8,357 research outputs found
Phase transitions of GUP-corrected charged AdS black hole
We study the thermodynamic properties and critical behaviors of the
topological charged black hole in AdS space under the consideration of the
generalized uncertainty principle (GUP). It is found that only in the spherical
horizon case there are Van der Waals-like first-order phase transitions and
reentrant phase transitions. From the equation of state we find that the
GUP-corrected black hole can have one, two and three apparent critical points
under different conditions. However, it is verified by the Gibbs free energy
that in either case there is at most one physical critical point.Comment: 13 pages, 9 figures, Advances in High Energy Physics, in pres
Topological nodal points in two coupled SSH chains
We study two coupled Su-Schrieffer-Heeger (SSH) chains system, which is shown
to contain rich quantum phases associated with topological invariants protected
by symmetries. In the weak coupling region, the system supports two non-trivial
topological insulating phases, characterized by winding number N = +1 or -1,
and two types of edge states. The boundary between the two topological phases
arises from two band closing points, which exhibit topological characteristics
in one-dimensional k space. By mapping Bloch states on a vector field in k
space, the band degenerate points correspond to a pair of kinks of the field,
with opposite topological charges. Two topological nodal points move and merge
as the inter-chain coupling strength varies. This topological invariant is
protected by the translational and inversion symmetries, rather than the
antiunitary operation. Furthermore, we find that when a pair of nodal points is
created, a second order quantum phase transition (QPT) occurs, associating with
a gap closing and spontaneously symmetry breaking. This simple model
demonstrates several central concepts in the field of quantum materials and
provides a theoretical connection between them.Comment: 8 pages, 8 figure
Zooming Network
Structural information is important in natural language understanding.
Although some current neural net-based models have a limited ability to take
local syntactic information, they fail to use high-level and large-scale
structures of documents. This information is valuable for text understanding
since it contains the author's strategy to express information, in building an
effective representation and forming appropriate output modes. We propose a
neural net-based model, Zooming Network, capable of representing and leveraging
text structure of long document and developing its own analyzing rhythm to
extract critical information. Generally, ZN consists of an encoding neural net
that can build a hierarchical representation of a document, and an interpreting
neural model that can read the information at multi-levels and issuing labeling
actions through a policy-net. Our model is trained with a hybrid paradigm of
supervised learning (distinguishing right and wrong decision) and reinforcement
learning (determining the goodness among multiple right paths). We applied the
proposed model to long text sequence labeling tasks, with performance exceeding
baseline model (biLSTM-crf) by 10 F1-measure
Phase transition and thermodynamic stability of topological black holes in Ho\v{r}ava-Lifshitz gravity
On the basis of horizon thermodynamics, we study the thermodynamic stability
and criticality of topological black holes constructed in
Ho\v{r}ava-Lifshitz (HL) gravity without the detailed-balance condition (with
general ). In the framework of horizon thermodynamics, we do not need
the concrete black hole solution (the metric function) and the concrete matter
fields. It is shown that the HL black hole for is always
thermodynamically stable. For , the thermodynamic behaviors and
criticality of the HL black hole are similar to those of RN-AdS black hole for
some . For , the temperature is classified into six types by
their different features. Among them, we mainly focus on the type with triply
degenerate thermodynamic state. It is also shown that there is a "thermodynamic
singularity" for the HL black hole, where the temperature and Gibbs free
energy both diverge apart from a special pressure .Comment: 10 pages, 11 figure
Event Identification as a Decision Process with Non-linear Representation of Text
We propose scale-free Identifier Network(sfIN), a novel model for event
identification in documents. In general, sfIN first encodes a document into
multi-scale memory stacks, then extracts special events via conducting
multi-scale actions, which can be considered as a special type of sequence
labelling. The design of large scale actions makes it more efficient processing
a long document. The whole model is trained with both supervised learning and
reinforcement learning.Comment: 8 pages, 8 figure
JUMPER: Learning When to Make Classification Decisions in Reading
In early years, text classification is typically accomplished by
feature-based machine learning models; recently, deep neural networks, as a
powerful learning machine, make it possible to work with raw input as the text
stands. However, exiting end-to-end neural networks lack explicit
interpretation of the prediction. In this paper, we propose a novel framework,
JUMPER, inspired by the cognitive process of text reading, that models text
classification as a sequential decision process. Basically, JUMPER is a neural
system that scans a piece of text sequentially and makes classification
decisions at the time it wishes. Both the classification result and when to
make the classification are part of the decision process, which is controlled
by a policy network and trained with reinforcement learning. Experimental
results show that a properly trained JUMPER has the following properties: (1)
It can make decisions whenever the evidence is enough, therefore reducing total
text reading by 30-40% and often finding the key rationale of prediction. (2)
It achieves classification accuracy better than or comparable to
state-of-the-art models in several benchmark and industrial datasets.Comment: Accepted by IJCAI 201
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves
two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the
whole-lung/pulmonary malignancy. Currently, there are many studies about the
first step, but few about the second step. Since the existence of nodule does
not definitely indicate cancer, and the morphology of nodule has a complicated
relationship with cancer, the diagnosis of lung cancer demands careful
investigations on every suspicious nodule and integration of information of all
nodules. We propose a 3D deep neural network to solve this problem. The model
consists of two modules. The first one is a 3D region proposal network for
nodule detection, which outputs all suspicious nodules for a subject. The
second one selects the top five nodules based on the detection confidence,
evaluates their cancer probabilities and combines them with a leaky noisy-or
gate to obtain the probability of lung cancer for the subject. The two modules
share the same backbone network, a modified U-net. The over-fitting caused by
the shortage of training data is alleviated by training the two modules
alternately. The proposed model won the first place in the Data Science Bowl
2017 competition. The code has been made publicly available.Comment: 12 pages, 9 figure
Quantum nondemolition measurement of the Werner state
We propose a theoretical scheme of quantum nondemolition measurement of
two-qubit Werner state. We discuss our scheme with the two qubits restricted in
a local place and then extend the scheme to the case in which two qubits are
separated. We also consider the experimental realization of our scheme based on
cavity quantum electrodynamics. It is very interesting that our scheme is
robust against the dissipative effects introduced by the probe process. We also
give a brief interpretation of our scheme finally.Comment: 5 pages, 3 figure
Generation of long-living entanglement between two distant three-level atoms in non-Markovian environments
In this paper, a scheme for the generation of long-living entanglement
between two distant {\Lambda}-type three-level atoms separately trapped in two
dissipative cavities is proposed. In this scheme, two dissipative cavities are
coupled to their own non-Markovian environments and two three-level atoms are
driven by the classical fields. The entangled state between the two atoms is
produced by performing Bell state measurement (BSM) on photons leaving the
dissipative cavities. Using the time-dependent Sch\"ordinger equation, we
obtain the analytical results for the evolution of the entanglement. It is
revealed that, by manipulating the detunings of classical field, the
long-living stationary entanglement between two atoms can be generated in the
presence of dissipationComment: 11 pages, 7 figure
The Dual Roles of Quantum Discord in a Non-demolition Probing Task
We present a non-demolition quantum information processing task of probing
the information of a class of quantum state. In this task, the information is
extracted by some unitary evolution with the introduced probing qubit assisted,
but the probed quantum state (density matrix) is undisturbed at any time and
independent of the choice of the initial probing state. We give a sufficient
and necessary condition on the Hamiltonian that can lead to the successful
realization of such a task. We prove that, for any feasible scheme, the probed
plus probing system will always stay at a disentangled state with one side
quantum discord absent and the other side one inevitably produced in the
probing process. An explicit example is given for the demonstration, whilst the
example shows that the ratio of quantum discord to the total correlation will
have to reduce to zero for the maximal accessible information. In this sense,
we say that quantum discord plays the dual roles in this case.Comment: 5 pages and 1 figur
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