\rho-$mode and $\rho-\lambda$ hybrid states, their strong decays are highly suppressed and they can survive as extremely narrow states. Moreover, the mass spectra and strong decays for bottom-charmed baryons preserve the heavy quark symmetry well. We hope our calculations can provide helpful information for further experimental and theoretical researches., Comment: 13 pages, 7 figures. Some modifications, accepted version

In isolated nonlinear optical waveguide arrays with bounded energy spectrum, simultaneous conservation of energy and power of the optical modes enables study of coupled thermal and particle transport in the negative temperature regime. Here, based on exact numerical simulation and rationale from Landauer formalism, we predict generic violation of the Wiedemann-Franz law in such systems. This is rooted in the spectral decoupling of thermal and power current of optical modes, and their different temperature dependence. Our work extends the study of coupled thermal and particle transport into unprecedented regimes, not reachable in natural condensed matter and atomic gas systems.

Numerous modern technologies are reliant on the low-phase noise and exquisite timing stability of microwave signals. Substantial progress has been made in the field of microwave photonics, whereby low noise microwave signals are generated by the down-conversion of ultra-stable optical references using a frequency comb. Such systems, however, are constructed with bulk or fiber optics and are difficult to further reduce in size and power consumption. Our work addresses this challenge by leveraging advances in integrated photonics to demonstrate low-noise microwave generation via two-point optical frequency division. Narrow linewidth self-injection locked integrated lasers are stabilized to a miniature Fabry-P\'{e}rot cavity, and the frequency gap between the lasers is divided with an efficient dark-soliton frequency comb. The stabilized output of the microcomb is photodetected to produce a microwave signal at 20 GHz with phase noise of -96 dBc/Hz at 100 Hz offset frequency that decreases to -135 dBc/Hz at 10 kHz offset--values which are unprecedented for an integrated photonic system. All photonic components can be heterogeneously integrated on a single chip, providing a significant advance for the application of photonics to high-precision navigation, communication and timing systems.

In this paper, we study the Manin triples associated to $n$-Lie bialgebras. We introduce the concept of operad matrices for $n$-Lie bialgebras. In particular, by studying a special case of operad matrices, it leads to the notion of local cocycle $n$-Lie bialgebras. Furthermore, we establish a one-to-one correspondence between the double of $n$-Lie bialgebras and Manin triples of $n$-Lie algebras.

The actinides, such as the uranium (U) element, are typically synthesized through the rapid neutron-capture process (r-process), which can occur in core-collapse supernovae or double neutron star mergers. There exist nine r-process giant stars exhibiting conspicuousUabundances, commonly referred to as U-rich giants. However, the origins of these U-rich giants remain ambiguous. We propose an alternative formation scenario for these U-rich giants whereby a red giant (RG) engulfs an Earth-like planet. To approximate the process of a RG engulfing an Earth-like planet, we employ an accretion model wherein the RG assimilates materials from said planet. Our findings demonstrate that this engulfment event can considerably enhance the presence of heavy elements originating from Earth-like planets on the surfaces of very metal-poor stars (Z = 0.00001), while its impact on solar-metallicity stars is comparatively modest. Importantly, the structural and evolutionary properties of both very metalpoor and solar-metallicity stars remain largely unaffected. Notably, our engulfment model effectively accounts for the observed U abundances in known U-rich giants. Furthermore, the evolutionary trajectories of U abundances on the surfaces of RGs subsequent to the engulfment of Earth-like planets encompass all known U-rich giants. Therefore, it is plausible that U-rich giants are formed when a RG engulfs an Earth-like planet., Comment: 9 pages, 8 figures, accepted 2023 July 10 by MNRAS

Simplicial complexes have recently been in the limelight of higher-order network analysis, where a minority of simplices play crucial roles in structures and functions due to network heterogeneity. We find a significant inconsistency between identifying influential nodes and simplices. Therefore, it remains elusive how to characterize simplices' influence and identify influential simplices, despite the relative maturity of research on influential nodes (0-simplices) identification. Meanwhile, graph neural networks (GNNs) are potent tools that can exploit network topology and node features simultaneously, but they struggle to tackle higher-order tasks. In this paper, we propose a higher-order graph learning model, named influential simplices mining neural network (ISMnet), to identify vital h-simplices in simplicial complexes. It can tackle higher-order tasks by leveraging novel higher-order presentations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where targeted simplices are grouped into a hub set and can interact with other simplices. Furthermore, ISMnet employs learnable graph convolutional operators in each HoH Laplacian domain to capture interactions among simplices, and it can identify influential simplices of arbitrary order by changing the hub set. Empirical results demonstrate that ISMnet significantly outperforms existing methods in ranking 0-simplices (nodes) and 2-simplices. In general, this novel framework excels in identifying influential simplices and promises to serve as a potent tool in higher-order network analysis.

Let $\mathbb{N}$ be the set of all nonnegative integers. For any positive integer $k$ and any subset $A$ of nonnegative integers, let $r_{1,k}(A,n)$ be the number of solutions $(a_1,a_2)$ to the equation $n=a_1+ka_2$. In 2016, Qu proved that $$\liminf_{n\rightarrow\infty}r_{1,k}(A,n)=\infty$$ providing that $r_{1,k}(A,n)=r_{1,k}(\mathbb{N}\setminus A,n)$ for all sufficiently large integers, which answered affirmatively a 2012 problem of Yang and Chen. In a very recent article, another Chen (the first named author) slightly improved Qu's result and obtained that $$\liminf_{n\rightarrow\infty}\frac{r_{1,k}(A,n)}{\log n}>0.$$ In this note, we further improve the lower bound on $r_{1,k}(A,n)$ by showing that $$\liminf_{n\rightarrow\infty}\frac{r_{1,k}(A,n)}{n}>0.$$ Our bound reflects the correct order of magnitude of the representation function $r_{1,k}(A,n)$ under the above restrictions due to the trivial fact that $r_{1,k}(A,n)\le n/k.$

We study the holographic dual of the two dimensional non-relativistic conformal field theory with anisotropic scaling from a symmetry perspective. We construct a new four dimensional metric with two dimensional global anisotropic scaling isometry. The four dimensional spacetime is homogeneous and is a solution of Einstein gravity with quadratic-curvature extension. We consider this spacetime dual to the vacuum of the conformal field theory. By introducing proper solution phase space, we find that the asymptotic symmetry of the gravity theory is the two dimensional local anisotropic conformal symmetry, which recovers precisely the results from the dual non-relativistic conformal field theory side., Comment: v2: interpretation improved, typos fixed, references added, published version

We introduce the notion of {\em iterated residue} to study generalized Bott manifolds. When applying the iterated residues to compute the Borisov-Gunnells toric form and the Witten genus of certain toric varieties as well as complete intersections, we obtain interesting vanishing results and some theta function identities, one of which is a twisted version of a classical Rogers-Ramanujan type formula., Comment: 19 pages

The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo. Our findings suggest that the information-cascading process is best described as an activate-decay dynamical process. Building on these insights, we developed an Activate-Decay (AD)-based algorithm that can accurately predict the long-term popularity of online content based solely on its early repost amount. We tested our algorithm using data from WeChat and Weibo, demonstrating that we could fit the evolution trend of content propagation and predict the longer-term dynamics of message forwarding from earlier data. We also discovered a close correlation between the peak forwarding amount of information and the total amount of dissemination. Finding the peak of the amount of information dissemination can significantly improve the prediction accuracy of our model. Our method also outperformed existing baseline methods for predicting the popularity of information.

By self-injection-locking a 1560 nm distributed feedback semiconductor laser to a high-$Q$ silicon nitride resonator, a high-coherence 780 nm second harmonic signal is generated via the photogalvanic-induced second-order nonlinearity. A record-low frequency noise floor of 4 Hz$^2$/Hz is achieved for the 780 nm emission. The approach can be generalized for signal generation over a wide range of visible and near-visible bands.

Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development procedure at the cost of losing an end-to-end unified solution to the problem. In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this, we introduce a novel method called PanoOcc, which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme, integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to verify the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore, our method can be easily extended to dense occupancy prediction and has shown promising performance on the Occ3D benchmark. The code will be released at https://github.com/Robertwyq/PanoOcc., Comment: technical report

Link prediction has been widely studied as an important research direction. Higher-order link prediction has gained especially significant attention since higher-order networks provide a more accurate description of real-world complex systems. However, higher-order networks contain more complex information than traditional pairwise networks, making the prediction of higher-order links a formidable challenging task. Recently, researchers have discovered that local features have advantages over long-range features in higher-order link prediction. Therefore, it is necessary to develop more efficient and concise higher-order link prediction algorithms based on local features. In this paper, we proposed two similarity metrics via local information, simplicial decomposition weight (SDW) and closed ratio weight (CRW), to predict possible future higher-order interactions (simplices) in simplicial networks. These two algorithms capture local higher-order information at two aspects: simplex decomposition and cliques' state (closed or open). We tested their performance in eight empirical simplicial networks, and the results show that our proposed metrics outperform other benchmarks in predicting third-order and fourth-order interactions (simplices) in most cases. In addition, we explore the robustness of the proposed algorithms, and the results suggest that the performance of these novel algorithms is advanced under different sizes of training sets.

Data association is a knotty problem for 2D Multiple Object Tracking due to the object occlusion. However, in 3D space, data association is not so hard. Only with a 3D Kalman Filter, the online object tracker can associate the detections from LiDAR. In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space. Unlike the existing depth-based MOT methods, the 3D object representation can be jointly learned with the object association module. Besides, the object's 3D representation is learned from the video and supervised by the 2D tracking labels without additional manual annotations from LiDAR or pretrained depth estimator. With 3D object representation learning from Pseudo 3D object labels in monocular videos, we propose a new 2D MOT paradigm, called P3DTrack. Extensive experiments show the effectiveness of our method. We achieve new state-of-the-art performance on the large-scale Waymo Open Dataset., Comment: Technical report