289 research outputs found
Biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridyl-cobalt complexes: synthesis, characterization and ethylene polymerization behavior
A series of biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridine derivatives reacted with cobalt dichloride in dichloromethane/ethanol to afford the corresponding binuclear cobalt complexes. The cobalt complexes were characterized by FT-IR spectroscopy and elemental analysis, and the structure of a representative complex was confirmed by single-crystal X-ray diffraction. Upon activation with either MAO or MMAO, these cobalt complexes performed with high activities of up to 1.2 × 10⁷ g (mol of Co)⁻¹ h⁻¹ in ethylene polymerization, which represents one of the most active cobalt-based catalytic systems in ethylene reactivity. These biphenyl-bridged bis(imino)pyridylcobalt precatalysts exhibited higher activities than did their mononuclear bis(imino)pyridylcobalt precatalyst counterparts, and more importantly, the binuclear precatalysts revealed a better thermal stability and longer lifetimes. The polyethylenes obtained were characterized by GPC, DSC, and high-temperature NMR spectroscopy and mostly possessed unimodal and highly linear features
AFM characterization of physical properties in coal adsorbed with different cations induced by electric pulse fracturing
Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin
Numerical Study on Reasonable Entry Layout of Lower Seam in Multi-seam Mining
Abstract: According to the geological conditions of 6# coal seam and 8# coal seam in Xieqiao Coal Mine, reasonable entry layout of lower seam in multi-seam mining has been studied by FLAC3D numerical simulation. Three ways of entry layout including alternate internal entry layout, alternate exterior entry layout and overlapping entry layout has been put forward for discussing on reasonable entry layout. Then stress distribution and displacement characteristics of surrounding rock have been analyzed in the three ways of entry layout by numerical simulation, leading to the conclusion that alternate internal entry layout pattern, which make the entry located in stress reduce zone and avoid the influence of abutment pressure of upper coal seam mining to a certain extent, is a better choice for multi-seam mining. The research results herein can offer beneficial reference for entry layout with similar geological conditions in multi-seam minin
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Generative Adversarial Networks (GANs) have significantly advanced image
synthesis through mapping randomly sampled latent codes to high-fidelity
synthesized images. However, applying well-trained GANs to real image editing
remains challenging. A common solution is to find an approximate latent code
that can adequately recover the input image to edit, which is also known as GAN
inversion. To invert a GAN model, prior works typically focus on reconstructing
the target image at the pixel level, yet few studies are conducted on whether
the inverted result can well support manipulation at the semantic level. This
work fills in this gap by proposing in-domain GAN inversion, which consists of
a domain-guided encoder and a domain-regularized optimizer, to regularize the
inverted code in the native latent space of the pre-trained GAN model. In this
way, we manage to sufficiently reuse the knowledge learned by GANs for image
reconstruction, facilitating a wide range of editing applications without any
retraining. We further make comprehensive analyses on the effects of the
encoder structure, the starting inversion point, as well as the inversion
parameter space, and observe the trade-off between the reconstruction quality
and the editing property. Such a trade-off sheds light on how a GAN model
represents an image with various semantics encoded in the learned latent
distribution. Code, models, and demo are available at the project page:
https://genforce.github.io/idinvert/
Reaction kinetics of CN + toluene and its implication on the productions of aromatic nitriles in the Taurus molecular cloud and Titan's atmosphere
Reactions between cyano radical and aromatic hydrocarbons are believed to be
important pathways for the formation of aromatic nitriles in the interstellar
medium (ISM) including those identified in the Taurus molecular cloud (TMC-1).
Aromatic nitriles might participate in the formation of polycyclic aromatic
nitrogen containing hydrocarbons (PANHs) in Titan's atmosphere. Here, ab initio
kinetics simulations reveal a high efficiency of and the competition of the different products of
30-1800 K and -100 atm of the CN + toluene reaction. In the
star-forming region of TMC-1 environment, the product yields of benzonitrile
and tolunitriles for CN reacting with toluene may be approximately 17 and
83, respectively. The detection of main products, tolunitriles, can serve
as proxies for the undetected toluene in the ISM due to their much larger
dipole moments. The competition between bimolecular and unimolecular products
is extremely intense under the warmer and denser PANH forming region of Titan's
stratosphere. The computational results show that the fractions of
tolunitriles, adducts, and benzonitrile are 19-68, 15-64 and
17, respectively, at 150-200 K and 0.0001-0.001 atm (Titan's stratosphere).
Then, benzonitrile and tolunitriles may contribute to the formation of PANHs by
consecutive additions. Kinetic information of aromatic nitriles
for the CN + toluene reaction calculated here helps to explain the formation
mechanism of polycyclic aromatic hydrocarbons (PAHs) or PANHs under different
interstellar environments and constrains corresponding astrochemical models
Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration
This paper tackles the task of singulating and grasping paper-like deformable
objects. We refer to such tasks as paper-flipping. In contrast to manipulating
deformable objects that lack compression strength (such as shirts and ropes),
minor variations in the physical properties of the paper-like deformable
objects significantly impact the results, making manipulation highly
challenging. Here, we present Flipbot, a novel solution for flipping paper-like
deformable objects. Flipbot allows the robot to capture object physical
properties by integrating exteroceptive and proprioceptive perceptions that are
indispensable for manipulating deformable objects. Furthermore, by
incorporating a proposed coarse-to-fine exploration process, the system is
capable of learning the optimal control parameters for effective paper-flipping
through proprioceptive and exteroceptive inputs. We deploy our method on a
real-world robot with a soft gripper and learn in a self-supervised manner. The
resulting policy demonstrates the effectiveness of Flipbot on paper-flipping
tasks with various settings beyond the reach of prior studies, including but
not limited to flipping pages throughout a book and emptying paper sheets in a
box.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 202
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
3D-aware image synthesis aims at learning a generative model that can render
photo-realistic 2D images while capturing decent underlying 3D shapes. A
popular solution is to adopt the generative adversarial network (GAN) and
replace the generator with a 3D renderer, where volume rendering with neural
radiance field (NeRF) is commonly used. Despite the advancement of synthesis
quality, existing methods fail to obtain moderate 3D shapes. We argue that,
considering the two-player game in the formulation of GANs, only making the
generator 3D-aware is not enough. In other words, displacing the generative
mechanism only offers the capability, but not the guarantee, of producing
3D-aware images, because the supervision of the generator primarily comes from
the discriminator. To address this issue, we propose GeoD through learning a
geometry-aware discriminator to improve 3D-aware GANs. Concretely, besides
differentiating real and fake samples from the 2D image space, the
discriminator is additionally asked to derive the geometry information from the
inputs, which is then applied as the guidance of the generator. Such a simple
yet effective design facilitates learning substantially more accurate 3D
shapes. Extensive experiments on various generator architectures and training
datasets verify the superiority of GeoD over state-of-the-art alternatives.
Moreover, our approach is registered as a general framework such that a more
capable discriminator (i.e., with a third task of novel view synthesis beyond
domain classification and geometry extraction) can further assist the generator
with a better multi-view consistency.Comment: Accepted by NeurIPS 2022. Project page:
https://vivianszf.github.io/geo
Learn to Grasp via Intention Discovery and its Application to Challenging Clutter
Humans excel in grasping objects through diverse and robust policies, many of
which are so probabilistically rare that exploration-based learning methods
hardly observe and learn. Inspired by the human learning process, we propose a
method to extract and exploit latent intents from demonstrations, and then
learn diverse and robust grasping policies through self-exploration. The
resulting policy can grasp challenging objects in various environments with an
off-the-shelf parallel gripper. The key component is a learned intention
estimator, which maps gripper pose and visual sensory to a set of sub-intents
covering important phases of the grasping movement. Sub-intents can be used to
build an intrinsic reward to guide policy learning. The learned policy
demonstrates remarkable zero-shot generalization from simulation to the real
world while retaining its robustness against states that have never been
encountered during training, novel objects such as protractors and user
manuals, and environments such as the cluttered conveyor.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
This work presents an easy-to-use regularizer for GAN training, which helps
explicitly link some axes of the latent space to a set of pixels in the
synthesized image. Establishing such a connection facilitates a more convenient
local control of GAN generation, where users can alter the image content only
within a spatial area simply by partially resampling the latent code.
Experimental results confirm four appealing properties of our regularizer,
which we call LinkGAN. (1) The latent-pixel linkage is applicable to either a
fixed region (\textit{i.e.}, same for all instances) or a particular semantic
category (i.e., varying across instances), like the sky. (2) Two or multiple
regions can be independently linked to different latent axes, which further
supports joint control. (3) Our regularizer can improve the spatial
controllability of both 2D and 3D-aware GAN models, barely sacrificing the
synthesis performance. (4) The models trained with our regularizer are
compatible with GAN inversion techniques and maintain editability on real
images
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