2,590 research outputs found
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 to , 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 , scalar mass parameter 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
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
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