Nitrogen (N) is a major ingredient of the atmosphere, but a trace component in the silicate Earth. Its initial inventory in these reservoirs during Earth's early differentiation requires knowledge of N speciation in magmas, for example, whether it outgasses as N 2 or is sequestered in silicate melts as N 3− , which remains largely unconstrained over the entire mantle regime. Here we examine N species in anhydrous and hydrous pyrolitic melts at varying P‐T‐redox conditions by ab‐initio calculations, and find N‐N bonding under oxidizing conditions from ambient to lower mantle pressures. Under reducing conditions, N interacts with the silicate network or forms N‐H bonds, depending on the availability of hydrogen. Redox control of N speciation is demonstrated valid over a P‐T space encompassing probable magma ocean depths. Finally, if the Earth accreted from increasingly oxidized materials toward the end of its accretion, an N‐enriched secondary atmosphere might be produced and persist until later impacts.
Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popularly used one-shot estimation is statistically inefficient if data are not randomly distributed across different workers. To fix the problem, a novel one-step estimation method is developed with the following nice properties. First, the algorithm is communication efficient. That is the communication cost demanded is practically acceptable. Second, the resulting estimator is statistically efficient. That is its asymptotic covariance is the same as that of the global estimator. Third, the estimator is robust against data distribution. That is its consistency is guaranteed even if data are not randomly distributed across different workers. Numerical experiments are provided to corroborate our findings. A real example is also presented for illustration.
Nutrient scarcity is pervasive for natural microbial communities, affecting species reproduction and co-existence. However, it remains unclear whether there are general rules of how microbial species abundances are shaped by biotic and abiotic factors. Here we show that the ribosomal RNA gene operon (rrn) copy number, a genomic trait related to bacterial growth rate and nutrient demand, decreases from the abundant to the rare biosphere in the nutrient-rich coastal sediment but exhibits the opposite pattern in the nutrient-scarce pelagic zone of the global ocean. Both patterns are underlain by positive correlations between community-level rrn copy number and nutrients. Furthermore, inter-species co-exclusion inferred by negative network associations is observed more in coastal sediment than in ocean water samples. Nutrient manipulation experiments yield effects of nutrient availability on rrn copy numbers and network associations that are consistent with our field observations. Based on these results, we propose a “hunger games” hypothesis to define microbial species abundance rules using the rrn copy number, ecological interaction, and nutrient availability.
As a stereo odor cue, internostril odor influx could help us in many spatial tasks, including localization and navigation. Studies have also revealed that this benefit could be modulated by the asymmetric concentrations of both influxes (left nose vs right nose). The interaction between olfaction and vision, such as in object recognition and visual direction judgment, has been documented; however, little has been revealed about the impact of odor cues on sound localization. Here we adopted the ventriloquist paradigm in auditory-odor interactions and investigated sound localization with the concurrent unilateral odor influx. Specifically, we teased apart both the "nature" of the odors (pure olfactory stimulus vs. mixed olfactory/trigeminal stimulus) and the location of influx (left nose vs. right nose) and examined sound localization with the method of constant stimuli. Forty-one participants, who passed the Chinese Smell Identification Test, perceived sounds with different azimuths (0°, 5°, 10°, and 20° unilaterally deflected from the sagittal plane by head-related transfer function) and performed sound localization (leftward or rightward) tasks under concurrent, different unilateral odor influxes (10% v/v phenylethyl alcohol, PEA, as pure olfactory stimulus, 1% m/v menthol as mixed olfactory/trigeminal stimulus, and propylene glycol as the control). Meanwhile, they reported confidence levels of the judgments. Results suggested that unilateral PEA influx did not affect human sound localization judgments. However, unilateral menthol influx systematically biased the perceived sound localization, shifting toward the odor source. Our study provides evidence that unilateral odor influx could bias perceived sound localization only when the odor activates the trigeminal nerves.
This paper discusses a widely accepted emendation to an earlier version of IG X 2.1 137. Early draft copies of the Herennia announcement show that Antoninus Pius was hailed as Σωτήρ by the city of Thessalonike, a rare epithet for this emperor. This reading was later replaced due to an expert’s claim that σωτῆρος has to be read σωτηρίας. Since this seems to conform to a well-known salutary formula, the emendation was adopted from then on. This paper suggests that the reading of σωτῆρος is based on reliable and published reports instead, and ought to be preferred over the expert claim. Empirical evidence is given to support reading σωτῆρος.
Recently, recommendation based on causal inference has gained much attention in the industrial community. The introduction of causal techniques into recommender systems (RS) has brought great development to this field and has gradually become a trend. However, a unified causal analysis framework has not been established yet. On one hand, the existing causal methods in RS lack a clear causal and mathematical formalization on the scientific questions of interest. Many confusions need to be clarified: what exactly is being estimated, for what purpose, in which scenario, by which technique, and under what plausible assumptions. On the other hand, technically speaking, the existence of various biases is the main obstacle to drawing causal conclusions from observed data. Yet, formal definitions of the biases in RS are still not clear. Both of the limitations greatly hinder the development of RS.In this paper, we attempt to give a causal analysis framework to accommodate different scenarios in RS, thereby providing a principled and rigorous operational guideline for causal recommendation. We first propose a step-by-step guideline on how to clarify and investigate problems in RS using causal concepts. Then, we provide a new taxonomy and give a formal definition of various biases in RS from the perspective of violating what assumptions are adopted in standard causal analysis. Finally, we find that many problems in RS can be well formalized into a few scenarios using the proposed causal analysis framework.
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN conversion has achieved comparable performance as ANNs on large-scale datasets. Despite this, it requires long time-steps to match the firing rates of SNNs to the activation of ANNs. As a result, the converted SNN suffers severe performance degradation problems with short time-steps, which hamper the practical application of SNNs. In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs. Then we propose the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs. We prove that the expected conversion error between SNNs and ANNs is zero, enabling us to achieve high-accuracy and ultra-low-latency SNNs. We evaluate our method on CIFAR-10/100 and ImageNet datasets, and show that it outperforms the state-of-the-art ANN-SNN and directly trained SNNs in both accuracy and time-steps. To the best of our knowledge, this is the first time to explore high-performance ANN-SNN conversion with ultra-low latency (4 time-steps).