Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition. Codes will be released.1

Given time and location information about digital photographs we can automatically generate an abundance of related contextual metadata, using off-the-shelf and Web-based data sources. Among these are the local daylight status and weather conditions at the time and place a photo was taken. This metadata has the potential of serving as memory cues and filters when browsing photo collections, especially as these collections grow into the tens of thousands and span dozens of years.We describe the contextual metadata that we automatically assemble for a photograph, given time and location, as well as a browser interface that utilizes that metadata. We then present the results of a user study and a survey that together expose which categories of contextual metadata are most useful for recalling and finding photographs. We identify among still unavailable metadata categories those that are most promising to develop next.

This paper presents a new method, called the equivalent force control method, for solving the nonlinear equations of motion in a real-time substructure test using an implicit time integration algorithm. The method replaces the numerical iteration in implicit integration with a force-feedback control loop, while displacement control is retained to control the motion of an actuator. The method is formulated in such a way that it represents a unified approach that also encompasses the effective force test method. The accuracy and effectiveness of the method have been demonstrated with numerical simulations of real-time substructure tests with physical substructures represented by spring and damper elements, respectively. The method has also been validated with actual tests in which a Magnetorheological damper was used as the physical substructure. 1128 B. WU ET AL.technique, in which a structure is split into a physical test specimen and a numerical model. While the effective force method is conceptually simple and it does not require real-time numerical computation during a test, it is not as versatile and efficient as RST methods. On the other hand, RST methods face a number of challenges in terms of the robustness and efficiency of the numerical computation schemes and the interaction between the actual and numerically simulated dynamics of the structural specimen.While many different numerical algorithms are available for RSTs [3][4][5][6][7][8][9], for structures with many degrees of freedom, an integration method with unconditional stability is highly desirable. Wu et al. [10] have investigated the numerical properties of the operator-splitting method (OSM) [15] for RSTs (OSM-RST) and found that the method is unconditionally stable as long as the nonlinear stiffness and damping are of the softening type (i.e. the tangent stiffness and damping never exceed the initial values). The advantage of the OSM-RST method over other unconditionally stable methods is its explicit formulation. However, with the OSM or OSM-RST, the accuracy of the numerical solution may be significantly impaired when the predictor stiffness or damping is very different from the actual stiffness or damping of a structure [10, 16]. Shing and his coworkers have implemented the unconditionally stable -method of Hilber, Hughes, and Taylor [16, 17] for RSTs [11,12]. Their method adopts a special iterative solution procedure for real-time testing. This iterative procedure is based on the initial stiffness of the structure and may require very small time steps when severe strain softening occurs in the structure. This paper proposes a new method, called the equivalent force control (EFC) method, which is aimed to replace the numerical iteration in an implicit scheme by a force-feedback control loop. Furthermore, this method is formulated in such a way that it encompasses both the effective force test method and RST method. Figure 12. Photograph of MR damper in RST.capacity of the actuator was 2500 kN and the sampling frequency of the MTS digital co...

SUMMARYIt has been shown that the operator-splitting method (OSM) provides explicit and unconditionally stable solutions for quasi-static pseudo-dynamic substructure testing. However, the OSM provides only an explicit target displacement but not an explicit target velocity, so that it is essentially an implicit method for real-time substructure testing (RST) when the velocity-dependent restoring force is considered. This paper proposes a target velocity formulation based on the forward di erence of the predicted displacements so as to render the OSM explicit for RST. The stability and accuracy of the resulting OSM-RST algorithm are investigated. It is shown that the OSM-RST is unconditionally stable so long as the non-linear sti ness and damping are of the softening type (i.e. the tangent sti ness and damping never exceed the initial values). The stability of the OSM-RST for structures with inÿnite tangent damping coe cient or sti ness is also proved, and the stability of the method for MDOF structures with a non-classical damping matrix is demonstrated by an energy criterion. The e ects of actuator delay and compensation are analysed based on the bilinear approximation of the actuator step response. Experiments on damped SDOF and MDOF structures verify that the stability of the OSM-RST is preserved when the experimental substructure generates velocity-dependent reaction forces, whereas the stability of real-time substructure tests based on the central di erence method is worsened by the damping of the specimen.

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal’s pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.

Japanese encephalitis virus (JEV) is a representative virus of the JEV serogroup in genus Flavivirus, family Flaviviridae. JEV is a mosquito-borne virus that causes Japanese encephalitis (JE), one of the most severe viral encephalitis diseases in the world. JEV is divided into five genotypes (G1-G5), and each genotype has its own distribution pattern. However, the distribution of different JEV genotypes has changed markedly in recent years. JEV G1 has replaced G3 as the dominant genotype in the traditional epidemic areas in Asia, while G3 has spread from Asia to Europe and Africa and caused domestic JE cases in Africa. G2 and G5, which were endemic in Malaysia, exhibited great geographical changes as well. G2 migrated southward and led to prevalence of JE in Australia, while G5 emerged in China and South Korea after decades of silence. Along with these changes, JE occurred in some non-traditional epidemic regions as an emerging infectious disease. The regional changes in JEV pose a great threat to human health, leading to huge disease burdens. Therefore, it is of great importance to strengthen the monitoring of JEV as well as virus genotypes, especially in non-traditional epidemic areas.

Key Points Question What are the risk factors associated with major cardiovascular events within 1 year among patients who survive an acute myocardial infarction? Findings In this cohort study of 4227 patients with acute myocardial infarction, 19 risk factors associated with major cardiovascular events 1 year after acute myocardial infarction, comprising 15 unique variables, were identified. A risk model to predict 1-year cardiovascular events after an acute myocardial infarction was developed and evaluated based on these risk factors. Meaning Identification of individuals at the highest risk of long-term cardiovascular events after acute myocardial infarction may aid in the provision of targeted, intensive, and higher-quality longitudinal care following discharge.

The elucidation of molecular events that confer tamoxifen resistance to estrogen receptor α (ER) positive breast cancer is of major scientific and therapeutic importance. Here, we report that LEM4 overexpression renders ER+ breast cancer cells resistant to tamoxifen by activating the cyclin D-CDK4/6 axis and the ERα signaling. We show that LEM4 overexpression accelerates tumor growth. Interaction with LEM4 stabilizes CDK4 and Rb, promotes Rb phosphorylation and the G1/S phase transition. LEM4 depletion or combined tamoxifen and PD0332991 treatment significantly reverses tamoxifen resistance. Furthermore, LEM4 interacts with and stabilizes both Aurora-A and ERα, promotes Aurora-A mediated phosphorylation of ERα-Ser167, leading to increase in ERα DNA-binding and transactivation activity. Elevated levels of LEM4 correlates with poorer relapse-free survival in patients with ER+ breast cancer undergoing endocrine therapy. Thus, LEM4 represents a prognostic marker and an attractive target for breast cancer therapeutics. Functional antagonism of LEM4 could overcome tamoxifen resistance.