H.Wanhong

The ising model energy is defined as:
$$
E(\sigma) = -\sum_{ij}J_{ij}\sigma_i\sigma_j - \sum_{i} H_i\sigma_i
$$
In which, $\sigma_i = {0, 1}$ in this work.

Let $\mathcal S$ is the set of all possible configuration. $|\mathcal S| = 2^n$,

where $n$ is the number of sites, and is equal to the length of $\sigma$.

In maximum entropy principle, we expect to maximize the entropy $S(p)=-\sum_\sigma p(\sigma)logp(\sigma)$, in the constraints that

$\langle \sigma_i \sigma_j\rangle^{emp} = \langle \sigma_i \sigma_j\rangle$, $\langle \sigma_i\rangle^{emp}=\langle \sigma_i\rangle$ and $\sum_\sigma p(\sigma) = 1$.

Combine to the Lagrange function:

$\mathcal L(p;J;H)=S(p) -\lambda((\sum_\sigma p(\sigma))-1)-\sum_{ij}J_{ij}(\langle \sigma_i \sigma_j\rangle -\langle \sigma_i \sigma_j\rangle^{emp})-\sum_{j}H_{i}(\langle \sigma_i \rangle -\langle \sigma_i\rangle^{emp})$​

Thank you for visiting this site ^_^.

I am currently an independent researcher conducting interdisciplinary studies across the social sciences, nomological sciences,natural sciences, and engineering.

My work includes, for example, applying natural language processing techniques to electroencephalogram (EEG) analysis, exploring modern nomology and the philosophy of law, and developing field-Theoretic models of structural emergence and dynamics.

My major interests are in domain of philosophy, linguistics (especially computational linguistics), neuroscience and social and nomological sciences.

More specifically, my research interests involve following keywords:

• Field-Theoretic Physics, Statistical Physics, and Dynamical Systems
• Philosophy
• Social and Nomological Sciences
• Statistical Learning and Deep Learning
• Emerging Computer Architectures
• Emerging Computing Paradigms (e.g., neuromorphic computing)
• Computational Linguistics
• Programming Language Theory and Programming Language Processing Systems
• Optimization Algorithms
• Bio-signal Processing

I am engaged in interdisciplinary research and study across the social sciences, nomological sciences, natural sciences, and engineering to better understand human beings and the world. My interests also include developing applications in healthcare, communication enhancement, well-being, policymaking, nomological development, and related fields.

This website aims to share some of my research discoveries and personal reflections.
Thank you again for taking the time to browse this site.

Best wishes,
Wanhong HUANG
November 7, 2025

---

Email: huangwanhong.g.official@gmail.com

Chinese link: https://zhuanlan.zhihu.com/p/717409759

1. Temporal Event Encoding in Dynamical Systems

Time is the basis of many interesting human behaviors [1].

In connectionist methods, such as neural networks, explicitly encoding temporal events based on their absolute positions can pose problems for recognizing similar patterns. A similar example of this issue is pointed out in work [1]. Pattern vectors $[0,0,0,1,1,1,0,0]$ and $[0,1,1,1,0,0,0,0]$ (where $1$ represent temporal events) exhibit similar patterns, even though the relative positions of the temporal events differ. However, the explicit encoding of absolute positions causes these vectors to have large differences.

Moreover, since it is difficult to explain how biological systems might use mechanisms similar to shift registers to process patterns with differences in relative positions, this encoding method lacks some degree of biological interpretability [1].

In Jeffrey L. Elman et al.’s work in 1990 [1], a method was proposed for implicitly representing the influence of time on temporal data processing during sequence processing. Specifically, a recurrent neural network was used, where the network’s internal hidden states encoded input events at each moment.