603 research outputs found
On Adaptive Extended Different Life Cycle of Product Design Strategy
AbstractThe article uses research ways of following the whole lifespan of product and enterprise's development course to research strategy of company's product design and development. It announces enterprises of different nature, enterprises at different developing stage will adopt different mode strategy. It also announces close causality between development course of company and central technology and product. The result indicated in different developing stages such as company development period, crisis predicament period, lasting steadies period, improving by payback period, issues steadies secondary period, declining go and live period, enterprise should pursue different mode product tactics of research and development such as shrinking strategy, consolidating strategy, innovation keeping forging ahead strategy. Enterprise should break regular management mode to introduce different research and development mode to promote enterprise's competitiveness effectively
A Comparative Analysis of the Internationalisation Strategy of Chinese and Japanese Firms---The Case of Geely and Toyota
In a long period of time, internationalisation has been related to MNEs from developed markets. For instance, after the World War II, the development of Japanese MNEs should be regarded as a “miracle”. Recently, the internationalisation of enterprises from emerging markets is on the rise. Considering the growth of Chinese MNEs, the development path after economic reforms in 1978 is similar to that of Japanese MNEs since World War II. As more Chinese enterprises start to be engaged in internationalisation, especially in mature market, a crucial issue should be addressed: whether Chinese MNEs will follow the steps of Japanese MNEs? Based on both the traditional international business models and Peng’s integrated framework, this dissertation will answer the question above. This study employs two case studies—Geely and Toyota, to illustrate the differences of internationalisation strategies between Chinese and Japanese MNEs in terms of expansion path and speed, entry mode, etc.
An amount of research findings have emerged from this research. Firstly, the different industrial factors, resource features and institutional characteristics which lied in the growth background and competitive advantages would lead to the divergence of internationalisation strategies between Chinese and Japanese MNEs, especially in terms of expansion path and speed, preferred entry mode, etc. Secondly, the dissertation also examines the traditional models, including Dunning’s eclectic paradigm and the Uppsala model, which developed to explain internationalization of enterprises from developed economies. They may not have equal explanatory power for the internationalization of emerging market enterprises in the modern market. In contrast, Peng’s framework, as an integrated framework instead of complement or substitute of the traditional theories, is generally consistent with the internationalisation strategies of MNEs from both developed economies and emerging economies. Further, the boundaries of the research and future avenues for future research have also been identified in this dissertation
Semantic Parsing for Question Answering over Knowledge Graphs
In this paper, we introduce a novel method with graph-to-segment mapping for
question answering over knowledge graphs, which helps understanding question
utterances. This method centers on semantic parsing, a key approach for
interpreting these utterances. The challenges lie in comprehending implicit
entities, relationships, and complex constraints like time, ordinality, and
aggregation within questions, contextualized by the knowledge graph. Our
framework employs a combination of rule-based and neural-based techniques to
parse and construct highly accurate and comprehensive semantic segment
sequences. These sequences form semantic query graphs, effectively representing
question utterances. We approach question semantic parsing as a sequence
generation task, utilizing an encoder-decoder neural network to transform
natural language questions into semantic segments. Moreover, to enhance the
parsing of implicit entities and relations, we incorporate a graph neural
network that leverages the context of the knowledge graph to better understand
question representations. Our experimental evaluations on two datasets
demonstrate the effectiveness and superior performance of our model in semantic
parsing for question answering.Comment: arXiv admin note: text overlap with arXiv:2401.0296
Rapid Diagnosis by Microfluidic Techniques
Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields
Turning a CLIP Model into a Scene Text Detector
The recent large-scale Contrastive Language-Image Pretraining (CLIP) model
has shown great potential in various downstream tasks via leveraging the
pretrained vision and language knowledge. Scene text, which contains rich
textual and visual information, has an inherent connection with a model like
CLIP. Recently, pretraining approaches based on vision language models have
made effective progresses in the field of text detection. In contrast to these
works, this paper proposes a new method, termed TCM, focusing on Turning the
CLIP Model directly for text detection without pretraining process. We
demonstrate the advantages of the proposed TCM as follows: (1) The underlying
principle of our framework can be applied to improve existing scene text
detector. (2) It facilitates the few-shot training capability of existing
methods, e.g., by using 10% of labeled data, we significantly improve the
performance of the baseline method with an average of 22% in terms of the
F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene
text detection methods, we further achieve promising domain adaptation ability.
The code will be publicly released at https://github.com/wenwenyu/TCM.Comment: CVPR202
Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
We present a new algorithm to estimate quasar photometric redshifts
(photo-s), by considering the asymmetries in the relative flux distributions
of quasars. The relative flux models are built with multivariate Skew-t
distributions in the multi-dimensional space of relative fluxes as a function
of redshift and magnitude. For 151,392 quasars in the SDSS, we achieve a
photo- accuracy, defined as the fraction of quasars with the difference
between the photo- and the spectroscopic redshift , within 0.1, of 74%. Combining the WISE W1 and W2 infrared
data with the SDSS data, the photo- accuracy is enhanced to 87%. Using the
Pan-STARRS1 or DECaLS photometry with WISE W1 and W2 data, the photo-
accuracies are 79% and 72%, respectively. The prior probabilities as a function
of magnitude for quasars, stars and galaxies are calculated respectively based
on (1) the quasar luminosity function; (2) the Milky Way synthetic simulation
with the Besan\c{c}on model; (3) the Bayesian Galaxy Photometric Redshift
estimation. The relative fluxes of stars are obtained with the Padova
isochrones, and the relative fluxes of galaxies are modeled through galaxy
templates. We test our classification method to select quasars using the DECaLS
, , , and WISE W1 and W2 photometry. The quasar selection completeness
is higher than 70% for a wide redshift range , and a wide magnitude
range mag. Our photo- regression and classification method has
the potential to extend to future surveys. The photo- code will be publicly
available.Comment: 22 pages, 17 figure, accepted by AJ. The code is available at
https://doi.org/10.5281/zenodo.101440
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
Multimodal tasks in the fashion domain have significant potential for
e-commerce, but involve challenging vision-and-language learning problems -
e.g., retrieving a fashion item given a reference image plus text feedback from
a user. Prior works on multimodal fashion tasks have either been limited by the
data in individual benchmarks, or have leveraged generic vision-and-language
pre-training but have not taken advantage of the characteristics of fashion
data. Additionally, these works have mainly been restricted to multimodal
understanding tasks. To address these gaps, we make two key contributions.
First, we propose a novel fashion-specific pre-training framework based on
weakly-supervised triplets constructed from fashion image-text pairs. We show
the triplet-based tasks are an effective addition to standard multimodal
pre-training tasks. Second, we propose a flexible decoder-based model
architecture capable of both fashion retrieval and captioning tasks. Together,
our model design and pre-training approach are competitive on a diverse set of
fashion tasks, including cross-modal retrieval, image retrieval with text
feedback, image captioning, relative image captioning, and multimodal
categorization.Comment: 14 pages, 4 figures. To appear at Conference on Empirical Methods in
Natural Language Processing (EMNLP) 202
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