arXiv subjects

Tong Liu

Publications and source records attributed to Tong Liu.

At least 235 records · Page 13Linked to original sources

arXiv2022

Construction of a qudit using Schrodinger cat states and generation of hybrid entanglement between a discrete-variable qudit and a continuous-variable qudit

We show that a continuous-variable (CV) qudit can be constructed using quasiorthogonal cat states of a bosonic mode, when the phase encoded in each cat state is chosen appropriately. With the constructed CV qudit and the discrete-variable (DV) qudit encoded with Fock states, we propose an approach to generate the hybrid maximally entangled state of a CV qudit and a DV qudit by using two microwave cavities coupled to a superconducting flux qutrit. This proposal relies on the initial preparation of a superposition of Fock states of one cavity and the initial preparation of a cat state of the other cavity. After the initial state of each cavity is prepared, this proposal requires only two basic operations, i.e., the first operation employs the dispersive coupling of both cavities with the qutrit while the second operation uses the dispersive coupling of only one cavity with the qutrit. The entangled state production is deterministic and the operation time decreases as the dimensional size of each qudit increases. In addition, during the entire operation, the coupler qutrit remains in the ground state and thus decoherence from the qutrit is significantly reduced. As an example, we further discuss the experimental feasibility for generating the hybrid maximally entangled state of a DV qutrit and a CV qutrit based on circuit QED. This proposal is universal and can be extended to accomplish the same task, by using two microwave or optical cavities coupled to a natural or artificial three-level atom.

quant-ph↗

arXiv2022

Black hole hyperaccretion in collapsars. III. GRB timescale

Gamma-ray bursts (GRBs) are classified into long and short populations (i.e., LGRBs and SGRBs) based on the observed bimodal distribution of duration $T_{90}$. Multimessenger observations indicated that most SGRBs and LGRBs should be powered by ultrarelativistic jets launched from black hole (BH) hyperaccretion in compact object mergers and massive collapsars, respectively. However, the duration criterion sometimes cannot correctly reflect the physical origin of a particular GRB. In the collapsar scenario, a GRB can be observed when the jet breaks out from the envelope and circumstellar medium successfully. The observed GRB duration reflects only the time that the engine operates after the jet breaks out. This work studies the propagation of jets driven by the neutrino annihilation or Blandford-Znajek mechanism in massive collapsars. The signatures of the progenitors for producing LGRBs, SGRBs, and failed GRBs in the collapsar scenario are exhibited. The competition between the mass supply onto the BH hyperaccretion and jet propagation into the envelope are definitely dependent on the density profiles of the collapsars. We show that duration and isotropic energy $E_{\rm{γ,iso}}$ of GRBs can help constrain the density profiles of collapsars. Finally, we propose that a collapsar-origin SGRB, GRB 200826A, might originate from a neutrino-annihilation-dominated jet launched by a $\sim 10~M_\odot$ collapsar whose progenitor's envelope has been stripped.

astro-ph.HE↗

arXiv2022

A 40Gb/s Linear Redriver with Multi-Band Equalization in 130nm SiGe BiCMOS

A linear redriver circuit implements multi-band equalization techniques to efficiently compensate for high-frequency channel loss and extend high-speed wireline link reach. Input and output stage emitter-follower buffers with dual AC and DC paths provide programmable low-frequency peaking for channel skin effect, while a continuous-time linear equalizer (CTLE) utilizes RC degeneration in the input stage for mid-band peaking and a subsequent feedback structure contributes to additional high-frequency peaking to compensate for the additional dielectric loss effects. A variable-gain amplifier (VGA) stage provides up to 7.1dB tunable gain and utilizes negative capacitive loads for bandwidth extension. Input and output return loss of -11.0dB and -12.2dB is respectively achieved at 20GHz with input and output T-coil stages that distribute the ESD circuitry capacitance. Fabricated in a 130nm SiGe BiCMOS process, the redriver achieves 23.5dB max peaking at 20GHz and supports a 1Vppd linear output swing. Per-channel power consumption is 115.2mW from a 1.8V supply.

physics.ins-det↗

arXiv2022

Eigenvalues restricted by Lyapunov exponent of eigenstates

We point out that the Lyapunov exponent of the eigenstate places restrictions on the eigenvalue. Consequently, with regard to non-Hermitian systems, even without any symmetry, the non-conservative Hamiltonians can exhibit real spectra as long as Lyapunov exponents of eigenstates inhibit imaginary parts of eigenvalues. Our findings open up a new route to study non-Hermitian physics.

quant-ph↗

arXiv2022

Nucleosynthesis Contribution of Neutrino-dominated Accretion Flows to the Chemical Evolution of Active Galactic Nuclei

Recent observations of quasars show high line-flux ratios in their broad emission lines and the ratios appear to be independent of redshift up to $z \gtrsim 6$, which indicate that the broad-line regions of these early quasars are surprisingly metal-rich. Here, we revisit the chemical evolution of high-redshift quasars by adding a new ingredient, i.e., the neutrino-dominated accretion flows (NDAFs) with outflows, on top of the conventional core-collapse supernovae (CCSNe). In the presence of the chemical contribution from NDAFs with outflows, the total metal mass (i.e., the summation of the conventional CCSN and NDAFs with outflows) per CCSN depends weakly upon the mass of the progenitor star if the mass is in the range of $\sim 25-55~M_{\odot}$. We model the chemical evolution by adopting a improved open-box model with three typical initial mass functions (IMFs). We find that, with the additional chemical contribution from NDAFs with outflows, the quasar metallicity can be enriched more rapidly in the very early Universe ($z \sim 10$) and reaches higher saturation than the no-NDAF case at $z \sim 8$, after which they evolve slowly with redshift. The quasar metallicity can reach $\sim 20~Z_{\odot}$ ($Z_\odot$ denotes the metallicity of the Sun; and $\sim 20\%$ of which is produced by NDAF outflows) at $z \sim 8$ for the ``top-heavy'' IMF model in \citet{Toyouchi2022}, which readily explains the quasar observations on the super-solar metal abundance and redshift-independent evolution.

astro-ph.HE↗

arXiv2022

On Calibration of Graph Neural Networks for Node Classification

Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networks have been developed, which learn entity and edge embeddings for tasks such as node classification and link prediction. These models achieve good performance with respect to accuracy, but the confidence scores associated with the predictions might not be calibrated. That means that the scores might not reflect the ground-truth probabilities of the predicted events, which would be especially important for safety-critical applications. Even though graph neural networks are used for a wide range of tasks, the calibration thereof has not been sufficiently explored yet. We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration. Further, we propose a topology-aware calibration method that takes the neighboring nodes into account and yields improved calibration compared to baseline methods.

cs.LG↗

arXiv2022

Polarization in early optical afterglows of gamma-ray bursts driven by precessing jets

Jet precessions are widely involved in astrophysical phenomena from galaxies to X-ray binaries and gamma-ray bursts (GRBs). Polarization presents a unique probe of the magnetic fields in GRB jets. The precession of GRBs relativistic jets will change the geometry within the observable emitting region of the jet, which can potentially affect the polarization of the afterglow. In this paper, we take into account jet precession to study the polarization evolution and corresponding light curves in GRB early optical afterglows with ordered and random magnetic field geometries. We find that the jet precession in long-lived engines can significantly reduce the polarization degree (PD) regardless of the magnetic field structure. The strongest PD attenuation is found when the line of sight is aligned with the precession axis. Our results show that jet precession can provide new insight into the low PD measured in the early optical afterglows of GRBs.

astro-ph.HE↗

arXiv2022

Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are two main difficulties in this prediction task. First, from the historical facts point of view, how to model the evolutionary patterns of the facts to predict the query accurately. Second, from the query perspective, how to handle the two cases where the query contains seen and unseen entities in a unified framework. Driven by the two problems, we propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. And in sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess our model performance, we conduct link prediction on four benchmark datasets, the experimental results demonstrate that our method obtains considerable performance compared with existing methods.

cs.AI↗

arXiv2022

Relativistic global solutions of neutrino-dominated accretion flows with magnetic coupling

A Kerr black hole (BH) surrounded by a neutrino-dominated accretion flow (NDAF) is one of plausible candidates of the central engine in gamma-ray bursts. The accretion material might inherit and restructure strong magnetic fields from the compact object mergers or massive collapsars. The magnetic coupling (MC) process between a rapid rotating BH and an accretion disc is one of possible magnetic configurations that transfers the energy and angular momentum from the BH to the disc. In this paper, we investigate one-dimensional global solutions of NDAFs with MC (MCNDAFs), taking into account general relativistic effects, detailed neutrino physics, different MC geometries, and reasonable nucleosynthesis processes. Six cases with different accretion rates and power-law indices of magnetic fields are presented and compared with NDAFs without MC. Our results indict that the MC process can prominently impact the structure, thermal properties, and microphysics of MCNDAFs, increase luminosities of neutrinos and their annihilations, result in the changing of radial distributions of nucleons, and push the region of heavy nuclei synthesis to a larger radius than counterparts in NDAFs.

astro-ph.HE↗

arXiv2022

Finite volume based film flow and ice accretion models on aircraft wings

The thin runback water films driven by the gas flow, the pressure gradient and the gravity on the iced aircraft surface are investigated in this paper. A three-dimensional film flow model based on Finite Volume Method (FVM) and the lubrication theory is proposed to describe the flow. The depth-averaged velocity of the film is stored in Cartesian coordinates to avoid the appearance of the metric tensors. The governing equations are discretized in the first layer structured grid cell which is selected as the grids for film flow. In order to verify this method, comparisons between numerical results and experimental results of ice shapes on NACA 0012 airfoil and GLC-305 swept wing are presented, both showing a good agreement for rime and glaze ice condition. Overall, this model shows great potential to model ice accretion reasonably under different icing conditions. Besides, the present method doesn't require analytic metric terms, and can be easily coupled to existing finite volume solvers for logically Cartesian meshes.

physics.flu-dyn↗

arXiv2022

Comparison of prismatic cohomology and derived de Rham cohomology

We establish a comparison isomorphism between prismatic cohomology and derived de Rham cohomology respecting various structures, such as their Frobenius actions and filtrations. As an application, when $X$ is a proper smooth formal scheme over $\mathcal O_K$ with $K$ being a $p$-adic field, we improve Breuil--Caruso's theory on comparison between torsion crystalline cohomology and torsion étale cohomology.

math.AG↗

arXiv2022

On the u^{\infty}-torsion submodule of prismatic cohomology

We investigate the maximal finite length submodule of the Breuil-Kisin prismatic cohomology of a smooth proper formal scheme over a p-adic ring of integers. This submodule governs pathology phenomena in integral p-adic cohomology theories. Geometric applications include a control, in low degrees and mild ramifications, of (1) the discrepancy between two naturally associated Albanese varieties in characteristic p, and (2) kernel of the specialization map in p-adic étale cohomology. As an arithmetic application, we study the boundary case of the theory due to Fontaine-Laffaille, Fontaine-Messing, and Kato. Also included is an interesting example, generalized from a construction in Bhatt-Morrow-Scholze's work, which (1) illustrates some of our theoretical results being sharp, and (2) negates a question of Breuil.

math.AG↗

arXiv2022

Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM

In this note, we develop an adaptive observer for a class of nonlinear systems with switched unknown parameters to estimate the states and parameters simultaneously. The main challenge lies in how to eliminate the disturbance effect of zero-input responses caused by the switching on the parameter estimation. These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero. Our solution is to treat the zero-input responses as excitations instead of disturbances. This is realized by first augmenting the system parameter with the SASI and then developing an estimator for the augmented parameter using the \textit{dynamic regression extension and mixing} (DREM) technique. Thanks to its property of element-wise parameter adaptation, the system parameter estimation is decoupled from the SASI. As a result, the estimation errors of system states and parameters converge to zero asymptotically. Furthermore, the robustness of the proposed adaptive observer is guaranteed in the presence of disturbances and noise. A numerical example validates the effectiveness of the proposed approach.

eess.SY↗

arXiv2022

Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle's behavior. In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality. Our objective is to find the specific set of information that should be shared among the vehicles for the construction of the most appropriate state space. SSDP models are conceived for platoon control under different information topologies (IFT) by taking into account `just sufficient' information. Furthermore, theorems are established for comparing the performance of their optimal policies. In order to determine whether a piece of information should or should not be transmitted for improving the DRL-based control policy, we quantify its value by deriving the conditional KL divergence of the transition models. More meritorious information is given higher priority in transmission, since including it in the state space has a higher probability in offsetting the negative effect of having higher state dimensions. Finally, simulation results are provided to illustrate the theoretical analysis.

eess.SY↗

arXiv2022

Revisiting black hole hyperaccretion in the center of gamma-ray bursts for the lower mass gap

The ultrarelativistic jets triggered by neutrino annihilation processes or Blandford-Znajek (BZ) mechanisms in stellar-mass black hole (BH) hyperaccretion systems are generally considered to power gamma-ray bursts (GRBs). Due to the high accretion rate, the central BHs might grow rapidly on a short timescale, providing a new way to understand "the lower mass gap" problem. In this paper, we use the BH hyperaccretion model to investigate BH mass growth based on observational GRB data. The results show that (i) if the initial BH mass is set as $3~M_\odot$, the neutrino annihilation processes are capable of fueling the BHs to escape the lower mass gap for more than half of long-duration GRBs (LGRBs), while the BZ mechanism is inefficient on triggering BH growths for LGRBs; (ii) the mean BH mass growths in the case of LGRBs without observable supernova (SN) association are much larger than these in the case of LGRBs associated with SNe for both mechanisms, which imply that more massive progenitors or lower SN explosion energies prevail throughout the former cases; (iii) for the short-duration GRBs, the mean BH mass growths are satisfied with the mass supply limitation in the scenario of compact object mergers, but the hyperaccretion processes are unable to rescue BHs from the gap in binary neutron star (NS) mergers or the initial BH mass being $3~M_\odot$ after NS-BH mergers.

astro-ph.HE↗

arXiv2022

Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adversarial perturbation (SAP) which targets ECG classification; however, to the best of our knowledge, no study has completely explored the defense against adversarial attacks targeting ECG classification. Thus, we did different experiments to explore the effects of defense methods against white-box adversarial attack and black-box adversarial attack targeting ECG classification, and we found that some common defense methods performed well against these attacks. Besides, we proposed a new defense method called Adversarial Distillation Training (ADT) which comes from defensive distillation and can effectively improve the generalization performance of DNNs. The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization. Furthermore, we found that our method performed better against PGD attacks with low noise levels, which means that our method has stronger robustness.

eess.SP↗

arXiv2022

Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection

The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on. Existing generative adversarial networks (GANs), are primarily used for creating synthetic samples from reals. They also have been proved successful in anomaly detection tasks. In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting anomalous traffic from normal traffic other than calculating expensive and complex anomaly scores or thresholds. Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on the NSL-KDD dataset.

cs.LG↗

arXiv2022

On the evaluation of (meta-)solver approaches

Meta-solver approaches exploits a number of individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can simply adopt the metrics typically used for individual solvers (e.g., runtime or solution quality), or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers, by underlying their strengths and weaknesses.

cs.AI↗