A gauge invariant dressed holon and spinon description of the normal-state of underdoped cuprates

A partial charge-spin separation fermion-spin theory is developed to study the normal-state properties of the underdoped cuprates. In this approach, the physical electron is decoupled as a gauge invariant dressed holon and spinon, with the dressed holon behaving like a spinful fermion, and represents the charge degree of freedom together with the phase part of the spin degree of freedom, while the dressed spinon is a hard-core boson, and representing the amplitude part of the spin degree of freedom. The electron local constraint for single occupancy is satisfied. Within this approach, the charge and spin dynamics of the underdoped cuprates are studied based on the t-t'-J model. It is shown that the charge dynamics is mainly governed by the scattering from the dressed holons due to the dressed spinon fluctuation, while the scattering from the dressed spinons due to the dressed holon fluctuation dominates the spin dynamics.Comment: 10 pages, 7 figures, corrected typo

Gauge invariant dressed holon and spinon in doped cuprates

We develop a partial charge-spin separation fermion-spin theory implemented the gauge invariant dressed holon and spinon. In this novel approach, the physical electron is decoupled as the gauge invariant dressed holon and spinon, with the dressed holon behaviors like a spinful fermion, and represents the charge degree of freedom together with the phase part of the spin degree of freedom, while the dressed spinon is a hard-core boson, and represents the amplitude part of the spin degree of freedom, then the electron single occupancy local constraint is satisfied. Within this approach, the charge transport and spin response of the underdoped cuprates is studied. It is shown that the charge transport is mainly governed by the scattering from the dressed holons due to the dressed spinon fluctuation, while the scattering from the dressed spinons due to the dressed holon fluctuation dominates the spin response.Comment: 8 pages, Revtex, three figures are include

Nano-scale Heat Transfer in Nanostructures: Toward Understanding and Engineering Thermal Transport

University of Minnesota Ph.D. dissertation. May 2017. Major: Mechanical Engineering. Advisor: Traian Dumitrica. 1 computer file (PDF); xiv, 145 pages.Heat transfer is vital throughout research and industry. This thesis focuses on heat transfer in nanostructures and amorphous materials, in which the arrangement of atoms is crucial for the effectiveness of heat transport. Defects and mechanical deformations in a material which cause displacement or reconfiguration of atoms relative to that material’s “normal” or “pristine” condition can dramatically influence its heat transport efficiency. Since the 1950’s, there has been little progress in understanding the defects–thermal transport property relationship. Using novel numerical techniques and large-scale computations performed on modern supercomputers, I have studied heat transport in nanomaterials containing various defects and mechanical deformations. From the properties of atomic vibrations in my simulations, the effects these deformations have on heat transport can be deduced. Three research projects are presented in this thesis. The study of heat transport in screw-dislocated nanowires with low thermal conductivities in their bulk form represents the knowledge base needed for engineering thermal transport in advanced thermoelectric and electronic materials. This research also suggests a new potential route to lower thermal conductivity, which could promote thermoelectricity. The study of high-temperature coating composite materials helps with the understanding of the role played by composition and the structural characterization, which is difficult to be approached by experiments. The method applied in studying the composition-structure-property relationship of amorphous Silicon-Boron-Nitride networks could also be used in the investigation of various other similar composite materials. Such studies can further provide guidance in designing ultra-high-temperature ceramics, including space shuttle thermal protection system materials and high-temperature-resistance coating. The understanding of the impact of bending and collapsing on thermal transport along carbon nanotubes is important as carbon nanotubes are excellent materials candidates in a variety of applications, including thermal interface materials, thermal switches and composite materials. The atomistic study of carbon nanotubes can also provide crucial guidance in multi-scale study of the materials to enable large-scale thermal behavior prediction

Breakdown of Conventional Winding Number Calculation in One-Dimensional Lattices with Interactions Beyond Nearest Neighbors

Topological indices, such as winding numbers, have been conventionally used to predict the number of topologically protected edge states (TPES) in topological insulators. In this Letter, we experimentally observe its breakdown in Su-Schrieffer-Heeger (SSH) lattices with beyond-nearest-neighbor interactions. We hereby resort to the Berry connection for accurate TPES prediction. Moreover, we decouple the complex phonon modes by examining the torsional ones, which have received much less attention than their transverse and longitudinal counterparts in existing metamaterial studies

ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition

For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language. Despite the impressive performance achieved by these methods, we argue that they have two limitations. Firstly, the teacher network fails to effectively learn language-independent knowledge shared across languages due to the differences in the feature distribution between the source and target languages. Secondly, the student network acquires all of its knowledge from the teacher network and ignores the learning of target language-specific knowledge. Undesirably, these limitations would hinder the model's performance in the target language. This paper proposes an unsupervised prototype knowledge distillation network (ProKD) to address these issues. Specifically, ProKD presents a contrastive learning-based prototype alignment method to achieve class feature alignment by adjusting the distance among prototypes in the source and target languages, boosting the teacher network's capacity to acquire language-independent knowledge. In addition, ProKD introduces a prototypical self-training method to learn the intrinsic structure of the language by retraining the student network on the target data using samples' distance information from prototypes, thereby enhancing the student network's ability to acquire language-specific knowledge. Extensive experiments on three benchmark cross-lingual NER datasets demonstrate the effectiveness of our approach.Comment: AAAI 202