124 research outputs found
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks (RNN) to handle raw skeletons only focus on the contextual
dependency in the temporal domain and neglect the spatial configurations of
articulated skeletons. In this paper, we propose a novel two-stream RNN
architecture to model both temporal dynamics and spatial configurations for
skeleton based action recognition. We explore two different structures for the
temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed
according to human body kinematics. We also propose two effective methods to
model the spatial structure by converting the spatial graph into a sequence of
joints. To improve generalization of our model, we further exploit 3D
transformation based data augmentation techniques including rotation and
scaling transformation to transform the 3D coordinates of skeletons during
training. Experiments on 3D action recognition benchmark datasets show that our
method brings a considerable improvement for a variety of actions, i.e.,
generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
La banca central en tiempos de guerra: China 1927-1949
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 05-11-2020The central banking system has been a worldwide-adopted banking system since the 20th century, which influences political and economic structures profoundly in different countries to this day. Chinese Mainland is the second biggest economic entity, whose economic growth in the last 30 years gets the attention of the world. One hot topic is the function of China’s central banking system. But the works for studying the establishment of China’s central bank and the monetary thought debate behind the event still require further analysis. This thesis explains the establishment of China’s central banking system by following the monetary thought debate among Chinese economists, politicians, and foreign financial specialists. They provided advice for monetary policy from 1927 to 1949.This research has the following three main objectives: Revise the monetary thought debate from 1927 to 1949 among Chinese economists, politicians, and Western financial specialists on how to improve the Chinese monetary system and how to build an effective central-banking system during wartime. Demonstrate how the establishment of central banking was supported by the monetary theories provided in the monetary thought debate...El sistema de banca central ha sido el sistema bancario adoptado en todo el mundo desde el siglo XX, influyendo profundamente en las estructuras políticas y económicas de muchos países hasta nuestros días. China continental es la segunda potencia económica más grande a nivel internacional, cuyo crecimiento económico en los últimos 30 años ha llamado la atención en todo el mundo. Un tema candente relacionado con China es la función de su sistema de banca central. Sin embargo, los trabajos para estudiar la creación del banco central de China y el debate sobre el pensamiento monetario detrás de la aparición de dicha institución, aún requieren un análisis más detallado. Esta tesis explica el establecimiento del sistema de banca central de China siguiendo el debate del pensamiento monetario entre economistas y políticos chinos y especialistas financieros extranjeros, que brindaron asesoramiento para la política monetaria de 1927 a 1949...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes
Federated learning shows promise as a privacy-preserving collaborative
learning technique. Existing heterogeneous federated learning mainly focuses on
skewing the label distribution across clients. However, most approaches suffer
from catastrophic forgetting and concept drift, mainly when the global
distribution of all classes is extremely unbalanced and the data distribution
of the client dynamically evolves over time. In this paper, we study the new
task, i.e., Dynamic Heterogeneous Federated Learning (DHFL), which addresses
the practical scenario where heterogeneous data distributions exist among
different clients and dynamic tasks within the client. Accordingly, we propose
a novel federated learning framework named Federated Multi-Level Prototypes
(FedMLP) and design federated multi-level regularizations. To mitigate concept
drift, we construct prototypes and semantic prototypes to provide fruitful
generalization knowledge and ensure the continuity of prototype spaces. To
maintain the model stability and consistency of convergence, three
regularizations are introduced as training losses, i.e., prototype-based
regularization, semantic prototype-based regularization, and federated
inter-task regularization. Extensive experiments show that the proposed method
achieves state-of-the-art performance in various settings
Chatbots in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with ChatGPT
The birth of ChatGPT, a cutting-edge language model chatbot developed by
OpenAI, ushered in a new era in AI, and this paper vividly showcases its
innovative application within the field of drug discovery. Focused specifically
on developing anti-cocaine addiction drugs, the study employs GPT-4 as a
virtual guide, offering strategic and methodological insights to researchers
working on generative models for drug candidates. The primary objective is to
generate optimal drug-like molecules with desired properties. By leveraging the
capabilities of ChatGPT, the study introduces a novel approach to the drug
discovery process. This symbiotic partnership between AI and researchers
transforms how drug development is approached. Chatbots become facilitators,
steering researchers towards innovative methodologies and productive paths for
creating effective drug candidates. This research sheds light on the
collaborative synergy between human expertise and AI assistance, wherein
ChatGPT's cognitive abilities enhance the design and development of potential
pharmaceutical solutions. This paper not only explores the integration of
advanced AI in drug discovery but also reimagines the landscape by advocating
for AI-powered chatbots as trailblazers in revolutionizing therapeutic
innovation
Покращення ресурсних характеристик літака шляхом оптимізації заклепкових з’єднань
Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник роботи: професор, д.т.н. Карускевич Михайло ВіталійовичThis master thesis is dedicated to optimization of aviation riveted joints by the reduction of secondary bending of aircraft skin components under the action of operational loads/
The methods of computer aided design CAD/CAE have been used, particularly CATIA та ABAQUS.
The practical value of the diploma work lays in the improvement of the reliability and extension of the lifespan of aviation constructions by the optimization of the riveted joints geometry.
The materials of the master's diploma can be used in the aviation industry and in the educational process of aviation specialties.Дана дипломна робота присвячена оптимізації заклепкових авіаційних з’єднань шляхом зменшення вторинного вигину елементів обшивки літака при дії експлуатаційних навантажень.
В роботі було використано методи компьютерного проєктування за допомогою CAD/CAE систем проєктування та розрахунку, зокрема CATIA та ABAQUS.
Практичне значення результату дипломної роботи магістра полягає в підвищенні надійності та довговічності авіаційних конструкцій, за рахунок оптимізації геометрії заклепкових з’єднань.
Матеріали дипломної роботи магістра можуть бути використані в навчальному процесі та в практичній діяльності конструкторів спеціалізованих проєктних установ
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training
Image-text retrieval is a central problem for understanding the semantic
relationship between vision and language, and serves as the basis for various
visual and language tasks. Most previous works either simply learn
coarse-grained representations of the overall image and text, or elaborately
establish the correspondence between image regions or pixels and text words.
However, the close relations between coarse- and fine-grained representations
for each modality are important for image-text retrieval but almost neglected.
As a result, such previous works inevitably suffer from low retrieval accuracy
or heavy computational cost. In this work, we address image-text retrieval from
a novel perspective by combining coarse- and fine-grained representation
learning into a unified framework. This framework is consistent with human
cognition, as humans simultaneously pay attention to the entire sample and
regional elements to understand the semantic content. To this end, a
Token-Guided Dual Transformer (TGDT) architecture which consists of two
homogeneous branches for image and text modalities, respectively, is proposed
for image-text retrieval. The TGDT incorporates both coarse- and fine-grained
retrievals into a unified framework and beneficially leverages the advantages
of both retrieval approaches. A novel training objective called Consistent
Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra-
and inter-modal semantic consistencies between images and texts in the common
embedding space. Equipped with a two-stage inference method based on the mixed
global and local cross-modal similarity, the proposed method achieves
state-of-the-art retrieval performances with extremely low inference time when
compared with representative recent approaches.Comment: Code is publicly available: https://github.com/LCFractal/TGD
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