285 research outputs found
Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration
Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for
multiple agents from their initial locations to destinations, visiting a set of
intermediate target locations in the middle of the paths, while minimizing the
sum of arrival times. While a few approaches have been developed to handle
MCPF, most of them simply direct the agent to visit the targets without
considering the task duration, i.e., the amount of time needed for an agent to
execute the task (such as picking an item) at a target location. MCPF is
NP-hard to solve to optimality, and the inclusion of task duration further
complicates the problem. This paper investigates heterogeneous task duration,
where the duration can be different with respect to both the agents and
targets. We develop two methods, where the first method post-processes the
paths planned by any MCPF planner to include the task duration and has no
solution optimality guarantee; and the second method considers task duration
during planning and is able to ensure solution optimality. The numerical and
simulation results show that our methods can handle up to 20 agents and 50
targets in the presence of task duration, and can execute the paths subject to
robot motion disturbance
BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy
A popular approach for constructing bird's-eye-view (BEV) representation in
3D detection is to lift 2D image features onto the viewing frustum space based
on explicitly predicted depth distribution. However, depth distribution can
only characterize the 3D geometry of visible object surfaces but fails to
capture their internal space and overall geometric structure, leading to sparse
and unsatisfactory 3D representations. To mitigate this issue, we present
BEV-IO, a new 3D detection paradigm to enhance BEV representation with instance
occupancy information. At the core of our method is the newly-designed instance
occupancy prediction (IOP) module, which aims to infer point-level occupancy
status for each instance in the frustum space. To ensure training efficiency
while maintaining representational flexibility, it is trained using the
combination of both explicit and implicit supervision. With the predicted
occupancy, we further design a geometry-aware feature propagation mechanism
(GFP), which performs self-attention based on occupancy distribution along each
ray in frustum and is able to enforce instance-level feature consistency. By
integrating the IOP module with GFP mechanism, our BEV-IO detector is able to
render highly informative 3D scene structures with more comprehensive BEV
representations. Experimental results demonstrate that BEV-IO can outperform
state-of-the-art methods while only adding a negligible increase in parameters
(0.2%) and computational overhead (0.24%in GFLOPs).Comment: v
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair
modeling tasks, such as answer selection and natural language inference (NLI).
These models generally perform cross-attention over input pairs, leading to
prohibitive computational costs. Recent studies propose dual-encoder and late
interaction architectures for faster computation. However, the balance between
the expressive of cross-attention and computation speedup still needs better
coordinated. To this end, this paper introduces a novel paradigm MixEncoder for
efficient sentence pair modeling. MixEncoder involves a light-weight
cross-attention mechanism. It conducts query encoding only once while modeling
the query-candidate interaction in parallel. Extensive experiments conducted on
four tasks demonstrate that our MixEncoder can speed up sentence pairing by
over 113x while achieving comparable performance as the more expensive
cross-attention models.Comment: Accepted to EMNLP 202
Average Polarization of Electromagnetic Gaussian Schell-Model Beams through Anisotropic Non-Kolmogorov Turbulence
Polarization properties of electromagnetic Gaussian Schell-model beams propagating through the anisotropic non-Kolmogorov turbulence of marine-atmosphere channel are studied based on the cross-spectral density matrix. Detailed analysis shows that the average polarization decreases with increasing the spectral index, inner scale of turbulence and generalized refractive-index structure parameter. We find the effects of anisotropic turbulence on the average polarization is less than that of the isotropic turbulence and the depolarization effect of turbulence in marine-atmosphere is larger than terrene-atmosphere. The electromagnetic Gaussian Schell-model beam with the parameters of smaller σxx ,σyy and Ax, but larger Ay will reduce the interference of turbulence
Dilated FCN: Listening Longer to Hear Better
Deep neural network solutions have emerged as a new and powerful paradigm for
speech enhancement (SE). The capabilities to capture long context and extract
multi-scale patterns are crucial to design effective SE networks. Such
capabilities, however, are often in conflict with the goal of maintaining
compact networks to ensure good system generalization. In this paper, we
explore dilation operations and apply them to fully convolutional networks
(FCNs) to address this issue. Dilations equip the networks with greatly
expanded receptive fields, without increasing the number of parameters.
Different strategies to fuse multi-scale dilations, as well as to install the
dilation modules are explored in this work. Using Noisy VCTK and AzBio
sentences datasets, we demonstrate that the proposed dilation models
significantly improve over the baseline FCN and outperform the state-of-the-art
SE solutions.Comment: 5 pages; will appear in WASPAA conferenc
College Towns: Handle Data With Care
EconomicGrowth_Development_TechnicalChangeAlthough the term ‘college town’ may invoke idyllic images from our past, government statistics paint a different picture. College towns often appear as poverty – ridden, with unaffordable housing and low incomes. However, by their very nature college students are young, often have very little income, and usually have an ability to spend far more than their official income. In this paper, authors Dennis W. Jansen, Carlos I. Navarro and Yuanhang Wang show how government statistics for college towns can be misleading with respect to income, poverty, and housing affordability, as well as ways in which college towns are not so different from other areas with respect to statistics on crime or unemployment rates
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