3,610 research outputs found
2019 Overview
The CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews, and reports of novel findings of therapeutic relevance to the central nervous system. Its focus includes clinical pharmacology, drug development, and novel methodologies for drug evaluation in neurological and psychiatric diseases. We are pleased to announce that CNS Neuroscience & Therapeutics has become an OpenâAccess Journal as of January 2019. This would allow wider dissemination of scientific knowledge and facilitate collaborative efforts toward advancing novel and solid research on the maintenance of brain homeostasis and repairing the aging and dysfunctional brain
Data-Driven Design-by-Analogy: State of the Art and Future Directions
Design-by-Analogy (DbA) is a design methodology wherein new solutions,
opportunities or designs are generated in a target domain based on inspiration
drawn from a source domain; it can benefit designers in mitigating design
fixation and improving design ideation outcomes. Recently, the increasingly
available design databases and rapidly advancing data science and artificial
intelligence technologies have presented new opportunities for developing
data-driven methods and tools for DbA support. In this study, we survey
existing data-driven DbA studies and categorize individual studies according to
the data, methods, and applications in four categories, namely, analogy
encoding, retrieval, mapping, and evaluation. Based on both nuanced organic
review and structured analysis, this paper elucidates the state of the art of
data-driven DbA research to date and benchmarks it with the frontier of data
science and AI research to identify promising research opportunities and
directions for the field. Finally, we propose a future conceptual data-driven
DbA system that integrates all propositions.Comment: A Preprint Versio
Deep Learning for Technical Document Classification
In large technology companies, the requirements for managing and organizing
technical documents created by engineers and managers have increased
dramatically in recent years, which has led to a higher demand for more
scalable, accurate, and automated document classification. Prior studies have
only focused on processing text for classification, whereas technical documents
often contain multimodal information. To leverage multimodal information for
document classification to improve the model performance, this paper presents a
novel multimodal deep learning architecture, TechDoc, which utilizes three
types of information, including natural language texts and descriptive images
within documents and the associations among the documents. The architecture
synthesizes the convolutional neural network, recurrent neural network, and
graph neural network through an integrated training process. We applied the
architecture to a large multimodal technical document database and trained the
model for classifying documents based on the hierarchical International Patent
Classification system. Our results show that TechDoc presents a greater
classification accuracy than the unimodal methods and other state-of-the-art
benchmarks. The trained model can potentially be scaled to millions of
real-world multimodal technical documents, which is useful for data and
knowledge management in large technology companies and organizations.Comment: 16 pages, 8 figures, 9 table
Patent Data for Engineering Design: A Critical Review and Future Directions
Patent data have long been used for engineering design research because of
its large and expanding size, and widely varying massive amount of design
information contained in patents. Recent advances in artificial intelligence
and data science present unprecedented opportunities to develop data-driven
design methods and tools, as well as advance design science, using the patent
database. Herein, we survey and categorize the patent-for-design literature
based on its contributions to design theories, methods, tools, and strategies,
as well as the types of patent data and data-driven methods used in respective
studies. Our review highlights promising future research directions in patent
data-driven design research and practice.Comment: Accepted by JCIS
Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces
Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting
elements to enhance the passive beam gain, improve Wireless Energy Transfer
(WET) efficiency, and enable its deployment for numerous Internet of Things
(IoT) devices. However, the increasing number of IRS elements presents
considerable channel estimation challenges. This is due to the lack of active
Radio Frequency (RF) chains in an IRS, while pilot overhead becomes
intolerable. To address this issue, we propose a Channel State Information
(CSI)-free scheme that maximizes received energy in a specific direction and
covers the entire space through phased beam rotation. Furthermore, we take into
account the impact of an imperfect IRS and meticulously design the active
precoder and IRS reflecting phase shift to mitigate its effects. Our proposed
technique does not alter the existing IRS hardware architecture, allowing for
easy implementation in the current system, and enabling access or removal of
any Energy Receivers (ERs) without additional cost. Numerical results
illustrate the efficacy of our CSI-free scheme in facilitating large-scale IRS
without compromising performance due to excessive pilot overhead. Furthermore,
our scheme outperforms the CSI-based counterpart in scenarios involving
large-scale ERs, making it a promising solution in the era of IoT
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Large language models (LLMs) with memory are computationally universal.
However, mainstream LLMs are not taking full advantage of memory, and the
designs are heavily influenced by biological brains. Due to their approximate
nature and proneness to the accumulation of errors, conventional neural memory
mechanisms cannot support LLMs to simulate complex reasoning. In this paper, we
seek inspiration from modern computer architectures to augment LLMs with
symbolic memory for complex multi-hop reasoning. Such a symbolic memory
framework is instantiated as an LLM and a set of SQL databases, where the LLM
generates SQL instructions to manipulate the SQL databases. We validate the
effectiveness of the proposed memory framework on a synthetic dataset requiring
complex reasoning. The project website is available at
https://chatdatabase.github.io/
A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation
The patent database is often used in searches of inspirational stimuli for
innovative design opportunities because of its large size, extensive variety
and rich design information in patent documents. However, most patent mining
research only focuses on textual information and ignores visual information.
Herein, we propose a convolutional neural network (CNN)-based patent image
retrieval method. The core of this approach is a novel neural network
architecture named Dual-VGG that is aimed to accomplish two tasks: visual
material type prediction and international patent classification (IPC) class
label prediction. In turn, the trained neural network provides the deep
features in the image embedding vectors that can be utilized for patent image
retrieval and visual mapping. The accuracy of both training tasks and patent
image embedding space are evaluated to show the performance of our model. This
approach is also illustrated in a case study of robot arm design retrieval.
Compared to traditional keyword-based searching and Google image searching, the
proposed method discovers more useful visual information for engineering
design.Comment: 11 pages, 11 figure
- âŠ