12,842 research outputs found
Two-axis-twisting spin squeezing by multi-pass quantum erasure
Many-body entangled states are key elements in quantum information science
and quantum metrology. One important problem in establishing a high degree of
many-body entanglement using optical techniques is the leakage of the system
information via the light that creates such entanglement. We propose an
all-optical interference-based approach to erase this information. Unwanted
atom-light entanglement can be removed by destructive interference of three or
more successive atom-light interactions, with only the desired effective
atom-atom interaction left. This quantum erasure protocol allows implementation
of Heisenberg-limited spin squeezing using coherent light and a cold or warm
atomic ensemble. Calculations show that significant improvement in the
squeezing exceeding 10 dB is obtained compared to previous methods, and
substantial spin squeezing is attainable even under moderate experimental
conditions. Our method enables the efficient creation of many-body entangled
states with simple setups, and thus is promising for advancing technologies in
quantum metrology and quantum information processing.Comment: 10 pages, 4 figures. We have improved the presentation and added a
new section, in which we have generalized the scheme from a three-pass scheme
to multi-pass schem
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.Comment: CIKM 201
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201
Deducing topology of protein-protein interaction networks from experimentally measured sub-networks.
BackgroundProtein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear.ResultsBy analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks.ConclusionThe topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network
Phylogenetic networks: A tool to display character conflict and demographic history
Evolutionary trees have the assumption that evolution and phylogeny can be represented in a strictly bifurcating manner. Firmly speaking, from one ancestral taxon, two descendant taxa emerge. Nevertheless, hybridization, recombination and horizontal gene transfer is in conflict with this straightforward concept. In such cases, evolutionary lines do not only separate from each other, but have the possibility of melting again and are called reticulations. Consequently, networks can represent evolutionary events more realistically than phylogenetic trees. Networks can display alternative topologies and co-existence of ancestors and descendants, which are otherwise not obvious when a comparison is done on several single trees or a consensus tree. Therefore, networks have the ability to visualize the conflicting information in a given data set. Moreover, the distribution, frequencies and arrangement of haplotypes in populations can reveal the phylogenetic histories of the taxa, regarding predictions from the coalescent theory. This review aims to: (1) give a brief comparison between phylogenetic trees and networks, (2) provide the overall concept of the coalescent theory, (3) clarify how phylogenetic networks can be used to display conflict data and evaluate phylogenetic histories, and (4) offer a useful starting point and guide for sequence analysis, with the aim to discover population dynamics.Key words: Phylogenetic networks, reticulation, coalescent theory, population history, character conflict
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control
This paper addresses the challenges associated with decentralized voltage
control in power grids due to an increase in distributed generations (DGs).
Traditional model-based voltage control methods struggle with the rapid energy
fluctuations and uncertainties of these DGs. While multi-agent reinforcement
learning (MARL) has shown potential for decentralized secondary control,
scalability issues arise when dealing with a large number of DGs. This problem
lies in the dominant centralized training and decentralized execution (CTDE)
framework, where the critics take global observations and actions. To overcome
these challenges, we propose a scalable network-aware (SNA) framework that
leverages network structure to truncate the input to the critic's Q-function,
thereby improving scalability and reducing communication costs during training.
Further, the SNA framework is theoretically grounded with provable
approximation guarantee, and it can seamlessly integrate with multiple
multi-agent actor-critic algorithms. The proposed SNA framework is successfully
demonstrated in a system with 114 DGs, providing a promising solution for
decentralized voltage control in increasingly complex power grid systems
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