8,086 research outputs found
Random-Singlet Phase in Disordered Two-Dimensional Quantum Magnets
We study effects of disorder (randomness) in a 2D square-lattice
quantum spin system, the - model with a 6-spin interaction
supplementing the Heisenberg exchange . In the absence of disorder the
system hosts antiferromagnetic (AFM) and columnar valence-bond-solid (VBS)
ground states. The VBS breaks symmetry, and in the presence of
arbitrarily weak disorder it forms domains. Using QMC simulations, we
demonstrate two kinds of such disordered VBS states. Upon dilution, a removed
site leaves a localized spin in the opposite sublattice. These spins form AFM
order. For random interactions, we find a different state, with no order but
algebraically decaying mean correlations. We identify localized spinons at the
nexus of domain walls between different VBS patterns. These spinons form
correlated groups with the same number of spinons and antispinons. Within such
a group, there is a strong tendency to singlet formation, because of
spinon-spinon interactions mediated by the domain walls. Thus, no long-range
AFM order forms. We propose that this state is a 2D analog of the well-known 1D
random singlet (RS) state, though the dynamic exponent in 2D is finite. By
studying the T-dependent magnetic susceptibility, we find that varies, from
at the AFM--RS phase boundary and larger in the RS phase The RS state
discovered here in a system without geometric frustration should correspond to
the same fixed point as the RS state recently proposed for frustrated systems,
and the ability to study it without Monte Carlo sign problems opens up
opportunities for further detailed characterization of its static and dynamic
properties. We also discuss experimental evidence of the RS phase in the
quasi-two-dimensional square-lattice random-exchange quantum magnets
SrCuTeWO.Comment: 31 pages, 29 figures; substantial additions in v2; additional
analysis in v
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods
Random-singlet phase in disordered two-dimensional quantum magnets
We study effects of disorder (randomness) in a 2D square-lattice S=1/2 quantum spin system, the J-Q model with a 6-spin interaction Q supplementing the Heisenberg exchange J. In the absence of disorder the system hosts antiferromagnetic (AFM) and columnar valence-bond-solid (VBS) ground states. The VBS breaks Z4 symmetry, and in the presence of arbitrarily weak disorder it forms domains. Using QMC simulations, we demonstrate two kinds of such disordered VBS states. Upon dilution, a removed site leaves a localized spin in the opposite sublattice. These spins form AFM order. For random interactions, we find a different state, with no order but algebraically decaying mean correlations. We identify localized spinons at the nexus of domain walls between different VBS patterns. These spinons form correlated groups with the same number of spinons and antispinons. Within such a group, there is a strong tendency to singlet formation, because of spinon-spinon interactions mediated by the domain walls. Thus, no long-range AFM order forms. We propose that this state is a 2D analog of the well-known 1D random singlet (RS) state, though the dynamic exponent z in 2D is finite. By studying the T-dependent magnetic susceptibility, we find that z varies, from z=2 at the AFM--RS phase boundary and larger in the RS phase The RS state discovered here in a system without geometric frustration should correspond to the same fixed point as the RS state recently proposed for frustrated systems, and the ability to study it without Monte Carlo sign problems opens up opportunities for further detailed characterization of its static and dynamic properties. We also discuss experimental evidence of the RS phase in the quasi-two-dimensional square-lattice random-exchange quantum magnets Sr2CuTe1−xWxO6.Accepted manuscrip
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
Reverberation, which is generally caused by sound reflections from walls,
ceilings, and floors, can result in severe performance degradation of acoustic
applications. Due to a complicated combination of attenuation and time-delay
effects, the reverberation property is difficult to characterize, and it
remains a challenging task to effectively retrieve the anechoic speech signals
from reverberation ones. In the present study, we proposed a novel integrated
deep and ensemble learning algorithm (IDEA) for speech dereverberation. The
IDEA consists of offline and online phases. In the offline phase, we train
multiple dereverberation models, each aiming to precisely dereverb speech
signals in a particular acoustic environment; then a unified fusion function is
estimated that aims to integrate the information of multiple dereverberation
models. In the online phase, an input utterance is first processed by each of
the dereverberation models. The outputs of all models are integrated
accordingly to generate the final anechoic signal. We evaluated the IDEA on
designed acoustic environments, including both matched and mismatched
conditions of the training and testing data. Experimental results confirm that
the proposed IDEA outperforms single deep-neural-network-based dereverberation
model with the same model architecture and training data
Investigation of ultra-thin Al₂O₃ film as Cu diffusion barrier on low-k (k=2.5) dielectrics
Ultrathin Al(2)O(3) films were deposited by PEALD as Cu diffusion barrier on low-k (k=2.5) material. The thermal stability and electrical properties of the Cu/low k system with Al(2)O(3) layers with different thickness were studied after annealing. The AES, TEM and EDX results revealed that the ultrathin Al(2)O(3) films are thermally stable and have excellent Cu diffusion barrier performance. The electrical measurements of dielectric breakdown and TDDB tests further confirmed that the ultrathin Al(2)O(3) film is a potential Cu diffusion barrier in the Cu/low-k interconnects system
- …