Connectivity of workflow nets: the foundations of stepwise verification

Behavioral models capture operational principles of real-world or designed systems. Formally, each behavioral model defines the state space of a system, i.e., its states and the principles of state transitions. Such a model is the basis for analysis of the system’s properties. In practice, state spaces of systems are immense, which results in huge computational complexity for their analysis. Behavioral models are typically described as executable graphs, whose execution semantics encodes a state space. The structure theory of behavioral models studies the relations between the structure of a model and the properties of its state space. In this article, we use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations. At each decomposition step, the discovered compositional structure of a model is used for reasoning on properties of the whole state space of the system. The approach is exemplified by means of a concrete behavioral model and verification criterion. That is, we analyze workflow nets, a well-established tool for modeling behavior of distributed systems, with respect to the soundness property, a basic correctness property of workflow nets. Stepwise verification allows the detection of violations of the soundness property by inspecting small portions of a model, thereby considerably reducing the amount of work to be done to perform soundness checks. Besides formal results, we also report on findings from applying our approach to an industry model collection.     

New high-spin bis-o-semiquinonato cobalt(II) complexes with neutral donor ligands

Two novel bis-o-semiquinonato cobalt complexes Co(3,6-SQ)₂L (1, L¹ = 2,6-dimethyl-N-(pyridin-2-ylmethylene)benzenamine; 2, L² = 2,6-dimethyl-N-(thiophen-2-ylmethylene)benzenamine; 3,6-SQ is 3,6-di-tert-butyl-o-benzosemiquinonato radical-anion) were synthesized. According to X-ray analysis, complex 2 adopts square pyramidal geometry with N-coordinated neutral ligand L² in the apical site. According to magnetic susceptibility measurements and spectroscopic studies, both complexes contain high-spin cobalt(II) (d⁷, S = 3/2) and two radical-anionic o-semiquinonato ligands. There is antiferromagnetic metal–ligand and ligand–ligand coupling in 1, while complex 2 demonstrates weak antiferromagnetic ligand–ligand coupling at low temperature and ferromagnetic metal–ligand exchange at 150–300 K.     

The role of fear and expectancies in capture of covert attention by spiders.

Fear-related stimuli are often prioritized during visual selection but it remains unclear whether capture by salient objects is more likely to occur when individuals fear those objects. In this study, participants with high and low fear of spiders searched for a circle while on some trials a completely irrelevant fear-related (spider) or neutral distractor (butterfly/leaf) was presented simultaneously in the display. Our results show that when you fear spiders and you are not sure whether a spider is going to be present, then any salient distractor (i.e., a butterfly) grabs your attention, suggesting that mere expectation of a spider triggered compulsory monitoring of all irrelevant stimuli. However, neutral stimuli did not grab attention when high spider fearful people knew that a spider could not be present during a block of trials, treating the neutral stimuli just as the low spider fearful people do. Our results show that people that fear spiders inspect potential spider-containing locations in a compulsory fashion even though directing attention to this location is completely irrelevant for the task. Reduction of capture can only be accomplished when people that fear spiders do not expect a spider to be present. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

DK公司陈列式办公室

来自俄罗斯的Megabudka建筑事务所为一家名为＂DK＂、专业从事家具和灯具制造的公司设计了新的办公室。＂办公室是一个矛盾空间,既充满了对于活力和创造性充满渴望,也时常不得不为各种标准与规范而最终妥协。但是在办公室中,改变也是能够存在的,尤其是当你愿意去接近这种变化时。＂在介绍这间位于莫斯科的现代化陈列式多功能办公空间时,来自Megabudka的设计师们引用了这段法国设计师Ronan＆Erwan Bouroullec兄弟的话,一语道破了这个办公空间设计的灵感来源。     

DNA G-Quadruplexes Contribute to CTCF Recruitment

G-quadruplex (G4) sites in the human genome frequently colocalize with CCCTC-binding factor (CTCF)-bound sites in CpG islands (CGIs). We aimed to clarify the role of G4s in CTCF positioning. Molecular modeling data suggested direct interactions, so we performed in vitro binding assays with quadruplex-forming sequences from CGIs in the human genome. G4s bound CTCF with Kd values similar to that of the control duplex, while respective i-motifs exhibited no affinity for CTCF. Using ChIP-qPCR assays, we showed that G4-stabilizing ligands enhance CTCF occupancy at a G4-prone site in STAT3 gene. In view of the reportedly increased CTCF affinity for hypomethylated DNA, we next questioned whether G4s also facilitate CTCF recruitment to CGIs via protecting CpG sites from methylation. Bioinformatics analysis of previously published data argued against such a possibility. Finally, we questioned whether G4s facilitate CTCF recruitment by affecting chromatin structure. We showed that three architectural chromatin proteins of the high mobility group colocalize with G4s in the genome and recognize parallel-stranded or mixed-topology G4s in vitro. One of such proteins, HMGN3, contributes to the association between G4s and CTCF according to our bioinformatics analysis. These findings support both direct and indirect roles of G4s in CTCF recruitment.     

How evolutionary crystal structure prediction works--and why

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties.     

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification.

Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.