405 research outputs found
Spatial Throughput Maximization of Wireless Powered Communication Networks
Wireless charging is a promising way to power wireless nodes' transmissions.
This paper considers new dual-function access points (APs) which are able to
support the energy/information transmission to/from wireless nodes. We focus on
a large-scale wireless powered communication network (WPCN), and use stochastic
geometry to analyze the wireless nodes' performance tradeoff between energy
harvesting and information transmission. We study two cases with battery-free
and battery-deployed wireless nodes. For both cases, we consider a
harvest-then-transmit protocol by partitioning each time frame into a downlink
(DL) phase for energy transfer, and an uplink (UL) phase for information
transfer. By jointly optimizing frame partition between the two phases and the
wireless nodes' transmit power, we maximize the wireless nodes' spatial
throughput subject to a successful information transmission probability
constraint. For the battery-free case, we show that the wireless nodes prefer
to choose small transmit power to obtain large transmission opportunity. For
the battery-deployed case, we first study an ideal infinite-capacity battery
scenario for wireless nodes, and show that the optimal charging design is not
unique, due to the sufficient energy stored in the battery. We then extend to
the practical finite-capacity battery scenario. Although the exact performance
is difficult to be obtained analytically, it is shown to be upper and lower
bounded by those in the infinite-capacity battery scenario and the battery-free
case, respectively. Finally, we provide numerical results to corroborate our
study.Comment: 15 double-column pages, 8 figures, to appear in IEEE JSAC in February
2015, special issue on wireless communications powered by energy harvesting
and wireless energy transfe
Convergence to global equilibrium for Fokker-Planck equations on a graph and Talagrand-type inequalities
In recent work, Chow, Huang, Li and Zhou introduced the study of
Fokker-Planck equations for a free energy function defined on a finite graph.
When is the number of vertices of the graph, they show that the
corresponding Fokker-Planck equation is a system of nonlinear ordinary
differential equations defined on a Riemannian manifold of probability
distributions. The different choices for inner products on the space of
probability distributions result in different Fokker-Planck equations for the
same process. Each of these Fokker-Planck equations has a unique global
equilibrium, which is a Gibbs distribution. In this paper we study the {\em
speed of convergence} towards global equilibrium for the solution of these
Fokker-Planck equations on a graph, and prove that the convergence is indeed
exponential. The rate as measured by the decay of the norm can be bound
in terms of the spectral gap of the Laplacian of the graph, and as measured by
the decay of (relative) entropy be bound using the modified logarithmic Sobolev
constant of the graph.
With the convergence result, we also prove two Talagrand-type inequalities
relating relative entropy and Wasserstein metric, based on two different
metrics introduced in [CHLZ] The first one is a local inequality, while the
second is a global inequality with respect to the "lower bound metric" from
[CHLZ]
Poly[di-μ5-adipato-κ4 O:O′:O′′:O′′′,O′′′-μ4-adipato-κ4 O:O′:O′′:O′′′-bisÂ[2-phenyl-1H-1,3,7,8-tetraÂazacycloÂpentaÂ[l]phenanthrene-κ2 N 7,N 8]tricobalt(II)]
In the title polymer, [Co3(C6H8O4)3(C19H12N4)2]n, two adipate dianions (C6H8O4
2−) occupy general positions and two are situated on different inversion centres. The two on general positions bind through their four O atoms to five 2-phenyl-1H-1,3,7,8-tetraÂazacycloÂpentaÂ[l]phenanthrene-chelated CoII ions, whereas the two on special positions bind to only four. Of the three Co atoms, two are chelated by N-heterocycles; the third is bonded to six O atoms. The bonding mode of the dianion gives rise to a three-dimensional network structure; the network is further consolidated by N—H⋯O hydrogen bonds
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
In this paper, we introduce the task of learning unsupervised dialogue
embeddings. Trivial approaches such as combining pre-trained word or sentence
embeddings and encoding through pre-trained language models (PLMs) have been
shown to be feasible for this task. However, these approaches typically ignore
the conversational interactions between interlocutors, resulting in poor
performance. To address this issue, we proposed a self-guided contrastive
learning approach named dial2vec. Dial2vec considers a dialogue as an
information exchange process. It captures the conversational interaction
patterns between interlocutors and leverages them to guide the learning of the
embeddings corresponding to each interlocutor. The dialogue embedding is
obtained by an aggregation of the embeddings from all interlocutors. To verify
our approach, we establish a comprehensive benchmark consisting of six
widely-used dialogue datasets. We consider three evaluation tasks: domain
categorization, semantic relatedness, and dialogue retrieval. Dial2vec achieves
on average 8.7, 9.0, and 13.8 points absolute improvements in terms of purity,
Spearman's correlation, and mean average precision (MAP) over the strongest
baseline on the three tasks respectively. Further analysis shows that dial2vec
obtains informative and discriminative embeddings for both interlocutors under
the guidance of the conversational interactions and achieves the best
performance when aggregating them through the interlocutor-level pooling
strategy. All codes and data are publicly available at
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.Comment: Accepted as Long Paper at "EMNLP,2022
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