Modeling Information Acquisition and Social Learning Dynamics: A Rational Inattention Perspective

Social learning, a fundamental process through which individuals shape their beliefs and perspectives via observation and interaction with others, is critical for the development of our society and the functioning of social governance. Prior works on social learning usually assume that the initial beliefs are given and focus on the update rule. With the recent proliferation of online social networks, there is an avalanche amount of information, which may significantly influence users' initial beliefs. In this paper, we use the rational inattention theory to model how agents acquire information to form initial beliefs and assess its influence on their adjustments in beliefs. Furthermore, we analyze the dynamic evolution of belief distribution among agents. Simulations and social experiments are conducted to validate our proposed model and analyze the impact of model parameters on belief dynamics.Comment: 10 pages, 6 figures, submitted to ICASSP 202

Study on an adaptive multi-model predictive controller for the thermal management of a SOFC-GT hybrid system

A SOFC temperature control system based on adaptive multimodel predictive control (MMPC) method is designed for a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system with anode and cathode ejectors. Two multi-input and multi-output MPCs (under 100% and 90% load) are designed to control the anode and cathode inlet temperatures. The accuracy of the identified linear models are both more than 95%. The control performance of the designed MMPC is compared with a single MPC and traditional PI. The comparison results demonstrate that the proposed MMPC is most effective and competitive in SOFC thermal management. During the load following, the controller overshoot is less than 1.19K. The settling time is about 2000s, and the integral of time-weighted absolute error is less than 472

Generation of Sst-P2a-Mcherry Reporter Human Embryonic Stem Cell Line Using the Crispr/cas9 System (WAe001-A-2C)

Somatostatin (SST)-producing pancreatic delta-cells play an important role in maintaining the balance of insulin and glucagon secretion within the islets. This study aimed to generate a human embryonic stem cell (hESC) line with a SST-P2A-mCherry reporter using CRISPR/Cas9 system. The SST-P2A-mCherry reporter cell line was shown to maintain typical pluripotent characteristics and able to be induced into SST-producing pancreatic delta-cells. The generation of the cell line would provide useful platform for the characterization of stem cell-derived delta-cells, discovery of delta-cell surface markers and investigation of paracrine mechanisms, which will ultimately promote the drug discovery and cell therapy of diabetes mellitus

Delay-dependent stabilization of stochastic interval delay systems with nonlinear disturbances

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this paper, a delay-dependent approach is developed to deal with the robust stabilization problem for a class of stochastic time-delay interval systems with nonlinear disturbances. The system matrices are assumed to be uncertain within given intervals, the time delays appear in both the system states and the nonlinear disturbances, and the stochastic perturbation is in the form of a Brownian motion. The purpose of the addressed stochastic stabilization problem is to design a memoryless state feedback controller such that, for all admissible interval uncertainties and nonlinear disturbances, the closed-loop system is asymptotically stable in the mean square, where the stability criteria are dependent on the length of the time delay and therefore less conservative. By using Itô's differential formula and the Lyapunov stability theory, sufficient conditions are first derived for ensuring the stability of the stochastic interval delay systems. Then, the controller gain is characterized in terms of the solution to a delay-dependent linear matrix inequality (LMI), which can be easily solved by using available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed design procedure.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Small-molecule antagonists of the oncogenic Tcf/β-catenin protein complex

AbstractKey molecular lesions in colorectal and other cancers cause β-catenin-dependent transactivation of T cell factor (Tcf)-dependent genes. Disruption of this signal represents an opportunity for rational cancer therapy. To identify compounds that inhibit association between Tcf4 and β-catenin, we screened libraries of natural compounds in a high-throughput assay for immunoenzymatic detection of the protein-protein interaction. Selected compounds disrupt Tcf/β-catenin complexes in several independent in vitro assays and potently antagonize cellular effects of β-catenin-dependent activities, including reporter gene activation, c-myc or cyclin D1 expression, cell proliferation, and duplication of the Xenopus embryonic dorsal axis. These compounds thus meet predicted criteria for disrupting Tcf/β-catenin complexes and define a general standard to establish mechanism-based activity of small molecule inhibitors of this pathogenic protein-protein interaction

PolyMaX: General Dense Prediction with Mask Transformer

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.Comment: WACV 202