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 $s$-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 $p$-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 $D$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $D$ 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 $(1-\epsilon)$ for any $\epsilon > 0$. 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 $\mathit{P} = \mathit{NP}$. 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