The finite element analysis of the in plane and out of plane harmonic responses of piezoresponse force microscopy cantilever

The harmonic response under the in plane and out of plane driving force separately and model analysis of the widely used SCM-PIT probe were carried out in the consideration of the typical piezoresponse force microscopy working condition by finite element method. It is shown that there are symmetric modes of the resonance at 68, 408, 1139, 2244 kHz, and antisymmetric modes at 646, 1020, and 3077 kHz in the first seven eigenmodes. The symmetric modes of the harmonic response are verified by the frequency sweep method from the piezoresponse phase signals experimentally. It is also revealed that different driving frequencies should be used in the resonance-enhanced PFM imaging in the consideration of the domain structures. The driving frequency of 68, 408, 1139, 2244 kHz should be preferred in the resonance-enhanced PFM imaging of the out of plane domains, while the driving frequency of 646, 1020 and 3077 kHz should be used for the imaging of in plane domains in order of achieved the best resonance-enhanced effect

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and system design in DRS. Finally, we discuss appealing research directions and summarize the survey

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

The finite element analysis of the in plane and out of plane harmonic responses of piezoresponse force microscopy cantilever

The harmonic response under the in plane and out of plane driving force separately and model analysis of the widely used SCM-PIT probe were carried out in the consideration of the typical piezoresponse force microscopy working condition by finite element method. It is shown that there are symmetric modes of the resonance at 68, 408, 1139, 2244 kHz, and antisymmetric modes at 646, 1020, and 3077 kHz in the first seven eigenmodes. The symmetric modes of the harmonic response are verified by the frequency sweep method from the piezoresponse phase signals experimentally. It is also revealed that different driving frequencies should be used in the resonance-enhanced PFM imaging in the consideration of the domain structures. The driving frequency of 68, 408, 1139, 2244 kHz should be preferred in the resonance-enhanced PFM imaging of the out of plane domains, while the driving frequency of 646, 1020 and 3077 kHz should be used for the imaging of in plane domains in order of achieved the best resonance-enhanced effect

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific information. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information. The multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that our HierRec outperforms existing models significantly

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

Research progress on the relationship between axonal transport dysfunction in neuronal cells and Alzheimer’s disease

Alzheimer’s disease is known as one of the “top ten killers in the world”. Due to lack of effective therapy at present， early pathological changes have captivated widespread attention. Axonal transport dysfunction has been reported as an early pathological feature of many neurodegenerative diseases. However， multiple factors can cause axonal transport dysfunction. In this article， the relationship between axonal transport dysfunction caused by kinesins， microtubules and mitochondria and Alzheimer’s disease was discussed， aiming to provide new ideas for the prevention and treatment of Alzheimer’s disease by in-depth study on axonal transport mechanism of neure

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead