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Researcher profile

Yu Pan

Yu Pan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.COComputer VisionMachine Learningquant-phArtificial Intelligencemath.SGcond-mat.mtrl-sciCryptography and Securityeess.IVhep-thmath.GTeess.SPeess.SYgr-qchep-phphysics.chem-phphysics.comp-phphysics.flu-dynRoboticsSoundSystems and Control

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Published work

30 published item(s)

preprint2026arXiv

SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton

Generating symphonic music requires simultaneously managing high-level structural form and dense, multi-track orchestration. Existing symbolic models often struggle with a "complexity-control imbalance", in which scaling bottlenecks limit long-term granular steerability. We present SymphonyGen, a 3D hierarchical framework for contemporary cinematic orchestration. SymphonyGen employs a cascading decoder architecture that decomposes the Bar, Track, and Event axes, improving computational efficiency and scalability over conventional 1D or 2D models. We introduce "short-score" conditioning via a beat-quantized multi-voice harmony skeleton, enabling outline control while preserving textural diversity. The model is further refined using Group Relative Policy Optimization (GRPO) with a cross-modal audio-perceptual reward, aligning symbolic output with modern acoustic expectations. Additionally, we implement a dissonance-averse sampling algorithm to suppress unintended tonal clashes during inference. Objective evaluations show that both reinforcement learning and dissonance-averse sampling effectively enhance harmonic cleanliness while maintaining melodic expression. Subjective evaluations demonstrate that SymphonyGen outperforms baselines in musicality and preference for orchestral music generation. Demo page: https://symphonygen.github.io/

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

BadBlocks: Lightweight and Stealthy Backdoor Threat in Text-to-Image Diffusion Models

Diffusion models have recently achieved remarkable success in image generation, yet growing evidence shows their vulnerability to backdoor attacks, where adversaries implant covert triggers to manipulate outputs. While existing defenses can detect many such attacks via visual inspection and neural network-based analysis, we identify a more lightweight and stealthy threat, termed BadBlocks. BadBlocks selectively contaminates specific blocks within the UNet architecture while preserving the normal behavior of the remaining components. Compared with prior methods, it requires only about 30% of the computation and 20% of the GPU time, yet achieves high attack success rates with minimal perceptual degradation. Extensive experiments demonstrate that BadBlocks can effectively evade state-of-the-art defenses, particularly attention-based detection frameworks. Ablation studies further reveal that effective backdoor injection does not require fine-tuning the entire network and highlight the critical role of certain layers in backdoor mapping. Overall, BadBlocks substantially lowers the barrier for backdooring large-scale diffusion models, even on consumer-grade GPUs.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Augmentations and immersed Lagrangian fillings

For a Legendrian link $Λ\subset J^1M$ with $M = \mathbb{R}$ or $S^1$, immersed exact Lagrangian fillings $L \subset \mbox{Symp}(J^1M) \cong T^*(\mathbb{R}_{>0} \times M)$ of $Λ$ can be lifted to conical Legendrian fillings $Σ\subset J^1(\mathbb{R}_{>0} \times M)$ of $Λ$. When $Σ$ is embedded, using the version of functoriality for Legendrian contact homology (LCH) from [30], for each augmentation $α: \mathcal{A}(Σ) \rightarrow \mathbb{Z}/2$ of the LCH algebra of $Σ$, there is an induced augmentation $ε_{(Σ,α)}: \mathcal{A}(Λ) \rightarrow \mathbb{Z}/2$. With $Σ$ fixed, the set of homotopy classes of all such induced augmentations, $I_Σ\subset \mathit{Aug}(Λ)/{\sim}$, is a Legendrian isotopy invariant of $Σ$. We establish methods to compute $I_Σ$ based on the correspondence between Morse complex families and augmentations. This includes developing a functoriality for the cellular DGA from [31] with respect to Legendrian cobordisms, and proving its equivalence to the functoriality for LCH. For arbitrary $n \geq 1$, we give examples of Legendrian torus knots with $2n$ distinct conical Legendrian fillings distinguished by their induced augmentation sets. We prove that when $ρ\neq 1$ and $Λ\subset J^1\mathbb{R}$ every $ρ$-graded augmentation of $Λ$ can be induced in this manner by an immersed Lagrangian filling. Alternatively, this is viewed as a computation of cobordism classes for an appropriate notion of $ρ$-graded augmented Legendrian cobordism.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail to generate appropriate weights for TCNNs. Meanwhile, although there are ad-hoc approaches for specific architectures (e.g., Tensor Ring Nets), they are not applicable to TCNNs with other tensor decomposition methods (e.g., CP or Tucker decomposition). To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs. Specifically, we first present the Reproducing Transformation to convert the backward process in TCNNs to an equivalent convolution process. Then, based on the convolution operators in the forward and backward processes, we build a unified paradigm to control the variance of features and gradients in TCNNs. Thus, we can derive fan-in and fan-out initialization for various TCNNs. We demonstrate that our paradigm can stabilize the training of TCNNs, leading to faster convergence and better results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Automatic Depth Optimization for Quantum Approximate Optimization Algorithm

Quantum Approximate Optimization Algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. Control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on 7-node and 10-node Max-Cut problems, which show that the control depth can be significantly reduced during the iteration while achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on Noisy Intermediate-scale Quantum (NISQ) devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Cosmological-model-independent tests of cosmic distance duality relation with Type Ia supernovae and radio quasars

In this paper, we investigate the possible deviations of the cosmic distance duality relation (CDDR) using the combination of the largest SNe Ia (Pantheon) and compact radio quasar (QSO) samples through two model-independent approaches. The deviation of CDDR is written as $D_L(z)/D_A(z)(1+z)^{-2}=η(z)$ and $η(z)=e^{τ(z)/2}$, with the parameterizations of $F_1$ ($τ(z) = 2ε_1 z$) and $F_2$ ($τ(z) = (1+z)^{2ε_2}-1$). Furthermore, in order to compare the two resulting distances, two cosmological-model-independent methods, i.e., the nearby SNe Ia method and the GP method are employed to match the two distinct data at the same redshift. Our findings indicate that, compared with the results obtained in the literature, there is an improvement in precision when the latest SNe Ia and QSO samples are used. Specially, in the framework of nearby SNe Ia method, the CDDR would be constrained at the precision of $Δε_{1} = 0.013$ in Model $F_1$ and $Δε_{2}=0.018$ in Model $F_2$. Regarding the GP method, one observes that a larger data size would produce more stringent constraints on the CDDR parameters. Therefore, accompanied by further developments in cosmological observations and the analysis methods, our analysis provides an insight into the evidence for unaccounted opacity sources at an earlier stage of the universe, or at the very least the new physics involved.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High precision measurement of cosmic curvature: from gravitational waves and cosmic chronometer

Although the spatial curvature has been measured with very high precision, it still suffers from the well known cosmic curvature tension. In this paper, we propose an improved method to determine the cosmic curvature, by using the simulated data of binary neutron star mergers observed by the second generation space-based DECi-hertz Interferometer Gravitational-wave Observatory (DECIGO). By applying the Hubble parameter observations of cosmic chronometers to the DECIGO standard sirens, we explore different possibilities of making measurements of the cosmic curvature referring to a distant past: one is to reconstruct the Hubble parameters through the Gaussian process without the influence of hypothetical models, and the other is deriving constraints on $Ω_K$ in the framework of non-flat $Λ$ cold dark matter model. It is shown that in the improved method DECIGO could provide a reliable and stringent constraint on the cosmic curvature ($Ω_{K} = -0.007\pm0.016$), while we could only expect the zero cosmic curvature to be established at the precision of $ΔΩ_K=0.12$ in the second model-dependent method. Therefore, our results indicate that in the framework of methodology proposed in this paper, the increasing number of well-measured standard sirens in DECIGO could significantly reduce the bias of estimations for cosmic curvature. Such constraint is also comparable to the precision of Planck 2018 results with the newest cosmic microwave background (CMB) observations ($ΔΩ_{K} \approx 0.018$), based on the concordance $Λ$CDM model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning

In general, multiple domain cyberspace security assessments can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning methods are also based on expert-given rules, resulting in inefficiency and a low degree of intelligence. To address this challenge, we create a Knowledge Graph (KG) of multiple domain cyberspace in order to provide a standard semantic description of the multiple domain cyberspace. Following that, we proposed a user's permissions reasoning method based on reinforcement learning. All permissions in cyberspace are represented as nodes, and an agent is trained to find all permissions that user can have according to user's initial permissions and cyberspace KG. We set 10 reward setting rules based on the features of cyberspace KG in the reinforcement learning of reward information setting, so that the agent can better locate user's all permissions and avoid blindly finding user's permissions. The results of the experiments showed that the proposed method can successfully reason about user's permissions and increase the intelligence level of the user's permissions reasoning method. At the same time, the F1 value of the proposed method is 6% greater than that of the Translating Embedding (TransE) method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning

The existing network attack and defense method can be regarded as game, but most of the game only involves network domain, not multiple domain cyberspace. To address this challenge, this paper proposed a multiple domain cyberspace attack and defense game model based on reinforcement learning. We define the multiple domain cyberspace include physical domain, network domain and digital domain. By establishing two agents, representing the attacker and the defender respectively, defender will select the multiple domain actions in the multiple domain cyberspace to obtain defender's optimal reward by reinforcement learning. In order to improve the defense ability of defender, a game model based on reward randomization reinforcement learning is proposed. When the defender takes the multiple domain defense action, the reward is randomly given and subject to linear distribution, so as to find the better defense policy and improve defense success rate. The experimental results show that the game model can effectively simulate the attack and defense state of multiple domain cyberspace, and the proposed method has a higher defense success rate than DDPG and DQN.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Obstructions to reversing Lagrangian surgery in Lagrangian fillings

Given an immersed, Maslov-$0$, exact Lagrangian filling of a Legendrian knot, if the filling has a vanishing index and action double point, then through Lagrangian surgery it is possible to obtain a new immersed, Maslov-$0$, exact Lagrangian filling with one less double point and with genus increased by one. We show that it is not always possible to reverse the Lagrangian surgery: not every immersed, Maslov-$0$, exact Lagrangian filling with genus $g \geq 1$ and $p$ double points can be obtained from such a Lagrangian surgery on a filling of genus $g-1$ with $p+1$ double points. To show this, we establish the connection between the existence of an immersed, Maslov-$0$, exact Lagrangian filling of a Legendrian $Λ$ that has $p$ double points with action $0$ and the existence of an embedded, Maslov-$0$, exact Lagrangian cobordism from $p$ copies of a Hopf link to $Λ$. We then prove that a count of augmentations provides an obstruction to the existence of embedded, Maslov-$0$, exact Lagrangian cobordisms between Legendrian links.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem

Mixed-integer linear programming (MILP) plays a crucial role in artificial intelligence, biochemistry, finance, cryptography, etc. Notwithstanding popular for decades, the researches of MILP solvers are still limited by the resource consumption caused by complexity and failure of Moore's Law. Quantum-inspired Ising machines, as a new computing paradigm, can be used to solve integer programming problems by reducing them into Ising models. Therefore, it is necessary to understand the technical evolution of quantum inspired solvers to break the bottleneck. In this paper, the concept and traditional algorithms for MILP are introduced. Then, focused on Ising model, the principle and implementations of annealers and coherent Ising machines are summarized. Finally, the paper discusses the challenges and opportunities of miniaturized solvers in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reheating constraints on modified single-field Natural Inflation models

In this paper, we discuss three modified single-field natural inflation models in detail, including Special generalized Natural Inflation model(SNI), Extended Natural Inflation model(ENI) and Natural Inflation inspired model(NII). We derive the analytical expression of the tensor-to-scalar ratio $r$ and the spectral index $n_s$ for those models. Then the reheating temperature $T_{re}$ and reheating duration $N_{re}$ are analytically derived. Moreover, considering the CMB constraints, the feasible space of the SNI model in $(n_s, r)$ plane is almost covered by that of the NII, which means the NII is more general than the SNI. In addition, there is no overlapping space between the ENI and the other two models in $(n_s, r)$ plane, which indicates that the ENI and the other two models exclude each other, and more accurate experiments can verify them. Furthermore, the reheating brings tighter constraints to the inflation models, but they still work for a different reheating universe. Considering the constraints of $n_s$, $r$, $N_k$ and choosing $T_{re}$ near the electroweak energy scale, one can find that the decay constants of the three models have no overlapping area and the effective equations of state $ω_{re}$ should be within $\frac{1}{4}\lesssim ω_{re} \lesssim \frac{4}{5}$ for the three models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations

States of quantum many-body systems are defined in a high-dimensional Hilbert space, where rich and complex interactions among subsystems can be modelled. In machine learning, complex multiple multilinear correlations may also exist within input features. In this paper, we present a quantum-inspired multilinear model, named Residual Tensor Train (ResTT), to capture the multiple multilinear correlations of features, from low to high orders, within a single model. ResTT is able to build a robust decision boundary in a high-dimensional space for solving fitting and classification tasks. In particular, we prove that the fully-connected layer and the Volterra series can be taken as special cases of ResTT. Furthermore, we derive the rule for weight initialization that stabilizes the training of ResTT based on a mean-field analysis. We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models. Numerical experiments demonstrate that ResTT outperforms the state-of-the-art tensor network and benchmark deep learning models on MNIST and Fashion-MNIST datasets. Moreover, ResTT achieves better performance than other statistical methods on two practical examples with limited data which are known to have complex feature interactions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust optimization for quantum reinforcement learning control using partial observations

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust photon-efficient imaging using a pixel-wise residual shrinkage network

Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios. However, limited signal photon counts and high noises in the collected data have posed great challenges for predicting the depth image precisely. In this paper, we propose a pixel-wise residual shrinkage network for photon-efficient imaging from high-noise data, which adaptively generates the optimal thresholds for each pixel and denoises the intermediate features by soft thresholding. Besides, redefining the optimization target as pixel-wise classification provides a sharp advantage in producing confident and accurate depth estimation when compared with existing research. Comprehensive experiments conducted on both simulated and real-world datasets demonstrate that the proposed model outperforms the state-of-the-arts and maintains robust imaging performance under different signal-to-noise ratios including the extreme case of 1:100.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Semantically Proportional Patchmix for Few-Shot Learning

Few-shot learning aims to classify unseen classes with only a limited number of labeled data. Recent works have demonstrated that training models with a simple transfer learning strategy can achieve competitive results in few-shot classification. Although excelling at distinguishing training data, these models are not well generalized to unseen data, probably due to insufficient feature representations on evaluation. To tackle this issue, we propose Semantically Proportional Patchmix (SePPMix), in which patches are cut and pasted among training images and the ground truth labels are mixed proportionally to the semantic information of the patches. In this way, we can improve the generalization ability of the model by regional dropout effect without introducing severe label noise. To learn more robust representations of data, we further take rotate transformation on the mixed images and predict rotations as a rule-based regularizer. Extensive experiments on prevalent few-shot benchmarks have shown the effectiveness of our proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Anomalous thermoelectric effects and quantum oscillations in the kagome metal CsV$_3$Sb$_5$

The kagome metal compounds $A$V$_3$Sb$_5$ ($A$ = K, Rb, and Cs) feature a wealth of phenomena including nontrivial band topology, charge density wave (CDW), and superconductivity. One intriguing property is the time-reversal symmetry breaking in the CDW state without local moments, which leads to anomalous transport responses. Here, we report the investigation of magneto-thermoelectric effects on high-quality CsV$_3$Sb$_5$ single crystals. A large anomalous Nernst effect is observed at temperatures below 30 K. Multiple Fermi surfaces with small effective masses are revealed by quantum oscillations in Nernst and Seebeck signals under high magnetic field. Furthermore, we find an unknown frequency, and attribute it to the magnetic breakdown across two smaller Fermi surfaces. A gap around 20 meV can be resolved from the breakdown threshold field, which we propose to be introduced by the CDW. These results shed new light on the CDW-related phenomena, particularly in $A$V$_3$Sb$_5$ compounds.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Giant anomalous Nernst signal in the antiferromagnet YbMnBi2

Searching for a high anomalous Nernst effect (ANE) is crucial for thermoelectric energy conversion applications because the associated unique transverse geometry facilitates module fabrication. Topological ferromagnets with large Berry curvatures show high ANEs; however, they face drawbacks such as strong magnetic disturbances and low mobility due to high magnetization. Herein, we demonstrate that YbMnBi2, a canted antiferromagnet, has a large ANE conductivity of ~10 Am-1K-1 that surpasses the common high values (i.e. 3-5 Am-1K-1) observed so far in ferromagnets. The canted spin structure of Mn guarantees a nonzero Berry curvature but generates only a weak magnetization three orders of magnitude lower than that of general ferromagnets. The heavy Bi with a large spin-orbit coupling enables a high ANE and low thermal conductivity, whereas its highly dispersive px/y orbitals ensure low resistivity. The high anomalous transverse thermoelectric performance and extremely small magnetization makes YbMnBi2 an excellent candidate for transverse thermoelectrics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function. To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architecture. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Spectrum Attention Mechanism for Time Series Classification

Time series classification(TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning models to solve TSC problems. Since time series always contains a lot of noise, which has a negative impact on network training, people usually filter the original data before training the network. The existing schemes are to treat the filtering and training as two stages, and the design of the filter requires expert experience, which increases the design difficulty of the algorithm and is not universal. We note that the essence of filtering is to filter out the insignificant frequency components and highlight the important ones, which is similar to the attention mechanism. In this paper, we propose an attention mechanism that acts on spectrum (SAM). The network can assign appropriate weights to each frequency component to achieve adaptive filtering. We use L1 regularization to further enhance the frequency screening capability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of time domain information caused by using the spectrum of the whole sequence. In which, a tumbling window is introduced to segment the original data. Then SAM is applied to each segment to generate new features. We propose a heuristic strategy to search for the appropriate number of segments. Experimental results show that SSAM can produce better feature representations, make the network converge faster, and improve the robustness and classification accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Constructions of Lagrangian cobordisms

Lagrangian cobordisms between Legendrian knots arise in Symplectic Field Theory and impose an interesting and not well-understood relation on Legendrian knots. There are some known "elementary" building blocks for Lagrangian cobordisms that are smoothly the attachment of $0$- and $1$-handles. An important question is whether every pair of non-empty Legendrians that are related by a connected Lagrangian cobordism can be related by a ribbon Lagrangian cobordism, in particular one that is "decomposable" into a composition of these elementary building blocks. We will describe these and other combinatorial building blocks as well as some geometric methods, involving the theory of satellites, to construct Lagrangian cobordisms. We will then survey some known results, derived through Heegaard Floer Homology and contact surgery, that may provide a pathway to proving the existence of nondecomposable (nonribbon) Lagrangian cobordisms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Model-independent constraints on Lorentz invariance violation: implication from updated Gamma-ray burst observations

Astrophysical observations provide a unique opportunity to test possible signatures of Lorentz Invariance Violation (LIV), due to the high energies and long distances involved. In quantum theory of gravity, one may expect the modification of the dispersion relation between energy and momentum for photons, which can be probed with the time-lag (the arrival time delay between light curves in different energy bands) of Gamma-ray bursts (GRBs). In this paper, by using the detailed time-delay measurements of GRB 160625B at different energy bands, as well as 23 time-delay GRBs covering the redshifts range of $z=0.168-2.5$ (which were measured at different energy channels from the light curves), we propose an improved model-independent method (based on the newly-compiled sample of $H(z)$ measurements) to probe the energy-dependent velocity due to the modified dispersion relation for photons. In the framework of a more complex and reasonable theoretical expression to describe the time delays, our results imply that the intrinsic time lags can be better described with more GRBs time delay data. More importantly, through direct fitting of the time-delay measurements of a sample of GRBs, our limit on the LIV energy scale is comparable to that with unknown constant for the intrinsic time lag, much lower than the Planck energy scale in both linear LIV and quadratic LIV cases.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Nektar++: Design and implementation of an implicit, spectral/$hp$ element, compressible flow solver using a Jacobian-free Newton Krylov approach

At high Reynolds numbers, the use of explicit in time compressible flow simulations with spectral/$hp$ element discretization can become significantly limited by time step. To alleviate this limitation we extend the capability of the spectral/$hp$ element open-source software framework, Nektar++, to include an implicit discontinuous Galerkin compressible flow solver. The integration in time is carried out by a singly diagonally implicit Runge-Kutta method. The non-linear system arising from the implicit time integration is iteratively solved by the Jacobian-free Newton Krylov (JFNK) method. A favorable feature of the JFNK approach is its extensive use of the explicit operators available from the previous explicit in time implementation. The functionalities of different building blocks of the implicit solver are analyzed from the point of view of software design and placed in appropriate hierarchical levels in the C++ libraries. In the detailed implementation, the contributions of different parts of the solver to computational cost, memory consumption, and programming complexity are also analyzed. A combination of analytical and numerical methods is adopted to simplify the programming complexity in forming the preconditioning matrix. The solver is verified and tested using cases such as manufactured compressible Poiseuille flow, Taylor-Green vortex, turbulent flow over a circular cylinder at $\text{Re}=3900$ and shock wave boundary-layer interaction. The results show that the implicit solver can speed-up the simulations while maintaining good simulation accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

nuScenes: A multimodal dataset for autonomous driving

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the dynamics of two photons interacting with a two-qubit coherent feedback network}

The purpose of this paper is to study the dynamics of a quantum coherent feedback network composed of two two-level systems (qubits) driven by two counter-propagating photons, one in each input channel. The coherent feedback network enhances the nonlinear photon-photon interaction inside the feedback loop. By means of quantum stochastic calculus and the input-output framework, the analytic form of the steady-state output two-photon state is derived. Based on the analytic form, the applications on the Hong-Ou-Mandel (HOM) interferometer and marginally stable single-photon devices using this coherent feedback structure have been demonstrated. The difference between continuous-mode and single-mode few-photon states is demonstrated.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Phase transition and entropic force of de Sitter black hole in massive gravity

It is well known that de Sitter(dS) black holes generally have a black hole horizon and a cosmological horizon, both of which have Hawking radiation. But the radiation temperature of the two horizons is generally different, so dS black holes do not meet the requirements of thermal equilibrium stability, which brings certain difficulties to the study of the thermodynamic characteristics of black holes. In this paper, dS black hole is regarded as a thermodynamic system, and the effective thermodynamic quantities of the system are obtained. The influence of various state parameters on the effective thermodynamic quantities in the massive gravity space-time is discussed. The condition of the phase transition of the de Sitter black hole in massive gravity space-time is given. We consider that the total entropy of the dS black hole is the sum of the corresponding entropy of the two horizons plus an extra term from the correlation of the two horizons. By comparing the entropic force of interaction between black hole horizon and the cosmological horizon with Lennard-Jones force between two particles, we find that the change rule of entropic force between the two system is surprisingly the same. The research will help us to explore the real reason of accelerating expansion of the universe.

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preprint2020arXiv

Thermodynamic properties of higher-dimensional dS black holes in dRGT massive gravity

On the basis of the state parameter of de Sitter space-time satisfying the first law of thermodynamics,we can derive some effective thermodynamic quantities.When the temperature of the black hole horizon is equal to that of the cosmological horizon, we think that the effective temperature of the space-time should have the same value. Using this condition, we obtain a differential equation of the entropy of the de Sitter black hole in the higherdimensional de Rham, Gabadadze and Tolley (dRGT) massive gravity. Solving the differential equation, we obtain the corrected entropy and effective thermodynamic quantities of the de Sitter black hole. The results show that for multiparameter black holes, the entropy satisfied differential equation is invariable with different independent state parameters. Therefore, the entropy of higher-dimensional dS black holes in dRGT massive gravity is only a function of the position of the black hole horizon, and is independent of other state parameters. It is consistent with the corresponding entropy of the black hole horizon and the cosmological horizon. The thermodynamic quantities of self-consistent de Sitter spacetime are given theoretically, and the equivalent thermodynamic quantities have the second-order phase transformation similar to AdS black hole, but unlike AdS black hole, the equivalent temperature of de Sitter space-time has a maximum value. By satisfying the requirement of thermodynamic equilibrium and stability of space-time, the conditions for the existence of dS black holes in the universe are obtained.

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preprint2019arXiv

Cosmic opacity: cosmological-model-independent tests from gravitational waves and Type Ia Supernova

In this paper, we present a scheme to investigate the opacity of the Universe in a cosmological-model-independent way, with the combination of current and future available data in gravitational wave (GW) and electromagnetic (EM) domain. In the FLRW metric, GWs propagate freely through a perfect fluid without any absorption and dissipation, which provides a distance measurement unaffected by the cosmic opacity. Focusing on the simulated data of gravitational waves from the third-generation gravitational wave detector (the Einstein Telescope, ET), as well as the newly-compiled SNe Ia data (JLA and Pantheon sample), we find an almost transparent universe is strongly favored at much higher redshifts ($z\sim 2.26$). Our results suggest that, although the tests of cosmic opacity are not significantly sensitive to its parametrization, a strong degeneracy between the cosmic opacity parameter and the absolute \textit{B}-band magnitude of SNe Ia is revealed in this analysis. More importantly, we obtain that future measurements of the luminosity distances of gravitational waves sources will be much more competitive than the current analyses, which makes it expectable more vigorous and convincing constraints on the cosmic opacity (and consequently on background physical mechanisms) and a deeper understanding of the intrinsic properties of type Ia supernovae in a cosmological-model-independent way.

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preprint2015arXiv

Constraints on Lorentz Invariance Violation with gamma-ray bursts via a Markov Chain Monte Carlo approach

In quantum theory of gravity, we expect the Lorentz Invariance Violation (LIV) and the modification of the dispersion relation between energy and momentum for photons. The effect of the energy-dependent velocity due to the modified dispersion relation for photons was studied in the standard cosmological context by using a sample of Gamma Ray Bursts (GRBs). In this paper we mainly discuss the possible LIV effect by using different cosmological models for the accelerating universe. Due to the degeneracies among model parameters, the GRBs' time delay data are combined with the cosmic microwave background data from the Planck first year release, the baryon acoustic oscillation data at six different redshifts, as well as Union2 type Ia supernovae data, to constrain both the model parameters and the LIV effect. We find no evidence of LIV.

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