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, $am=0.020,\, 0.018, \, 0.015, \, 0.013,\, 0.010$, and four different lattice volumes $N_s=8, \, 12,\, 16, \, 20$ with temporal extent $N_t=4$ 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 $B_4(am,a\mu_c)$ is around $3$ which deviates from the $Z(2)$ universality class value 1.604. However, based on the expectations of $Z(2)$ criticality, the fitting result only with the data from the largest lattice volume $N_s=20$ 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 $N_t=4$ lattices, larger lattices with spatial extent $N_s>=20$ 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

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

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

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 $\kappa$ 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 $\kappa$ in our simulations.Comment: 23 figures, 9 pages; 23 figures,10 pages; some slight change