Miracle-dz/UniFAR-FTUs huggingface dataset:
Response to Referee # 1 , 28 . 2 . 2017 by Thum et al .

and Conclusions – Mostly a discussion of methods and no mention of new results. I suggest discussing at least one new and interesting result from your study. Something about spring or autumn photosynthesis, or using leaf level measurements with satellite data, or comparing model simulations to active and passive fluorescence data. We added some results, including two points: i) the ability of observed SIF to capture seasonal cycle of photosynthesis at site scale, ii) the goodness of simulated SIF values vs. observations at regional scale. Figure 1 Figure legend is difficult to read and it’s not clear from figure or caption what is being plotted in panel (A) – legend appears to suggest fluorescence yield as solid red but text refers to photochemical yield. GPP is not shown in panel (A) as stated in caption – please correct. The font size in the figure legend was increased to make it easier to read. The text was corrected, so that it states that the fluorescence yield is the modelled yield (shown in solid red in the figure). The caption was corrected to say that GPP is in panel B. Figure 3 – color scheme is confusing especially with multiple variables on 1 plot. keep observations in black and models in color like in figure 1. Use same line styles for same variables (solid for GPP, dashed for SIF). We remade the figure like suggested. P5L10: *an indication of the fraction of electrons in the leaf that follow the ChlF pathway
Monocular Depth Estimation and Feature Tracking

In the previous section, we discussed the idea of representation learning which leverages unsupervised and self-supervised methods to learn an intermediate, low-dimensional representation of high-dimensional sensory data. These learned features can then be used to solve downstream visual inference tasks. Here, we will examine how representation learning in the context of two common computer vision problems: monocular depth estimation and feature tracking.
The Curse of Depth in Kernel Regime

Recent work by Jacot et al. (2018) has shown that training a neural network of any kind with gradient descent is strongly related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Empirical results in (Lee et al., 2019) demonstrated high performance of a linearized version of training using the so-called NTK regime. In this paper, we show that the large depth limit of this regime is unexpectedly trivial
M ASAI : Multi-agent Summative Assessment Improvement for Unsupervised Environment Design

Reinforcement Learning agents require a distribution of environments for their policy to be trained on. The method or process of deﬁning these environments directly impacts robustness and generalization of the learned agent policies. In single agent reinforcement learning, this problem is often solved by domain randomization, or random-izing the environment and tasks within the scope of the desired operating domain of the agent. The challenge here is to generate both structured and solvable environments that guide the agent’s learning process. Most recently, works have sought to produce the environments under the Unsuper-vised Environment Design (UED) formulation. However, these methods lead to a proliferation of adversarial agents to train one agent for a single agent problem in a discretized task domain. In this work, we aim to automatically generate environments that are solvable and challenging for the continuous multi-agent setting. We base our solution on the Teacher-Student relationship with parameter sharing Students where we re-imagine the Teacher as an environment generator for UED. Our approach uses one environment generator agent ( Teacher ) for any number of learning agents ( Students ). We qualitatively and quantitatively demonstrate that, in terms of multi-agent ( ≥ 8 agents) navigation and steering, Students trained by our approach outperform agents using heuristic search, as well as agents trained by domain randomization. Our code is available at https:
Ideas and perspectives: Allocation of carbon from net primary production in models is inconsistent with observations of the age of respired carbon

Abstract. Carbon allocation in vegetation is an important process in the terrestrial carbon cycle; it determines the fate of photoassimilates, and it has an impact on the time carbon spends in the terrestrial biosphere. Although previous studies have highlighted important conceptual issues in the definition and metrics used to assess carbon allocation, very little emphasis has been placed on the distinction between the allocation of carbon from gross primary production (GPP) and the allocation from net primary production (NPP). An important number of simulation models and conceptual frameworks are based on the concept that C is allocated from NPP, which implies that C is respired immediately after photosynthetic assimilation. However, empirical work that estimates the age of respired CO2 from vegetation tissue (foliage, stems, roots) shows that it may take from years to decades to respire previously produced photosynthates. The transit time distribution of carbon in vegetation and ecosystems, a metric that provides an estimate of the age of respired carbon, indicates that vegetation pools respire carbon of a wide range of ages, on timescales that are in conflict with the assumption that autotrophic respiration only consumes recently fixed carbon. In this contribution, we attempt to provide compelling evidence based on recent research on the age of respired carbon and the theory of timescales of carbon in ecosystems, with the aim to promote a change in the predominant paradigm implemented in ecosystem models where carbon allocation is based on NPP. In addition, we highlight some implications for understanding and modeling carbon dynamics in terrestrial ecosystems.
Active Damping Current Control for Current-Source Inverter-Based PMSM Drives

Current-source inverter is considered as a promising candidate in the permanent-magnet synchronous machine drive system for its superior current waveforms and inherent capability of withstanding the short-circuit failure. However, the resonance issue is yielded, resulting from the paralleling of machine inductances and filter capacitors. Very limited control bandwidth can be retained if only considering the passive damping of winding resistance. In this article, the decoupled active damping current controller in the synchronous frame is investigated. The impedance analysis is performed to make a more intuitive demonstration on the damping mechanism, which reveals that the virtual impedance is not always linearly correlated with the damping gain. The digital delay is crucial for the accurate assessment on the damping effectiveness, and the z-domain discrete model is established to emulate the real circumstance. The quantified analysis is performed on deriving the stable boundaries of controller parameters. The frequency responses and root trajectories are illustrated to validate the conclusions. The optimized parameters are obtained by the proposed tuning procedure, which realizes a balance between the damping effect and the bandwidth performance. Finally, the experiments are performed to verify the proposed control schemes.
Assessment of the Retinal Vessels in Keratoconus: An OCT Angiography Study

This study investigated vascular density and foveal avascular zone (FAZ) parameters using optical coherence tomography angiography (OCT-A) in patients with keratoconus (KC). Participants with KC and healthy controls were included and underwent best-corrected visual acuity (BCVA), keratometry, anterior segment OCT, and macular OCT-A examinations. Of the 70 subjects (mean age 42.9 ± 15.31 years), 79 KC and 47 healthy eyes were included. Significant reductions in the KC group were recorded for the FAZ area, with a mean (±SD) of 0.19 ± 0.12 vs. 0.25 ± 0.09 mm2 p < 0.001. Central vascular density in KC patients was lower compared with the controls: 6.78 ± 4.74 vs. 8.44 ± 3.33 mm−1 p = 0.049; the inner density was also decreased in the study group (13.64 ± 5.13 vs. 16.54 ± 2.89 mm−1, p = 0.002), along with the outer density (14.71 ± 4.12 vs. 16.88 ± 2.42 mm−1, p = 0.004) and full density (14.25 ± 4.30 vs. 16.57 ± 2.48) p = 0.003. Furthermore, BCVA was positively correlated with central vascular density (R = 0.42 p = 0.004, total R = 0.40, p = 0.006) and inner density (R = 0.44, p = 0.002) in patients with KC but not in controls. Additionally, we found a correlation between K2 and inner vascular density (R = −0.30, p = 0.043) and central epithelium thickness and outer density (R = 0.03, p = 0.046). KC patients had lower macular vascular density and a smaller FAZ than healthy participants. The BCVA in KC patients was correlated with the vascular density.
A Driver Gaze Estimation Method Based on Deep Learning

Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver’s attention to build an efficient ADAT. To obtain this “attention value”, the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver’s eyes. To detect the driver’s face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations.
On competition and product differentiation in urban transportation: the

This article considers Cournot-like competition among two public transit oligopolists (a rapid-rail system modeled on the Bay Area's BART, and a parallel bus system) in the presence ofa competitively supplied third alternative. The oligopolists compete in both service quality and price, anddo so myopically. With demand and cost data from the Bay Area numerically estimated equilibria are obtained and studied. The results indicate that: (I) withfares and product characteristics freely variable, neither mode need operate at a loss; (2) the rapidrail mode can cover costs from the farebox even if the bus mode offers moneylosing service; (3) the equilibria exhibit product differentiation, in sharp con? trast to the presently observed situation.
Deep Neural Networks for Form-Finding of Tensegrity Structures

Analytical paradigms have limited conventional form-finding methods of tensegrities; therefore, an innovative approach is urgently needed. This paper proposes a new form-finding method based on state-of-the-art deep learning techniques. One of the statical paradigms, a force density method, is substituted for trained deep neural networks to obtain necessary information of tensegrities. It is based on the differential evolution algorithm, where the eigenvalue decomposition process of the force density matrix and the process of the equilibrium matrix are not needed to find the feasible sets of nodal coordinates. Three well-known tensegrity examples including a 2D two-strut, a 3D-truncated tetrahedron and an icosahedron tensegrity are presented for numerical verifications. The cases of the ReLU and Leaky ReLU activation functions show better results than those of the ELU and SELU. Moreover, the results of the proposed method are in good agreement with the analytical super-stable lines. Three examples show that the proposed method exhibits more uniform final shapes of tensegrity, and much faster convergence history than those of the conventional one.
Combination of HPLC-Q-TOF-MS/MS, Network Pharmacology, and Molecular Docking to Reveal the Mechanism of Apple Pollen in the Treatment of Type 2 Diabetes Mellitus

Studies have found that apple pollen can restrain the activity of amylase. Therefore, we speculate that it may be prescribed to treat patients with type 2 diabetes mellitus (T2DM), while its chemical and pharmacologic profiles remain to be further explained. In this study, the potential bioactive compounds of apple pollen and the underlying mechanism of action were investigated by performing chemical and network pharmacology analysis. Therefore, HPLC-QTOF-MS/MS analysis based on chemical compound libraries was applied in identifying the chemical profiles of apple pollen and network pharmacology was adopted for predicting the potential targets of the active components of apple pollen. Initially, the chemical map of apple pollen was identified and characterized. Secondly, the potential targets of active compounds of apple pollen were predicted with the Swiss Target Prediction and PharmMapper databases, whereas targets of T2DM were collected from the GeneCards and OMIM database. Thereafter, the target of active compounds and T2DM targets established common targets using Venn. Afterwards, the common targets were imported into the STRING database in order to construct the protein-protein interaction (PPI) network and select the core targets of apple pollen treatment of T2DM. In addition, GO and KEGG signaling pathway enrichment analyses were conducted on the selected core targets using the DAVID database. As a result, totally 28 compounds were identified. Meanwhile, network pharmacological analysis showed that 3-hydroxy-3-methyl glutaric acid, 5-hydroxyindoleacetic acid, DL-3-phenyllactic acid, isorhamnetin-3-glucoside-4′-glucoside, isorhamnetin-3-O-glucoside, syringetin-3-O-galactoside, rhamnetin, m-coumaric acid, quercitrin, isorhamnetin-3-galactoside-6″-rhamnoside, and kaempferol-3-O-alpha-L-arabinoside might be the active compounds of apple pollen. Moreover, AKT1, PPARG, SRC, EGFR, CASP3, ESR1, and the other potential core targets might be involved in the treatment of T2DM by modulating the following pathways, containing insulin resistance, hepatitis C, pancreatic cancer, insulin signaling pathway, TNF signaling pathway, and PI3K-AKT signaling pathway. Quercitrin, kaempferol, and isorhamnetin-3-O-glucoside bound most stably to AKT1. Isorhamnetin-3-O-glucoside and quercitrin bound most stably to SRC. In addition, arachidonic acid bound most stably to PPARG.
Performance Analysis of Collaborative Transmission Schemes in Industrial Wireless Sensor Networks

This paper analyzes the energy efficiency and throughput performance of Single-hop, Multi-hop, Decodeand-Forward (DF) and Incremental Decode-and-Forward (IDF) transmission schemes. This paper studies the impact of propagation channel in both Line-Of-Sight (LOS) and Non Line-Of-Sight (NLOS) environments based on the Nakagami-m fading channel. Our simulation results show that IDF protocol, exploiting cooperative transmission scheme and a feedback bit at the destination, achieves better performance than the other schemes.
Benchmarking process of knowledge management best practice model for higher learning institution

This paper aims to propose the benchmarking process for KM Best Practice Model to suits Higher Learning Institution (HLI) environment. The need for Benchmarking of KM Best Practice Model is due to the lack of research made to confirm the Best KM Model for HLI. Benchmarking process will helps HLI to ensure continuous improvement and hopefully will uplift KM state in HLI by implementing benchmarked KM Best Practice Model due to the stagnant state of KM in HLI for the past 10 years. Method: Literature for Benchmarking were extracted to propose the right benchmarking process for HLI environment.Several KM models were extracted from the literature and reviewed. The KM Model were selected to suit the HLI environment in Malaysia, this is to ensure the KM Model selected served as the best KM model to be practice in the future or practically named internal benchmarking. Conclusion: The paper proposed Benchmarking process for KM Best Practice Model to suits HLI. The moderator for KM Best Practice Model were identified to proceed with the KM Best Practice Components effectiveness validation in the future study.This study also proposed a conceptual Model for KM Best Practice.
Investigation of the Specification Degradation Mechanism of CMOS Power Amplifier under Thermal Shock Test

To investigate the critical specifications of a power amplifier (PA) under rapidly changing temperature conditions, we fabricated and tested a 0.3–1.1 GHz complementary metal–oxide–semiconductor (CMOS) PA under thermal shock tests. The results show that high- and low-temperature shocks can accelerate the degradation of critical specifications of PAs and that the critical specifications of PAs degrade with the increasing number of shocks. The main reason for the degradation of critical specifications of PAs with increasing thermal shock tests is the mismatch of thermal expansion coefficients of printed circuit boards (PCB) with FR-4 as a substrate. The results of this paper can provide a reference for the development of temperature-robust PAs design guidelines for the implementation of temperature-robust PAs using low-cost silicon technology.
An investigation of unsteady 3D effects on trailing edge flaps

The present study investigates the impact of unsteady and viscous three-dimensional aerodynamic effects on a wind turbine blade with trailing edge flap by means of CFD. Harmonic oscillations are simulated on the DTU 10 MW rotor with a flap of 10% chord extent ranging from 70% to 80% blade radius. The deflection frequency is varied in the range between 1p and 6p. To quantify 3D effects, rotor simulations are compared to 2D airfoil computations. A significant influence of trailing and shed vortex structures has been found which leads to a reduction of the lift amplitude and hysteresis effects in the lift response with regard to the flap deflection. In the 3D rotor results greater amplitude reductions and less hystereses have been found compared to the 2D airfoil simulations.
Exosomal EPHA2 derived from highly metastatic breast cancer cells promotes angiogenesis by activating the AMPK signaling pathway through Ephrin A1-EPHA2 forward signaling

Rationale: Angiogenesis is a fundamental process of tumorigenesis, growth, invasion and metastatic spread. Extracellular vesicles, especially exosomes, released by primary tumors promote angiogenesis and cancer progression. However, the mechanism underlying the pro-angiogenic potency of cancer cell-derived exosomes remains poorly understood. Methods: Exosomes were isolated from breast cancer cells with high metastatic potential (HM) and low metastatic potential (LM). The pro-angiogenic effects of these exosomes were evaluated by in vitro tube formation assays, wound healing assays, rat arterial ring budding assays and in vivo Matrigel plug assays. Subsequently, RNA sequencing, shRNA-mediated gene knockdown, overexpression of different EPHA2 mutants, and small-molecule inhibitors were used to analyze the angiogenesis-promoting effect of exosomal EPHA2 and its potential downstream mechanism. Finally, xenograft tumor models were established using tumor cells expressing different levels of EPHA2 to mimic the secretion of exosomes by tumor cells in vivo, and the metastasis of cancer cells were monitored using the IVIS Spectrum imaging system and Computed Tomography. Results: Herein, we demonstrated that exosomes produced by HM breast cancer cells can promote angiogenesis and metastasis. EPHA2 was rich in HM-derived exosomes and conferred the pro-angiogenic effect. Exosomal EPHA2 can be transferred from HM breast cancer cells to endothelial cells. Moreover, it can stimulate the migration and tube-forming abilities of endothelial cells in vitro and promote angiogenesis and tumor metastasis in vivo. Mechanistically, exosomal EPHA2 activates the AMPK signaling via the ligand Ephrin A1-dependent canonical forward signaling pathway. Moreover, inhibition of the AMPK signaling impairs exosomal EPHA2-mediated pro-angiogenic effects. Conclusion: Our findings identify a novel mechanism of exosomal EPHA2-mediated intercellular communication from breast cancer cells to endothelial cells in the tumor microenvironment to provoke angiogenesis and metastasis. Targeting the exosomal EPHA2-AMPK signaling may serve as a potential strategy for breast cancer therapy.
A Novel Approach to Control of Autonomous Microgrid Systems

In this paper, reinforcement learning techniques are proposed for the control of autonomous microgrids. A type of approximate dynamic programming method is used to solve the Bellman equation, namely heuristic dynamic programming. The proposed control strategy is based on actor-critic networks. The control strategy is designed using a dynamic model of islanded microgrids and makes use of an internal oscillator for frequency control. The proposed control technique is based on a value iterations algorithm which is implemented online. Using only partial knowledge of the microgrid dynamics, the simulation results showed that the proposed control technique stabilizes the system and is robust to the load disturbances.