Nanofabrication and its application in developing high aspect ratio (HAR) and edge Atomic Force Microscopy (AFM) probes

This thesis focuses on nanofabrication and its applications which are related to producing atomic force microscope (AFM) probes. This thesis is divided into four chapters. The first chapter brings a preliminary introduction to nanofabrication. The second chapter reviews the history of AFM and fabrication process of AFM probe. Equipping with the basic knowledge, Chapter 3, the main chapter, goes to our work on the batch fabrication of high aspect ratio (HAR) AFM tips. Last but not least, Chapter 4 focuses on another work, batch fabrication of edge probes. In order to obtain a more accurate image of surfaces, high aspect ratio tips are needed to reach the bottom of very deep and narrow trenches. However, currently all commercial HAR tips are produced in a slow, high-cost (~5-20x that of regular AFM tips) way. We have developed a new method to batch fabricate HAR tips. In this fabrication, two kinds of hard masks were deposited at a specific angle followed by two etching processes (dry etching and wet etching respectively). As a result, a small piece of hard mask was formed just on the apex of pyramid tip, which would be the protection layer in the following RIE step. The batch and lithography-free process makes it an efficient and low-cost method. The controllable profile, radius of curvature and aspect ratio of tips can be easily obtained by adjusting gas ratio and etching time in RIE. All the parameters and results were demonstrated clearly assisted by images and schematics. For edge probes, our method to batch fabricate tips on the edge is introduced step by step as well. The objective of every step is presented in detail assisted with schematics and tables

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Synthesis, properties, and optical applications of noble metal nanoparticle-biomolecule conjugates

Noble metal nanoparticles, such as gold or silver nanoparticles and nanorods, exhibit unique photonic, electronic and catalytic properties. Functionalization of noble metal nanoparticles with biomolecules (e. g., protein and DNA) produces systems that possess numerous applications in catalysis, delivery, therapy, imaging, sensing, constructing nanostructures and controlling the structure of biomolecules. In this paper, the recent development of noble metal nanoparticle-biomolecule conjugates is reviewed from the following three aspects: (1) synthesis of noble metal nanoparticle-biomolecule systems by electrostatic adsorption, direct chemisorption of thiol derivatives, covalent binding through bifunctional linkers and specific affinity interactions; (2) the photonic properties and bioactivation of noble metal nanoparticle-biomolecule conjugates; and (3) the optical applications of such systems in biosensors, and medical imaging, diagnosis, and therapy. The conjugation of Au and Ag nanoparticles with biomolecules and the most recent optical applications of the resulting systems have been focused on

Zinc-Chelating Mechanism of Sea Cucumber (Stichopus japonicus)-Derived Synthetic Peptides

In this study, three synthetic zinc-chelating peptides (ZCPs) derived from sea cucumber hydrolysates with limited or none of the common metal-chelating amino-acid residues were analyzed by flame atomic absorption spectroscopy, circular dichroism spectroscopy, size exclusion chromatography, zeta-potential, Fourier transform infrared spectroscopy, Raman spectroscopy and nuclear magnetic resonance spectroscopy. The amount of zinc bound to the ZCPs reached maximum values with ZCP:zinc at 1:1, and it was not further increased by additional zinc presence. The secondary structures of ZCPs were slightly altered, whereas no formation of multimers was observed. Furthermore, zinc increased the zeta-potential value by neutralizing the negatively charged residues. Only free carboxyl in C-terminus of ZCPs was identified as the primary binding site of zinc. These results provide the theoretical foundation to understand the mechanism of zinc chelation by peptides

DiCLET-TTS: Diffusion Model based Cross-lingual Emotion Transfer for Text-to-Speech -- A Study between English and Mandarin

While the performance of cross-lingual TTS based on monolingual corpora has been significantly improved recently, generating cross-lingual speech still suffers from the foreign accent problem, leading to limited naturalness. Besides, current cross-lingual methods ignore modeling emotion, which is indispensable paralinguistic information in speech delivery. In this paper, we propose DiCLET-TTS, a Diffusion model based Cross-Lingual Emotion Transfer method that can transfer emotion from a source speaker to the intra- and cross-lingual target speakers. Specifically, to relieve the foreign accent problem while improving the emotion expressiveness, the terminal distribution of the forward diffusion process is parameterized into a speaker-irrelevant but emotion-related linguistic prior by a prior text encoder with the emotion embedding as a condition. To address the weaker emotional expressiveness problem caused by speaker disentanglement in emotion embedding, a novel orthogonal projection based emotion disentangling module (OP-EDM) is proposed to learn the speaker-irrelevant but emotion-discriminative embedding. Moreover, a condition-enhanced DPM decoder is introduced to strengthen the modeling ability of the speaker and the emotion in the reverse diffusion process to further improve emotion expressiveness in speech delivery. Cross-lingual emotion transfer experiments show the superiority of DiCLET-TTS over various competitive models and the good design of OP-EDM in learning speaker-irrelevant but emotion-discriminative embedding.Comment: accepted by TASL

Analysis of risk factors and prognostic factors of synchronous breast and thyroid cancer

Objective To analyze the risk and prognostic factors for synchronous breast cancer（BC）and thyroid cancer（TC）. Methods The Surveillance，Epidemiology，and End Results Program（SEER）2020 database was utilized to collect the information of patients with synchronous BC and TC（BC and TC group）and those with BC alone（BC group）. Clinical data and survival were compared between two groups. Clinical data of patients with synchronous BC and TC（BC and TC group A）and those with BC alone（BC group B）admitted to a certain hospital were retrospectively analyzed. Clinical data and survival were also compared between two groups. Results ①Analysis of SEER database ，482 patients in BC and TC group and 500 patients in BC group. Univariate analysis revealed that age at first diagnosis and progesterone receptor（PR）were the risk factors for synchronous BC and TC（both P < 0.05）. Multivariate analysis found that age at first diagnosis（OR=1.800，95% CI：1.387-2.337，P < 0.001）and PR（OR=1.364，95% CI：1.023-1.818，P = 0.034）were the independent risk factors for synchronous BC and TC. Excluding those with incomplete follow-up data，univariate analysis indicated that tumor diameter and PR were the prognostic factors for synchronous BC and TC（both P < 0.05）；multivariate analysis revealed that tumor diameter was an independent prognostic factor for synchronous BC and TC（OR=4.328，95% CI：1.410-13.288，P = 0.010）. Univariate analysis found that age at first diagnosis and tumor diameter were the prognostic factors for BC alone（both P < 0.05）；multivariate analysis identified that age at first diagnosis（OR = 2.443，95% CI ：1.014-5.889，P = 0.047）and tumor diameter（OR = 2.030，95% CI：1.039-3.969，P = 0.038）were the independent prognostic factors for BC alone. ②Analysis of inpatients，there were 40 patients each in BC and TC group A and BC group A. Univariate analysis indicated that menstrual status，PR，proliferation index Ki-67，and TT3 were the risk factors for synchronous BC and TC（all P < 0.05），multivariate analysis found that menstrual status（synchronous BC and TC versus BC alone，OR=0.175，95% CI：0.052-0.591，P = 0.005），PR（OR=5.686，95% CI：1.677-19.282，P = 0.005），Ki-67（OR=3.966，95% CI：1.133-13.875，P = 0.031）were the independent risk factors for synchronous BC and TC. Eighty patients were subject to follow-up，6 patients died，27 survived，and 7 were lost to follow-up in BC and TC group A；2 patients died，29 survived，and 9 were lost to follow-up in BC group A. Cox regression analysis revealed no statistical significance in both groups. Conclusions Age at first diagnosis，menstrual status，PR，and Ki-67 are the risk factors for synchronous BC and TC. Tumor diameter is an independent prognostic factor for synchronous BC and TC. Age at first diagnosis and tumor diameter are the independent prognostic factors for BC alone

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie