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1
Huang, Sheng-Jun, e Zhi-Hua Zhou. "Multi-Label Learning by Exploiting Label Correlations Locally". Proceedings of the AAAI Conference on Artificial Intelligence 26, n. 1 (20 settembre 2021): 949–55. http://dx.doi.org/10.1609/aaai.v26i1.8287.
Abstract (sommario):
It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, however, different instances may share different label correlations, and few correlations are globally applicable. In this paper, we propose the ML-LOC approach which allows label correlations to be exploited locally. To encode the local influence of label correlations, we derive a LOC code to enhance the feature representation of each instance. The global discrimination fitting and local correlation sensitivity are incorporated into a unified framework, and an alternating solution is developed for the optimization. Experimental results on a number of image, text and gene data sets validate the effectiveness of our approach.
2
Bikondoa, Oier. "On the use of two-time correlation functions for X-ray photon correlation spectroscopy data analysis". Journal of Applied Crystallography 50, n. 2 (17 febbraio 2017): 357–68. http://dx.doi.org/10.1107/s1600576717000577.
Abstract (sommario):
Multi-time correlation functions are especially well suited to study non-equilibrium processes. In particular, two-time correlation functions are widely used in X-ray photon correlation experiments on systems out of equilibrium. One-time correlations are often extracted from two-time correlation functions at different sample ages. However, this way of analysing two-time correlation functions is not unique. Here, two methods to analyse two-time correlation functions are scrutinized, and three illustrative examples are used to discuss the implications for the evaluation of the correlation times and functional shape of the correlations.
3
Li, Yi-An, Dong-Fang Wang, Song Zhang e Yu-Gang Ma. "System scan of the multiplicity correlation between forward and backward rapidities in relativistic heavy-ion collisions using a multi-phase transport model *". Chinese Physics C 46, n. 4 (1 aprile 2022): 044101. http://dx.doi.org/10.1088/1674-1137/ac3bc9.
Abstract (sommario):
Abstract A systematic study on forward–backward (FB) multiplicity correlations from large systems to small ones through a multi-phase transport model (AMPT) has been performed and the phenomenon that correlation strength increases with centrality can be explained by taking the distribution of events as the superposition of a series of Gaussian distributions. It is also found that correlations in the plane can imply the shape of the event. Furthermore, long-range correlations originate from the fluctuations associated with the source information. FB correlations allow us to decouple long-range correlations from short-range correlations, and may provide a chance to investigate the α-clustering structure in initial colliding light nuclei as well. It seems the tetrahedron 16O + 16O collision gives a more uniform and symmetrical fireball, that emits the final particles more isotropically or independently in the longitudinal direction, indicating that the forward–backward multiplicity correlation could be used to identify the pattern of α-clustered 16O in future experiments.
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MARABEL ROMO, JACINTO. "WORST-OF OPTIONS AND CORRELATION SKEW UNDER A STOCHASTIC CORRELATION FRAMEWORK". International Journal of Theoretical and Applied Finance 15, n. 07 (novembre 2012): 1250051. http://dx.doi.org/10.1142/s0219024912500513.
Abstract (sommario):
This article considers a multi-asset model based on Wishart processes that accounts for stochastic volatility and for stochastic correlations between the underlying assets, as well as between their volatilities. The model accounts for the existence of correlation term structure and correlation skew. The article shows that the Wishart specification can generate different patterns corresponding to the correlation skew for a wide range of correlation term structures. Another advantage of the model is that it is analytically tractable and, hence, it is possible to obtain semi-closed-form solutions for the prices of plain vanilla options, as well as for the price of exotic derivatives. In this sense, this article develops semi-closed-form formulas for the price of European worst-of options with barriers and/or forward-start features. To motivate the introduction of the Wishart volatility model, the article compares the prices obtained under this model and under a multi-asset stochastic volatility model with constant instantaneous correlations. The results reveal the existence of a stochastic correlation premium and show that the consideration of stochastic correlation is a key element for the valuation of these structures.
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Feng, Lei, Bo An e Shuo He. "Collaboration Based Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 3550–57. http://dx.doi.org/10.1609/aaai.v33i01.33013550.
Abstract (sommario):
It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.
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Cao, Yang, Detian Liu, Qizheng Yin, Fei Xue e Hengliang Tang. "MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting". Journal of Advanced Transportation 2022 (17 giugno 2022): 1–15. http://dx.doi.org/10.1155/2022/2811961.
Abstract (sommario):
Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.
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Lin, Bridget M., Hunyong Cho, Chuwen Liu, Jeff Roach, Apoena Aguiar Ribeiro, Kimon Divaris e Di Wu. "BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data". Microorganisms 11, n. 3 (16 marzo 2023): 766. http://dx.doi.org/10.3390/microorganisms11030766.
Abstract (sommario):
Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome–metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite–species and species–species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome–metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.
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Pumpe, Daniel, Martin Reinecke e Torsten A. Enßlin. "Denoising, deconvolving, and decomposing multi-domain photon observations". Astronomy & Astrophysics 619 (novembre 2018): A119. http://dx.doi.org/10.1051/0004-6361/201832781.
Abstract (sommario):
Astronomical imaging based on photon count data is a non-trivial task. In this context we show how to denoise, deconvolve, and decompose multi-domain photon observations. The primary objective is to incorporate accurate and well motivated likelihood and prior models in order to give reliable estimates about morphologically different but superimposed photon flux components present in the data set. Thereby we denoise and deconvolve photon counts, while simultaneously decomposing them into diffuse, point-like and uninteresting background radiation fluxes. The decomposition is based on a probabilistic hierarchical Bayesian parameter model within the framework of information field theory (IFT). In contrast to its predecessor D3PO, D4PO reconstructs multi-domain components. Thereby each component is defined over its own direct product of multiple independent domains, for example location and energy. D4PO has the capability to reconstruct correlation structures over each of the sub-domains of a component separately. Thereby the inferred correlations implicitly define the morphologically different source components, except for the spatial correlations of the point-like flux. Point-like source fluxes are spatially uncorrelated by definition. The capabilities of the algorithm are demonstrated by means of a synthetic, but realistic, mock data set, providing spectral and spatial information about each detected photon. D4PO successfully denoised, deconvolved, and decomposed a photon count image into diffuse, point-like and background flux, each being functions of location as well as energy. Moreover, uncertainty estimates of the reconstructed fields as well as of their correlation structure are provided employing their posterior density function and accounting for the manifolds the domains reside on.
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Hoffreumon, Timothée, e Ognyan Oreshkov. "The Multi-round Process Matrix". Quantum 5 (20 gennaio 2021): 384. http://dx.doi.org/10.22331/q-2021-01-20-384.
Abstract (sommario):
We develop an extension of the process matrix (PM) framework for correlations between quantum operations with no causal order that allows multiple rounds of information exchange for each party compatibly with the assumption of well-defined causal order of events locally. We characterise the higher-order process describing such correlations, which we name the multi-round process matrix (MPM), and formulate a notion of causal nonseparability for it that extends the one for standard PMs. We show that in the multi-round case there are novel manifestations of causal nonseparability that are not captured by a naive application of the standard PM formalism: we exhibit an instance of an operator that is both a valid PM and a valid MPM, but is causally separable in the first case and can violate causal inequalities in the second case due to the possibility of using a side channel.
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Dobrowolski, Janusz A. "Noise Characterization of Differential Multi- Element Multiport Networks - the Wave Approach". International Journal of Electronics and Telecommunications 61, n. 4 (1 dicembre 2015): 395–401. http://dx.doi.org/10.2478/eletel-2015-0052.
Abstract (sommario):
Abstract In this paper there is presented and discussed a general analysis method for noise characterization of noisy multielement multiport differential networks. It is based on mixed mode, differential and common mode, noise waves representation of noise, generalized mixed-mode scattering parameters and generalized mixed-mode noise wave correlation parameters for the network. There are derived analytical relation between the noise figure for a given output port and the noise matrix and the scattering parameters of the network, as well as the correlations between the input port noise waves. The signal to noise ratio degradation factor is derived and discussed, too. Presented results can be implemented directly in a CAD software for noise analysis of differential microwave multi-element multiport networks with differential as well as with conventional single ended ports.
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Guo, Tao, e Gui Yang Li. "An Improved Multi-Label Classifier Chain Derived from Binary Relevance". Applied Mechanics and Materials 551 (maggio 2014): 302–8. http://dx.doi.org/10.4028/www.scientific.net/amm.551.302.
Abstract (sommario):
In multi-label classification, each training example is associated with a set of labels and the task for classification is to predict the proper label set for each unseen instance. Recently, multi-label classification methods mainly focus on exploiting the label correlations to improve the accuracy of individual multi-label learner. In this paper, an improved method derived from binary relevance named double layer classifier chaining (DCC) is proposed. This algorithm decomposes the multi-label classification problem into two stages classification process to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space, augmented by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with other well-established methods.
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MARABEL, JACINTO. "PRICING DIGITAL OUTPERFORMANCE OPTIONS WITH UNCERTAIN CORRELATION". International Journal of Theoretical and Applied Finance 14, n. 05 (agosto 2011): 709–22. http://dx.doi.org/10.1142/s0219024911006425.
Abstract (sommario):
Multi-asset options exhibit sensitivity to the correlations between the underlying assets and these correlations are notoriously unstable. Moreover, some of these options such as the digital outperformance options, have a cross-gamma that changes sign depending on the relative evolution of the underlying assets. In this paper, I present a model to price digital outperformance options when there is uncertainty about correlation, but it is assumed to lie within a certain range. Under the assumption that assets prices follow a Geometric Brownian motion with constant instantaneous volatilities I present an analytic expression for the price of the digital outperformance option under the constant correlation assumption, as well as the partial differential equation corresponding to the uncertain correlation model. The comparison of the prices obtained using both models shows that there is no constant correlation which allows attaining the price obtained under the uncertain correlation model. This fact shows that it can be dangerous to assume a constant instantaneous correlation for products with a cross-gamma that changes sign.
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Aronoff, Justin, Prajna BK, Simin Soleimanifar e Leslie Bernstein. "Binaural fusion with ecologically valid stimuli". Journal of the Acoustical Society of America 154, n. 4_supplement (1 ottobre 2023): A71. http://dx.doi.org/10.1121/10.0022834.
Abstract (sommario):
Binaural fusion, or the perception of a single auditory “image” can arise from simultaneous inputs across ears. Fusion is fundamental to binaural abilities, including sound source localization. One potent determiner of degree of fusion is the interaural correlation of the binaurally presented waveforms. Noise waveforms can be used to manipulate the interaural correlation, but the normalized correlation of the envelopes of such noises is limited to between 1.0 and 0.785 (π/4). As a result of differing properties between noises and speech, pairs of speech sounds can be generated having normalized interaural envelope correlations well below 0.785. We measured cochlear implant users’ perceived degree of binaural fusion while varying the interaural correlation of speech-like signals from 0.4 to 1.0. Stimuli were generated using speech-based envelopes from multi-channel vocoders that modulated 1000 pulse-per-second pulse trains. Envelopes derived from different vocoder channels were extracted to yield interaural correlations that varied over a wider range than would be possible using noises. Preliminary results suggest that the speech-based stimuli, while differing qualitatively from noises and being more ecologically valid, yield similar effects of interaural correlation on binaural fusion, while allowing a larger range of interaural correlations to be employed.
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Wang, Li, Qianhui Tang, Xiaoyi Wang, Jiping Xu, Zhiyao Zhao, Huiyan Zhang, Jiabin Yu et al. "Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network". PLOS ONE 18, n. 12 (22 dicembre 2023): e0287781. http://dx.doi.org/10.1371/journal.pone.0287781.
Abstract (sommario):
In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data’s temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.
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Liu, X., e V. L. Markine. "Correlation Analysis and Verification of Railway Crossing Condition Monitoring". Sensors 19, n. 19 (26 settembre 2019): 4175. http://dx.doi.org/10.3390/s19194175.
Abstract (sommario):
This paper presents a correlation analysis of the structural dynamic responses and weather conditions of a railway crossing. Prior to that, the condition monitoring of the crossing as well as the indicators for crossing condition assessment are briefly introduced. In the correlation analysis, strong correlations are found between acceleration responses with irregular contact ratios and the fatigue area. The correlation results between the dynamic responses and weather variables indicate the influence of weather on the performance of the crossing, which is verified using a numerical vehicle-crossing model developed using the multi-body system (MBS) method. The combined correlation and simulation results also indicate degraded track conditions of the monitored crossing. In the condition monitoring of railway crossings, the findings of this study can be applied to data measurement simplification and regression, as well as to assessing the conditions of railway crossings.
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Bogolubov, Nikolai N., e Andrey V. Soldatov. "Algebraic aspects of the driven dynamics in the density operator and correlation functions calculation for multi-level open quantum systems". International Journal of Modern Physics B 31, n. 32 (18 dicembre 2017): 1850044. http://dx.doi.org/10.1142/s0217979218500443.
Abstract (sommario):
Exact and approximate master equations were derived by the projection operator method for the reduced statistical operator of a multi-level quantum system with finite number N of quantum eigenstates interacting with arbitrary external classical fields and dissipative environment simultaneously. It was shown that the structure of these equations can be simplified significantly if the free Hamiltonian driven dynamics of an arbitrary quantum multi-level system under the influence of the external driving fields as well as its Markovian and non-Markovian evolution, stipulated by the interaction with the environment, are described in terms of the SU(N) algebra representation. As a consequence, efficient numerical methods can be developed and employed to analyze these master equations for real problems in various fields of theoretical and applied physics. It was also shown that literally the same master equations hold not only for the reduced density operator but also for arbitrary nonequilibrium multi-time correlation functions as well under the only assumption that the system and the environment are uncorrelated at some initial moment of time. A calculational scheme was proposed to account for these lost correlations in a regular perturbative way, thus providing additional computable terms to the correspondent master equations for the correlation functions.
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Zhong, MingHao, Fenghuan Li e Weihong Chen. "Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks". Mathematical Biosciences and Engineering 19, n. 12 (2022): 12448–71. http://dx.doi.org/10.3934/mbe.2022581.
Abstract (sommario):
<abstract><p>Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.</p></abstract>
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Di Nicola, G., S. Tomassetti, M. Pierantozzi e P. F. Muciaccia. "Semi-empirical correlations and an artificial neural network for liquid dynamic viscosity of low GWP refrigerants". IOP Conference Series: Earth and Environmental Science 1106, n. 1 (1 novembre 2022): 012018. http://dx.doi.org/10.1088/1755-1315/1106/1/012018.
Abstract (sommario):
Abstract In this work, simple semi-empirical correlations to describe the temperature and the pressure dependence of the dynamic viscosity of low GWP refrigerants, namely HydroFluoroOlefins (HFOs) and HydroChloroFluoroOlefins (HCFOs), in the liquid phase are presented. Firstly, the experimental liquid dynamic viscosity data available in scientific literature and databases were collected and statistically analyzed. From the data collected for low pressures, the Latini et al. (2002, 1990) correlation for the dynamic viscosity of liquid refrigerants in saturated conditions was re-fitted and constants expressly dedicated to the studied low GWP refrigerants were obtained. Then, the proposed temperature-dependent correlation was modified to represent liquid dynamic viscosity dependence on pressure. In addition, an artificial neural network was developed to predict the dependence of the liquid viscosity of the studied refrigerants on temperature and pressure. This model was trained, validated, and tested for the selected dataset. The results of the proposed correlations and the multi-layer perceptron neural network were compared with the liquid viscosity calculations provided by some of the most well-known literature correlations and REFPROP 10.0, proving the accuracy of the proposed models for engineering applications.
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Ma, Yiran, Xinyi He, Rui Wu e Chenhua Shen. "Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO2 Concentration Time Series during 2010–2018 over China". Entropy 24, n. 6 (11 giugno 2022): 817. http://dx.doi.org/10.3390/e24060817.
Abstract (sommario):
Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO2 concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO2) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO2 data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO2 concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO2 concentration. Highlight: (1) An XCO2 concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO2 concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed.
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Choi, Hwanseok, Cheolwoo Lee e Jin Q. Jeon. "A Simulation Study on Clustering Multivariate Time Series Using Kernel Variant Multi-Way Principal Component Analysis". Journal of Derivatives and Quantitative Studies 25, n. 2 (31 maggio 2017): 229–53. http://dx.doi.org/10.1108/jdqs-02-2017-b0003.
Abstract (sommario):
Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component Analysis (KMPCA) to cluster multivariate time series data which havemultiple dimensions with auto- and cross-correlations. We then check whether this method works well in clustering those data by employing simulation for generalization. Two simulation studies with two different mean structures with nine combinations of auto- and cross-correlations were conducted. The results showed that KMPCA cluster two different mean structure groups over 90% success rates with an appropriate kernel function. We also found that when the mean structures are the same, auto-correlation, the number of temporal points, and the kernel function parameter have the statistically significant effects on clustering performance. The second and third order interaction effects with each of those factors also have effects on clustering success rates. Among the effects of the main factors, the kernel function parameter is the most critical factor to consider for obtaining better performance. A similar error structure may obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and a larger number of temporal points. The paper also discussed some limitations of the KMPCA model and suggested directions for future research that could improve the model.
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Voulgarakis, A., P. J. Telford, A. M. Aghedo, P. Braesicke, G. Faluvegi, N. L. Abraham, K. W. Bowman, J. A. Pyle e D. T. Shindell. "Global multi-year O<sub>3</sub>-CO correlation patterns from models and TES satellite observations". Atmospheric Chemistry and Physics Discussions 11, n. 2 (10 febbraio 2011): 5079–125. http://dx.doi.org/10.5194/acpd-11-5079-2011.
Abstract (sommario):
Abstract. The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used as benchmarks in future studies. The highest positive correlations (around 0.8) are found in the Northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and reflects a mixture of different processes such as transport.
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Voulgarakis, A., P. J. Telford, A. M. Aghedo, P. Braesicke, G. Faluvegi, N. L. Abraham, K. W. Bowman, J. A. Pyle e D. T. Shindell. "Global multi-year O<sub>3</sub>-CO correlation patterns from models and TES satellite observations". Atmospheric Chemistry and Physics 11, n. 12 (22 giugno 2011): 5819–38. http://dx.doi.org/10.5194/acp-11-5819-2011.
Abstract (sommario):
Abstract. The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used in future model evaluations. The highest positive correlations (around 0.8) are found in the northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and may reflect a mixture of different processes such as transport.
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Zibetti, Stefano, e Brent Groves. "Resolved optical-infrared SEDs of galaxies: universal relations and their break-down on local scales". Proceedings of the International Astronomical Union 7, S284 (settembre 2011): 117–21. http://dx.doi.org/10.1017/s1743921312008861.
Abstract (sommario):
AbstractA large body of evidence has demonstrated that the global rest-frame optical and IR colours of galaxies correlate well with each other, which can be readily interpreted as a sign of typically smooth star formation histories. However the processes that lead to the observed correlations are contrary: the stellar light that contributes to the optical is readily absorbed by dust which emits in the IR. Thus on small scales we expect these correlations to break down. In this contribution we present our recent results (Zibetti & Groves 2011) from a pixel-by-pixel multi-wavelength (u-band to 8μm) analysis of seven nearby galaxies ranging from early- to late-types. We show that such a break-down occurs already on scales on few 100 pc, as a result of the different physical conditions in spatially distinct regions inside the galaxy, as we demonstrate by means of a Principal Component Analysis. Despite the lack of internal correlation between optical and IR within individual galaxies, when the pixels of all galaxies are compared the well known optical-IR colour correlations return, demonstrating that the variance observed within galaxies is limited around a mean which follows the well-known trends. We also examine the extremely strong correlations between the mid IR (Spitzer-IRAC)-NIR colours which extend continuously across all galaxies. These correlations arise from the differing contribution of stellar light and dust to the IRAC bands, enabling us to determine pure stellar colours for these bands, but still demonstrating the need for dust (or stellar) corrections in these bands when being used as stellar (dust) tracers.
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Sirangelo, Tiziana M., Richard A. Ludlow e Natasha D. Spadafora. "Multi-Omics Approaches to Study Molecular Mechanisms in Cannabis sativa". Plants 11, n. 16 (22 agosto 2022): 2182. http://dx.doi.org/10.3390/plants11162182.
Abstract (sommario):
Cannabis (Cannabis sativa L.), also known as hemp, is one of the oldest cultivated crops, grown for both its use in textile and cordage production, and its unique chemical properties. However, due to the legislation regulating cannabis cultivation, it is not a well characterized crop, especially regarding molecular and genetic pathways. Only recently have regulations begun to ease enough to allow more widespread cannabis research, which, coupled with the availability of cannabis genome sequences, is fuelling the interest of the scientific community. In this review, we provide a summary of cannabis molecular resources focusing on the most recent and relevant genomics, transcriptomics and metabolomics approaches and investigations. Multi-omics methods are discussed, with this combined approach being a powerful tool to identify correlations between biological processes and metabolic pathways across diverse omics layers, and to better elucidate the relationships between cannabis sub-species. The correlations between genotypes and phenotypes, as well as novel metabolites with therapeutic potential are also explored in the context of cannabis breeding programs. However, further studies are needed to fully elucidate the complex metabolomic matrix of this crop. For this reason, some key points for future research activities are discussed, relying on multi-omics approaches.
25
Jamshidi, Neema, Alborz Feizi, Claude B. Sirlin, Joel E. Lavine e Michael D. Kuo. "Multi-Modality, Multi-Dimensional Characterization of Pediatric Non-Alcoholic Fatty Liver Disease". Metabolites 13, n. 8 (8 agosto 2023): 929. http://dx.doi.org/10.3390/metabo13080929.
Abstract (sommario):
Non-alcoholic fatty liver disease is a multifaceted disease that progresses through multiple phases; it involves metabolic as well as structural changes. These alterations can be measured directly or indirectly through blood, non-invasive imaging, and/or tissue analyses. While some studies have evaluated the correlations between two sets of measurements (e.g., histopathology with cross-sectional imaging or blood biomarkers), the interrelationships, if any, among histopathology, clinical blood profiles, cross-sectional imaging, and metabolomics in a pediatric cohort remain unknown. We created a multiparametric clinical MRI–histopathologic NMR network map of pediatric NAFLD through multimodal correlation networks, in order to gain insight into how these different sets of measurements are related. We found that leptin and other blood markers were correlated with many other measurements; however, upon filtering out the blood biomarkers, the network was decomposed into three independent hubs centered around histopathological features, each with associated MRI and plasma metabolites. These multi-modality maps could serve as a framework for characterizing disease status and progression and could potentially guide medical interventions.
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Al-Suhili, Rafa H., e Nawbahar F. Mustafa. "A Multi-variables Multi -sites Model for Forecasting Hydrological Data Series". Journal of Engineering 20, n. 07 (19 giugno 2023): 85–102. http://dx.doi.org/10.31026/j.eng.2014.07.06.
Abstract (sommario):
A multivariate multisite hydrological data forecasting model was derived and checked using a case study. The philosophy is to use simultaneously the cross-variable correlations, cross-site correlations and the time lag correlations. The case study is of two variables, three sites, the variables are the monthly rainfall and evaporation; the sites are Sulaimania, Dokan, and Darbandikhan.. The model form is similar to the first order auto regressive model, but in matrices form. A matrix for the different relative correlations mentioned above and another for their relative residuals were derived and used as the model parameters. A mathematical filter was used for both matrices to obtain the elements. The application of this model indicates it's capability of preserving the statistical characteristics of the observed series. The preservation was checked by using (t-test) and (F-test) for the monthly means and variances which gives 98.6% success for means and 81% success for variances. Moreover for the same data two well-known models were used for the sake of comparison with the developed model. The single-site singlevariable auto regressive first order and the multi-variable single-site models. The results of the three models were compared using (Akike test) which indicates that the developed model is more successful ,since it gave minimum (AIC) value for Sulaimania rainfall, Darbandikhan rainfall, and Darbandikhan evaporation, while Matalas model gave minimum (AIC) value for Sulaimania evaporation and Dokan rainfall, and Markov AR (1) model gave minimum (AIC) value for only Dokan evaporation).However, for these last cases the (AIC) given by the developed model is slightly greater than the minimum corresponding value.
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METZ, ANDREAS, DANIEL PITONYAK, ANDREAS SCHÄFER, MARC SCHLEGEL, WERNER VOGELSANG e JIAN ZHOU. "SINGLE SPIN ASYMMETRIES IN INCLUSIVE DIS AND MULTI-PARTON CORRELATIONS IN THE NUCLEON". International Journal of Modern Physics: Conference Series 20 (gennaio 2012): 137–44. http://dx.doi.org/10.1142/s2010194512009178.
Abstract (sommario):
Transverse single spin asymmetries in inclusive deep-inelastic lepton-nucleon scattering can be generated through multi-photon exchange between the leptonic and the hadronic part of the process. Here we consider two-photon exchange, and mainly focus on the transverse target spin asymmetry. In particular, we investigate the case where two photons couple to different quarks. Such a contribution involves a quark-photon-quark correlator in the nucleon, which has a (model-dependent) relation to the Efremov-Teryaev-Qiu-Sterman quark-gluon-quark correlator TF. Using different parameterizations for TF we compute the transverse target spin asymmetry for both a proton and a neutron target, and compare the results to recent experimental data. Potential implications on our general understanding of single spin asymmetries in hard scattering processes are discussed as well.
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Abdel-Khalek, S., K. Berrada e H. Eleuch. "Quantum correlation and non-classical properties in semiconductor microcavities for multi-photon excitation". International Journal of Quantum Information 17, n. 05 (agosto 2019): 1950047. http://dx.doi.org/10.1142/s0219749919500473.
Abstract (sommario):
We study how the interplay of weak excitation regime of quantum well confined in a semiconductor microcavity affects the dynamics of correlations and the statistical properties of photons. We discuss how the system parameters can impact the quantum entropy and Mandel’s parameter, and illustrate our concerns with numerical simulations. We have found that the enhancement of the correlations and the control of the statistical properties of photons in semiconductor microcavities for multi-photon excitation highly benefits from the combination of the strength of the field, excitonic spontaneous emission rate, cavity dissipative rate, and the coupling between the exciton and photons. Our results may have important implications not only for those specially interested in quantum optics and information, but also for the general physics community.
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Khanal, Piush, Christian Maltecca, Clint Schwab, Justin Fix e Francesco Tiezzi. "214 Correlation among host gut microbiome and their relationship with meat quality and carcass composition traits of swine". Journal of Animal Science 97, Supplement_3 (dicembre 2019): 44. http://dx.doi.org/10.1093/jas/skz258.087.
Abstract (sommario):
Abstract Study on correlation among host gut microbiome and their relationship with meat quality and carcass composition traits remains limited. The objectives of this study were 1) to estimate the microbial correlation between meat quality and carcass traits; and 2) to estimate the genetic correlation between microbial alpha diversity, and meat quality and carcass traits in commercial swine population. Data were collected from Duroc sired three-way cross individuals (n = 1,123) genotyped with 60K SNP chips. Fecal 16S microbial sequences for all individuals were obtained at three different stages: weaning (WEAN: 18.64 ± 1.09 days); week 15 (W_15: 118.2 ± 1.18 days); and off test (OT: 196.4 ± 7.80 days). Alpha diversity was measured at each stage [WEAN (alpha_w), W_15 (alpha_15) and OT (alpha_off)] using the Shannon index, which was computed as: ∑ ni=1piln(pi) where pi was the proportional abundance of ith operational taxonomic unit. Microbial correlations were estimated using multi-trait model, which included fixed effects of dam line, contemporary group and sex, as well as random effects of pen, additive genetic and microbiome. Bivariate analyses were conducted between different traits and alpha_w, alpha_15 and alpha_off with the same fixed effects and random pen and additive genetic effect. Analyses were conducted in ASREML v.4. Microbial correlations ranged from -0.93 ± 0.11 between firmness and slice shear force to 0.97 ± 0.02 between carcass average daily gain (CADG) and loin weight. For meat quality traits, correlations were weak, except for alpha_15 with Minolta a* (-0.45±0.19). Alpha_15 showed weak correlations except with CADG (-0.43±0.19). All correlations between alpha_ot and growth, carcass and meat quality traits were weak. These results may establish a newer approach of genetic evaluation process by utilizing gut microbiome information.
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Nagpal, Sunil, Rashmi Singh, Deepak Yadav e Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks". Nucleic Acids Research 48, W1 (27 aprile 2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
Abstract (sommario):
Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates ‘continuous and categorical’ meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.
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Pemmasani, Sai Pramod, Krishna Valleti, M. Ramakrishna, K. V. Rajulapati, Ravi C. Gundakaram e S. V. Joshi. "Structure-Property Correlations in Cathodic Arc Deposited TiAlN Coatings". Materials Science Forum 702-703 (dicembre 2011): 967–70. http://dx.doi.org/10.4028/www.scientific.net/msf.702-703.967.
Abstract (sommario):
PVD hard coatings, notably transition metal nitrides and carbides, are being increasingly used by industry for improving the life and machining speeds of cutting and forming tools. There has been an increasing trend towards use of complex coatings, based on ternary and even more complex multi-component systems, as well as in novel configurations such as multilayers, superlattices, nanolayers and graded coatings, to achieve superior properties in the tool as well as the finished product. The service properties of the coatings are known to be influenced by their microstructure, phase assembly and composition, apart from the orientation and stress states which can be suitably tailored for diverse applications. In the present study, a ternary coating based on Titanium Aluminum Nitride was deposited on high speed steel substrates by cathodic arc evaporation under varied bias voltage conditions. As-deposited coatings were characterized by X-ray diffraction, Residual Stress Analysis, Scanning Electron Microscopy (SEM), EBSD and FIB. Mechanical and tribological characteristics of the coatings were evaluated by nanoindentation and nanoscratch testing, respectively. The variations in coating hardness and adhesion with the bias voltage were studied. The changes in coating microstructure as a consequence of variation in bias voltage were also examined. Results from the above investigations are presented to illustrate how a combination of electron microscopy with nanoindentation and adhesion testing can be utilized to ascertain structure-property correlations in coatings.
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Huang, Kaixuan, Yubao Sun e Quan Gu. "Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction". Applied Sciences 13, n. 23 (29 novembre 2023): 12795. http://dx.doi.org/10.3390/app132312795.
Abstract (sommario):
Coded aperture snapshot spectral imaging (CASSI) is a new imaging mode that captures the spectral characteristics of materials in real scenes. It encodes three-dimensional spatial–spectral data into two-dimensional snapshot measurements, and then recovers the original hyperspectral image (HSI) through a reconstruction algorithm. Hyperspectral data have multi-scale coupling correlations in both spatial and spectral dimensions. Designing a network architecture that effectively represents this coupling correlation is crucial for enhancing reconstruction quality. Although the convolutional neural network (CNN) can effectively represent local details, it cannot capture long-range correlation well. The Transformer excels at representing long-range correlation within the local window, but there are also issues of over-smoothing and loss of details. In order to cope with these problems, this paper proposes a dual-branch CNN-Transformer complementary module (DualCT). Its CNN branch mainly focuses on learning the spatial details of hyperspectral images, and the Transformer branch captures the global correlation between spectral bands. These two branches are linked through bidirectional interactions to promote the effective fusion of spatial–spectral features of the two branches. By utilizing characteristics of CASSI imaging, the residual mask attention is also designed and encapsulated in the DualCT module to refine the fused features. Furthermore, by using the DualCT module as a basic component, a multi-scale encoding and decoding model is designed to capture multi-scale spatial–spectral features of hyperspectral images and achieve end-to-end reconstruction. Experiments show that the proposed network can effectively improve reconstruction quality, and ablation experiments also verify the effectiveness of our network design.
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Zhao, Qi, Chuqiao Chen, Guangcan Liu, Qingshan Liu e Shengyong Chen. "Parallel Connected LSTM for Matrix Sequence Prediction with Elusive Correlations". ACM Transactions on Intelligent Systems and Technology 12, n. 4 (31 agosto 2021): 1–16. http://dx.doi.org/10.1145/3469437.
Abstract (sommario):
This article is about a challenging problem called matrix sequence prediction, which is motivated from the application of taxi order prediction. Remarkably, the problem differs greatly from previous sequence prediction tasks in the sense that the time-wise correlations are quite elusive; namely, distant entries could be strongly correlated and nearby entries are unnecessarily related. Such distinct specifics make prevalent convolution-recurrence-based methods inadequate to apply. To remedy this trouble, we propose a novel architecture called Parallel Connected LSTM (PcLSTM), which integrates two new mechanisms, Multi-channel Linearized Connection (McLC) and Adaptive Parallel Unit (APU), into the framework of LSTM. Benefiting from the strengths of McLC and APU, our PcLSTM is able to handle well both the elusive correlations within each timestamp and the temporal dependencies across different timestamps, achieving state-of-the-art performance in a set of experiments demonstrated on synthetic and real-world datasets.
34
Perdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence e G. E. Karniadakis. "Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, n. 2198 (febbraio 2017): 20160751. http://dx.doi.org/10.1098/rspa.2016.0751.
Abstract (sommario):
Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.
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Zhao, Ning, Heng Wang, Quanlong Cui e Lan Wu. "U-ETMVSNet: Uncertainty-Epipolar Transformer Multi-View Stereo Network for Object Stereo Reconstruction". Applied Sciences 14, n. 6 (7 marzo 2024): 2223. http://dx.doi.org/10.3390/app14062223.
Abstract (sommario):
The Multi-View Stereo model (MVS), which utilizes 2D images from multiple perspectives for 3D reconstruction, is a crucial technique in the field of 3D vision. To address the poor correlation between 2D features and 3D space in existing MVS models, as well as the high sampling rate required for static sampling, we proposeU-ETMVSNet in this paper. Initially, we employ an integrated epipolar transformer module (ET) to establish 3D spatial correlations along epipolar lines, thereby enhancing the reliability of aggregated cost volumes. Subsequently, we devise a sampling module based on probability volume uncertainty to dynamically adjust the depth sampling range for the next stage. Finally, we utilize a multi-stage joint learning method based on multi-depth value classification to evaluate and optimize the model. Experimental results demonstrate that on the DTU dataset, our method achieves a relative performance improvement of 27.01% and 11.27% in terms of completeness error and overall error, respectively, compared to CasMVSNet, even at lower depth sampling rates. Moreover, our method exhibits excellent performance with a score of 58.60 on the Tanks &Temples dataset, highlighting its robustness and generalization capability.
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Blaxton, Jessica, Niccole Nelson e Cindy Bergeman. "The Positive and Negative Affect Relation in the Context of Stress and Age". Innovation in Aging 5, Supplement_1 (1 dicembre 2021): 840. http://dx.doi.org/10.1093/geroni/igab046.3075.
Abstract (sommario):
Abstract Research suggests that the within-person inverse relationship between negative affect (NA) and positive affect (PA) indicates poorer emotional well-being, and this interaffect correlation fluctuates in relation to the context of the individual. Specifically, age, stress, and global PA all relate to changes in the interaffect correlation. The current study uses comprehensive data from the Notre Dame Study of Health & Well Being (NDHWB), which allows us to uniquely examine between-person differences in within-person change and variability in the interaffect correlation, thereby examining these constructs from a process-oriented perspective. Midlife and later life participants (N = 965) completed daily questionnaires assessing stress, NA, and PA. Three-level multi-level models illustrated that the interaffect correlation becomes more negative during times of stress, adults with greater global PA experience a stronger inverse interaffect correlation during times of stress, and days of higher stress relate to a stronger inverse interaffect correlation for older adults compared to midlife adults. The findings illustrate the idiographic nature of these relationships and suggest that later life adults and adults with high levels of global PA undergoing higher than typical stressful situations experience stronger inverse interaffect correlations.
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Vandicke, Jonas, Katrien De Visschere, Siska Croubels, Sarah De Saeger, Kris Audenaert e Geert Haesaert. "Mycotoxins in Flanders’ Fields: Occurrence and Correlations with Fusarium Species in Whole-Plant Harvested Maize". Microorganisms 7, n. 11 (18 novembre 2019): 571. http://dx.doi.org/10.3390/microorganisms7110571.
Abstract (sommario):
Mycotoxins are well-known contaminants of several food- and feedstuffs, including silage maize for dairy cattle. Climate change and year-to-year variations in climatic conditions may cause a shift in the fungal populations infecting maize, and therefore alter the mycotoxin load. In this research, 257 maize samples were taken from fields across Flanders, Belgium, over the course of three years (2016–2018) and analyzed for 22 different mycotoxins using a multi-mycotoxin liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. DNA of Fusarium graminearum, F. culmorum and F. verticillioides was quantified using the quantitative polymerase chain reaction (qPCR). Multi-mycotoxin contamination occurred frequently, with 47% of samples containing five or more mycotoxins. Nivalenol (NIV) was the most prevalent mycotoxin, being present in 99% of the samples, followed by deoxynivalenol (DON) in 86% and zearalenone (ZEN) in 50% of the samples. Fumonisins (FUMs) were found in only 2% of the samples in the wet, cold year of 2016, but in 61% in the extremely hot and dry year of 2018. Positive correlations were found between DON and NIV and between F. graminearum and F. culmorum, among others. FUM concentrations were not correlated with any other mycotoxin, nor with any Fusarium sp., except F. verticillioides. These results show that changing weather conditions can influence fungal populations and the corresponding mycotoxin contamination of maize significantly, and that multi-mycotoxin contamination increases the risk of mycotoxicosis in dairy cattle.
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Que, Qingmin, Chunmei Li, Buye Li, Huiyun Song, Pei Li, Ruiqi Pian, Huaqing Li, Xiaoyang Chen e Kunxi Ouyang. "Multi-Level Genetic Variation and Selection Strategy of Neolamarckia cadamba in Successive Years". Forests 12, n. 11 (25 ottobre 2021): 1455. http://dx.doi.org/10.3390/f12111455.
Abstract (sommario):
Neolamarckia cadamba (Roxb.) Bosser is a tropical evergreen broadleaf tree species that could play an important role in meeting the increasing demand for wood products. However, multi-level genetic variation and selection efficiency for growth traits in N. cadamba is poorly characterized. We therefore investigated the efficiency of early selection in N. cadamba by monitoring the height (HT), diameter at breast height (DBH), and tree volume (V) in 39 half-sib families from 11 provenances at ages 2, 3, 4, 5, and 6 years in a progeny test. Age-related trends in growth rate, genetic parameters in multi-level, efficiency of early selection, and realized gain in multi-level for growth traits were analyzed. The result showed that genetic variation among families within provenances was higher than that among provenances. The estimated individual heritability values for the growth traits ranged from 0.05 to 0.26, indicating that the variation of growth traits in N. cadamba was subject to weak or intermediate genetic control. The age–age genetic correlations for growth traits were always positive and high (0.51–0.99), and the relationships between the genetic/phenotypic correlations and the logarithm of the age ratio (LAR) were described well by linear models (R2 > 0.85, except the fitting coefficient of genetic correlation and LAR for HT was 0.35). On the basis of an early selection efficiency analysis, we found that it is the best time to perform early selection for N. cadamba at age 5 before half-rotation, and the selection efficiencies were 157.28%, 151.56%, and 127.08% for V, DBH, and HT, respectively. Higher realized gain can be obtained by selecting superior trees from superior families. These results can be expected to provide theoretical guidance and materials for breeding programs in N. cadamba and can even be a reference for breeding strategies of other fast-growing tree species.
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Kalugin, A. Yu. "ON SOME METHODOLOGICAL ASPECTS OF THE STUDY OF HUMAN INDIVIDUALITY". Психология. Психофизиология 12, n. 2 (15 luglio 2019): 29–40. http://dx.doi.org/10.14529/jpps190203.
Abstract (sommario):
Human individuality, presented on different levels (from biological to social ones), is of a high interest in Russian psychology, and the method of correlation design is widely used among researches, because it allows revealing relationships between multi-level properties of individuality. The present article examines several methodical aspects of the correlation analysis implementation, discussing problems and possible solutions. In particular, it considers the issue of nonlinear dependencies (parabolic, hyperbolic etc.), which are impossible to reveal by common correlation methods, but which can be uncovered by using nonlinear correlations, such as correlation index, correlation ratio, maximal information coefficient, distance correlation, maximal correlation, “partial moments” method. Furthermore, it considers the necessity of visualizing variables correlation (scatterplots) that enables to reveal hidden data structures, for example, subgroups. Special attention is paid to correlations corrections for restriction of range and related difficulties that are well-known, but scarcely researched in Russian psychology. In process of investigating plentiful pairwise correlations between individuality properties on different levels it is important to consider anissue of multiple comparisons, which, however, is rarely taken into the account by researches, leading to false results in many occasions. Moreover, the article examines robust statistical methods, particularly permutation tests and bootstrap. These methods combine robustness and high power. Finally, the study observes such issues as the completeness of results presentation and current debates about significance level, effect size and confidence intervals, reproducibility of psychological researches, and meta-analysis approach. Significance level has often been criticized; interval estimates and effect size were supposed to replace it. However, the problem of Null Hypothesis Significance Testing (NHST) has not been completely solved yet. A possible solution is presentation of complete data on research results including precise significance level, confidence intervals, effect size and etc. These estimations can be then applied in meta-analysis, which allows moving on to a new level of scientific generalizations.
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Zhu, Fangqi, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang e Ruifeng Xu. "A Generative Approach for Script Event Prediction via Contrastive Fine-Tuning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 11 (26 giugno 2023): 14056–64. http://dx.doi.org/10.1609/aaai.v37i11.26645.
Abstract (sommario):
Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge (e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze (MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at https://github.com/zhufq00/mcnc.
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Liu, Jing, Huibin Lu, Xiuru Zhang, Xiaoli Li, Lei Wang, Shimin Yin e Dong Cui. "Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?" Entropy 25, n. 3 (21 febbraio 2023): 396. http://dx.doi.org/10.3390/e25030396.
Abstract (sommario):
So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.
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Liese, Angela D., Jamie L. Crandell, Janet A. Tooze, Mary T. Fangman, Sarah C. Couch, Anwar T. Merchant, Ronny A. Bell e Elizabeth J. Mayer-Davis. "Relative validity and reliability of an FFQ in youth with type 1 diabetes". Public Health Nutrition 18, n. 3 (28 marzo 2014): 428–37. http://dx.doi.org/10.1017/s1368980014000408.
Abstract (sommario):
AbstractObjectiveTo evaluate the relative validity and reliability of the SEARCH FFQ that was modified from the Block Kids Questionnaire.DesignStudy participants completed the eighty-five-item FFQ twice plus three 24 h dietary recalls within one month. We estimated correlations between frequencies obtained from participants with the true usual intake for food groups and nutrients, using a two-part model for episodically consumed foods and measurement error adjustment.SettingThe multi-centre SEARCH for Diabetes in Youth Nutrition Ancillary Study.SubjectsA subgroup of 172 participants aged 10–24 years with type 1 diabetes.ResultsThe mean correlations, adjusted for measurement error, of food groups and nutrients between the FFQ and true usual intake were 0·41 and 0·38, respectively, with 57 % of food groups and 70 % of nutrients exhibiting correlations >0·35. Correlations were high for low-fat dairy (0·80), sugar-sweetened beverages (0·54), cholesterol (0·59) and saturated fat (0·51), while correlations were poor for high-fibre bread and cereal (0·16) and folate (0·11). Reliability of FFQ intake based on two FFQ administrations was also reasonable, with 54 % of Pearson correlation coefficients ≥0·5. Reliability was high for low-fat dairy (0·7), vegetables (0·6), carbohydrates, fibre, folate and vitamin C (all 0·5), but less than desirable for low-fat poultry and high-fibre bread, cereal, rice and pasta (0·2–0·3).ConclusionsWhile there is some room for improvement, our findings suggest that the SEARCH FFQ performs quite well for the assessment of many nutrients and food groups in a sample of youth with type 1 diabetes.
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Brommundt, J., e A. Bárdossy. "Spatial correlation of radar and gauge precipitation data in high temporal resolution". Advances in Geosciences 10 (26 aprile 2007): 103–9. http://dx.doi.org/10.5194/adgeo-10-103-2007.
Abstract (sommario):
Abstract. A multi-sites precipitation time series generator for engineering designs is currently being developed. The objective is to generate several time series' simultaneously with correct inter-station relationships. Therefore, a model to estimate correlation between stations for arbitrary points in a project area is needed, using rain gauge data as well as radar data. Two methods are applied to compare the spatial behaviour of precipitation in both the rain gauge data and the radar data. The first approach is to calculate precipitation intensities from radar reflectivity and use it as gauge data. The results show that the spatial structure in both data sets is similar, but cross correlation varies too much to use radar derived spatial correlation to describe gauge inter-station relationship. Thus, a second approach was tested to account for the differences in the spatial correlation associated to the distribution. Using the indicator time series, cross correlations for different quantiles were calculated from both the rain gauge and radar data. This approach shows that cross correlation varies depending on the chosen quantile. In the lower quantiles, the correlation is very similar in rain gauge and radar data, hence a transfer is possible. This insight is useful to derive cross correlations of rain gauges from radar images. Correlation data for rain gauges thus obtained contains all the information about heterogeneity and anisotropy of the spatial structure of rainfall, which is in the radar data.
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KİREMİTÇİ CANIÖZ, Elvan. "Yoga and Psychological Well-Being: Sequential Mediating Role of Happiness and Mindfulness". Psikiyatride Guncel Yaklasimlar - Current Approaches in Psychiatry 15, Ek 1 (27 dicembre 2023): 87–94. http://dx.doi.org/10.18863/pgy.1264898.
Abstract (sommario):
Nowadays, individuals do yoga as a way to improve their physical and psychological health with the pursuit of feeling good. Therefore, examining yoga in terms of psychological factors is very important in literature and life. This study aimed to examine psychological well-being, happiness, and mindfulness according to yoga and non-yoga practitioners and the sequential mediating role of happiness and mindfulness in the relationship between yoga and psychological well-being. The sample group of this study consists of 263 participants (129 yoga practitioners and 134 non-yoga practitioners). Participants completed the Demographic Information Form, Psychological Well-Being Scale, Oxford Happiness Scale, and Conscious Awareness Scale online. The data obtained in the study were analyzed by applying the Pearson correlation coefficient and Serial Multi-Mediator Variable Analysis (PROCESS Model 6). According to the study results, it was seen that between psychological well-being and happiness .56, between psychological well-being and mindfulness .34, and between happiness and mindfulness .30 correlations are positive and significant relationships. In conclusion, the results remark on the significant sequential mediating role of happiness and mindfulness in the relationship between yoga and psychological well-being.
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Gupta, Vivek, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain e Piyush Rai. "Distributional Semantics Meets Multi-Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 3747–54. http://dx.doi.org/10.1609/aaai.v33i01.33013747.
Abstract (sommario):
We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as label-label correlations; this is crucial especially when many training instances are only partially annotated. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings for the new input to be annotated, via efficient gradient based methods. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed models perform favorably as compared to state-of-the-art methods for large-scale multi-label learning.
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Xu, Ning, Yun-Peng Liu e Xin Geng. "Partial Multi-Label Learning with Label Distribution". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 04 (3 aprile 2020): 6510–17. http://dx.doi.org/10.1609/aaai.v34i04.6124.
Abstract (sommario):
Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identifying the ground-truth label via utilizing the confidence of each candidate label or estimating the noisy labels in the candidate label sets. Nonetheless, these strategies ignore considering the essential label distribution corresponding to each instance since the label distribution is not explicitly available in the training set. In this paper, a new partial multi-label learning strategy named Pml-ld is proposed to learn from partial multi-label examples via label enhancement. Specifically, label distributions are recovered by leveraging the topological information of the feature space and the correlations among the labels. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the recovered label distributions. Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of Pml-ld for solving PML problems.
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Lum, Danny, G. Gregory Haff e Tiago M. Barbosa. "The Relationship between Isometric Force-Time Characteristics and Dynamic Performance: A Systematic Review". Sports 8, n. 5 (15 maggio 2020): 63. http://dx.doi.org/10.3390/sports8050063.
Abstract (sommario):
The purpose of this article was to review the data on the relationship between multi-joint isometric strength test (IsoTest) force-time characteristics (peak force, rate of force development and impulse) and dynamic performance that is available in the current literature. Four electronic databases were searched using search terms related to IsoTest. Studies were considered eligible if they were original research studies that investigated the relationships between multi-joint IsoTest and performance of dynamic movements; published in peer-reviewed journals; had participants who were athletes or active individuals who participate in recreational sports or resistance training, with no restriction on sex; and had full text available. A total of 47 studies were selected. These studies showed significant small to large correlations between isometric bench press (IBP) force-time variables and upper body dynamic performances (r2 = 0.221 to 0.608, p < 0.05) and significant small to very large correlation between isometric squat (ISqT) (r2 = 0.085 to 0.746, p < 0.05) and isometric mid-thigh pull (IMTP) (r2 = 0.120 to 0.941, p < 0.05) force-time variables with lower body dynamic performances. IsoTest force-time characteristics were shown to have small to very large correlations with dynamic performances of the upper and lower limbs as well as performance of sporting movements (r2 = 0.118 to 0.700, p < 0.05). These data suggest that IsoTest force-time characteristics provide insights into the force production capability of athletes which give insight into dynamic performance capabilities.
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Couture, Frédéric, Kévin Ly, Christine Levesque, Anna Kwiatkowska, Samia Ait-Mohand, Roxane Desjardins, Brigitte Guérin e Robert Day. "Multi-Leu PACE4 Inhibitor Retention within Cells Is PACE4 Dependent and a Prerequisite for Antiproliferative Activity". BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/824014.
Abstract (sommario):
The overexpression as well as the critical implication of the proprotein convertase PACE4 in prostate cancer progression has been previously reported and supported the development of peptide inhibitors. The multi-Leu peptide, a PACE4-specific inhibitor, was further generated and its capability to be uptaken by tumor xenograft was demonstrated with regard to its PACE4 expression status. To investigate whether the uptake of this inhibitor was directly dependent of PACE4 levels, uptake and efflux from cancer cells were evaluated and correlations were established with PACE4 contents on both wild type and PACE4-knockdown cell lines. PACE4-knockdown associated growth deficiencies were established on the knockdown HepG2, Huh7, and HT1080 cells as well as the antiproliferative effects of the multi-Leu peptide supporting the growth capabilities of PACE4 in cancer cells.
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Gao, Quanxue, Wei Xia, Zhizhen Wan, Deyan Xie e Pu Zhang. "Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 04 (3 aprile 2020): 3930–37. http://dx.doi.org/10.1609/aaai.v34i04.5807.
Abstract (sommario):
Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD, which does not make sense in the existence of noise and illumination change. To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. We further apply the WTNNM algorithm to multi-view subspace clustering by exploiting the high order correlations embedded in different views. Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance.
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Kolluri, Bharat, Susan Wahab e Mahmoud Wahab. "Systematic Covariations and Emerging Asian Equity Markets’ Diversification Benefits to US Equity Investors". Review of Pacific Basin Financial Markets and Policies 23, n. 02 (giugno 2020): 2050009. http://dx.doi.org/10.1142/s0219091520500095.
Abstract (sommario):
Unconditional and conditional correlations have played a central role in portfolio analysis, optimization, and performance measurement. However, recent studies show these two correlation measures are inappropriate for measuring both financial integration and, therefore, diversification benefits. We use an alternative correlation measure that we refer to by factor model-implied correlation estimated from the systematic (predictable) portion of returns of a multi-factor model with several global risk factors. Estimated implied correlations, covariances, variances, and in-sample (predicted) mean returns are used to calculate optimal US and Asian equities’ asset allocation weights in alternative Global equity portfolios varying by Asian equity market combined with US equities, as well as by whether: (i) implied or unconditional statistics are used; and (ii) portfolios are optimized by Sharpe’s ratio-maximization or variance-minimization. Risk-adjusted returns of alternative actively-managed Global equity portfolios are compared to US equities’ risk-adjusted returns. We find Global equity portfolios with asset allocation weights calculated using factor model-implied statistics uniformly yield higher risk-adjusted returns than US equities and Global equity portfolios with asset allocation weights calculated using unconditional portfolio statistics. In actively-managed Global equity portfolios with asset allocation weights calculated using implied statistics, India and Taiwan consistently rank as top contributors, while South Korea, Singapore, and Hong Kong consistently rank as bottom contributors to enhancing US equities’ risk-adjusted returns. While our analyses are dynamic, they use implied portfolio statistics estimated from historical returns’ distributions. Future studies can extend this research using conditional(out-of-sample) ex-ante estimates of systematic returns, covariances, variances, and correlations in examining emerging markets’ contributions to developed markets’ equities.