Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

In recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution. Specifically, we found this problem commonly exists in real-world DS datasets, and without special handing, typical DS-RE models cannot automatically adapt to this shift, thus achieving deteriorated performance. To further validate our intuition, we develop a simple yet effective adaptation method for DS-trained models, bias adjustment, which updates models learned over the source domain (i.e., DS training set) with a label distribution estimated on the target domain (i.e., test set). Experiments demonstrate that bias adjustment achieves consistent performance gains on DS-trained models, especially on neural models, with an up to 23% relative F1 improvement, which verifies our assumptions. Our code and data can be found at \url{https://github.com/INK-USC/shifted-label-distribution}.Comment: 13 pages: 10 pages paper, 3 pages appendix. Appears at EMNLP 201

The simulation model of sucker rod string transverse vibration under the space buckling deformation excitation and rod-tubing eccentric wear in vertical wells

Considering the limitations of the static buckling theory on the eccentric wear of sucker rod and tubing, a new dynamic analysis method for the transverse vibration of sucker rod in the tubing is proposed. Taking the axial distribution load at the rod body and the dynamic load at the bottom into account, the dynamic model of transverse vibration is established based on the space buckling configuration of rod string which regarded as the deformation excitation during the down stroke. To solve the mathematical equations, the finite difference method is used to discretize the well depth, and the Newmark-beta method is used to discretize the time. Meanwhile, a restitution coefficient is introduced to depict the change of velocity and the momentum after the collision. The result shows the phenomenon of rod-tubing collision occurs mainly in the down stroke after the rod string post buckling; the collision force from the wellhead to the bottom increases gradually, of which distributed almost along the entire well depth; and the high frequency collision occurs below the neutral point where the collision force is also the biggest. Further, the collision frequency and the collision force decrease successively from the neutral point to the wellhead direction. But during the up stroke, few collisions occur, and the collision force is also very small. The simulation model is suitable for the eccentric wear analysis of rod-tubing, and provides a new theoretical basis for the optimal allocation of the centralizer

Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.Comment: Accepted in AAAI 202

3-(Diphenyl­methyl­idene)indolin-2-one

The title mol­ecule, C21H15NO, has an indoline-2-one and two benzene substituent groups which are arranged in a propeller-like fashion around the central C atom. The dihedral angle between the two benzene rings is 73.32 (16)° and those between the benzene rings and the indoline-2-one group are 76.54 (14) and 67.69 (14)°. In the crystal, there is an inter­molecular N—H⋯O hydrogen-bonding inter­action, which links the mol­ecules into chains extending along c