56,596 research outputs found
Active Discriminative Text Representation Learning
We propose a new active learning (AL) method for text classification with
convolutional neural networks (CNNs). In AL, one selects the instances to be
manually labeled with the aim of maximizing model performance with minimal
effort. Neural models capitalize on word embeddings as representations
(features), tuning these to the task at hand. We argue that AL strategies for
multi-layered neural models should focus on selecting instances that most
affect the embedding space (i.e., induce discriminative word representations).
This is in contrast to traditional AL approaches (e.g., entropy-based
uncertainty sampling), which specify higher level objectives. We propose a
simple approach for sentence classification that selects instances containing
words whose embeddings are likely to be updated with the greatest magnitude,
thereby rapidly learning discriminative, task-specific embeddings. We extend
this approach to document classification by jointly considering: (1) the
expected changes to the constituent word representations; and (2) the model's
current overall uncertainty regarding the instance. The relative emphasis
placed on these criteria is governed by a stochastic process that favors
selecting instances likely to improve representations at the outset of
learning, and then shifts toward general uncertainty sampling as AL progresses.
Empirical results show that our method outperforms baseline AL approaches on
both sentence and document classification tasks. We also show that, as
expected, the method quickly learns discriminative word embeddings. To the best
of our knowledge, this is the first work on AL addressing neural models for
text classification.Comment: This paper got accepted by AAAI 201
Control of Four-Level Quantum Coherence via Discrete Spectral Shaping of an Optical Frequency Comb
We present an experiment demonstrating high-resolution coherent control of a
four-level atomic system in a closed (diamond) type configuration. A
femtosecond frequency comb is used to establish phase coherence between a pair
of two-photon transitions in cold Rb atoms. By controlling the spectral phase
of the frequency comb we demonstrate the optical phase sensitive response of
the diamond system. The high-resolution state selectivity of the comb is used
to demonstrate the importance of the signs of dipole moment matrix elements in
this type of closed-loop excitation. Finally, the pulse shape is optimized
resulting in a 256% increase in the two-photon transition rate by forcing
constructive interference between the mode pairs detuned from an intermediate
resonance.Comment: 5 pages, 4 figures Submitted to Physical Review Letter
Quantum nonlocality of four-qubit entangled states
Quantum nonlocality of several four-qubit states is investigated by
constructing a new Bell inequality. These include the
Greenberger-Zeilinger-Horne (GHZ) state, W state, cluster state, and the state
that has been recently proposed in [PRL, {\bf 96}, 060502 (2006)]. The
Bell inequality is optimally violated by but not violated by the GHZ
state. The cluster state also violates the Bell inequality though not
optimally. The state can thus be discriminated from the cluster state
by using the inequality. Different aspects of four-partite entanglement are
also studied by considering the usefulness of a family of four-qubit mixed
states as resources for two-qubit teleportation. Our results generalize those
in [PRL, {\bf 72}, 797 (1994)].Comment: 13 pages, 1 figur
Evaluation of heating effects on atoms trapped in an optical trap
We solve a stochastic master equation based on the theory of Savard et al. [T. A. Savard. K. M. O'Hara, and J. E. Thomas, Phys, Rev. A 56, R1095 (1997)] for heating arising from fluctuations in the trapping laser intensity. We compare with recent experiments of Ye et al. [J. Ye, D. W. Vernooy, and H. J. Kimble, Phys. Rev. Lett. 83, 4987 (1999)], and find good agreement with the experimental measurements of the distribution of trap occupancy times. The major cause of trap loss arises from the broadening of the energy distribution of the trapped atom, rather than the mean heating rate, which is a very much smaller effect
Harmonized Cellular and Distributed Massive MIMO: Load Balancing and Scheduling
Multi-tier networks with large-array base stations (BSs) that are able to
operate in the "massive MIMO" regime are envisioned to play a key role in
meeting the exploding wireless traffic demands. Operated over small cells with
reciprocity-based training, massive MIMO promises large spectral efficiencies
per unit area with low overheads. Also, near-optimal user-BS association and
resource allocation are possible in cellular massive MIMO HetNets using simple
admission control mechanisms and rudimentary BS schedulers, since scheduled
user rates can be predicted a priori with massive MIMO.
Reciprocity-based training naturally enables coordinated multi-point
transmission (CoMP), as each uplink pilot inherently trains antenna arrays at
all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP,
which improves cell-edge performance without requiring channel state
information exchanges among cooperating BSs. We present methods for harmonized
operation of distributed and cellular massive MIMO in the downlink that
optimize resource allocation at a coarser time scale across the network. We
also present scheduling policies at the resource block level which target
approaching the optimal allocations. Simulations reveal that the proposed
methods can significantly outperform the network-optimized cellular-only
massive MIMO operation (i.e., operation without CoMP), especially at the cell
edge
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