2-{2,6-Bis[bis(4-fluorophenyl)methyl]-4-chlorophenylimino} -3-aryliminobutylnickel(II) bromide complexes: Synthesis, characterization, and investigation of their catalytic behavior

The series of 2-{2,6-bis[di(4-fluorophenyl)methyl]-4-chlorophenylimino}-3- aryliminobutane derivatives (L1-L5) and their nickel(II) dibromide complexes (Ni1-Ni5) were synthesized, and all organic compounds were fully characterized by the Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy and by elemental analysis, while the nickel complexes were characterized by FT-IR spectroscopy, elemental analysis, as well as by single-crystal X-ray diffraction for two representative examples, namely Ni1 and Ni4. A distorted tetrahedral geometry was observed for these four-coordinate nickel complexes. Upon the activation with either Methylaluminoxane or modified methylaluminoxane as co-catalyst, all nickel complex precatalysts showed very high activity toward ethylene polymerization with activities of up to 10 7 g(PE)·mol -1 (Ni)·h -1 , and afforded highly branched polyethylene with a bimodal distribution. © 2014 Elsevier B.V

ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training

Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cross-modal tasks and high computational efficiency. They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality. Actually, an image or a text contains various potential views, just as humans could capture a real-world scene via diverse descriptions or photos. In this paper, we propose ERNIE-ViL 2.0, a Multi-View Contrastive learning framework to build intra-modal and inter-modal correlations between diverse views simultaneously, aiming at learning a more robust cross-modal representation. Specifically, we construct multiple views within each modality to learn the intra-modal correlation for enhancing the single-modal representation. Besides the inherent visual/textual views, we construct sequences of object tags as a special textual view to narrow the cross-modal semantic gap on noisy image-text pairs. Pre-trained with 29M publicly available datasets, ERNIE-ViL 2.0 achieves competitive results on English cross-modal retrieval. Additionally, to generalize our method to Chinese cross-modal tasks, we train ERNIE-ViL 2.0 through scaling up the pre-training datasets to 1.5B Chinese image-text pairs, resulting in significant improvements compared to previous SOTA results on Chinese cross-modal retrieval. We release our pre-trained models in https://github.com/PaddlePaddle/ERNIE.Comment: 14 pages, 6 figure

ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph

We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%.Comment: Paper has been published in the AAAI2021 conferenc

Diffusion Model-Augmented Behavioral Cloning

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a) (e.g., implicit behavioral cloning). Despite its simplicity, modeling the conditional probability with BC usually struggles with generalization. While modeling the joint probability can lead to improved generalization performance, the inference procedure can be time-consuming and it often suffers from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed diffusion model-augmented behavioral cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution as well as compare different generative models

Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game

Aggregating the demand side flexibility is essential to complementing the inflexible and variable renewable energy supply in achieving low carbon energy systems. Sources of demand side flexibility, e.g., dispatchable generators, storages, and flexible loads, can be structured in a form of microgrids and collectively provided to utility grids through transactive energy in local energy markets. This paper proposes a framework of local energy markets to manage the transactive energy and facilitate the flexibility provision. The distribution system operator aims to achieve local energy balance by scheduling the operation of multi-microgrids and determining the imbalance prices. Multiple microgrid traders aim to maximise profits for their prosumers through dispatching flexibility sources and participating in localised energy trading. The decision making and interactions between a distribution system operator and multiple microgrid traders are formulated as the Stackelberg game-theoretic problem. Case studies using the IEEE 69-bus distribution system demonstrate the effectiveness of the developed model in terms of facilitating the local energy balance and reducing the dependency on the utility grids

2-(1-(2-Benzhydrylnaphthylimino)ethyl)pyridylnickel halides: Synthesis, characterization, and ethylene polymerization behavior

A series of 2-(1-(2-benzhydrylnaphthylimino)ethyl)pyridine derivatives (L1–L3) was synthesized and fully characterized. The organic compounds acted as bi-dentate ligands on reacting with nickel halides to afford two kinds of nickel complexes, either mononuclear bis-ligated L₂NiBr₂ (Ni1–Ni3) or chloro-bridged dinuclear L₂Ni₂Cl₄ (Ni4–Ni6) complexes. The nickel complexes were fully characterized, and the single crystal X-ray diffraction revealed for Ni2, a distorted square pyramidal geometry at nickel comprising four nitrogens of two ligands and one bromide; whereas for Ni4, a centrosymmetric dimer possessing a distorted octahedral geometry at nickel was formed by two nitrogens of one ligand, two bridging chlorides and one terminal chloride along with oxygen from methanol (solvent). When activated with diethylaluminium chloride (Et₂AlCl), all nickel complexes performed with high activities (up to 1.22 × 10⁷ g (PE) mol⁻¹(Ni) h⁻¹) towards ethylene polymerization; the obtained polyethylene possessed high branching, low molecular weight and narrow polydispersity, suggestive of a single-site active species. The effect of the polymerization parameters, including the nature of the ligands/halides on the catalytic performance is discussed