High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization

Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference method focused on constructing confidence intervals with efficient computation and fast convergence to the nominal level. Specifically, we propose to use a small number of independent multi-runs to acquire distribution information and construct a t-based confidence interval. Our method requires minimal additional computation and memory beyond the standard updating of estimates, making the inference process almost cost-free. We provide a rigorous theoretical guarantee for the confidence interval, demonstrating that the coverage is approximately exact with an explicit convergence rate and allowing for high confidence level inference. In particular, a new Gaussian approximation result is developed for the online estimators to characterize the coverage properties of our confidence intervals in terms of relative errors. Additionally, our method also allows for leveraging parallel computing to further accelerate calculations using multiple cores. It is easy to implement and can be integrated with existing stochastic algorithms without the need for complicated modifications

What drives housing consumption in China? Based on a dynamic optimal general equilibrium model and spatial panel data analysis

Abstract. This paper examines the housing sales in China from 2004 to 2015 utilizing an optimal dynamic general equilibrium theoretical framework combined with a macroeconomic model. The spatial panel econometric empirical results suggest that housing prices and economic growth have increased housing sales in China. However, since house is considered as a special commodity in China, and unemployment show negative impacts on housing sales.Keywords. Energy use, Housing values, Optimal dynamic general equilibrium, Spatial panel econometrics, China.JEL. Q41, R31, E10

Programmed Design of a Lithium–Sulfur Battery Cathode by Integrating Functional Units

Sulfur is considered to be one of the most promising cathode materials due to its high theoretical specific capacity and low cost. However, the insulating nature of sulfur and notorious “shuttle effect” of lithium polysulfides (LiPSs) lead to severe loss of active sulfur, poor redox kinetics, and rapid capacity fade. Herein, a hierarchical electrode design is proposed to address these issues synchronously, which integrates multiple building blocks with specialized functions into an ensemble to construct a self‐supported versatile cathode for lithium–sulfur batteries. Nickel foam acts as a robust conductive scaffold. The heteroatom‐doped host carbon with desired lithiophilicity and electronic conductivity serving as a reservoir for loading sulfur can trap LiPSs and promote electron transfer to interfacial adsorbed LiPSs and Ni3S2 sites. The sulfurized carbon nanofiber forest can facilitate the Li‐ion and electron transport and retard the LiPSs diffusion as a barrier layer. Sulfiphilic Ni3S2 acts as both a chemical anchor with strong adsorption affinity to LiPSs and an efficient electrocatalyst for accelerating kinetics for redox conversion reactions. Synergistically, all functional units promote the lithium ion coupled electron transfer for binding and redox conversion of LiPSs, resulting in high reversible capacities, remarkable cycle stability, and excellent rate capability

Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding

As a pivotal approach in machine learning and data science, manifold learning aims to uncover the intrinsic low-dimensional structure within complex nonlinear manifolds in high-dimensional space. By exploiting the manifold hypothesis, various techniques for nonlinear dimension reduction have been developed to facilitate visualization, classification, clustering, and gaining key insights. Although existing manifold learning methods have achieved remarkable successes, they still suffer from extensive distortions incurred in the global structure, which hinders the understanding of underlying patterns. Scalability issues also limit their applicability for handling large-scale data. Here, we propose a scalable manifold learning (scML) method that can manipulate large-scale and high-dimensional data in an efficient manner. It starts by seeking a set of landmarks to construct the low-dimensional skeleton of the entire data, and then incorporates the non-landmarks into the learned space based on the constrained locally linear embedding (CLLE). We empirically validated the effectiveness of scML on synthetic datasets and real-world benchmarks of different types, and applied it to analyze the single-cell transcriptomics and detect anomalies in electrocardiogram (ECG) signals. scML scales well with increasing data sizes and embedding dimensions, and exhibits promising performance in preserving the global structure. The experiments demonstrate notable robustness in embedding quality as the sample rate decreases.Comment: 33 pages, 10 figure

Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay. [ABSTRACT FROM PUBLISHER

Epithelial-Mesenchymal Transition in tumor microenvironment

The epithelial to mesenchymal transition (EMT) plays crucial roles in the formation of the body plan and also in the tumor invasion process. In addition, EMT also causes disruption of cell-cell adherence, loss of apico-basal polarity, matrix remodeling, increased motility and invasiveness in promoting tumor metastasis. The tumor microenvironment plays an important role in facilitating cancer metastasis and may induce the occurrence of EMT in tumor cells. A large number of inflammatory cells infiltrating the tumor site, as well as hypoxia existing in a large area of tumor, in addition many stem cells present in tumor microenvironment, such as cancer stem cells (CSCs), mesenchymal stem cells (MSCs), all of these may be the inducers of EMT in tumor cells. The signaling pathways involved in EMT are various, including TGF-β, NF-κB, Wnt, Notch, and others. In this review, we discuss the current knowledge about the role of the tumor microenvironment in EMT and the related signaling pathways as well as the interaction between them