Suppression of spin-density-wave transition and emergence of ferromagnetic ordering of Eu2+^{2+} moments in EuFe2−x_{2-x}Nix_{x}As2_{2}

We present a systematic study on the physical properties of EuFe$_{2-x}$Ni$_{x}$As$_{2}$ (0$\leq$\emph{x}$\leq$0.2) by electrical resistivity, magnetic susceptibility and thermopower measurements. The undoped compound EuFe$_{2}$As$_{2}$ undergoes a spin-density-wave (SDW) transition associated with Fe moments at 195 K, followed by antiferromagnetic (AFM) ordering of Eu$^{2+}$ moments at 20 K. Ni doping at the Fe site simultaneously suppresses the SDW transition and AFM ordering of Eu$^{2+}$ moments. For $x\geq$0.06, the magnetic ordering of Eu$^{2+}$ moments evolves from antiferromagnetic to ferromagnetic (FM). The SDW transition is completely suppressed for $x\geq$0.16, however, no superconducting transition was observed down to 2 K. The possible origins of the AFM-to-FM transition and the absence of superconductivity in EuFe$_{2-x}$Ni$_{x}$As$_{2}$ system are discussed.Comment: 5 pages, 5 figures, accepted for publication in PR

Gravity-based models for evaluating urban park accessibility: Why does localized selection of attractiveness factors and travel modes matter?

Gravity-based models have been extensively utilized in urban studies for measuring geographic disparities in access to urban parks over the past several decades. However, despite methodological advancements incorporating various aspects of accessibility, there has been limited focus on the impact of variable selection (e.g., attractiveness factors) and transport modes on accessibility evaluations. This study investigates the differences in gravity-based models for assessing park accessibility based on varying assumptions about attractiveness factors and travel impedance. Semi-structured interviews with local residents were conducted to identify the reasons for park visits in Shanghai. Our bivariate correlation analyses reveal that factors such as park openness and access to public transport were crucial, in addition to conventional factors identified in the literature (i.e., park size and driving accessibility). This insight led to the development of localized accessibility measurements that incorporate park inclusiveness (i.e., entrance fees and opening hours) and multimodal travel options (based on multinomial logistic mode choice models). The results indicate that the refined model produces lower and more varied accessibility levels, which can better capture accessibility gaps across different geographic contexts. This accurate and practical identification of accessibility gaps can assist local planners and decision-makers in formulating effective policies and strategies to promote equitable access to urban public parks

Partial entropy in finite-temperature phase transitions

It is shown that the von Neumann entropy, a measure of quantum entanglement, does have its classical counterpart in thermodynamic systems, which we call partial entropy. Close to the critical temperature the partial entropy shows perfect finite-size scaling behavior even for quite small system sizes. This provides a powerful tool to quantify finite-temperature phase transitions as demonstrated on the classical Ising model on a square lattice and the ferromagnetic Heisenberg model on a cubic lattice.Comment: 4 pages, 6 figures, Revised versio

Probing potential priming: Defining, quantifying, and testing the causal priming effect using the potential outcomes framework

Having previously seen an item helps uncover the item another time, given a perceptual or cognitive cue. Oftentimes, however, it may be difficult to quantify or test the existence and size of a perceptual or cognitive effect, in general, and a priming effect, in particular. This is because to examine the existence of and quantify the effect, one needs to compare two outcomes: the outcome had one previously seen the item vs. the outcome had one not seen the item. But only one of the two outcomes is observable. Here, we argue that the potential outcomes framework is useful to define, quantify, and test the causal priming effect. To demonstrate its efficacy, we apply the framework to study the priming effect using data from a between-subjects study involving English word identification. In addition, we show that what has been used intuitively by experimentalists to assess the priming effect in the past has a sound mathematical foundation. Finally, we examine the links between the proposed method in studying priming and the multinomial processing tree (MPT) model, and how to extend the method to study experimental paradigms involving exclusion and inclusion instructional conditions

Antiferromagnetic transition in EuFe2_2As2_2: A possible parent compound for superconductors

Ternary iron arsenide EuFe$_2$As$_2$ with ThCr$_2$Si$_2$-type structure has been studied by magnetic susceptibility, resistivity, thermopower, Hall and specific heat measurements. The compound undergoes two magnetic phase transitions at about 200 K and 20 K, respectively. The former was found to be accompanied with a slight drop in magnetic susceptibility (after subtracting the Curie-Weiss paramagnetic contribution), a rapid decrease in resistivity, a large jump in thermopower and a sharp peak in specific heat with decreasing temperature, all of which point to a spin-density-wave-like antiferromagnetic transition. The latter was proposed to be associated with an A-type antiferromagnetic ordering of Eu$^{2+}$ moments. Comparing with the physical properties of the iso-structural compounds BaFe$_2$As$_2$ and SrFe$_2$As$_2$, we expect that superconductivity could be induced in EuFe$_2$As$_2$ through appropriate doping.Comment: 4 pages, 4 figure

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations of a widely adopted epidemiological model. We use latent instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction.Comment: AAAI 202

Carbon emissions in China's thermal electricity and heating industry: An input-output structural decomposition analysis

CO2 emissions from China accounted for 27 per cent of global emisions in 2019. More than one third of China's CO2 emissions come from the thermal electricity and heating sector. Unfortunately, this area has received limited academic attention. This research aims to find the key drivers of CO2 emissions in the thermal electricity and heating sector, as well as investigating how energy policies affect those drivers. We use data from 2007 to 2018 to decompose the drivers of CO2 emissions into four types, namely: energy structure; energy intensity; input-output structure; and the demand for electricity and heating. We find that the demand for electricity and heating is the main driver of the increase in CO2 emissions, and energy intensity has a slight effect on increasing carbon emissions. Improving the input-output structure can significantly help to reduce CO2 emissions, but optimising the energy structure only has a limited influence. This study complements the existing literature and finds that the continuous upgrading of power generation technology is less effective at reducing emissions and needs to be accompanied by the market reform of thermal power prices. Second, this study extends the research on CO2 emissions and enriches the application of the IO-SDA method. In terms of policy implications, we suggest that energy policies should be more flexible and adaptive to the varying socio-economic conditions in different cities and provinces in China. Accelerating the market-oriented reforms with regard to electricity pricing is also important if the benefits of technology upgrading and innovation are to be realised

Augmented Tensor Decomposition with Stochastic Optimization

Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstream classification tasks. Also, real-world tensor data are usually high-ordered and have large dimensions with millions or billions of entries. Thus, it is expensive to decompose the whole tensor with traditional algorithms. In practice, raw tensor data also contains redundant information while data augmentation techniques may be used to smooth out noise in samples. This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations to boost downstream classification. To reduce the memory footprint of the decomposition, we propose a stochastic algorithm that updates the factor matrices in a batch fashion. We evaluate ATD on multiple signal datasets. It shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines with less than 5% of model parameters, achieves 0.6% ~ 1.3% accuracy gain over other tensor-based baselines, and reduces the memory footprint by 9X when compared to standard tensor decomposition algorithms.Comment: Fixed some typo

Transplantation of Ciliary Neurotrophic Factor-Expressing Adult Oligodendrocyte Precursor Cells Promotes Remyelination and Functional Recovery after SpinalCord Injury

Demyelination contributes to the dysfunction after traumatic spinal cord injury (SCI). We explored whether the combination of neurotrophic factors and transplantation of adult rat spinal cord oligodendrocyte precursor cells (OPCs) could enhance remyelination and functional recovery after SCI. Ciliary neurotrophic factor (CNTF) was the most effective neurotrophic factor to promote oligodendrocyte (OL) differentiation and survival of OPCs in vitro. OPCs were infected with retroviruses expressing enhanced green fluorescent protein (EGFP) or CNTF and transplanted into the contused adult thoracic spinal cord 9 d after injury. Seven weeks after transplantation, the grafted OPCs survived and integrated into the injured spinal cord. The survival of grafted CNTF-OPCs increased fourfold compared with EGFP-OPCs. The grafted OPCs differentiated into adenomatus polyposis coli (APC+) OLs, and CNTF significantly increased the percentage of APC+ OLs from grafted OPCs. Immunofluorescent and immunoelectron microscopic analyses showed that the grafted OPCs formed central myelin sheaths around the axons in the injured spinal cord. The number of OL-remyelinated axons in ventrolateral funiculus (VLF) or lateral funiculus (LF) at the injured epicenter was significantly increased in animals that received CNTF-OPC grafts compared with all other groups. Importantly, 75% of rats receiving CNTF-OPC grafts recovered transcranial magnetic motor-evoked potential and magnetic interenlargement reflex responses, indicating that conduction through the demyelinated axons in VLF or LF, respectively, was partially restored. More importantly, recovery of hindlimb locomotor function was significantly enhanced in animals receiving grafts of CNTF-OPCs. Thus, combined treatment with OPC grafts expressing CNTF can enhance remyelination and facilitate functional recovery after traumatic SCI