Search for Continuous Gravitational Wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling

Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semi-analytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling shown the capability of coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including $mathcal{O}(10^2)$ pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also high beneficial to include DNest in current toolboxes of PTA analysis.

Graph Convolution for Re-ranking in Person Re-identification

Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID) and have achieved great success. However, due to the non-overlapping between training and testing IDs, the difference between the data used for model training and the testing data makes the performance of learned feature degraded during testing. Hence, re-ranking is proposed to mitigate this issue and various algorithms have been developed. However, most of existing re-ranking methods focus on replacing the Euclidean distance with sophisticated distance metrics, which are not friendly to downstream tasks and hard to be used for fast retrieval of massive data in real applications. In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric. Inspired by graph convolution networks, we develop an operator to propagate features over an appropriate graph. Since graph is the essential key for the propagation, two important criteria are considered for designing the graph, and three different graphs are explored accordingly. Furthermore, a simple yet effective method is proposed to generate a profile vector for each tracklet in videos, which helps extend our method to video re-ID. Extensive experiments on three benchmark data sets, e.g., Market-1501, Duke, and MARS, demonstrate the effectiveness of our proposed approach.

General theory of quantum fingerprinting network

The purpose of fingerprinting is to compare long messages with low communication complexity. Compared with its classical version, the quantum fingerprinting can realize exponential reduction in communication complexity. Recently, the multi-party quantum fingerprinting is studied on whether the messages from many parties are the same. However, sometimes it is not enough just to know whether these messages are the same, we usually need to know the relationship among them. We provide a general model of quantum fingerprinting network, defining the relationship function $f^R$ and giving the corresponding decision rules. In this work, we take the four-party quantum fingerprinting protocol as an example for detailed analysis. We also choose the optimal parameters to minimize communication complexity in the case of asymmetric channels. Furthermore, we compare the multi-party quantum fingerprinting with the protocol based on the two-party quantum fingerprinting and find that the multi-party protocol has obvious advantages, especially in terms of communication time. Finally, the method of encoding more than one bit on each coherent state is used to further improve the performance of the protocol.

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but for some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.

Convergence Rates of Two-Component MCMC Samplers

Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings samplers, are commonly used for sampling from multivariate probability distributions. A long-standing question regarding Gibbs algorithms is whether a deterministic-scan (systematic-scan) sampler converges faster than its random-scan counterpart. We answer this question when the samplers involve two components by establishing an exact quantitative relationship between the $L^2$ convergence rates of the two samplers. The relationship shows that the deterministic-scan sampler converges faster. We also establish qualitative relations among the convergence rates of two-component Gibbs samplers and some conditional Metropolis-Hastings variants. For instance, it is shown that if some two-component conditional Metropolis-Hastings samplers are geometrically ergodic, then so are the associated Gibbs samplers.

GR-MHD disk winds and jets from black holes and resistive accretion disks

We perform GR-MHD simulations of outflow launching from thin accretion disks. As in the non-relativistic case, resistivity is essential for the mass loading of the disk wind. We implemented resistivity in the ideal GR-MHD code HARM3D, extending previous works (Qian et al. 2017, 2018) for larger physical grids, higher spatial resolution, and longer simulation time. We consider an initially thin, resistive disk orbiting the black hole, threaded by a large-scale magnetic flux. As the system evolves, outflows are launched from the black hole magnetosphere and the disk surface. We mainly focus on disk outflows, investigating their MHD structure and energy output in comparison with the Poynting-dominated black hole jet. The disk wind encloses two components -- a fast component dominated by the toroidal magnetic field and a slower component dominated by the poloidal field. The disk wind transitions from sub to super-Alfvenic speed, reaching velocities $simeq 0.1c$. We provide parameter studies varying spin parameter and resistivity level, and measure the respective mass and energy fluxes. A higher spin strengthens the $B_{phi}$-dominated disk wind along the inner jet. We disentangle a critical resistivity level that leads to a maximum matter and energy output for both, resulting from the interplay between re-connection and diffusion, which in combination govern the magnetic flux and the mass loading. For counter-rotating black holes the outflow structure shows a magnetic field reversal. We estimate the opacity of the inner-most accretion stream and the outflow structure around it. This stream may be critically opaque for a lensed signal, while the axial jet funnel remains optically thin.

On the relation between transition region network jets and coronal plumes

Both coronal plumes and network jets are rooted in network lanes. The relationship between the two, however, has yet to be addressed. For this purpose, we perform an observational analysis using images acquired with the Atmospheric Imaging Assembly (AIA) 171{AA} passband to follow the evolution of coronal plumes, the observations taken by the Interface Region Imaging Spectrograph (IRIS) slit-jaw 1330{AA} to study the network jets, and the line-of-sight magnetograms taken by the Helioseismic and Magnetic Imager (HMI) to overview the the photospheric magnetic features in the regions. Four regions in the network lanes are identified, and labeled ``R1--R4. We find that coronal plumes are clearly seen only in ``R1&R2 but not in ``R3&``R4, even though network jets abound in all these regions. Furthermore, while magnetic features in all these regions are dominated by positive polarity, they are more compact (suggesting stronger convergence) in ``R1&``R2 than that in ``R3&``R4. We develop an automated method to identify and track the network jets in the regions. We find that the network jets rooted in ``R1&``R2 are higher and faster than that in ``R3&``R4,indicating that network regions producing stronger coronal plumes also tend to produce more dynamic network jets. We suggest that the stronger convergence in ``R1&``R2 might provide a condition for faster shocks and/or more small-scale magnetic reconnection events that power more dynamic network jets and coronal plumes.

Guessing probability in quantum key distribution

On the basis of the existing trace distance result, we present a simple and efficient method to tighten the upper bound of the guessing probability. The guessing probability of the final key k can be upper bounded by the guessing probability of another key k, if k can be mapped from the final key k. Compared with the known methods, our result is more tightened by thousands of orders of magnitude. For example, given a 10^{-9}-secure key from the sifted key, the upper bound of the guessing probability obtained using our method is 2*10^(-3277). This value is smaller than the existing result 10^(-9) by more than 3000 orders of magnitude. Our result shows that from the perspective of guessing probability, the performance of the existing trace distance security is actually much better than what was assumed in the past.

Continuous gravitational wave searches with pulsar timing arrays: Maximization versus marginalization over pulsar phase parameters

Resolvable Supermassive Black Hole Binaries are promising sources for Pulsar Timing Array based gravitational wave searches. Search algorithms for such targets must contend with the large number of so-called pulsar phase parameters in the joint log-likelihood function of the data. We compare the localization accuracy for two approaches: Maximization over the pulsar phase parameters (MaxPhase) against marginalization over them (AvPhase). Using simulated data from a pulsar timing array with 17 pulsars, we find that for weak and moderately strong signals, AvPhase outperforms MaxPhase significantly, while they perform comparably for strong signals.