The Economic and Product Complexity Indices, introduced as an attempt to measure these capabilities from a country's basket of exported products, have become popular to study economic development, the geography of innovation, and industrial policies. Despite this reception, the interpretation of these indicators proved difficult. Although the original Method of Reflections suggested a direct interconnection between country and product metrics, it has been proved that the Economic and Product Complexity Indices result from a spectral clustering algorithm that separately groups similar countries or similar products, respectively. This recent approach to economic and product complexity conflicts with the original one and treats separately countries and products. However, building on previous interpretations of the indices and the recent evolution in spectral clustering, we show that these indices simultaneously identify two co-clusters of similar countries and products. This viewpoint reconciles the spectral clustering interpretation of the indices with the original Method of Reflections interpretation. By proving the often neglected intimate relationship between country and product complexity, this approach emphasizes the role of a selected set of products in determining economic development while extending the range of applications of these indicators in economics.

Evaluating the significance of a paper is pivotal yet challenging for the scientific community. While the citation count is the most commonly used proxy for this purpose, they are widely criticized for failing to accurately reflect a paper's true impact. In this work, we propose a causal inference method, TextMatch, which adapts the traditional matching framework to high-dimensional text embeddings. Specifically, we encode each paper using the text embeddings by large language models (LLMs), extract similar samples by cosine similarity, and synthesize a counterfactual sample by the weighted average of similar papers according to their similarity values. We apply the resulting metric, called CausalCite, as a causal formulation of paper citations. We show its effectiveness on various criteria, such as high correlation with paper impact as reported by scientific experts on a previous dataset of 1K papers, (test-of-time) awards for past papers, and its stability across various sub-fields of AI. We also provide a set of findings that can serve as suggested ways for future researchers to use our metric for a better understanding of a paper's quality. Our code and data are at https://github.com/causalNLP/causal-cite.

Patient motion during PET is inevitable. Its long acquisition time not only increases the motion and the associated artifacts but also the patient's discomfort, thus PET acceleration is desirable. However, accelerating PET acquisition will result in reconstructed images with low SNR, and the image quality will still be degraded by motion-induced artifacts. Most of the previous PET motion correction methods are motion type specific that require motion modeling, thus may fail when multiple types of motion present together. Also, those methods are customized for standard long acquisition and could not be directly applied to accelerated PET. To this end, modeling-free universal motion correction reconstruction for accelerated PET is still highly under-explored. In this work, we propose a novel deep learning-aided motion correction and reconstruction framework for accelerated PET, called Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables modeling-free motion correction by estimating quasi-continuous motion from ultra-short frame reconstructions and using this information for motion-compensated reconstruction. Then, the SL-Recon converts the accelerated UMC image with low counts to a high-quality image with high counts for our final reconstruction output. Our experimental results on human studies show that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes acquisition to generate high-quality reconstruction images that outperform/match previous motion correction reconstruction methods using standard 15 minutes long acquisition data.

School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK; a. mosavi@brookes.ac.uk Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods . The traditional road inspecti on systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, t he proposed models utilize surface deflection data from falling weight deflectometer (FWD) test s to predict the PC I. Machine learning methods are the single multi - layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., L eve nberg - M arquardt (MLP - LM), scaled conjugate gradient (MLP - SCG), imperialist competitive (RBF - ICA), and g enetic algorithms (RBF - GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accur acy of the modeling. The results of the analysis have been verified through using four criteria of aver age percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS mode l outperforms other models with the promising results of APRE 2.3303, AAPRE 11.6768, RMSE 12.0056, and SD 0.0210. Introduction In road transportation, pavement plays a vital role as th e part of the road that is in direct contact with vehicles . U sers' judgment about the quality of road service is primarily predicated upon pavement conditions. The Maintena nce, Rehabilitation, and Reconstruction (MR&R) program of pavement network is a multidimensional decision - making process that takes into account several consideration s.

In a related editorial, R. Jeffrey Westcott, MD, and James E. Tcheng, MD, said Zack and colleagues' findings support the idea that machine learning could outperform classical statistical approaches to risk prediction--but it'll take some work to make it an industry standard. "Transforming healthcare, and, more specifically, transforming the management of data within healthcare to enable AI and its siblings, requires foundational investment and culture change," the editorialists wrote. They said artificial intelligence and machine learning will undoubtedly become "increasingly important in clinical medicine" as we move forward, with equity funding for healthcare-related AI ventures topping $2.4 billion in 2018. "Machine learning has proven to be valuable and is therefore the future," Westcott and Tcheng wrote. "Data warehouses and data lakes contain amazing amounts of structured and unstructured data that will change how medical research, drug and device trials, and device tracking are done. A collaborative effort is needed with EHR vendors, third-party vendors, professional societies and others to start meaningful standardized data collection and workflow redesign now."

For the first time in the literature, we develop a quantitative indicator of the Chinese government's policy priorities over a long period of time, which we call the Policy Change Index (PCI) of China. The PCI is a leading indicator of policy changes that runs from 1951 to the third quarter of 2018, and it can be updated in the future. It is designed with two building blocks: the full text of the People's Daily -- the official newspaper of the Communist Party of China -- as input data and a set of machine learning techniques to detect changes in how this newspaper prioritizes policy issues. Due to the unique role of the People's Daily in China's propaganda system, detecting changes in this newspaper allows us to predict changes in China's policies. The construction of the PCI does not require the researcher's understanding of the Chinese context, which suggests a wide range of applications in other settings, such as predicting changes in other (ex-)Communist regimes' policies, measuring decentralization in central-local government relations, quantifying media bias in democratic countries, and predicting changes in lawmaker's voting behavior and in judges' ideological leaning.

The performance of the Self-Organizing Map (SOM) algorithm is dependent on the initial weights of the map. The different initialization methods can broadly be classified into random and data analysis based initialization approach. In this paper, the performance of random initialization (RI) approach is compared to that of principal component initialization (PCI) in which the initial map weights are chosen from the space of the principal component. Performance is evaluated by the fraction of variance unexplained (FVU). Datasets were classified into quasi-linear and non-linear and it was observed that RI performed better for non-linear datasets; however the performance of PCI approach remains inconclusive for quasi-linear datasets.