620 research outputs found
Effect of Crosslinking on Carbon Nanotube Materials through Chemical Treatment and Irradiation
Carbon nanotubes (CNTs) exhibit a variety of exceptional properties, especially their ultrahigh tensile strength on the order of 100GPa show promise for constituting the next-generation carbon fiber. However, challenges remain to translate these properties into useful technology, primarily due to the sliding of the tubes past one another under tensile loading. The work presented in this dissertation is focused on enhancing the interaction between the CNTs and their bundles in a macro-assembly, in order to improve the tensile properties of the material.
Applying inter-tube crosslinks has been predicted to significantly enhance the stress transfer between the CNT components. We developed a novel chemical crosslinking method for reinforcement of carbon nanotube fibers (CNTFs) employing benzocyclobutene (BCB)-based polymers. This simple one-step mechanism achieves covalent functionalization of CNT surface and 3D crosslinking simultaneously, in the solid-state. The denser packing of CNTF, impregnated polymer and establishment of covalent crosslinking after the treatment all contributed to the enhancement of tensile strength by 250%.
Several process parameters were investigated for the BCB-induced crosslinking of CNTF. A series of poly(styrene-r-4-vinyl-benzocyclobutene) (PS-VBCB) with varying BCB/PS content were synthesized and the copolymer with PS content of 80% lead to the optimum result, possibly due to the BCB-BCB collapsing to form undesired defects at high percentage of BCB. The concentration of the
polymer solution for infiltration into CNTF was found to be critical in determining the amount of infiltrated PS-VBCB polymer. 0.05% wt or 0.1% wt results in highest mechanical properties. Pure heating of CNTF free from BCB degrades the load transfer, which further supports the positive effect of BCB crosslinking on CNTF.
Electron-beam (e-beam) induced crosslinking alters the morphology and properties of carbon structures. In this dissertation, the effect of e-beam irradiation on CNTF was observed, both positive and negative phenomena. At the presence of functional species, e-beam initiates functionalization by creating radicals on CNT. CNTF grafted with acrylic acid exhibited improvement in tensile strength by 77%. Finally, a preliminary attempt was made to combine the e-beam and BCB chemistry on the CNT materials
The Impact Of Social Capital On The Propensity And Properties Of Management Earnings Forecasts
This paper examines the relationship between the regional variation in social capital in the United States and the propensity and properties of the management earnings forecasts. Social capital refers to connections among individuals–social networks and the norms of reciprocity and trustworthiness that arise from them (Putnam 2000). Using a comprehensive sample of companies in the United States, we find that firms located in region with higher social capital are more likely to issue a management earnings forecast and are inclined to forecast more frequently. In addition, earnings forecasts made by those firms tend to be more specific. Our findings suggest that mangers of firms in the high social capital regions are more likely to be concerned about their reputation of providing transparent information regarding their businesses because of the close connections among individuals and the greater propensities to honor obligations. This study contributes to the accounting literature by identifying a non-financial factor (i.e., social capital) that affects management’s voluntary disclosure practices
Existence and uniqueness of solutions for a singular system of higher-order nonlinear fractional differential equations with integral boundary conditions
In this paper, we study the existence and uniqueness of solutions for a singular system of nonlinear fractional differential equations with integral boundary conditions. We obtain existence and uniqueness results of solutions by using the properties of the Green’s function, a nonlinear alternative of Leray–Schauder-type, Guo–Krasnoselskii’s fixed point theorem in a cone and the Banach fixed point theorem. Some examples are included to show the applicability of our results
Cross-domain Few-shot Segmentation with Transductive Fine-tuning
Few-shot segmentation (FSS) expects models trained on base classes to work on
novel classes with the help of a few support images. However, when there exists
a domain gap between the base and novel classes, the state-of-the-art FSS
methods may even fail to segment simple objects. To improve their performance
on unseen domains, we propose to transductively fine-tune the base model on a
set of query images under the few-shot setting, where the core idea is to
implicitly guide the segmentation of query images using support labels.
Although different images are not directly comparable, their class-wise
prototypes are desired to be aligned in the feature space. By aligning query
and support prototypes with an uncertainty-aware contrastive loss, and using a
supervised cross-entropy loss and an unsupervised boundary loss as
regularizations, our method could generalize the base model to the target
domain without additional labels. We conduct extensive experiments under
various cross-domain settings of natural, remote sensing, and medical images.
The results show that our method could consistently and significantly improve
the performance of prototypical FSS models in all cross-domain tasks.Comment: 12 pages, 8 figure
Elucidating the solution space of extended reverse-time SDE for diffusion models
Diffusion models (DMs) demonstrate potent image generation capabilities in
various generative modeling tasks. Nevertheless, their primary limitation lies
in slow sampling speed, requiring hundreds or thousands of sequential function
evaluations through large neural networks to generate high-quality images.
Sampling from DMs can be seen alternatively as solving corresponding stochastic
differential equations (SDEs) or ordinary differential equations (ODEs). In
this work, we formulate the sampling process as an extended reverse-time SDE
(ER SDE), unifying prior explorations into ODEs and SDEs. Leveraging the
semi-linear structure of ER SDE solutions, we offer exact solutions and
arbitrarily high-order approximate solutions for VP SDE and VE SDE,
respectively. Based on the solution space of the ER SDE, we yield mathematical
insights elucidating the superior performance of ODE solvers over SDE solvers
in terms of fast sampling. Additionally, we unveil that VP SDE solvers stand on
par with their VE SDE counterparts. Finally, we devise fast and training-free
samplers, ER-SDE-Solvers, achieving state-of-the-art performance across all
stochastic samplers. Experimental results demonstrate achieving 3.45 FID in 20
function evaluations and 2.24 FID in 50 function evaluations on the ImageNet
dataset
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
Correspondence pruning aims to search consistent correspondences (inliers)
from a set of putative correspondences. It is challenging because of the
disorganized spatial distribution of numerous outliers, especially when
putative correspondences are largely dominated by outliers. It's more
challenging to ensure effectiveness while maintaining efficiency. In this
paper, we propose an effective and efficient method for correspondence pruning.
Inspired by the success of attentive context in correspondence problems, we
first extend the attentive context to the first-order attentive context and
then introduce the idea of attention in attention (ANA) to model second-order
attentive context for correspondence pruning. Compared with first-order
attention that focuses on feature-consistent context, second-order attention
dedicates to attention weights itself and provides an additional source to
encode consistent context from the attention map. For efficiency, we derive two
approximate formulations for the naive implementation of second-order attention
to optimize the cubic complexity to linear complexity, such that second-order
attention can be used with negligible computational overheads. We further
implement our formulations in a second-order context layer and then incorporate
the layer in an ANA block. Extensive experiments demonstrate that our method is
effective and efficient in pruning outliers, especially in high-outlier-ratio
cases. Compared with the state-of-the-art correspondence pruning approach
LMCNet, our method runs 14 times faster while maintaining a competitive
accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral
Employment hysteresis in the United States during the COVID-19 pandemic
In this paper, we test the validity of the employment hysteresis
hypothesis. For this purpose, we use daily employment data at
the national and state levels in the United States from January 8,
2020, to May 30, 2020. We apply the modified version of the
Kapetanios-Shin unit root test, along with finite-sample critical values. We find that the employment hysteresis hypothesis is valid
in the United States during the COVID-19 era. The validity of the
findings does not change when data at the national and state
levels are used. The evidence is also valid when the employment
levels for all firms and small firms are considered. The results are
also robust to employment levels for workers at different income
levels and employment in five different sectors
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel
geometric-invariant coordinate representation for image correspondence
problems. In contrast to standard Cartesian coordinates, PCFs encode
coordinates in correspondence-specific barycentric coordinate systems (BCS)
with affine invariance. To know \textit{when and where to trust} the encoded
coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which
parameterizes the distribution of coordinate fields as Gaussian mixture models.
By jointly optimizing coordinate fields and their confidence conditioned on
dense flows, PCF-Net can work with various feature descriptors when quantifying
the reliability of PCFs by confidence maps. An interesting observation of this
work is that the learned confidence map converges to geometrically coherent and
semantically consistent regions, which facilitates robust coordinate
representation. By delivering the confident coordinates to keypoint/feature
descriptors, we show that PCF-Net can be used as a plug-in to existing
correspondence-dependent approaches. Extensive experiments on both indoor and
outdoor datasets suggest that accurate geometric invariant coordinates help to
achieve the state of the art in several correspondence problems, such as sparse
feature matching, dense image registration, camera pose estimation, and
consistency filtering. Further, the interpretable confidence map predicted by
PCF-Net can also be leveraged to other novel applications from texture transfer
to multi-homography classification.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligenc
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