Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models' variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.Comment: 15 pages, 12 figures, 5 table

Scale-free resilience of real traffic jams

The concept of resilience can be realized in natural and engineering systems, representing the ability of system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation system, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations is still missing. Here we define city traffic resilience based on the spatio-temporal clusters of congestion in real traffic, and find that the resilience follows a scale free distribution in two-dimensional city road networks and one-dimensional highways, with different exponents, but similar exponents in different days and different cities. The traffic resilience is also revealed to have a novel scaling relation between the cluster size of the spatio-temporal jam and its recovery duration, independent of microscopic details. Our findings of universal traffic resilience can provide indication towards better understanding and designing these complex engineering systems under internal and external disturbances. Comment: 6 pages, 4 figure Document type: Articl

Scale-free Resilience of Real Traffic Jams

The concept of resilience can be realized in natural and engineering systems, representing the ability of system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation system, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations is still missing. Here we define city traffic resilience based on the spatio-temporal clusters of congestion in real traffic, and find that the resilience follows a scale free distribution in two-dimensional city road networks and one-dimensional highways, with different exponents, but similar exponents in different days and different cities. The traffic resilience is also revealed to have a novel scaling relation between the cluster size of the spatio-temporal jam and its recovery duration, independent of microscopic details. Our findings of universal traffic resilience can provide indication towards better understanding and designing these complex engineering systems under internal and external disturbances.Comment: 6 pages, 4 figure

Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm

The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

BackgroundBetter outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.MethodsWe retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.ResultsA total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.ConclusionMultifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU

Nonlinear optical properties of meso-Tetra(fluorenyl)porphyrins peripherally functionalized with one to four ruthenium alkynyl substituents

The synthesis of a series of four porphyrin derivatives based on a meso-tetrafluorenylporphyrin core functionalized with one to four trans-chlorobis(dppe)ruthenium alkynyl units (dppe = 1,2-bis(diphenylphosphino)ethane) at the periphery, together with cyclic voltammetry (CV) and UV–Vis absorption and emission spectroscopy studies, are reported. In these multipolar assemblies, the organoruthenium endgroups are potential electron-donors and the central porphyrin core is a potential electron-acceptor. The third-order nonlinear optical (NLO) responses have been assessed by Z-scan, revealing that these extended π-networks incorporating polarizable organometallic units behave as nonlinear absorbers in the near-IR range. The role of the peripheral transition metal centers on the third-order NLO properties is discussed

A new triggering mechanism of the boiling crisis based on the percolation theory and its implication

Boiling is a very effective heat transfer process, used in nuclear reactors and other applications such as high-performance computing cooling, sterilization, water desalination. However, this process is limited by the boiling crisis. The boiling crisis is an instability that causes a sudden transition from a nucleate boiling regime to a film boiling regime. The value of the heat flux at which this boiling crisis occurs is known as critical heat flux (CHF). The boiling crisis is likely to make overheated heaters to burn out and fail. Thus, systems are typically operated with an adequate margin to the CHF limit. Researchers have spent decades exploring the triggering mechanism of the boiling crisis. Historically, most models assumed that boiling crisis is triggered by a macroscale hydrodynamics instability in the far-field liquid-vapor flows. However, there is a growing consensus that the boiling crisis is a near-wall phenomenon. In summary, while the boiling crisis has been studied for almost a century, there is still no consensus on the triggering mechanism of the boiling crisis. Recent observations from our group and other groups suggest that the boiling crisis is a scale-free phenomenon and belongs to the universal class of critical phenomena including earthquakes, traffic jams, and the outbreak of COVID-19. Inspired by the new observations, we propose a new way to view boiling heat transfer and the boiling crisis by modeling boiling as a bubble percolation process. By combining high-resolution experimental data and a stochastic model, we posit that the boiling crisis is triggered by an instability in the near-wall stochastic bubble interaction process. We formulate a Monte Carlo (MC) simulation model based on the continuum percolation theory that elucidates how the scale-free distribution emerges from the bubble percolation process. This model allows formulating a unifying nondimensional law of the boiling crisis, which we verify using data from eleven different surface and operating conditions. Inspired by the concurrence of scale-free criticality and complexity in many of the aforementioned physical systems, we analyze the fractal behavior of the bubble interaction process and show that the critical phase transition in this phenomenon (i.e., the boiling crisis), coincides with a maximum in the fractal dimension (i.e., the maximum complexity in the system). We also reveal that nucleate boiling (not the boiling crisis) is a self-organized process that belongs to the universal class of phenomena with a self-organized criticality.Ph.D

Percolative Scale-Free Behavior in the Boiling Crisis

We present the first experimental observations of scale-free behavior in the bubble footprint distribution during the boiling crisis of water, in pool and flow boiling conditions. We formulate a continuum percolation model that elucidates how the scale-free behavior emerges from the near-wall stochastic interaction of bubbles and provides a criterion to predict the boiling crisis. It also offers useful insights on how to engineer surfaces that enhance the critical heat flux limit.Chinese Scholarship Council (CSC, 201706020179)United States. Department of Energy (Contract No. DE-AC05-00OR22725

MiR-497-5p promotes osteogenic/odontogenic differentiation of stem cells from the apical papilla by regulation of the TGF-β Smad pathway through Smurf2

Purpose: To study the influence of miR-497-5p on osteogenic/odontogenic differentiation (OOD) of SCAP, and the signal route involved. Methods: Four groups were set up: miR-497-5p overexpression group (OEG), overexpression control OEC), miR-497-5p inhibition group, and inhibition control group. Alkaline phosphatase (ALP) activity was assayed, and calcified nodules measured. Protein expression levels of dentine salivary phosphoprotein (DSPP), collagen type I, ALP, osteoblast-related factors (Runx2, OSX and OPN) were also assayed. The mRNA expression levels of osteogenesis/dentin-related genes were determined. Results: ALP activity was significantly higher in miR-497-5p overexpression cells than in control, but was reduced, relative to inhibition control group (p < 0.05). The miR-497-5p OEG had significantly more calcified nodules than OEC (p < 0.05). There were markedly up-regulated protein expressions in cells of miR-497-5p OEG than in OCG. Furthermore, protein expressions of Smad2, Smad3 and Smad4 in cells of miR-497-5p OEG were significantly up-regulated, relative to those in OEC, but wer lower in miR-497- 5p inhibitory cells than in inhibitory cells. Conclusion: MiR-497-5p enhances the OOD of SCAP via a mechanism involving TGF-β Smad pathway and Smurf2. Thus, Mir-497-5p may be used as a target for OOD-related drugs