Relation-Oriented: Toward Knowledge-Aligned Causal AI

In machine learning, we naturally apply an Observation-Oriented principle, in which observational variables preexist and set the stage for constructing relationships. While sufficient for traditional models, the integration of AI with big data exposes the misalignment between the observational models and our actual comprehension. Contrarily, humans shape cognitive entities defined by relationships, enabling us to formulate knowledge across temporal and hyper-dimensional spaces, rather than being confined to observational constructs. From an innovative Relation-Oriented perspective, this study examines the roots of this misalignment within our current modeling paradigm, illuminated by intuitive examples from computer vision and health informatics. We also introduce the relation-defined representation learning methodology as a practical implementation of Relation-Oriented modeling, supported by extensive experimental validation

Two-particle dark state cooling of a nanomechanical resonator

The steady-state cooling of a nanomechanical resonator interacting with three coupled quantum dots is studied. General conditions for the cooling to the ground state with single and two-electron dark states are obtained. The results show that in the case of the interaction of the resonator with a single-electron dark state, no cooling of the resonator occurs unless the quantum dots are not identical. The steady-state cooling is possible only if the energy state of the quantum dot coupled to the drain electrode is detuned from the energy states of the dots coupled to the electron source electrode. The detuning has the effect of unequal shifting of the effective dressed states of the system that the cooling and heating processes occur at different frequencies. For the case of two electrons injected to the quantum dot system, the creation of a two-particle dark state is established to be possible with spin-antiparallel electrons. The results predict that with the two-particle dark state, an effective cooling can be achieved even with identical quantum dots subject of an asymmetry only in the charging potential energies coupling the injected electrons. It is found that similar to the case of the single-electron dark state, the asymmetries result in the cooling and heating processes to occur at different frequencies. However, an important difference between the single and two-particle dark state cases is that the cooling process occurs at significantly different frequencies. This indicates that the frequency at which the resonator could be cooled to its ground state can be changed by switching from the one-electron to the two-electron Coulomb blockade process.Comment: Published versio

Increasing Vegetable Oil Demand in China: Impacts on the International Soybean Market

Increases in income and urbanization lead to strong demand for edible oil in China, which impacts global soybean trade. This study reviews the history of China’s soybean industry and vegetable oil consumption patterns, compares the demand for vegetable oil with demand in Japan and South Korea, and then forecasts China’s demand for vegetable oil and soybean imports in the coming decades

Realization of Causal Representation Learning and Redefined DAG for Causal AI

DAG(Directed Acyclic Graph) from causal inference does not differentiate causal effects and correlated changes. And the general effect of a population is usually approximated by averaging correlations over all individuals. Since AI(Artificial Intelligence) enables large-scale structure modeling on big data, the complex hidden confoundings have made these approximation errors no longer ignorable but snowballed to considerable modeling bias - Such Causal Representation Bias (CRB) leads to many problems: ungeneralizable causal models, unrevealed individual-level features, hardly utilized causal knowledge in DL(Deep Learning), etc. In short, DAG must be redefined to enable a new framework for causal AI. The observational time series in statistics can only represent correlated changes, while the DL-based autoencoder can represent them as individualized feature changes in latent space to estimate the causal effects directly. In this paper, we introduce the redefined do-DAG to visualize CRB, propose a generic solution Causal Representation Learning (CRL) framework, along with a novel architecture for its realization, and experimentally verify the feasibility