Letter to the Editor

Mathematical Notes -     

Finding the response matrix to the external action from a subspace for a discrete linear stochastic dynamical system

For a discrete linear stochastic dynamical system, computation of the response matrix to the external action from a subspace using given observational data is examined. An algorithm is proposed and substantiated that makes it possible to improve the numerical accuracy and to reduce the amount of observational data compared to the general case where an arbitrary external action is allowed. As an illustration, a discrete system arising in the analysis of a linear stochastic dynamical continuous-time system is considered more thoroughly. Some numerical results are presented.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Correction: Click activated protodrugs against cancer increase the therapeutic potential of chemotherapy through local capture and activation

Correction for ‘Click activated protodrugs against cancer increase the therapeutic potential of chemotherapy through local capture and activation’ by Kui Wu et al., Chem. Sci., 2021, 12, 1259–1271, DOI: 10.1039/D0SC06099B.

The authors regret that the reference to the bond-breaking bioorthogonal chemistry, termed ‘click-to-release’ was omitted from the original article. In addition, we would like to include a reference describing the synthesis of compound 1, which is an intermediate to the protodrugs described in the original article. These references are listed below as ref. 1 and 2.The Royal Society of Chemistry apologizes for these errors and any consequent inconvenience to authors and readers.     

Synthesis of 5-Aryl-1,4-naphthoquinone and 1-Aryl-9,10-anthraquinone Derivatives by Cycloaddition of 1-(Dimethoxyphenyl)-3-trimethylsiloxy-1,3-butadienes to 1,4-Benzoquinones and 1,4-Naphthoquinones

The Diels-Alder reaction of new 1-(3,4-dimethoxyphenyl)- or 1-(2,4-dimethoxyphenyl)-2-R-3-trimethylsiloxy-1,3-butadienes with 2,5- and 2,6-dibromo-, and 2-bromo-6-methyl-1,4-benzoquinones regioselectively yields substituted 7-hydroxy-5-(dimethoxyphenyl)-1,4-naphthoquinones. By cycloaddition of the siloxydienes to naphthoquinone, bromonaphthoquinone, and juglone the corresponding substituted 3-hydroxy-1-(dimethoxyphenyl)-9,10-anthraquinones or their 4,4a-dihydro or 1,1a,4,4a-tetrahydro derivatives were obtained.     

Components of the ethylacetate extract of <Emphasis Type="Italic">Hedysarum theinum</Emphasis> roots

Isoflavonoids (-)-medicarpin, (-)-vestitol, and formononetin and butylphenols raspberry ketone and rhododendrol were isolated for the first time from the ethylacetate extract of Hedysarum thienum roots by column chromatography. GC-MS showed that the ethylacetate extract contained fatty acids, the principal ones being palmitic, linoleic, oleic, behenic, and lignocerinic. __________ Translated from Khimiya Prirodnykh Soedinenii, No. 1, pp. 6–9, January–February, 2007.     

Room temperature MgI2-catalyzed Friedel–Crafts reaction between electron-rich (het)arenes and ethyl glyoxylate

Magnesium iodide-catalyzed addition of electron-rich (het)arenes to ethyl glyoxylate proceeds at room temperature with high chemoselectivity to afford ethyl 2-(het)aryl- 2-hydroxyacetates in yields up to 95%.     

NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

NIFTy , “Numerical Information Field Theory,” is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high‐dimensional real‐world problems. This paper introduces NIFTy  3, a major upgrade to the original NIFTy  framework. NIFTy  3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy  3 is able to handle non‐scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real‐world algorithm from the realm of information field theory. NIFTy  3 is open‐source software available under the GNU General Public License v3 (GPL‐3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/NIFTy_3 .