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 $P-V$ criticality of topological black holes constructed in Ho\v{r}ava-Lifshitz (HL) gravity without the detailed-balance condition (with general $\epsilon$). 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 $k=0$ is always thermodynamically stable. For $k=1$, the thermodynamic behaviors and $P-V$ criticality of the HL black hole are similar to those of RN-AdS black hole for some $\epsilon$. For $k=-1$, 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 $k=-1$ HL black hole, where the temperature and Gibbs free energy both diverge apart from a special pressure $P_s$.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