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Ant Abstract kongre yönetim sistemimiz, online ve yüz yüze kongrelerinizde kayıt işleminden kongre programı yapmaya kadar her türlü kongre iş ve işlemlerini dijital ortamda yürütebilen bir programdır.

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With our system, you can seamlessly manage all aspects of your conferences without the need for a website, abstract management system, dealing with acceptance letters, participation certificates, abstract books, conference proceedings, and conference programs.

Bilimsel Etkinlik Düzenleme Antabstract, Ulusal veya uluslararası kongre, sempozyum, konferans, çalıştay veya seminerlerin A’dan Z’ye tüm süreçlerini online organize edebileceğiniz işlevsel bir yazılım sistemidir Ant abstract Bilimsel dergi yayıncılığında lider olan şirketimiz, güçlü bir hakem süreciyle desteklenen ...

ANT ("Adiabatic and Nonadiabatic Trajectories”) is a Fortran 90 trajectory program designed primarily for classical, quasiclassical, and semiclassical calculations of the dynamics of atoms, molecules, and clusters in the gas phase.

2004年11月8日 · Abstract: Everyone uses build tools. We have Make, GNU Make, Nmake, Jam, and many others. But Apache's Ant is different from typical build tools. Instead of using inherently evil shell-based commands, it's based on Java classes. Its use of XML for configuration files and architecture enables Ant's big advantages: simplicity, portability, and power.

Our data type ANT leverages two key innovations to exploit the intra-tensor and inter-tensor adaptive opportunities in DNN models. First, we propose a particular data type, flint, that combines the advantages of float and int for adapting to …

2021年6月24日 · In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology.

2022年8月3日 · We resolve the first high-resolution (~20-km) global diversity map for a major invertebrate clade, ants, using biodiversity informatics, range modeling, and machine learning to synthesize existing knowledge and predict the distribution of undiscovered diversity.