The electronic structure and magnetic interactions in the mixed transition-metal oxide La(Co,Ni)O3 studied by x-ray absorption spectroscopies

XAS findet klare Hinweise auf einen gemischtvalenten Zustand für Co ebenso wie für Ni. Während der allmähliche Spinübergang von Co3+ von einem Niedrigspin-Zustand hin zu einem Hochspin-Zustand für geringe Dotierungsgrade x erhalten bleibt. Insbesondere finden wir, dass es die Koexistenz des HS-Zustandes sowohl in Co3+ als auch in Ni3+ sein muß, die zu der verantwortlichen ferromagnetischen Wechselwirkung führt

CasGCN: Predicting future cascade growth based on information diffusion graph

Sudden bursts of information cascades can lead to unexpected consequences such as extreme opinions, changes in fashion trends, and uncontrollable spread of rumors. It has become an important problem on how to effectively predict a cascade' size in the future, especially for large-scale cascades on social media platforms such as Twitter and Weibo. However, existing methods are insufficient in dealing with this challenging prediction problem. Conventional methods heavily rely on either hand crafted features or unrealistic assumptions. End-to-end deep learning models, such as recurrent neural networks, are not suitable to work with graphical inputs directly and cannot handle structural information that is embedded in the cascade graphs. In this paper, we propose a novel deep learning architecture for cascade growth prediction, called CasGCN, which employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism on both the extracted features and the temporal information before conducting cascade size prediction. We conduct experiments on two real-world cascade growth prediction scenarios (i.e., retweet popularity on Sina Weibo and academic paper citations on DBLP), with the experimental results showing that CasGCN enjoys a superior performance over several baseline methods, particularly when the cascades are of large scale

On Large Language Models' Selection Bias in Multi-Choice Questions

Multi-choice questions (MCQs) serve as a common yet important task format in the research of large language models (LLMs). Our work shows that LLMs exhibit an inherent "selection bias" in MCQs, which refers to LLMs' preferences to select options located at specific positions (like "Option C"). This bias is prevalent across various LLMs, making their performance vulnerable to option position changes in MCQs. We identify that one primary cause resulting in selection bias is option numbering, i.e., the ID symbols A/B/C/D associated with the options. To mitigate selection bias, we propose a new method called PriDe. PriDe first decomposes the observed model prediction distribution into an intrinsic prediction over option contents and a prior distribution over option IDs. It then estimates the prior by permutating option contents on a small number of test samples, which is used to debias the subsequent test samples. We demonstrate that, as a label-free, inference-time method, PriDe achieves a more effective and computation-efficient debiasing than strong baselines. We further show that the priors estimated by PriDe generalize well across different domains, highlighting its practical potential in broader scenarios.Comment: Work in progress. 21 pages, 13 figure

Optimal integration of mobile battery energy storage in distribution system with renewables

published_or_final_versio

Observational constraints on cosmic neutrinos and dark energy revisited

Using several cosmological observations, i.e. the cosmic microwave background anisotropies (WMAP), the weak gravitational lensing (CFHTLS), the measurements of baryon acoustic oscillations (SDSS+WiggleZ), the most recent observational Hubble parameter data, the Union2.1 compilation of type Ia supernovae, and the HST prior, we impose constraints on the sum of neutrino masses (\mnu), the effective number of neutrino species (\neff) and dark energy equation of state ($w$), individually and collectively. We find that a tight upper limit on \mnu can be extracted from the full data combination, if \neff and $w$ are fixed. However this upper bound is severely weakened if \neff and $w$ are allowed to vary. This result naturally raises questions on the robustness of previous strict upper bounds on \mnu, ever reported in the literature. The best-fit values from our most generalized constraint read \mnu=0.556^{+0.231}_{-0.288}\rm eV, \neff=3.839\pm0.452, and $w=-1.058\pm0.088$ at 68% confidence level, which shows a firm lower limit on total neutrino mass, favors an extra light degree of freedom, and supports the cosmological constant model. The current weak lensing data are already helpful in constraining cosmological model parameters for fixed $w$. The dataset of Hubble parameter gains numerous advantages over supernovae when $w=-1$, particularly its illuminating power in constraining \neff. As long as $w$ is included as a free parameter, it is still the standardizable candles of type Ia supernovae that play the most dominant role in the parameter constraints.Comment: 39 pages, 15 figures, 7 tables, accepted to JCA