Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer

With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence. Following the recent success of the Transformer model, this paper proposes (1) a new variant of attention mechanism based on multi-head attention, called highway attention, and (2) a recurrent model based on transformer and the proposed highway attention, so-called Highway Recurrent Transformer. Experiments on the response selection task in the seventh Dialog System Technology Challenge (DSTC7) show the capability of the proposed model of modeling both utterance-level and dialogue-level information; the effectiveness of each module is further analyzed as well

Signals of New Gauge Bosons in Gauged Two Higgs Doublet Model

Recently a gauged two Higgs doublet model, in which the two Higgs doublets are embedded into the fundamental representation of an extra local $SU(2)_H$ group, is constructed. Both the new gauge bosons $Z^\prime$ and $W^{\prime (p,m)}$ are electrically neutral. While $Z^\prime$ can be singly produced at colliders, $W^{\prime (p,m)}$, which is heavier, must be pair produced. We explore the constraints of $Z^\prime$ using the current Drell-Yan type data from the Large Hadron Collider. Anticipating optimistically that $Z^\prime$ can be discovered via the clean Drell-Yan type signals at high luminosity upgrade of the collider, we explore the detectability of extra heavy fermions in the model via the two leptons/jets plus missing transverse energy signals from the exotic decay modes of $Z^\prime$. For the $W^{\prime (p,m)}$ pair production in a future 100 TeV proton-proton collider, we demonstrate certain kinematical distributions for the two/four leptons plus missing energy signals have distinguishable features from the Standard Model background. In addition, comparisons of these kinematical distributions between the gauged two Higgs doublet model and the littlest Higgs model with T-parity, the latter of which can give rise to the same signals with competitive if not larger cross sections, are also presented.Comment: 39 pages, 23 figures, 7 tables and two new appendixes, to appear in EPJ

Development of a DSS to Estimate the Sales for the Retailing Industry in Taiwan

An algorithm is constructed in this study to estimate the market sizes of daily commodity in Taiwan based on the sampled sales information provided by retailer chains. Though retailer chains provide sampled sales information from only small portions of their retailing stores, they expect to receive more valuable processed information from that. As result of this research, a DSS is proposed to compute value-added information from this joint sales information database, namely the estimation information. Through certain public accessible data such number of stores by each chain and retailers’ financial reports, the sampled sales information can be transferred to the market size information of each item in Taiwan. Two similar algorithms are constructed for convenient stores and supermarkets/hypermarkets separately. A simple integration method is used to combine these results. Finally, a DSS is built based on these estimation algorithms and is implemented successfull

Updated constraints on Georgi-Machacek model, and its electroweak phase transition and associated gravitational waves

With theoretical constraints such as perturbative unitarity and vacuum stability conditions and updated experimental data of Higgs measurements and direct searches for exotic scalars at the LHC, we perform an updated scan of the allowed parameter space of the Georgi-Machacek (GM) model. With the refined global fit, we examine the allowed parameter space for inducing strong first-order electroweak phase transitions (EWPTs) and find only the one-step phase transition is phenomenologically viable. Based upon the result, we study the associated gravitational wave (GW) signals and find most of which can be detected by several proposed experiments. We also make predictions on processes that may serve as promising probes to the GM model in the near future at the LHC, including the di-Higgs productions and several exotic scalar production channels.Comment: 42 pages, 11 figures, 9 table

Development of a 3D Parallel Mechanism Robot Arm with Three Vertical-Axial Pneumatic Actuators Combined with a Stereo Vision System

This study aimed to develop a novel 3D parallel mechanism robot driven by three vertical-axial pneumatic actuators with a stereo vision system for path tracking control. The mechanical system and the control system are the primary novel parts for developing a 3D parallel mechanism robot. In the mechanical system, a 3D parallel mechanism robot contains three serial chains, a fixed base, a movable platform and a pneumatic servo system. The parallel mechanism are designed and analyzed first for realizing a 3D motion in the X-Y-Z coordinate system of the robot’s end-effector. The inverse kinematics and the forward kinematics of the parallel mechanism robot are investigated by using the Denavit-Hartenberg notation (D-H notation) coordinate system. The pneumatic actuators in the three vertical motion axes are modeled. In the control system, the Fourier series-based adaptive sliding-mode controller with H∞ tracking performance is used to design the path tracking controllers of the three vertical servo pneumatic actuators for realizing 3D path tracking control of the end-effector. Three optical linear scales are used to measure the position of the three pneumatic actuators. The 3D position of the end-effector is then calculated from the measuring position of the three pneumatic actuators by means of the kinematics. However, the calculated 3D position of the end-effector cannot consider the manufacturing and assembly tolerance of the joints and the parallel mechanism so that errors between the actual position and the calculated 3D position of the end-effector exist. In order to improve this situation, sensor collaboration is developed in this paper. A stereo vision system is used to collaborate with the three position sensors of the pneumatic actuators. The stereo vision system combining two CCD serves to measure the actual 3D position of the end-effector and calibrate the error between the actual and the calculated 3D position of the end-effector. Furthermore, to verify the feasibility of the proposed parallel mechanism robot driven by three vertical pneumatic servo actuators, a full-scale test rig of the proposed parallel mechanism pneumatic robot is set up. Thus, simulations and experiments for different complex 3D motion profiles of the robot end-effector can be successfully achieved. The desired, the actual and the calculated 3D position of the end-effector can be compared in the complex 3D motion control

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

Recent algorithms designed for reinforcement learning tasks focus on finding a single optimal solution. However, in many practical applications, it is important to develop reasonable agents with diverse strategies. In this paper, we propose Diversity-Guided Policy Optimization (DGPO), an on-policy framework for discovering multiple strategies for the same task. Our algorithm uses diversity objectives to guide a latent code conditioned policy to learn a set of diverse strategies in a single training procedure. Specifically, we formalize our algorithm as the combination of a diversity-constrained optimization problem and an extrinsic-reward constrained optimization problem. And we solve the constrained optimization as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently finds diverse strategies in a wide variety of reinforcement learning tasks. We further show that DGPO achieves a higher diversity score and has similar sample complexity and performance compared to other baselines

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

E2F transcription factor 1 overexpression as a poor prognostic factor in patients with nasopharyngeal carcinomas

AbstractNasopharyngeal carcinoma (NPC) is an endemic head and neck epithelial malignancy in Southeastern Asia and Taiwan. The E2 factor (E2F) family of transcription factors is downstream targets of the retinoblastoma protein 1. The E2F family of transcription factors is the key regulator of genes involved in cell cycle progression, cell fate determination, DNA damage repair and apoptosis. E2F1 is unique in that it contributes both to the control of cellular proliferation and cellular death. However, the expression of E2F1 protein and its clinicopathological associations in patients with NPC are yet to be evaluated. Immunoexpression of E2F1 was retrospectively assessed in biopsies of 124 consecutive NPC patients without initial distant metastasis and treated with consistent guidelines. The outcomes were correlated with clinicopathological features and patient survivals. Results indicated that high E2F1 protein level (50%) was correlated with primary tumor (p < 0.001) and stage (p = 0.002; 7th American Joint Committee on Cancer). In multivariate analyses, high E2F1 expression emerged as an independent prognosticator for worse disease-specific survival (p = 0.003), distal metastasis-free survival (p = 0.003), and local recurrence-free survival (p = 0.039). In conclusion, high E2F1 protein level is common, associated with adverse prognosticators, and might confer tumor aggressiveness through tumor cell proliferation and metastasis