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 $64\times64$ 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