Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. 531107051063 and Guangxi Natural Science Foundation under Grant No. 2016JJD110017. We would like to thank Dr. Rautiainen Miina and Mottus Matti for sharing the field data and the USGS for making the EO-1 Hyperion hyperspectral data publically available. (531107051063 - Fundamental Research Funds for the Central Universities; 2016JJD110017 - Guangxi Natural Science Foundation)Accepted manuscrip

The Effect of Different Laser Irradiation on Cyclophosphamide-Induced Leucopenia in Rats

Objective. To assess the effect of different lasers on cyclophosphamide- (CTX-) induced leucopenia in rats. Methods. 11 rats were normal control and 55 rats were injected with a dose of 80 mg/kg CTX for the first time and 40 mg/kg on the 6th and the 11th days to establish a leucopenia model. Rats of the irradiation groups received a 5-minute laser irradiation with either single 10.6 μm or 650 nm laser or alternatively 10.6 μm–650 nm laser irradiation, besides a sham treatment on acupoint Dazhui (DU 14) and acupoint Zusanli (ST 36) of both sides, 8 times for 16 days. Normal and model control group received no treatment. Results. On day 16 after the first CTX injection, the WBC counts from all the laser irradiation groups were significantly higher than those from the model control and the sham group (P<0.05), while there were no significant differences compared with the normal control (P>0.05). The TI of 10.6 μm–650 nm laser irradiation group was significantly higher than that of the model control group (P<0.05). Conclusions. The single and combined 10.6 μm and 650 nm laser irradiation on ST36 and DU14 accelerated the recovery of the WBC count in the rats with leucopenia

Primary Disruption of the Memory-Related Subsystems of the Default Mode Network in Alzheimer’s Disease: Resting-State Functional Connectivity MRI Study

Background: Recent studies have indicated that the default mode network (DMN) comprises at least three subsystems: The medial temporal lobe (MTL) and dorsal medial prefrontal cortex (DMPFC) subsystems and a core comprising the anterior MPFC (aMPFC) and posterior cingulate cortex (PCC). Additionally, the disruption of the DMN is related to Alzheimer’s disease (AD). However, little is known regarding the changes in these subsystems in AD, a progressive disease characterized by memory impairment. Here, we performed a resting-state functional connectivity (FC) analysis to test our hypothesis that the memory-related MTL subsystem was predominantly disrupted in AD.Method: To reveal specific subsystem changes, we calculated the strength and number of FCS in the DMN intra- and inter-subsystems across individuals and compared the FC of the two groups. To further examine which pairs of brain regional functional connections contributed to the subsystem alterations, correlation coefficients between any two brain regions in the DMN were compared across groups. Additionally, to identify which regions made the strongest contributions to the subsystem changes, we calculated the regional FC strength (FCS), which was compared across groups.Results: For the intra-subsystem, decreased FC number and strength occurred in the MTL subsystem of AD patients but not in the DMPFC subsystem or core. For the inter-subsystems, the AD group showed decreased FCS and number between the MTL subsystem and PCC and a decreased number between the PCC and DMPFC subsystem. Decreased inter-regional FCS were found within the MTL subsystem in AD patients relative to controls: The posterior inferior parietal lobule (pIPL) showed decreased FC with the hippocampal formation (HF), parahippocampal cortex (PHC) and ventral MPFC (vMPFC). Decreased inter-regional FCS of the inter-subsystems were also found in AD patients: The HF and/or PHC showed decreased FC with dMPFC and TPJ, located in the DMPFC subsystem, and with PCC. AD patients also showed decreased FC between the PCC and TLC of the dMPFC subsystem. Furthermore, the HF and PHC in the MTL subsystem showed decreased regional FCS.Conclusion: Decreased intrinsic FC was mainly associated with the MTL subsystem of the AD group, suggesting that the MTL subsystem is predominantly disrupted

Learning quantum states and unitaries of bounded gate complexity

While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with $G$ two-qubit gates to a small trace distance, a sample complexity scaling linearly in $G$ is necessary and sufficient. We also prove that the optimal query complexity to learn a unitary generated by $G$ gates to a small average-case error scales linearly in $G$. While sample-efficient learning can be achieved, we show that under reasonable cryptographic conjectures, the computational complexity for learning states and unitaries of gate complexity $G$ must scale exponentially in $G$. We illustrate how these results establish fundamental limitations on the expressivity of quantum machine learning models and provide new perspectives on no-free-lunch theorems in unitary learning. Together, our results answer how the complexity of learning quantum states and unitaries relate to the complexity of creating these states and unitaries.Comment: 8 pages, 1 figure, 1 table + 56-page appendi

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Entropy Loading Design for the MIMO-OFDM Visible Light Communication System Using the OCT Precoding Technique

In this paper, an orthogonal circulant matrix transform (OCT) precoding technique is proposed to combine with the entropy loading in the multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) visible light communication (VLC) system where the space-time coding (STBC) is chosen for its robustness to the channel correlation. Benefitting from the OCT precoding technique, the uniform signal-to-noise ratio (SNR) among all the subchannels can be achieved. As a result, only one SNR value is required to be fed back, and the same distribution matcher is employed during probabilistic shaping (PS), which means much lower feedback overhead and system complexity than the conventional entropy loading scheme. Experimental results show that the OCT precoding does not cause the system performance loss where the achievable information rate (AIR) of the proposed system is comparable with the conventional system without precoding. With an available bandwidth of ∼25 MHz, the proposed scheme can realize the AIR of 50.75 Mb/s at the expense of 0.45% average forward error correction (FEC) overhead (OH)

The effect of corporate culture on firm performance: Evidence from China

This study examines whether corporate culture promotion affects firm performance in China in terms of firm market value, firm financial performance and innovation output. We find consistent evidence that corporate culture promotion is negatively related to firm market value, positively related to innovation output and not significantly related to firm financial performance. In addition, the negative effect of corporate culture promotion on firm market value is driven by small firms and firms located in less developed provinces. Furthermore, we find that some specific corporate culture promotions, such as innovation culture promotion and integrity culture promotion, are not related to firm value or financial performance. However, innovation culture promotion is positively associated with innovation output. Keywords: Corporate culture promotion, Firm performance, Chin

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

International audienceQuantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on the noiseless case. In this work, we numerically investigate the performance of different quantum state reconstruction techniques for mixed states: the finite-temperature Ising model. We show how to systematically reduce the quantum resource requirement of the algorithms by applying variance reduction techniques. Then, we compare the two leading neural quantum state encodings of the state, namely, the Neural Density Operator and the positive operator-valued measurement representation, and illustrate their different performance as the mixedness of the target state varies. We find that certain encodings are more efficient in different regimes of mixedness and point out the need for designing more efficient encodings in terms of both classical and quantum resources