A study exploring the impact of Chinese university performance appraisal on university teachers' academic innovation behaviour

The development of university teacher performance appraisal in China is later than that of European and American countries, so the existing university teacher performance appraisal in China is not mature. In addition, academic misconduct in academic innovation has appeared in China in recent years. Therefore, universities need to explore effective and reasonable methods to promote academic innovation of university teachers. Based on the self-determination theory, this paper takes teachers from certain universities in China as the research sample and collects data through questionnaires to explore the impact of university performance appraisal on teachers' academic innovation behaviour and motivation. The research results reveal the relationship between performance appraisal, innovation motivation and innovation behaviour

RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools

Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots remains limited due to challenges in understanding tool-object interactions. Here we develop an intelligent robotic system, RoboCook, which perceives, models, and manipulates elasto-plastic objects with various tools. RoboCook uses point cloud scene representations, models tool-object interactions with Graph Neural Networks (GNNs), and combines tool classification with self-supervised policy learning to devise manipulation plans. We demonstrate that from just 20 minutes of real-world interaction data per tool, a general-purpose robot arm can learn complex long-horizon soft object manipulation tasks, such as making dumplings and alphabet letter cookies. Extensive evaluations show that RoboCook substantially outperforms state-of-the-art approaches, exhibits robustness against severe external disturbances, and demonstrates adaptability to different materials.Comment: Project page: https://hshi74.github.io/robocook

Consensus Graph Representation Learning for Better Grounded Image Captioning

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i.e., the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i.e., the semantic inconsistency. In this paper, we take a novel perspective on the issue above - exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e.g., scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2.9 Cider) and grounding (+2.3 F1LOC).Comment: 9 pages, 5 figures, AAAI 202

catena-Poly[[diaqua­copper(II)]-μ-hy­drox­ido-κ2 O:O-μ-[4-(4H-1,2,4-triazol-4-yl)benzoato]-κ2 N 1:N 2]

The title compound, [Cu(C9H6N3O2)(OH)(H2O)2]n, adopts a chain motif along [010] in which the CuII atoms are bridged by hy­droxy groups and 4-(1,2,4-triazol-4-yl)benzoate (tab) ligands. The CuII atom lies on an inversion center and is six-coordinated by two N atoms from two tab ligands, two hy­droxy groups and two water mol­ecules, giving a distorted octa­hedral geometry. The hy­droxy group and the tab ligand are located on a mirror plane. One of the water H atoms is disordered over two positions with equal occupancy factors. Inter­molecular O—H⋯O hydrogen bonds extend the chains into a layer parallel to (100) and C—H⋯O hydrogen bonds connect the layers into a three-dimensional network

Poly[(μ3-camphorato-κ3 O:O′:O′′)(2-methyl-1H-imidazole-κN 3)zinc(II)]

In the title compound, [Zn(C10H14O4)(C4H6N2)]n, each ZnII ion is coordinated by one N atom from one 2-methyl-1H-imidazole ligand and three O atoms from two camphorate (cap) ligands in a distorted tetra­hedral geometry. In one of the cap ligands, one methyl group is disordered between positions 1 and 3 in a 0.518 (12):0.482 (12) ratio. Each cap ligand bridges three ZnII ions, forming two-dimensional layers, which inter­act further via N—H⋯O hydrogen bonds

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (~ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.Comment: Accepted by AAAI 202

catena-Poly[[[bis­(thio­cyanato-κN)zinc(II)]-μ-1,2-bis­{[2-(2-pyrid­yl)-1H-imidazol-1-yl]meth­yl}benzene] 0.28-hydrate]

The title one-dimensional coordination polymer, {[Zn(NCS)2(C24H20N6)2]·0.28H2O}n, was obtained by the reaction of Zn(OAc)2·2H2O, KSCN and 1,2-bis­{[2-(2-pyrid­yl)-1H-imid­azol-1-yl]meth­yl}benzene (hereafter L). The ZnII ion shows a distorted octa­hedral coordination geometry and is coordin­ated by two N atoms from two SCN− anions and four N atoms from two organic ligands. The L ligands act as bridging bis-chelating ligands with cis coordination modes at the ZnII ion. One-dimensional coordination polymers are arranged into layers by π–π stacking inter­actions between the imidazole rings of adjacent chains, with an inter­planar distance of 3.46 (1) Å and centroid–centroid distances of 3.8775 (16) Å. One of the thio­cyanate ligands is disordered over two positions with an occupancy factor of 0.564 (3) for the major component. The partially occupied water mol­ecule forms an O—H⋯S hydrogen bond with the disordered thio­cyanate group