Quasinormal modes and stability of higher dimensional rotating black holes under massive scalar perturbations

We consider the stability of six-dimensional singly rotating Myers-Perry black holes under massive scalar perturbations. Using Leaver's continued fraction method, we compute the quasinormal modes of the massive scalar fields. All modes found are damped under the quasinormal boundary conditions. It is also found that long-living modes called quasiresonances exist for large scalar masses as in the four-dimensional Kerr black hole case. Our numerical results provide a direct and complement evidence for the stability of six-dimensional MP black holes under massive scalar perturbation.Comment: 11 pages,9 figure

Learning Practically Feasible Policies for Online 3D Bin Packing

We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. In this problem, the items are delivered to the agent without informing the full sequence information. Agent must directly pack these items into the target bin stably without changing their arrival order, and no further adjustment is permitted. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP). We adopt deep reinforcement learning, in particular, the on-policy actor-critic framework, to solve this MDP with constrained action space. To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree. It attains a high analysis accuracy while reducing the computational complexity from $O(N^2)$ to $O(N \log N)$, making it especially suited for RL training. Second, we propose a decoupled packing policy learning for different dimensions of placement which enables high-resolution spatial discretization and hence high packing precision. Third, we introduce a reward function that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm. Furthermore, we provide a comprehensive discussion on several key implemental issues. The extensive evaluation demonstrates that our learned policy outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.Comment: Science China Information Science

Stability of five-dimensional Myers-Perry black holes under massive scalar perturbation: bound states and quasinormal modes

The stability of five-dimensional singly rotating Myers-Perry Black Holes against massive scalar perturbations is studied. Both the quasibound states and quasinormal modes of the massive scalar field are considered. For the quasibound states, we use an analytical method to discuss the effective potential felt by the scalar field, and found that there is no potential well outside the event horizon. Thus, singly rotating Myers-Perry Black Holes are stable against the perturbation of quasibound states of massive scalar fields. Then, We use continued fraction method based on solving a seven-term recurrence relations to compute the spectra of the quasinormal modes. For different values of the black hole rotation parameter $a$, scalar mass parameter $\mu$ and angular quantum numbers, all found quasinormal modes are damped. So singly rotating Myers-Perry Black Holes are also stable against the perturbation of quasinormal modes of massive scalar fields. Besides, when the scalar mass $\mu$ becomes relatively large, the long-living quasiresonances are also found as in other rotating black hole models. Our results complement previous arguments on the stability of five-dimensional singly rotating Myers-Perry black holes against massive scalar perturbations.Comment: references adde

One Point is All You Need: Directional Attention Point for Feature Learning

We present a novel attention-based mechanism for learning enhanced point features for tasks such as point cloud classification and segmentation. Our key message is that if the right attention point is selected, then "one point is all you need" -- not a sequence as in a recurrent model and not a pre-selected set as in all prior works. Also, where the attention point is should be learned, from data and specific to the task at hand. Our mechanism is characterized by a new and simple convolution, which combines the feature at an input point with the feature at its associated attention point. We call such a point a directional attention point (DAP), since it is found by adding to the original point an offset vector that is learned by maximizing the task performance in training. We show that our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks such as ModelNet40, ShapeNetPart, and S3DIS demonstrate that our DAP-enabled networks consistently outperform the respective original networks, as well as all other competitive alternatives, including those employing pre-selected sets of attention points

An Evaluation Model for Foreign Direct Investment Performance of Free Trade Port Zones

With the tendency of internationalisation and globalisation, signing regional economic agreements among multiple countries has become a trend. Under such an integration environment, some free economic zones with port transportation functions have become crucial for FDI (foreign direct investment) investors in selecting investment locations. The free trade port zone (FTPZ) is argued to be one of the most well-known. This paper aims to assess the FDI performance of FTPZs. On the basis of the FTPZ\u27s features and relevant literature, assessment criteria (ACs) are initially identified. An evaluation model based on the fuzzy AHP (Analytic Hierarchy Process) approach is then introduced to evaluate the FTPZs\u27 FDI performance from foreign investors\u27 viewpoints. Finally, the FTPZ of the Kaohsiung port in Taiwan was empirically investigated to verify the assessment model. Results point out that for the FTPZ of Kaohsiung port, ACs with higher priorities needing improvement are raw material acquired, local government efficiency, and political stability and social security. Theoretical and practical recommendations for the FTPZ managers are discussed based on the results

Adversarially Robust Submodular Maximization under Knapsack Constraints

We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings. For a single knapsack constraint, our algorithm outputs a robust summary of almost optimal (up to polylogarithmic factors) size, from which a constant-factor approximation to the optimal solution can be constructed. For multiple knapsack constraints, our approximation is within a constant-factor of the best known non-robust solution. We evaluate the performance of our algorithms by comparison to natural robustifications of existing non-robust algorithms under two objectives: 1) dominating set for large social network graphs from Facebook and Twitter collected by the Stanford Network Analysis Project (SNAP), 2) movie recommendations on a dataset from MovieLens. Experimental results show that our algorithms give the best objective for a majority of the inputs and show strong performance even compared to offline algorithms that are given the set of removals in advance.Comment: To appear in KDD 201

An endogenous factor enhances ferulic acid decarboxylation catalyzed by phenolic acid decarboxylase from Candida guilliermondii

Resource subsidies in the form of allochthonous primary production drive secondary production in many ecosystems, often sustaining diversity and overall productivity. Despite their importance in structuring marine communities, there is little understanding of how subsidies move through juxtaposed habitats and into recipient communities. We investigated the transport of detritus from kelp forests to a deep Arctic fjord (northern Norway). We quantified the seasonal abundance and size structure of kelp detritus in shallow subtidal (0‒12 m), deep subtidal (12‒85 m), and deep fjord (400‒450 m) habitats using a combination of camera surveys, dive observations, and detritus collections over 1 year. Detritus formed dense accumulations in habitats adjacent to kelp forests, and the timing of depositions coincided with the discrete loss of whole kelp blades during spring. We tracked these blades through the deep subtidal and into the deep fjord, and showed they act as a short-term resource pulse transported over several weeks. In deep subtidal regions, detritus consisted mostly of fragments and its depth distribution was similar across seasons (50% of total observations). Tagged pieces of detritus moved slowly out of kelp forests (displaced 4‒50 m (mean 11.8 m ± 8.5 SD) in 11‒17 days, based on minimum estimates from recovered pieces), and most (75%) variability in the rate of export was related to wave exposure and substrate. Tight resource coupling between kelp forests and deep fjords indicate that changes in kelp abundance would propagate through to deep fjord ecosystems, with likely consequences for the ecosystem functioning and services they provide