Global Structural Characteristics Research Articles

Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model

Stylish fonts are widely utilized in social networks, driving research interest in stylish Chinese font generation. Most existing methods for generating Chinese characters rely on GAN-based deep learning approaches. However, persistent issues of mode collapse and missing structure in CycleGAN-generated results make font generation a formidable task. This paper introduces a unique semi-supervised model specifically designed for generating stylish Chinese fonts. By incorporating a small amount of paired data and stroke encoding as information into CycleGAN, our model effectively captures both the global structural features and local characteristics of Chinese characters. Additionally, an attention module has been added and refined to improve the connection between strokes and character structures. To enhance the model’s understanding of the global structure of Chinese characters and stroke encoding reconstruction, two loss functions are included. The integration of these components successfully alleviates mode collapse and structural errors. To assess the effectiveness of our approach, comprehensive visual and quantitative assessments are conducted, comparing our method with benchmark approaches on six diverse datasets. The results clearly demonstrate the superior performance of our method in generating stylish Chinese fonts.

An Innovative Approach for Removing Stripe Noise in Infrared Images.

The non-uniformity of infrared detectors' readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm's effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm's superiority and efficacy.

Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework

The clinical efficacy and safety of a drug is determined by its molecular properties and targets in humans. However, proteome-wide evaluation of all compounds in humans, or even animal models, is challenging. In this study, we present an unsupervised pretraining deep learning framework, named ImageMol, pretrained on 10 million unlabelled drug-like, bioactive molecules, to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabelled molecular images on the basis of local and global structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (that is, the drug’s metabolism, brain penetration and toxicity) and molecular target profiles (that is, beta-secretase enzyme and kinases) across 51 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences. Via ImageMol, we identified candidate clinical 3C-like protease inhibitors for potential treatment of COVID-19.

Global Structure of the Intrinsically Disordered Protein Tau Emerges from Its Local Structure.

The paradigmatic disordered protein tau plays an important role in neuronal function and neurodegenerative diseases. To disentangle the factors controlling the balance between functional and disease-associated conformational states, we build a structural ensemble of the tau K18 fragment containing the four pseudorepeat domains involved in both microtubule binding and amyloid fibril formation. We assemble 129-residue-long tau K18 chains with atomic detail from an extensive fragment library constructed with molecular dynamics simulations. We introduce a reweighted hierarchical chain growth (RHCG) algorithm that integrates experimental data reporting on the local structure into the assembly process in a systematic manner. By combining Bayesian ensemble refinement with importance sampling, we obtain well-defined ensembles and overcome the problem of exponentially varying weights in the integrative modeling of long-chain polymeric molecules. The resulting tau K18 ensembles capture nuclear magnetic resonance (NMR) chemical shift and J-coupling measurements. Without further fitting, we achieve very good agreement with measurements of NMR residual dipolar couplings. The good agreement with experimental measures of global structure such as single-molecule Förster resonance energy transfer (FRET) efficiencies is improved further by ensemble refinement. By comparing wild-type and mutant ensembles, we show that pathogenic single-point P301L, P301S, and P301T mutations shift the population from the turn-like conformations of the functional microtubule-bound state to the extended conformations of disease-associated tau fibrils. RHCG thus provides us with an atomically detailed view of the population equilibrium between functional and aggregation-prone states of tau K18, and demonstrates that global structural characteristics of this intrinsically disordered protein emerge from its local structure.

Factors That Determine the Variation of Equilibrium and Kinetic Properties of QM/MM Enzyme Simulations: QM Region, Conformation, and Boundary Condition.

To analyze the impact of various technical details on the results of quantum mechanical (QM)/molecular mechanical (MM) enzyme simulations, including the QM region size, catechol-O-methyltransferase (COMT) is studied as a model system using an approximate QM/MM method (DFTB3/CHARMM). The results show that key equilibrium and kinetic properties for methyl transfer in COMT exhibit limited variations with respect to the size of the QM region, which ranges from ∼100 to ∼500 atoms in this study. With extensive sampling, local and global structural characteristics of the enzyme are largely conserved across the studied QM regions, while the nature of the transition state (e.g., secondary kinetic isotope effect) and reaction exergonicity are largely maintained. Deviations in the free energy profile with different QM region sizes are similar in magnitude to those observed with changes in other simulation protocols, such as different initial enzyme conformations and boundary conditions. Electronic structural properties, such as the covariance matrix of residual charge fluctuations, appear to exhibit rather long-range correlations, especially when the peptide backbone is included in the QM region; this observation holds when a range-separated DFT approach is used as the QM region, suggesting that delocalization error is unlikely the origin. Overall, the analyses suggest that multiple simulation details determine the results of QM/MM enzyme simulations with comparable contributions.

SSI influence on the seismic response of asymmetrical small, low-rise R/C buildings

In the current work, a series of seismic analyses of asymmetrical small, low-rise 3-D reinforced concrete (R/C) buildings is executed along with the investigation of the effect of deformable soil on the seismic structural response. The time-history analyses are performed by using earthquake accelerograms in two horizontal and one vertical direction, while the elastoplastic behavior of R/C elements is regarded with a fiber mechanical analytical model. The foundation of the buildings is designed in two ways, either as R/C footings interconnected with R/C beams or as foundation mat. The Soil-Structure Interaction (SSI) effect on the seismic response is studied not only through the global structural characteristics but also through detailed parameters, such as the redistribution of the internal forces of R/C elements. The simple maximum softening damage index is employed to evaluate the elastoplastic behavior of R/C structures.

Design and sizing of an aeroelastic composite model for a flying wing configuration with maneuver, gust, and landing loads

The paper addresses the application of a parametric design process for a flying wing configuration. The multi-disciplinary configuration (MULDICON) is a generic unmanned combat air vehicle (UCAV) developed for research purposes, a further development of the DLR-F19 configuration, which was used for research activities in the scope of the DLR project Mephisto and its predecessors FaUSST and UCAV2010. For the MULDICON, the DLR parametric design process MONA is applied. Special emphasis is placed on the structural modeling with composite material where each layer is modeled and analyzed. Various failure criteria are compared to define suitable constraints for the optimization of the load carrying structure. In contrast to optimize the thickness of composites with global allowable strains, such strategy allows for a detailed analysis of every layer. The number of constraints due to the set-up of every ply is substantially increased compared to the strain allowables but the structural optimization is still applicable. The detailed structural and mass models represent the global stiffness and structural dynamic characteristics of the aircraft. For the loads analysis part of the design process, 9 different mass configurations with a total of 306 maneuvering load cases as well as 336 1-cos gust load cases are taken into account. Furthermore, a new simplified landing impact simulation is introduced to consider 12 landing load cases. All load cases are defined according to regulations like CS-25. Such number of load cases is necessary to cover a sufficient number of flight conditions. For the selection of the design loads for the structural optimization, the essential loads are analyzed for a subset of locations. Together with a parametrized optimization model, the structural optimization is conducted. The result is a weight-optimized structural model for the MULDICON. This entire model allows for the investigation of physics-based effects already at an early stage of the design process.

Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection

It is vital for fault detection technology to extract features of industrial process data effectively. Local kernel principal component analysis (LKPCA) has proved its good performance in preserving global and local structural characteristics. However, it ignored useful high-order statistics of data, so multi-block statistics local kernel principal component analysis (MSLKPCA) algorithm integrating statistics pattern analysis (SPA) into LKPCA is proposed. The correlation coefficient matrix is first calculated and K-means clustering is adopted to divide the original variables into several blocks. Then the weighted SPA, which gives different weights to different samples in each window according to their distributions, is adopted to build statistic spaces containing both low-order and high-order statistics. After that, LKCPA is performed in each statistic space to realize feature extraction. To reduce the noise effect amplified by SPA, PCA is adopted in the residual space to remove noise. Bayesian strategy is used to fuse the results of each block and two monitoring statistics ET and ER are proposed to monitor the feature space and the residual space respectively. The Tennessee–Eastman (TE) process simulation shows the effectiveness and superiority of the proposed algorithm for process monitoring.

Effect of an asymmetry of branching on structural characteristics of dendrimers revealed by Brownian dynamics simulations

Dendrimers in dilute solution with asymmetry of branching were simulated by the Brownian dynamics method. In this simulation a coarse-grained dendrimer model and athermal solvent conditions were implemented, extending previous work to a wider range of branch asymmetries at fixed average spacer lengths and high generation numbers close to the theoretical limit. We considered both global and local structural characteristics of dendrimers. The global ones, such as the average distance of ends from the center, the radius of gyration, the hydrodynamic radius and the dendrimer shape anisotropy, are practically insensitive to the asymmetry of branching. The effect of the spacer asymmetry is revealed mainly in the local structure of dendrimers. In particular the radial density profile changes its shape from a convex to a concave one with an increase of the asymmetry. As compared to symmetrical case, the distribution of terminal monomer units in asymmetrical dendrimers shifts towards the dendrimers periphery. The terminal monomers in an asymmetrical dendrimer are on average in a denser environment compared to their symmetrical analogs. The shorter spacers are less stretched and more turned back to the core than the longer ones located at the same topological distance from the dendrimer periphery. The simulations also demonstrated that the asymmetry of branching leads to a smaller radial overlap of dendron fragments inside the dendrimer as compared to the symmetrical case. However, the total overlap was found to be independent of the asymmetry of branching.

Study on the Effect of Flame Instability on the Flame Structural Characteristics of Hydrogen/Air Mixtures Based on the Fast Fourier Transform

In this study, the effect of flame intrinsic instability on the flame structural characteristics of hydrogen/air mixtures premixed at various equivalence ratios were experimentally investigated from the macroscopic and microscopic perspectives, respectively. The correlation degree and the relative deformation degree were defined to quantitatively study the global flame structural characteristics. Peak detection was used to capture the characteristic length of the flame and fast Fourier transform was adopted to study the components of the fluctuation of the flame front. The results show that with the development of flames, the wrinkles in the flame front increase and the correlation degree of the flame decreases. The relative deformation degree of the flame first decreases and then increases. When the equivalence ratio is 0.6, the average characteristic length initially exhibits an increasing trend, followed by a decreasing trend. The average characteristic length scale gradually increases, and the growth rate gradually decreases when the equivalence ratio ranges from 0.70 to 0.99. With the increase in the wavenumber, the amplitude of the corresponding disturbance exhibited an increasing trend followed by a decreasing one. With the development of the flame, the maximum amplitude of the disturbance shows a reverse trend, i.e., first decreasing and then increasing. The disturbances with smaller wavelengths could be further developed.

General Graph Data De-Anonymization

When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively de-anonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs , ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015].

Matching based ground-truth annotation for online handwritten mathematical expressions

Assessment of mathematical expression recognition at expression level only is not sufficient to diagnose strengths and weaknesses of different recognition systems. In order to make assessment at different levels possible, large datasets annotated with ground-truth data at different levels, such as at symbol segmentation, symbol classification, symbol/sub-expression spatial relationships, baselines or whole expression levels, are needed. Creation of ground-truthed datasets of handwritten mathematical expressions is a challenging task due to the need to cope with a large variability of symbol classes, expression layouts, writing styles, among other issues including the fact that manual annotation is an error-prone procedure. We propose an expression matching approach where symbols in a transcribed expression are assigned to the corresponding symbols in the respective model expression. Matching is formulated as a simple linear assignment problem where matching cost is defined as a weighted linear combination of local (symbol) and global (structural) characteristics. Once a symbol-to-symbol assignment is computed, not only symbol labels but all other ground-truth data attached to the model expression can be automatically transferred to the transcribed expression. We use two independent large test sets to empirically evaluate the influence of the cost function terms on matching performance. Results show mean symbol assignment rates above 99% on both sets, suggesting the potential of the method as an useful tool for helping the creation of ground-truthed online mathematical expression datasets.

Human Action Recognition Using Pyramid Histograms of Oriented Gradients and Collaborative Multi-task Learning

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

Links between biota and climate-related variables in the Baltic region using Lake Onega as an example

This paper aims to reveal current changes (recent decades) in regional climatic variables like water temperature (WT), the duration of the ice-free period (ICE-FREE) and the precipitation rate (P), as exemplified by Petrozavodsk Bay (Lake Onega, European Russia), and to analyse their relationships with the global climatic indices NAO, AO and structural characteristics of biota (chlorophyll a concentration (Chl a), phytoplankton and zoobenthos abundance/biomass) in the lake ecosystem, which lies within the Baltic Sea catchment area. Spearman’s rank correlations yielded significant (p<0.05) relationships between the NAO and planktonic Cyanobacteria abundance, and also between NAO, AO, WT, P and the abundance and biomass of zoobenthos. Chl a correlates positively (R=0.66; p=0.03) with WT and negatively with ICE-FREE (R=−0.53; p=0.05). At the same time, multiple regression analysis confirmed that the global climate governs primarily the regional climatic variables and productivity level in the lake’s ecosystem, whereas most of the biotic characteristics respond to variability in the regional climate.

A computational approach to structural properties of glycoside hydrolase family 4 from bacteria.

Structural bioinformatics approaches applied to the alpha- and beta-glycosidases from the GH4 enzyme family reveal that, despite low sequence identity, these enzymes possess quite similar global structural characteristics reflecting a common reaction mechanism. Locally, there are a few distinctive structural characteristics of GH4 alpha- and beta-glycosidases, namely, surface cavities with different geometric characteristics and two regions with highly dissimilar structural organizations and distinct physicochemical properties in the alpha- and beta-glucosidases from Thermotoga maritima. We suggest that these structurally dissimilar regions may be involved in specific protein-protein interactions and this hypothesis is sustained by the predicted distinct functional partners of the investigated proteins. Also, we predict that alpha- and beta-glycosidases from the GH4 enzyme family interact with difenoconazole, a fungicide, but there are different features of these interactions especially concerning the identified structurally distinct regions of the investigated proteins.

The effect of complex solvents on the structure and dynamics of protein solutions: The case of Lysozyme in trehalose/water mixtures

We present a Molecular Dynamics simulation study of the effect of trehalose concentration on the structure and dynamics of individual proteins immersed in trehalose/water mixtures. Hen egg-white Lysozyme is used in this study and trehalose concentrations of 0%, 10%, 20%, 30% and 100% by weight are explored. Surprisingly, we have found that changes in trehalose concentration do not change the global structural characteristics of the protein as measured by standard quantities like the mean square deviation, radius of gyration, solvent accessible surface area, inertia tensor and asphericity. Only in the limit of pure trehalose these metrics change significantly. Specifically, we found that the protein is compressed by 2% when immersed in pure trehalose. At the amino acid level there is noticeable rearrangement of the surface residues due to the change in polarity of the surrounding environment with the addition of trehalose. From a dynamic perspective, our computation of the Incoherent Intermediate Scattering Function shows that the protein slows down with increasing trehalose concentration; however, this slowdown is not monotonic. Finally, we also report in-depth results for the hydration layer around the protein including its structure, hydrogen-bonding characteristics and dynamic behavior at different length scales.

Emerging Indian pharma multinationals: latecomer catch-up strategies in a globalised high-tech industry

Leading Indian Pharmaceutical Companies (IPCs) are frequently taken as prime examples of the new ‘emerging multinationals’ from developing countries. Business researchers have studied their catch-up and internationalisation strategies from a resource-based perspective, often in reference to the specific institutional changes in the Indian economy. While these studies identify significant advances in IPCs’ capabilities and internationalisation, the global competitive position they have achieved and the reach of this position remain elusive. By taking a ‘global industry perspective’ the paper argues that IPCs are still no serious competitive challenge to Big Pharma and have to largely rely on more subordinate collaborative strategies. More generally, the findings from this explorative case study suggest that certain global structural characteristics of various industries may be important variables in the explanation of the trajectories and dynamics of emerging multinationals’ international catch-up processes.

The perceived structure of Forest Nenets songs: A cross-cultural case study.

ABSTRACT - In this case study, several participants were ashed to identify the boundaries between successive melody lines in traditional vocal compositions of the Forest Nenets (people inhabiting Northeast Europe and Western Siberia). Unstructured interviews were conducted. The participants included an ethnic Forest Nenets (the insider), an Estonian composer (an outsider), and an Estonian ethnomusicologist (a mediator, one of the authors of this paper). The aim of the work was to investigate whether participants from different cultural background solve the segmentation task in a similar way, and whether the grouping rules that have been developed for the segmentation of Western tonal music have relevance for traditional vocal music of the Forest Nenets. Melody lines in the compositions were of either equal or unequal length, the latter called ad-lib compositions. In the first case, the segmentation results for att the participants concurred. In the second case, some discrepancies emerged because of different underlying principles in the interpretation of adlib compositions. A participant could rely on the melody, on the text, or on both when solving the task. Moreover, Ote native speaker of the Forest Nenets language was unable to separate the musical and verbal components of the composition. Onfy a few of aie grouping rules of Western tonal music seem to apply to the vocal compositions of the Forest Nenets. Rests may play a certain role as markers which refer to the possible end of a melody Une. A longer sequence of meaningless syllables tends to condition a listener to expect a new une of the composition to begin soon. The results demonstrate the interplay between universal and culture-specific factors in the decomposition of Ae Forest Nenets songs into structurally meaningful units. This case study provides a foundation for future experiments which examine groups of listeners of different skills and ethno-cuUural backgrounds in the boundary formation task, particularly in singing. KEYWORDS - grouping, singing, perceived boundary, segmentation, melody, cross-cultural, Nenets In the theory of Western tonal music, a musical work is usually treated as a hierarchical structure which consists of motifs, phrases, periods, sections, and movements (e.g., Riemann, 1900). In the contemporary urban environment, however, listening to music is becoming increasingly fragmentary (Sloboda & O'Neill, 2001). A concert situation, typical of 19th century Western culture, where listeners tried to concentrate on and to contemplate the music alone, is becoming progressively rare in contemporary society. This implies that lower-level processes like grouping or segmenting of music may become more important for listeners than the comprehension of the more global structural characteristics of a musical work (e.g., an ability to follow the subdivision of a movement written in sonata form) as listeners rarely encounter a work in its full shape. Lerdahl and Jackendoff (1983) have proposed a set of rules aimed at predicting where a listener is likely to perceive the boundaries in Western tonal music, which would help the listener to segment the flow of sounds into smaller units'. Lerdahl and Jackendoff distinguished between proximity rules and change rules which correspond to the well-known principles from Gestalt psychology of proximity and similarity, respectively. Examples of the proximity rules include formation of a boundary at a rest or after a longer note in a succession of shorter notes, which Lerdahl and Jackendoff (1983, p. 45) have indicated as GPR2. They write: Consider a sequence of four notes Wjn2nsn4. All else being equal, the transition n2n3 may be heard as a group boundary if [...] the interval of time from the end of n^sub 2^ to the beginning of n^sub 3^ is greater than that from the end of n^sub 1^ to the beginning of n^sub 2^ and that from the end of n^sub 3^ to the beginning of n^sub 4^. Examples of the change rules include formation of a boundary as a result of change in register, dynamics, or articulation, which Lerdahl and Jackendoff (1983, p. …

Rates of Unfolding, Rather than Refolding, Determine Thermal Stabilities of Thermophilic, Mesophilic, and Psychrotrophic 3-Isopropylmalate Dehydrogenases

The relationship between the thermal stability of proteins and rates of unfolding and refolding is still an open issue. The data are very scarce, especially for proteins with complex structure. Here, time-dependent denaturation-renaturation experiments on Thermus thermophilus, Escherichia coli, and Vibrio sp. I5 3-isopropylmalate dehydrogenases (IPMDHs) of different heat stabilities are presented. Unfolding, as monitored by several methods, occurs in a single first-order step with half-times of approximately 1 h, several minutes, and few seconds for the thermophilic, mesophilic, and psychrotrophic enzymes, respectively. The binding of Mn*IPM (the manganese complex of 3-isopropylmalate) markedly reduces the rates of unfolding; this effect is more prominent for the less stable enzyme variants. Refolding is a two-step or multistep first-order process involving an inactive intermediate(s). The restoration of the native structure and reactivation take place with a half-time of a few minutes for all three IPMDHs. Thus, the comparative experimental unfolding-refolding studies of the three IPMDHs with different thermostabilities have revealed a close relationship between thermostability and unfolding rate. Structural analysis has shown that the differences in the molecular contacts between selected nonconserved residues are responsible for the different rates of unfolding. On the other hand, the folding rates might be correlated with the absolute contact order, which does not significantly vary between IPMDHs with different thermostabilities. On the basis of our observations, folding rates appear to be dictated by global structural characteristics (such as native topology, i.e., contact order) rather than by thermodynamic stability.

Local and Global Effects in Texture and Microstructure Observed after Channel-Die Compression of Copper Single Crystals and after Cross-Rolling of Copper Sheets

The change of the deformation path leads to destabilization of the substructure and affects the texture of the deformed metal. The observed changes of texture and microstructure are, as a rule, significant and their characteristics depend on the geometry of the deformation process. Previous investigations on copper (and copper alloy) samples after deformation by rolling and channel-die compression were based on X-ray pole figure measurements and on observations in the light microscope. Hereby only global texture and structural characteristics have been obtained. The present study is mainly based on measurements of individual crystal orientations performed by ACOM (Automated Crystal Orientation Measurement, “Automated EBSD”) in the SEM which enables a precise local analysis of the investigated phenomena. For the channel-die experiments, (1 1 2)[1 1 -1] and (1 1 2)[1 -1 0] oriented copper single crystals have been used. After pre-deformation, a second deformation step has been carried out in transverse direction. The {1 1 2}&lt;1 1 0&gt; orientations are destabilized by channel-die compression, and clusters of layers develop which are composed of complementary {1 1 0}&lt;1 1 2&gt; components. The deformation process in polycrystalline sheets after rotating the rolling direction leads again to a distinct disintegration of the microstructure and destabilization of the b fiber. This process of microstructure reorganization after pre-deformation is fast and of high dynamics.