Genetic linkage maps of Pinus koraiensis Sieb. et Zucc. based on AFLP markers

Genetic linkage maps provide essential information for molecular breeding. In this paper, the genetic linkage map of Pinus koraiensis was constructed using an F1 progeny of 88 individuals. One hundred and thirty (130) of molecular markers were mapped onto 6 linkage groups, 4 triples and 15 pairs at the linkage criteria LOD 4.0. Nine primer combinations were applied to map construction. The consensus map gained covers 620.909 cM, with an average marker spacing of 4.776 cM. The presented map provides crucial information for future genomic studies of P. koraiensis, in particular for QTL (quantitative trait loci) mapping of economically important breeding target traits.Keywords: Genetic mapping, Korean pine, linkage map, marker-aided selectionAfrican Journal of Biotechnology Vol. 9(35), pp. 5659-5664, 30 August, 201

Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection

Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets

Multi-site fMRI-based mental disorder detection using adversarial learning: an ABIDE study

Heterogeneity in open fMRI datasets, caused by variations in scanning protocols, confounders, and population diversity, hinders representation learning and classification performance. To address these limitations, we propose a novel multi-site adversarial learning network (MSalNET) for fMRI-based mental disorder detection. Firstly, a representation learning module is introduced with a node information assembly (NIA) mechanism to extract features from functional connectivity (FC). This mechanism aggregates edge information from both horizontal and vertical directions, effectively assembling node information. Secondly, to generalize the feature across sites, we proposed a site-level feature extraction module that can learn from individual FC. Lastly, an adversarial learning network, is proposed to balance the trade-off between individual classification and site regression tasks. The proposed method was evaluated on Autism Brain Imaging Data Exchange (ABIDE). The results indicate that the proposed method achieves an accuracy of 75.56% with reducing variability from a data-driven perspective. The most discriminative brain regions revealed by NIA are consistent with statistical findings, uncovering the black box of deep learning to a certain extent. MSalNET offers a novel perspective on the detection of multi-site fMRI mental disorders and it considers the interpretability of the model, which is a crucial aspect in deep learning

Intraspecific predator interference promotes biodiversity in ecosystems

Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.Comment: Main text 14 pages, 3 figures. Appendices 34 pages, 15 Appendix-figure

Tackling Challenges in 21cm Global Spectrum Experiment: the Impact of Ionosphere and Beam Distortion

The HI 21cm global signal from the Cosmic Dawn and the Epoch of Reionization (EoR) offers critical insights into the evolution of our Universe. Yet, its detection presents significant challenges due to its extremely low signal-to-contamination ratio and complex instrumental systematics. In this paper, we examine the effects of the ionosphere and antenna beam on data analysis. The ionosphere, an ionized plasma layer in the Earth's atmosphere, refracts, absorbs, and emits radio waves in the relevant frequency range. This interaction results in additional spectral distortion of the observed signal, complicating the process of foreground subtraction. Additionally, chromatic variations in the beam can also introduce further contamination into the global spectrum measurement. Notably, the ionospheric effect, being dependent on the direction of incoming light, interacts with the instrumental beam, adding another layer of complexity. To address this, we evaluate three different fitting templates of foreground: the logarithmic polynomial, the physically motivated EDGES template, and a SVD-based template. Our findings indicate that the EDGES and SVD templates generally surpass logarithmic polynomials in performance. Recognizing the significance of beam chromaticity, we further investigate specific beam distortion models and their impacts on the signal extraction process.Comment: Accepted for publication in Ap

Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge, we propose a single-view and multi-view fused depth estimation system, which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth estimations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus, the system tends to choose the more reliable branch when facing textureless scenes, inaccurate calibration, dynamic objects, and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore, we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/AFNet/.Comment: Accepted to CVPR 202

Fast generation of arbitrary optical focus array

We report a novel method to generate arbitrary optical focus arrays (OFAs). Our approach rapidly produces computer-generated holograms (CGHs) to precisely control the positions and the intensities of the foci. This is achieved by replacing the fast Fourier transform (FFT) operation in the conventional iterative Fourier-transform algorithm (IFTA) with a linear algebra one, identifying/removing zero elements from the matrices, and employing a generalized weighting strategy. On the premise of accelerating the calculation speed by >70 times, we demonstrate OFA with 99% intensity precision in the experiment. Our method proves effective and is applicable for the systems in which real-time OFA generation is essential

Quantum Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning

Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage. Here, we propose a quantum resonant dimension reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data and accelerate the quantum machine learning algorithms. After QRDR, the dimension of input data $N$ can be reduced into desired scale $R$, and the effective information of the original data will be preserved correspondingly, which will reduce the computational complexity of subsequent quantum machine learning algorithms or quantum storage. QRDR operates with polylogarithmic time complexity and reduces the error dependency from the order of $1/\epsilon^3$ to the order of $1/\epsilon$, compared to existing algorithms. We demonstrate the performance of our algorithm combining with two types of quantum classifiers, quantum support vector machines and quantum convolutional neural networks, for classifying underwater detection targets and quantum many-body phase respectively. The simulation results indicate that reduced data improved the processing efficiency and accuracy following the application of QRDR. As quantum machine learning continues to advance, our algorithm has the potential to be utilized in a variety of computing fields