24,777 research outputs found
Gravitational-Wave Fringes at LIGO: Detecting Compact Dark Matter by Gravitational Lensing
Utilizing gravitational-wave (GW) lensing opens a new way to understand the
small-scale structure of the universe. We show that, in spite of its coarse
angular resolution and short duration of observation, LIGO can detect the GW
lensing induced by compact structures, in particular by compact dark matter
(DM) or primordial black holes of , which remain
interesting DM candidates. The lensing is detected through GW frequency
chirping, creating the natural and rapid change of lensing patterns:
\emph{frequency-dependent amplification and modulation} of GW waveforms. As a
highest-frequency GW detector, LIGO is a unique GW lab to probe such light
compact DM. With the design sensitivity of Advanced LIGO, one-year observation
by three detectors can optimistically constrain the compact DM density fraction
to the level of a few percent.Comment: 6 pages, 5 figures, v2: published version, Fig.5 updated with Poisson
distribution, improved discussion on the optical dept
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking
candidate answers, that adapts a hierarchical recurrent neural network and a
latent topic clustering module. With our proposed model, a text is encoded to a
vector representation from an word-level to a chunk-level to effectively
capture the entire meaning. In particular, by adapting the hierarchical
structure, our model shows very small performance degradations in longer text
comprehension while other state-of-the-art recurrent neural network models
suffer from it. Additionally, the latent topic clustering module extracts
semantic information from target samples. This clustering module is useful for
any text related tasks by allowing each data sample to find its nearest topic
cluster, thus helping the neural network model analyze the entire data. We
evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic
domain question answering dataset, which is related to Samsung products. The
proposed model shows state-of-the-art results for ranking question-answer
pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201
Distributed Averaging via Lifted Markov Chains
Motivated by applications of distributed linear estimation, distributed
control and distributed optimization, we consider the question of designing
linear iterative algorithms for computing the average of numbers in a network.
Specifically, our interest is in designing such an algorithm with the fastest
rate of convergence given the topological constraints of the network. As the
main result of this paper, we design an algorithm with the fastest possible
rate of convergence using a non-reversible Markov chain on the given network
graph. We construct such a Markov chain by transforming the standard Markov
chain, which is obtained using the Metropolis-Hastings method. We call this
novel transformation pseudo-lifting. We apply our method to graphs with
geometry, or graphs with doubling dimension. Specifically, the convergence time
of our algorithm (equivalently, the mixing time of our Markov chain) is
proportional to the diameter of the network graph and hence optimal. As a
byproduct, our result provides the fastest mixing Markov chain given the
network topological constraints, and should naturally find their applications
in the context of distributed optimization, estimation and control
Opportunistic Interference Mitigation Achieves Optimal Degrees-of-Freedom in Wireless Multi-cell Uplink Networks
We introduce an opportunistic interference mitigation (OIM) protocol, where a
user scheduling strategy is utilized in -cell uplink networks with
time-invariant channel coefficients and base stations (BSs) having
antennas. Each BS opportunistically selects a set of users who generate the
minimum interference to the other BSs. Two OIM protocols are shown according to
the number of simultaneously transmitting users per cell: opportunistic
interference nulling (OIN) and opportunistic interference alignment (OIA).
Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As
our main result, it is shown that DoFs are achievable under the OIN
protocol with selected users per cell, if the total number of users in
a cell scales at least as . Similarly, it turns out that
the OIA scheme with () selected users achieves DoFs, if scales
faster than . These results indicate that there exists a
trade-off between the achievable DoFs and the minimum required . By deriving
the corresponding upper bound on the DoFs, it is shown that the OIN scheme is
DoF optimal. Finally, numerical evaluation, a two-step scheduling method, and
the extension to multi-carrier scenarios are shown.Comment: 18 pages, 3 figures, Submitted to IEEE Transactions on Communication
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