scholarly journals ФИТОИНДИКАЦИОННОЕ ОЦЕНИВАНИЕ ИЗМЕРЕНИЙ, ПОЛУЧЕННЫХ ПРИ МНОГОМЕРНОМ ШКАЛИРОВАНИИ СТРУКТУРЫ РАСТИТЕЛЬНОГО СООБЩЕСТВА

2015 ◽  
Vol 5 (01) ◽  
pp. 69 ◽  
Author(s):  
A. V. Zhukov
Keyword(s):  
Multidimensional Scaling ◽  
Plant Community ◽  
Large Scale ◽  
Mantel Test ◽  
Specific Information ◽  
Scale Level ◽  
Complementary Information ◽  
Phenomenological Interpretation ◽  
Community Ordination ◽  
The Given

<p>The purpose of our work is to carry out plant community ordination by means of multidimensional scaling to reveal optimum ways of preliminary transformation of data and the similarity/dissimilarity measure, to identify multidimensional dimensions in terms of edafic properties and phytoindicator scales and to reveal character of interrelations of matrixes of plant community, phytoindicator scales and edafic properties. The received results testify that edafic and climatic scales matrixes bear the complementary information on edaphotop properties and possibly climatop. Most possibly that climatic scales at large-scale level bear the specific information on properties of environment. It is difficult to confirm, whether character of this information to adequate nominative properties of a scale at macrolevel is. But with confidence it is possible to say that climatic phytoindicator scales allow to differentiate ecological conditions in biogeocoenosis at large-scale level. Thus, at the given stage we tend to phenomenological interpretation of value of climatic phytoindicator scales at large-scale level.</p> <p><em>Keywords</em><em>: multidimensional scaling, community structure, phytoindicator scales, Mantel test</em></p>

Urban Perception: Sensing Cities via a Deep Interactive Multi-task Learning Framework

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-20
Author(s):  
Weili Guan ◽  
Zhaozheng Chen ◽  
Fuli Feng ◽  
Weifeng Liu ◽  
Liqiang Nie
Keyword(s):  
Large Scale ◽  
Pairwise Comparisons ◽  
Specific Information ◽  
Social Scientists ◽  
Learning Framework ◽  
Task Learning ◽  
Highly Correlated ◽  
Image Pairs ◽  
The Given

Social scientists have shown evidence that visual perceptions of urban attributes, such as safe, wealthy, and beautiful perspectives of the given cities, are highly correlated to the residents’ behaviors and quality of life. Despite their significance, measuring visual perceptions of urban attributes is challenging due to the following facts: (1) Visual perceptions are subjectively contradistinctive rather than absolute. (2) Perception comparisons between image pairs are usually conducted region by region, and highly related to the specific urban attributes. And (3) the urban attributes have both the shared and specific information. To address these problems, in this article, we present a Deep inteRActive Multi-task leArning scheme, DRAMA for short. DRAMA comparatively quantifies the perceptions of urban attributes by jointly integrating the pairwise comparisons, regional interactions, and urban attribute correlations within a unified deep scheme. In DRAMA, each urban attribute is treated as a task, whereby the task-sharing and the task-specific information is fully explored. By conducting extensive experiments over a public large-scale benchmark dataset, it is demonstrated that our proposed DRAMA scheme outperforms several state-of-the-art baselines. Meanwhile, we applied the pairwise comparisons of our DRAMA model to further quantify the urban attributes and hence rank cities with respect to the given urban attributes. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

scholarly journals BiPSim: a flexible and generic stochastic simulator for polymerization processes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stephan Fischer ◽  
Marc Dinh ◽  
Vincent Henry ◽  
Philippe Robert ◽  
Anne Goelzer ◽  
...  
Keyword(s):  
Large Scale ◽  
Stochastic Simulation Algorithm ◽  
Specific Information ◽  
Whole Cell ◽  
Simulation Speed ◽  
Genome Wide ◽  
Cell Simulation ◽  
Stochastic Simulator ◽  
Modeling Formalisms ◽  
Stochastic Phenomena

AbstractDetailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

Fungi and fire in Australian ecosystems: a review of current knowledge, management implications and future directions

2011 ◽  
Vol 59 (1) ◽  
pp. 70 ◽  
Author(s):  
Sapphire J. M. McMullan-Fisher ◽  
Tom W. May ◽  
Richard M. Robinson ◽  
Tina L. Bell ◽  
Teresa Lebel ◽  
...  
Keyword(s):  
Fungal Community ◽  
Large Scale ◽  
Current Knowledge ◽  
Vegetation Type ◽  
Fire Regimes ◽  
Trophic Group ◽  
Fire Intensity ◽  
Specific Information ◽  
Eastern Australia ◽  
Essential Components

Fungi are essential components of all ecosystems in roles including symbiotic partners, decomposers and nutrient cyclers and as a source of food for vertebrates and invertebrates. Fire changes the environment in which fungi live by affecting soil structure, nutrient availability, organic and inorganic substrates and other biotic components with which fungi interact, particularly mycophagous animals. We review the literature on fire and fungi in Australia, collating studies that include sites with different time since fire or different fire regimes. The studies used a variety of methods for survey and identification of fungi and focussed on different groups of fungi, with an emphasis on fruit-bodies of epigeal macrofungi and a lack of studies on microfungi in soil or plant tissues. There was a lack of replication of fire treatment effects in some studies. Nevertheless, most studies reported some consequence of fire on the fungal community. Studies on fire and fungi were concentrated in eucalypt forest in south-west and south-eastern Australia, and were lacking for ecosystems such as grasslands and tropical savannahs. The effects of fire on fungi are highly variable and depend on factors such as soil and vegetation type and variation in fire intensity and history, including the length of time between fires. There is a post-fire flush of fruit-bodies of pyrophilous macrofungi, but there are also fungi that prefer long unburnt vegetation. The few studies that tested the effect of fire regimes in relation to the intervals between burns did not yield consistent results. The functional roles of fungi in ecosystems and the interactions of fire with these functions are explained and discussed. Responses of fungi to fire are reviewed for each fungal trophic group, and also in relation to interactions between fungi and vertebrates and invertebrates. Recommendations are made to include monitoring of fungi in large-scale fire management research programs and to integrate the use of morphological and molecular methods of identification. Preliminary results suggest that fire mosaics promote heterogeneity in the fungal community. Management of substrates could assist in preserving fungal diversity in the absence of specific information on fungi.

Linguistically Annotated Reordering: Evaluation and Analysis

2010 ◽  
Vol 36 (3) ◽  
pp. 535-568 ◽  
Author(s):  
Deyi Xiong ◽  
Min Zhang ◽  
Aiti Aw ◽  
Haizhou Li
Keyword(s):  
Large Scale ◽  
Statistical Machine Translation ◽  
Training Data ◽  
Linguistic Knowledge ◽  
Analysis Method ◽  
Complementary Information ◽  
New Approach ◽  
Parse Trees ◽  
New Challenges ◽  
Insight Into

Linguistic knowledge plays an important role in phrase movement in statistical machine translation. To efficiently incorporate linguistic knowledge into phrase reordering, we propose a new approach: Linguistically Annotated Reordering (LAR). In LAR, we build hard hierarchical skeletons and inject soft linguistic knowledge from source parse trees to nodes of hard skeletons during translation. The experimental results on large-scale training data show that LAR is comparable to boundary word-based reordering (BWR) (Xiong, Liu, and Lin 2006), which is a very competitive lexicalized reordering approach. When combined with BWR, LAR provides complementary information for phrase reordering, which collectively improves the BLEU score significantly. To further understand the contribution of linguistic knowledge in LAR to phrase reordering, we introduce a syntax-based analysis method to automatically detect constituent movement in both reference and system translations, and summarize syntactic reordering patterns that are captured by reordering models. With the proposed analysis method, we conduct a comparative analysis that not only provides the insight into how linguistic knowledge affects phrase movement but also reveals new challenges in phrase reordering.

scholarly journals Neural mechanisms of context-dependent segmentation tested on large-scale recording data

2021 ◽  
Author(s):  
Toshitake Asabuki ◽  
Tomoki Fukai
Keyword(s):  
Cortical Neurons ◽  
Large Scale ◽  
Neural Mechanism ◽  
Imaging Data ◽  
Unsupervised Segmentation ◽  
Spike Sequences ◽  
Multiple Cell ◽  
Context Dependent ◽  
Current Flows ◽  
The Given

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning likely requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can context-dependently solve difficult segmentation tasks. Dendrites in this model learn to predict somatic responses in a self-supervising manner while recurrent connections learn a context-dependent gating of dendro-somatic current flows to minimize a prediction error. These connections select particular information suitable for the given context from input features redundantly learned by the dendrites. The model selectively learned salient segments in complex synthetic sequences. Furthermore, the model was also effective for detecting multiple cell assemblies repeating in large-scale calcium imaging data of more than 6,500 cortical neurons. Our results suggest that recurrent gating and dendrites are crucial for cortical learning of context-dependent segmentation tasks.

scholarly journals Algorithm for Counting Cars in Large-scale Video Surveillance Systems

2020 ◽  
pp. 100-108
Author(s):  
Arsenii Shirokov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Video Surveillance ◽  
Large Scale ◽  
Surveillance Systems ◽  
Motion Model ◽  
People Tracking ◽  
Detection Frequency ◽  
Computational Resources ◽  
Sparse Set ◽  
The Given ◽  
Distributed Tracking

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

scholarly journals Evaluating the parameters of a mobile maize dryer in practice

2013 ◽  
Vol 61 (6) ◽  
pp. 1779-1784
Author(s):  
Josef Los ◽  
Jiří Fryč ◽  
Zdeněk Konrád
Keyword(s):  
Large Scale ◽  
Electric Energy ◽  
Operating Parameters ◽  
The Czech Republic ◽  
Maize Hybrids ◽  
Post Harvest ◽  
Second Stage ◽  
Energy Intensiveness ◽  
Two Stages ◽  
The Given

The method of drying maize for grain has been recently employed on a large scale in the Czech Republic not only thanks to new maize hybrids but also thanks to the existence of new models of drying plants. One of the new post-harvest lines is a plant in Lipoltice (mobile dryer installed in 2010, storage base in 2012) where basic operational measurements were made of the energy intensiveness of drying and operating parameters of the maize dryer were evaluated. The process of maize drying had two stages, i.e. pre-drying from the initial average grain humidity of 28.55% to 19.6% in the first stage, and the additional drying from 16.7% to a final storage grain humidity of 13.7%. Mean volumes of natural gas consumed per 1 t% for drying in the first and second stage amounted to 1.275 m3 and 1.56 m3, respectively. The total mean consumption of electric energy per 1 t% was calculated to be 1.372 kWh for the given configuration of the post-harvest line.

scholarly journals Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

2020 ◽  
Author(s):  
Fayyaz Minhas ◽  
Dimitris Grammatopoulos ◽  
Lawrence Young ◽  
Imran Amin ◽  
David Snead ◽  
...  
Keyword(s):  
Large Scale ◽  
Resource Constraints ◽  
Large Population ◽  
Sampling Strategy ◽  
Population Surveillance ◽  
Large Scale Testing ◽  
Scale Population ◽  
Simulation Results ◽  
The Given ◽  
Test Outcomes

AbstractOne of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (<15%), therefore, most of the test outcomes will be negative. Here, we propose the use of agglomerative sampling which can prune out multiple negative cases in a single test by intelligently combining samples from different individuals. The proposed scheme builds on the assumption that samples from the population may not be independent of each other. Our simulation results show that the proposed sampling strategy can significantly increase testing capacity under resource constraints: on average, a saving of ~40% tests can be expected assuming a positive test probability of 10% across the given samples. The proposed scheme can also be used in conjunction with heuristic or Machine Learning guided clustering for improving the efficiency of large-scale testing further. The code for generating the simulation results for this work is available here: https://github.com/foxtrotmike/AS.

Sign in / Sign up

Export Citation Format

Share Document