Homotopy Diagrams of Algebras

In [math.AT/9907138] we proved that strongly homotopy algebras are homotopy invariant concepts in the category of chain complexes. Our arguments were based on the fact that strongly homotopy algebras are algebras over minimal cofibrant operads and on the principle that algebras over cofibrant operads are homotopy invariant. In our approach, algebraic models for colored operads describing diagrams of homomorphisms played an important role. The aim of this paper is to give an explicit description of these models. A possible application is an appropriate formulation of the `ideal' homological perturbation lemma for chain complexes with algebraic structures. Our results also provide a conceptual approach to `homotopies through homomorphism' for strongly homotopy algebras. We also argue that strongly homotopy algebras form a honest (not only weak Kan) category. The paper is a continuation of our program to translate the famous book "M. Boardman, R. Vogt: Homotopy Invariant Algebraic Structures on Topological Spaces" to algebra.Comment: 24 pages, LaTeX 2.0

The correction of the littlest Higgs model to the Higgs production process e−γ→νeW−He^{-}\gamma\to \nu_{e}W^{-}H in e−γe^{-}\gamma collisions

The littlest Higgs model is the most economical one among various little Higgs models. In the context of the littlest Higgs(LH) model, we study the process $e^{-}\gamma\to \nu_{e}W^{-}H$ and calculate the contributions of the LH model to the cross section of this process. The results show that, in most of parameter spaces preferred by the electroweak precision data, the value of the relative correction is larger than 10%. Such correction to the process $e^{-}\gamma\to \nu_{e}W^{-}H$ is large enough to be detected via $e^{-}\gamma$ collisions in the future high energy linear $e^{+}e^{-}$ collider($LC$) experiment with the c.m energy $\sqrt{s}$=500 GeV and a yearly integrated luminosity $\pounds=100fb^{-1}$, which will give an ideal way to test the model.Comment: 13 pages, 4 figure

Towards interpretable-by-design deep learning algorithms

The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set of prototypes derived from the training data, while taking advantage of existing latent spaces of large neural networks forming so-called Foundation Models (FM). This addresses the issue of explainability (stage B) while retaining the benefits from the tremendous achievements offered by DL models (e.g., visual transformers, ViT) pre-trained on huge data sets such as IG-3.6B + ImageNet-1K or LVD-142M (stage A). We show that one can turn such DL models into conceptually simpler, explainable-through-prototypes ones. The key findings can be summarized as follows: (1) the proposed models are interpretable through prototypes, mitigating the issue of confounded interpretations, (2) the proposed IDEAL framework circumvents the issue of catastrophic forgetting allowing efficient class-incremental learning, and (3) the proposed IDEAL approach demonstrates that ViT architectures narrow the gap between finetuned and non-finetuned models allowing for transfer learning in a fraction of time \textbf{without} finetuning of the feature space on a target dataset with iterative supervised methods

Semantic linking through spaces for cyber-physical-socio intelligence:a methodology

Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space — the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP3SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP3SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing