17,224 research outputs found
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss
Either human annotation or rule based automatic labeling is an effective
method to augment data for relation extraction. However, the inevitable wrong
labeling problem for example by distant supervision may deteriorate the
performance of many existing methods. To address this issue, we introduce a
practical end-to-end deep learning framework, including a standard feature
extractor and a novel noisy classifier with our proposed doubly transitional
mechanism. One transition is basically parameterized by a non-linear
transformation between hidden layers that implicitly represents the conversion
between the true and noisy labels, and it can be readily optimized together
with other model parameters. Another is an explicit probability transition
matrix that captures the direct conversion between labels but needs to be
derived from an EM algorithm. We conduct experiments on the NYT dataset and
SemEval 2018 Task 7. The empirical results show comparable or better
performance over state-of-the-art methods.Comment: 10 page
A matrix realignment method for recognizing entanglement
Motivated by the Kronecker product approximation technique, we have developed
a very simple method to assess the inseparability of bipartite quantum systems,
which is based on a realigned matrix constructed from the density matrix. For
any separable state, the sum of the singular values of the matrix should be
less than or equal to 1. This condition provides a very simple, computable
necessary criterion for separability, and shows powerful ability to identify
most bound entangled states discussed in the literature. As a byproduct of the
criterion, we give an estimate for the degree of entanglement of the quantum
state.Comment: 10 pages, 2 figures. Improved version with some new results and
references, including response to quant-ph/020505
Detection of entanglement and Bell's inequality violation
We propose a new method for detecting entanglement of two qubits and discuss
its relation with the Clauser-Horne-Shimony-Holt (CHSH) Bell inequality.
Without the need for full quantum tomography for the density matrix we can
experimentally detect the entanglement by measuring less than 9 local
observables for any given state. We show that this test is stronger than the
CHSH-Bell inequality and also gives an estimation for the degree of
entanglement. If prior knowledge is available we can further greatly reduce the
number of required local observables. The test is convenient and feasible with
present experimental technology.Comment: 4 pages, no figure, revtex4 styl
Nature of the chiral phase transition of two flavour QCD from imaginary chemical potential with HISQ fermions
The nature of the thermal phase transition of two flavor QCD in the chiral
limit has an important implication for the QCD phase diagram. We carry out
lattice QCD simulations in an attempt to address this problem. Simulations are
conducted with a Symanzik-improved gauge action and the HISQ fermion action.
Within the imaginary chemical potential formulation, five different quark
masses, , and four different
lattice volumes with temporal extent are
used to explore the scaling behavior. At each of the quark masses, the Binder
cumulants of the chiral condensate on different lattice volumes approximately
intersect at one point. We find that at the intersection point, the Binder
cumulant is around which deviates from the
universality class value 1.604. However, based on the expectations of
criticality, the fitting result only with the data from the largest lattice
volume agrees well with earlier result [ Phys. Rev., D90, 074030(2014)
]\cite{Bonati:2014kpa}. This fact implies that, although the finite cut-off
effects could be reduced with HISQ fermions even on lattices, larger
lattices with spatial extent for such studies are needed to control
finite volume effects.Comment: 7 pages, 7 figures; 8 pages, 8 figure
The Study of Mass Distribution of products in 7.0 AMeV U238+U238 Collisions
Within the Improved Quantum Molecular Dynamics (ImQMD) Model incorporating
the statistical decay Model, the reactions of U238+U238 at the energy of 7.0
AMeV have been studied. The charge, mass and excitation energy distributions of
primary fragments are investigated within the ImQMD model and de-excitation
processes of those primary fragments are described by the statistical decay
model. The mass distribution of the final products in U238+U238 collisions is
obtained and compared with the recent experimental data.Comment: 17 pages, 11 figures, to be published in PR
High Performance Data Persistence in Non-Volatile Memory for Resilient High Performance Computing
Resilience is a major design goal for HPC. Checkpoint is the most common
method to enable resilient HPC. Checkpoint periodically saves critical data
objects to non-volatile storage to enable data persistence. However, using
checkpoint, we face dilemmas between resilience, recomputation and checkpoint
cost. The reason that accounts for the dilemmas is the cost of data copying
inherent in checkpoint. In this paper we explore how to build resilient HPC
with non-volatile memory (NVM) as main memory and address the dilemmas. We
introduce a variety of optimization techniques that leverage high performance
and non-volatility of NVM to enable high performance data persistence for data
objects in applications. With NVM we avoid data copying; we optimize cache
flushing needed to ensure consistency between caches and NVM. We demonstrate
that using NVM is feasible to establish data persistence frequently with small
overhead (4.4% on average) to achieve highly resilient HPC and minimize
recomputation
Text-based depression detection on sparse data
Previous text-based depression detection is commonly based on large
user-generated data. Sparse scenarios like clinical conversations are less
investigated. This work proposes a text-based multi-task BGRU network with
pretrained word embeddings to model patients' responses during clinical
interviews. Our main approach uses a novel multi-task loss function, aiming at
modeling both depression severity and binary health state. We independently
investigate word- and sentence-level word-embeddings as well as the use of
large-data pretraining for depression detection. To strengthen our findings, we
report mean-averaged results for a multitude of independent runs on sparse
data. First, we show that pretraining is helpful for word-level text-based
depression detection. Second, our results demonstrate that sentence-level
word-embeddings should be mostly preferred over word-level ones. While the
choice of pooling function is less crucial, mean and attention pooling should
be preferred over last-timestep pooling. Our method outputs depression presence
results as well as predicted severity score, culminating a macro F1 score of
0.84 and MAE of 3.48 on the DAIC-WOZ development set
Nature of the Roberge-Weiss transition end points in two-flavor lattice QCD with Wilson quarks
We make simulations with 2 flavor Wilson fermions to investigate the nature
of the end points of Roberge-Weiss (RW) first order phase transition lines. The
simulations are carried out at 9 values of the hopping parameter
ranging from 0.155 to 0.198 on different lattice spatial volume. The Binder
cumulants, susceptibilities and reweighted distributions of the imaginary part
of Polyakov loop are employed to determine the nature of the end points of RW
transition lines. The simulations show that the RW end points are of first
order at the values of in our simulations.Comment: 23 figures, 9 pages; 23 figures,10 pages; some slight change
Collaborative Self-Attention for Recommender Systems
Recommender systems (RS), which have been an essential part in a wide range
of applications, can be formulated as a matrix completion (MC) problem. To
boost the performance of MC, matrix completion with side information, called
inductive matrix completion (IMC), was further proposed. In real applications,
the factorized version of IMC is more favored due to its efficiency of
optimization and implementation. Regarding the factorized version, traditional
IMC method can be interpreted as learning an individual representation for each
feature, which is independent from each other. Moreover, representations for
the same features are shared across all users/items. However, the independent
characteristic for features and shared characteristic for the same features
across all users/items may limit the expressiveness of the model. The
limitation also exists in variants of IMC, such as deep learning based IMC
models. To break the limitation, we generalize recent advances of
self-attention mechanism to IMC and propose a context-aware model called
collaborative self-attention (CSA), which can jointly learn context-aware
representations for features and perform inductive matrix completion process.
Extensive experiments on three large-scale datasets from real RS applications
demonstrate effectiveness of CSA.Comment: There are large modification
Unimem: Runtime Data Management on Non-Volatile Memory-based Heterogeneous Main Memory
Non-volatile memory (NVM) provides a scalable and power-efficient solution to
replace DRAM as main memory. However, because of relatively high latency and
low bandwidth of NVM, NVM is often paired with DRAM to build a heterogeneous
memory system (HMS). As a result, data objects of the application must be
carefully placed to NVM and DRAM for best performance. In this paper, we
introduce a lightweight runtime solution that automatically and transparently
manage data placement on HMS without the requirement of hardware modifications
and disruptive change to applications. Leveraging online profiling and
performance models, the runtime characterizes memory access patterns associated
with data objects, and minimizes unnecessary data movement. Our runtime
solution effectively bridges the performance gap between NVM and DRAM. We
demonstrate that using NVM to replace the majority of DRAM can be a feasible
solution for future HPC systems with the assistance of a software-based data
management.Comment: 11 page
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