Search Results for « Emergent Criticality »

The coevolution of network topology and dynamics is studied in an evolutionary Boolean network model that is a simple model of a gene regulatory network. We find that a critical state emerges spontaneously resulting from the interplay between topology and dynamics during the evolution. The final evolved state is shown to be independent of initial conditions. The network appears to be driven to a random Boolean network with uniform in-degree of 2 in the large-network limit. However, for biologically realized network sizes, significant finite-size effects are observed including a broad in-degree distribution and an average in-degree connections between 2 and 3. These results may be important for explaining the properties of gene regulatory networks.

Two case reports of emergent anesthesia of critical tracheal stenosis are presented. The use of extracorporeal circulation may be a lifesaving method for these patients. Two patients both with severe lower tracheal stenosis were admitted with severe inspiratory dyspnea. The first patient had a tracheal tube inserted above the stenosis in the operating room, but ventilation was unsatisfactory, high airway pressure and severe hypercarbia developed, therefore extracorporeal circulation was immediately initiated. For the second patient, we established femoral-femoral cardiopulmonary bypass prior to induction of anaesthesia, and intubated above the tracheal tumor orally under general anesthesia, then adjusted the endotracheal tube to appropriate depth after the tumor had been resected. The patient was gradually weaned from cardiopulmonary bypass. The two patients all recovered very well after surgery. Surgery is lifesaving for patients with critical tracheal stenosis, but how to ensure effective gas exchange is crucial to the anesthetic management. Extracorporeal circulation by the femoral artery and femoral vein cannulation can gain good gas exchange even if the trachea is totally obstructed. Therefore, before the induction of anesthesia, we should assess the site and degree of obstruction carefully and set up cardiopulmonary bypass to avoid exposing the patient to unexpected risks and the anesthesiologist to unexpected challenges.

We show, via explicit computation on a constrained bosonic model, that the presence of subsystem symmetries can lead to a quantum phase transition (QPT) where the critical point exhibits an emergent enhanced symmetry. Such a transition separates a unique gapped ground state from a gapless one; the latter phase exhibits a broken Z_2 Z 2 symmetry which we tie to the presence of the subsystem symmetries in the model. The intermediate critical point separating these phases exhibits an additional emergent Z_2 Z 2 symmetry which we identify. This emergence leads to a critical theory which seems to be different from those in the Ising universality class. Instead, within the data obtained from finite-size scaling analysis, we find the critical theory to be not inconsistent with Ashkin-Teller universality in the sense that the transitions of the model reproduces a critical line with variable correlation length exponent \nu ν but constant central charge c c close to unity. We verify this scenario via explicit exact-diagonalization computations, provide an effective Landau-Ginzburg theory for such a transition, and discuss the connection of our model to the PXP model describing Rydberg atom arrays.

Early embryonic development involves forming all specialized cells from a fluid-like mass of identical stem cells. The differentiation process consists of a series of symmetry-breaking events, starting from a high-symmetry state (stem cells) to a low-symmetry state (specialized cells). This scenario closely resembles phase transitions in statistical mechanics. To theoretically study this hypothesis, we model embryonic stem cell (ESC) populations through a coupled Boolean network (BN) model. The interaction is applied using a multilayer Ising model that considers paracrine and autocrine signaling, along with external interventions. It is demonstrated that cell-to-cell variability can be interpreted as a mixture of steady-state probability distributions. Simulations have revealed that such models can undergo a series of first- and second-order phase transitions as a function of the system parameters that describe gene expression noise and interaction strengths. These phase transitions result in spontaneous symmetry-breaking events that generate new types of cells characterized by various steady-state distributions. Coupled BNs have also been shown to self-organize in states that allow spontaneous cell differentiation.

One of the challenges in strongly correlated electron systems is to understand the anomalous electronic behavior that develops at an antiferromagnetic quantum critical point (QCP), a phenomenon that has been extensively studied in heavy-fermion materials. Current theories have focused on the critical spin fluctuations and associated breakdown of the Kondo effect. Here we argue that the abrupt change in the Fermi surface volume that accompanies heavy-fermion criticality leads to critical charge fluctuations. Using a model one-dimensional Kondo lattice, in which each moment is connected to a separate conduction bath, we show that a Kondo breakdown transition develops between a heavy Fermi liquid and a gapped spin liquid via a QCP with ω/T scaling, which features a critical charge mode directly associated with the breakup of Kondo singlets. We discuss the possible implications of this emergent charge mode for experiments.

The aim of this chapter is to describe how young learners' participation in dialogues around the picturebook Huskereisa enable emergent critical thinking.The data builds on observations of shared reading and classroom dialogues from second and fourth grade.The most salient critical thinking skills were interpretation, analysis and inference (Facione, 2020).This was visualised through students showing engagement, building on ideas, making connections, and elaborating and reasoning in the dialogues.

Abstract In a time characterised by widespread misinformation and the increasing sophistication of artificial intelligence, the capacity to think critically has become a foundational educational priority. While critical thinking initiatives have largely focused on adolescents and adults, far less attention has been paid to how such dispositions can be nurtured in early childhood. This paper draws on design-based doctoral research conducted in two preschool classrooms to explore how oral storytelling can serve as a dialogic and responsive pedagogical practice for fostering emergent critical thinking in young children. The present study offers a distinct focus on the pedagogical conditions through which critical thinking is enacted. Across a 10-week intervention, educators implemented structured oral storytelling sessions emphasising open-ended questioning, perspective-taking, and collaborative problem-solving. Data were generated through classroom observation, video recordings, audio-recorded transcripts, and collaborative researcher/educator reflection. The findings suggest that preschool children can meaningfully engage in emergent critical thinking when supported through intentional educator mediation. Three interrelated elements, reasoning, perspective-taking, and collaborative meaning-making, were observed to emerge dialogically within storytelling interactions. These were closely associated with the pedagogical conditions, including open-ended inquiry, the modelling of thinking language, provision of thinking time, and dialogic mediation. The study contributes to early childhood education by positioning critical thinking as a socially mediated and dialogically enacted process rather than an individual cognitive skill. The resulting StoryThinking framework offers a transferable pedagogical model to support educators in fostering critical thinking through everyday classroom practice.

In artificial neural networks, the activation dynamics of non-trainable variables are strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and boundary neurons are mapped to changes in trainable variables (e.g., weights and biases). For example, in feedforward neural networks, forward propagation is the activation pass and backward propagation is the learning pass. We show that a composition of the two maps establishes a duality map between a subspace of non-trainable boundary variables (e.g., dataset) and a tangent subspace of trainable variables (i.e., learning). In general, the dataset-learning duality is a complex nonlinear map between high-dimensional spaces. We use duality to study the emergence of criticality, or the power-law distribution of fluctuations of the trainable variables, using a toy and large models at learning equilibrium. In particular, we show that criticality can emerge in the learning system even from the dataset in a non-critical state, and that the power-law distribution can be modified by changing either the activation function or the loss function.

In artificial neural networks, the activation dynamics of non-trainable variables is strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and boundary neurons are mapped to changes in trainable variables (e.g., weights and biases). For example, in feed-forward neural networks, forward propagation is the activation pass and backward propagation is the learning pass. We show that a composition of the two maps establishes a duality map between a subspace of non-trainable boundary variables (e.g., dataset) and a tangent subspace of trainable variables (i.e., learning). In general, the dataset-learning duality is a complex non-linear map between high-dimensional spaces. We use duality to study the emergence of criticality, or the power-law distribution of fluctuations of the trainable variables, using a toy model at learning equilibrium. In particular, we show that criticality can emerge in the learning system even from the dataset in a non-critical state, and that the power-law distribution can be modified by changing either the activation function or the loss function.