556 research outputs found
A Dialectical Analysis on Upgrading Underdeveloped Guangdong Agriculture with Digital Ecological Industry
The upgrading of underdeveloped Guangdong agriculture is analysed by Materialist dialectics. Agriculture should not be seen as a symbol of backwardness, but rather as an important ecological industry that can be upgraded with advanced digital science and technology. Cognitive innovation and environmental innovation are emphasized in attracting innovative talents and supporting digital ecological industry. The upgrading path of the digital ecological industry highlights top-level design and systematic planning. Overall, the document emphasizes the strategic value of agriculture and the potential for Guangdong to play a dominant role in the Regional Comprehensive Economic Partnership (RCEP) through digital ecological industry upgrading. Digitalization may enable Guangdong integrate agriculture, industry, and service industries to achieve ecological civilization
Final bound-state formation effect on dark matter annihilation
If the annihilation products of dark matter (DM) are non-relativistic and
couples directly to a light force mediator, the non-perturbation effect like
final state bound state (FBS) formation and final state Sommerfeld (FSS) effect
must be considered. Non-relativistic region of final particles will appear when
there is small mass split between DM and products, so we study those effects in
the degenerate region of mass (including kinematics forbidden case) using two
specific models. We demonstrate that FBS effect will significantly modify the
DM relic abundance comparing to the standard perturbation calculation in some
mass split region. We emphasize that FBS effect is comparable to the FSS effect
in those mass split. The conservation angular momentum are subtle considering
FBS formation, in some cases there may be not -wave, so we use two models
exhibit the different partial wave FBS effect contribution. We also show that
the FBS formation with vector boson emission process also contributes in DM
relic abundance, and first calculate the -wave FSS effect in the specific
model.Comment: 23 pages, 13 figure
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
Biological systems in nature have evolved for millions of years to adapt and
survive the environment. Many features they developed can be inspirational and
beneficial for solving technical problems in modern industries. This leads to a
specific form of design-by-analogy called bio-inspired design (BID). Although
BID as a design method has been proven beneficial, the gap between biology and
engineering continuously hinders designers from effectively applying the
method. Therefore, we explore the recent advance of artificial intelligence
(AI) for a data-driven approach to bridge the gap. This paper proposes a
generative design approach based on the generative pre-trained language model
(PLM) to automatically retrieve and map biological analogy and generate BID in
the form of natural language. The latest generative pre-trained transformer,
namely GPT-3, is used as the base PLM. Three types of design concept generators
are identified and fine-tuned from the PLM according to the looseness of the
problem space representation. Machine evaluators are also fine-tuned to assess
the mapping relevancy between the domains within the generated BID concepts.
The approach is evaluated and then employed in a real-world project of
designing light-weighted flying cars during its conceptual design phase The
results show our approach can generate BID concepts with good performance.Comment: Accepted by J. Mech. Des. arXiv admin note: substantial text overlap
with arXiv:2204.0971
Dimensionality Reduction for General KDE Mode Finding
Finding the mode of a high dimensional probability distribution is a
fundamental algorithmic problem in statistics and data analysis. There has been
particular interest in efficient methods for solving the problem when is
represented as a mixture model or kernel density estimate, although few
algorithmic results with worst-case approximation and runtime guarantees are
known. In this work, we significantly generalize a result of (LeeLiMusco:2021)
on mode approximation for Gaussian mixture models. We develop randomized
dimensionality reduction methods for mixtures involving a broader class of
kernels, including the popular logistic, sigmoid, and generalized Gaussian
kernels. As in Lee et al.'s work, our dimensionality reduction results yield
quasi-polynomial algorithms for mode finding with multiplicative accuracy
for any . Moreover, when combined with gradient
descent, they yield efficient practical heuristics for the problem. In addition
to our positive results, we prove a hardness result for box kernels, showing
that there is no polynomial time algorithm for finding the mode of a kernel
density estimate, unless . Obtaining similar hardness
results for kernels used in practice (like Gaussian or logistic kernels) is an
interesting future direction.Comment: Full version of a paper published at ICML'2
Describing coevolution of business and IS alignment via agent-based modeling
The coevolution of business and IS alignment is a growing concern for researchers and practitioners alike. Extant literature on describing and modeling the coevolution is still in infancy, which makes it hard to capture the complexity and to offer reasonable decisions in the evolution of organizations. This paper focuses on the actors’ behaviors, and explores their emergent effects on the holistic alignment. We build an agent-based model to describe the complex alignment landscape and to improve the coevolution governance. The model embraces the emergent behaviors shaped by the interactions of business and IS agents, and guides the coevolution of alignment driven by the external changes. The development of this model forms a necessary step towards suggesting guidance how to analyze and implement coevolution in companies. The paper also shows the capability of an agent-based model to capture some of the emergent behaviors that emerge from bottom-level behaviors
How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
Our investigation into the Affective Reasoning in Conversation (ARC) task
highlights the challenge of causal discrimination. Almost all existing models,
including large language models (LLMs), excel at capturing semantic
correlations within utterance embeddings but fall short in determining the
specific causal relationships. To overcome this limitation, we propose the
incorporation of \textit{i.i.d.} noise terms into the conversation process,
thereby constructing a structural causal model (SCM). It explores how distinct
causal relationships of fitted embeddings can be discerned through independent
conditions. To facilitate the implementation of deep learning, we introduce the
cogn frameworks to handle unstructured conversation data, and employ an
autoencoder architecture to regard the unobservable noise as learnable
"implicit causes." Moreover, we curate a synthetic dataset that includes i.i.d.
noise. Through comprehensive experiments, we validate the effectiveness and
interpretability of our approach. Our code is available in
https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper.Comment: accepted via EMNLP2023-mai
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