Influence of antisymmetric exchange interaction on quantum tunneling of magnetization in a dimeric molecular magnet Mn6

We present magnetization measurements on the single molecule magnet Mn6, revealing various tunnel transitions inconsistent with a giant-spin description. We propose a dimeric model of the molecule with two coupled spins S=6, which involves crystal-field anisotropy, symmetric Heisenberg exchange interaction, and antisymmetric Dzyaloshinskii-Moriya exchange interaction. We show that this simplified model of the molecule explains the experimentally observed tunnel transitions and that the antisymmetric exchange interaction between the spins gives rise to tunneling processes between spin states belonging to different spin multiplets.Comment: 5 pages, 4 figure

Pseudopeptidic ligands: exploring the self-assembly of isopthaloylbisglycine (H2IBG) and divalent metal ions

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Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning molecular embeddings and their theoretical benefits, comparing molecular embeddings with each other and with traditional representations is not straightforward, which in turn hinders the process of choosing a suitable representation for QSAR modeling. A reason behind this issue is the difficulty of conducting a fair and thorough comparison of the different existing embedding approaches, which requires numerous experiments on various datasets and training scenarios. To close this gap, we reviewed the literature on methods for molecular embeddings and reproduced three unsupervised and two supervised molecular embedding techniques recently proposed in the literature. We compared these five methods concerning their performance in QSAR scenarios using different classification and regression datasets. We also compared these representations to traditional molecular representations, namely molecular descriptors and fingerprints. As opposed to the expected outcome, our experimental setup consisting of over 25,000 trained models and statistical tests revealed that the predictive performance using molecular embeddings did not significantly surpass that of traditional representations. While supervised embeddings yielded competitive results compared to those using traditional molecular representations, unsupervised embeddings tended to perform worse than traditional representations. Our results highlight the need for conducting a careful comparison and analysis of the different embedding techniques prior to using them in drug design tasks, and motivate a discussion about the potential of molecular embeddings in computer-aided drug design

Assessing Causality Structures learned from Digital Text Media

In this paper we describe a framework to uncover potential causal relations between event mentions from streaming text of news media. This framework relies on a dataset of manually labeled events to train a recurrent neural network for event detection. It then creates a time series of event clusters, where clusters are based on BERT contextual word embedding representations of the identified events. Using these time series dataset, we assess four methods based on Granger causality for inferring causal relations. Granger causality is a statistical concept of causality that is based on forecasting. It states that a cause occurs before the effect, and the cause produces unique changes in the effect, so past values of the cause help predict future values of the effect. The four analyzed methods are the pairwise Granger test, VAR(1), BigVar and SiMoNe. The framework is applied to the New York Times dataset, which covers news for a period of 246 months. This preliminary analysis delivers important insights into the nature of each method, identifies differences and commonalities, and points out some of their strengths and weaknesses.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Milios, Evangelos E.. Dalhousie University. Faculty of Computer Science; CanadáDocEng '20: ACM Symposium on Document Engineering 2020New YorkEstados UnidosAssociation for Computing Machiner

Visual analysis of interactive document clustering streams

Interactive clustering techniques play a key role by putting the user in the clustering loop, allowing her to interact with document group abstractions instead of full-length documents. It allows users to focus on corpus exploration as an incremental task. To explore Information Discovery's incremental aspect, this article proposes a visual component to depict clustering membership changes throughout a clustering iteration loop in both static and dynamic data sets. The visual component is evaluated with an expert user and with an experiment with data streams

A web platform for collaborative semi-automatic OCR Post-processing

Digital Humanities researchers often make use of software that helps them in the task of finding non-trivial relationships among characters in historical text. Usually, the source texts that contain such information come from OCR acquired volumes, carrying high amounts of errors within them. This work explains the development of a web platform for the task of OCR post-processing and ground-truth generation. This platform employs machine learning to predict the correct texts accurately from OCR noisy strings. The method used for this task involves transformers for character-based denoising language models. An active learning workflow is proposed, as the users can feed their corrections to the platform, generating new annotated data for re-training the underlying machine learning correction models.Fil: Mechaca, Ana Lidia. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Marmanillo, Walter Gabriel. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Xamena, Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Ciencias Sociales y Humanidades. Universidad Nacional de Salta. Facultad de Humanidades. Instituto de Investigaciones en Ciencias Sociales y Humanidades; Argentina. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Ramirez Orta, Juan. Dalhousie University Halifax; CanadáFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Milios, Evangelos E.. Dalhousie University Halifax; Canadá50º Jornada Argentina de Informática; Simposio Argentino de Ciencia de Datos y Grandes DatosCiudad Autónoma de Buenos AiresArgentinaSociedad Argentina de Investigación OperativaInstituto Nacional de Tecnología Agropecuari

A family of polynuclear cobalt complexes upon employment of an indeno-quinoxaline based oxime ligand

The reaction of Co(OAc)2·4H2O with LH (LH = 11H-indeno[1,2-b]quinoxalin-11-one oxime) in MeOH in the presence of NEt3 forms the complex [CoIII2CoIIO(OAc)3L3]·0.5MeOH·0.2H2O (1·0.5MeOH·0.2H2O), while repeating the reaction under solvothermal conditions yielded the heptanuclear cluster [CoII7L9 (OH)2(OAc)2.7(MeO)0.3(H2O)]·4.6MeOH·3.3H2O (2·4.6MeOH·3.3H2O). Changing the starting metal salt to Co(ClO4)2·6H2O and upon the reaction with LH in the presence of NEt3 under high temperature and pressure, we managed to isolate the decanuclear cluster [CoII10L14(OH)3.6(MeO)0.4](ClO4)2·8.5MeOH·5.75H2O (3·8.5MeOH·5.75H2O), while under normal bench conditions and upon employment of pivalates in the reaction mixture complex [CoII4L4(piv)4(MeOH)2]·MeOH·H2O (4·MeOH·H2O) was formed. Furthermore, the reaction of Co(ClO4)2·6H2O with LH and aibH (2-amino-isobutyric acid) in the presence of NEt3 in MeOH gave the mononuclear complex [CoIIIL(aib)2]·3H2O (5·3H2O), while upon increasing the metal–ligand ratio cluster [CoIII2CoIIL4(aib)2(OH)2]·7.9MeOH (6·7.9MeOH) was isolated. Finally, repeating the reaction that yielded the mononuclear complex 5·3H2O under solvothermal conditions, gave the octanuclear cluster [CoII8L10(aib)2(MeO)2](ClO4)2·6.8MeOH·7H2O (7·6.8MeOH·7H2O). Variable temperature dc magnetic susceptibility studies for complexes 2, 3, 4 and 7, reveal that all clusters display dominant antiferromagnetic interactions leading to small or diamagnetic ground-states,