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).
Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness. The most effective way to train deep SNNs is through ANN-to-SNN conversion, which have yielded the best performance in deep network structure and large-scale datasets. However, there is a trade-off between accuracy and latency. In order to achieve high precision as original ANNs, a long simulation time is needed to match the firing rate of a spiking neuron with the activation value of an analog neuron, which impedes the practical application of SNN. In this paper, we aim to achieve high-performance converted SNNs with extremely low latency (fewer than 32 time-steps). We start by theoretically analyzing ANN-to-SNN conversion and show that scaling the thresholds does play a similar role as weight normalization. Instead of introducing constraints that facilitate ANN-to-SNN conversion at the cost of model capacity, we applied a more direct way by optimizing the initial membrane potential to reduce the conversion loss in each layer. Besides, we demonstrate that optimal initialization of membrane potentials can implement expected error-free ANN-to-SNN conversion. We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps. For example, we reach top-1 accuracy of 93.38% on CIFAR-10 with 16 time-steps. Moreover, our method can be applied to other ANN-SNN conversion methodologies and remarkably promote performance when the time-steps is small.
Sulfite(S(IV))-induced advanced oxidation processes (AOPs) have great prospect in the field of removing organic pollutants, yet developing highly efficient sulfite activation systems and optimizing active sites for favorable catalytic processes are important but still challenging. Herein, we have achieved a composite catalyst with modulated Co electron structure for efficient AOPs by decorating Co(OH)2 on ultrathin graphitic carbon nitride (g-C3N4) nanosheet through an adjustable strategy, which exhibits high catalytic performance in S(IV) activation system. At optimal pH 9, 92% of paracetamol (APAP) (0.005 mM) is removed with the degradation rate constant of k1 = 0.193 min−1 within 30 min in presence of the composite material. The in-situ synthesis mode introduces strong heterogeneous interface interaction, resulting in directional electron transfer from cobalt hydroxide layer to g-C3N4 sheet revealed by X-ray photoelectron spectroscopy and density functional theory (DFT) calculations. The underlying activity enhanced mechanisms for APAP in S(IV) activation system using Co(OH)2/g-C3N4 are proposed: (i) The ultrathin g-C3N4 nanosheets provide more anchoring centers for generating small Co(OH)2 nanoparticles with abundant active sites which benefit to form metastable intermediates of Co(II)-SO3; (ii) The strong interface interaction induces charge redistribution between Co(OH)2 and g-C3N4 conformed by DFT calculation, which modulates the d-band center of Co site and strengthens the bind of Co(II)-SO3, thereby giving rise to radicals (•OH, SO4• and O2•) and nonradicals (1O2 and electron transfer) oxidation for highly-efficient removal APAP. Our work will pave the way to build an environmentally friendly strategy for emerging organic pollutant degradation in water through building efficient catalysts in sulfite activation system.