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Reinforcement learning (RL) is a promising technique for designing a model-free controller by interacting with the environment. Several researchers have applied RL to autonomous underwater vehicles (AUVs) for motion control, such as trajectory tracking. However, the existing RL-based controller usually assumes that the unknown AUV dynamics keep invariant during the operation period, limiting its further application in the complex underwater environment. In this article, a novel meta-RL-based control scheme is proposed for trajectory tracking control of AUV in the presence of unknown and time-varying dynamics. To this end, we divide the tracking task for AUV with time-varying dynamics into multiple specific tasks with fixed time-varying dynamics, to which we apply meta-RL for training to distill the general control policy. The obtained control policy can transfer to the testing phase with high adaptability. Inspired by the line-of-sight (LOS) tracking rule, we formulate each specific task as a Markov decision process (MDP) with a well-designed state and reward function. Furthermore, a novel policy network with an attention module is proposed to extract the hidden information of AUV dynamics. The simulation environment with time-varying dynamics is established, and the simulation results reveal the effectiveness of our proposed method.

Seizure prediction from intracranial electroencephalogram (iEEG) has great potentials to improve the life quality of epileptic patients, but faces big challenges. One major difficulty lies in that brain signal changes occasionally during long-term monitoring, due to electrode movements or the nonstationary brain dynamics. This leads to a serious situation that a predictor learned from historical data usually only works well in a short time period as long as the data do not change much. While in a long time span, the performance of the learned features decreases or even become totally invalid. To deal with the problem, we propose a domain adaptation convolutional neural network to learn robust preictal features that is invariant across different time periods. Specifically, the preictal feature is learned and enhanced by multi-scale temporal convolutions in the neural network. Based on this, a domain adaptation method is adopted to constrain that the learned features should be invariant across different time periods. Experimental results demonstrate that our approach can effectively improve seizure prediction performance against signal changes.

We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at https://w3id.org/synthmorph., 16 pages, 15 figures, 3 tables, deformable image registration, data independence, deep learning, MRI-contrast invariance, anatomy agnosticism, final published version

Recently, various fault diagnosis methods based on domain adaptation (DA) have been explored to solve the problem of discrepancy between the source and target domains. However, given complex industrial scenarios, DA-based methods usually fail when the working conditions of machines are unseen, i.e., target data are unavailable during model training. In this work, a generic domain-regressive framework for fault diagnosis, namely, adversarial domain-invariant generalization (ADIG), is proposed. ADIG leverages multiple available domain data to exploit domain-invariant knowledge through adversarial learning between the feature extractor and the domain classifier. Simultaneously, the fault classifier generalizes the knowledge from the source-related domain to diagnose the unseen but related target domain signals. Moreover, customized strategies of feature normalization and adaptive weight are proposed to promote diagnosis performance. Comprehensive case studies show that ADIG achieves satisfactory diagnosis accuracy and robustness under unseen conditions, indicating that ADIG is a remarkably potential diagnosis tool for real-case industrial machines. IEEE

Recognizing the same place undervariant viewpoint differences is the fundamental capability for human beings and animals. However, such a strong place recognition ability in robotics is still an unsolved problem. Extracting local invariant descriptors from the same place under various viewpoint differences is difficult. This article seeks to provide robots with a human-like place recognition ability using a new 3-D feature learning method. This article proposes a novel lightweight 3-D place recognition and fast sequence matching to achieve robust 3-D place recognition, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences. Specifically, we extracted the viewpoint-invariant place feature from 2-D spherical perspectives by leveraging spherical harmonics’ orientation-equivalent property. To improve sequence-matching efficiency, we designed a coarse-to-fine fast sequence-matching mechanism to balance the matching efficiency and accuracy. Despite the apparent simplicity, our proposed approach outperforms the relative state of the art. In both public and self-gathered datasets with orientation/translation differences or noise observations, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods.

Cross-domain face synthesis plays a positive role in law enforcement and digital entertainment. It is challenging to synthesize high-quality faces across multiple domains based on limited paired data because the multiple mappings between different domains may interfere with each other. Cognitive science investigates that the brain can recognize the same person with multiple different expressions by extracting invariant information on the face and we humans perceive instances by decomposing them into parts. Motivated by these cognitions, we propose a unified semi-supervised framework MFS for multi-domain face synthesis by extracting a domain-invariant representation and exploiting parts of multi-level features. Specifically, realized by adversarial training with additional ability to utilize domain-specific information, a encoder is trained to remove domain specific information and extract the domain-invariant representation from multiple inputs. Then, we utilize the multi-level feature parts extracted from inputs and reconstructed faces via a pretrained recognition model to ensure that the domain-invariant representation contains enough semantic information. we also utilize the feature parts extracted from inputs and limited paired data to compose pseudo features in target domain for supervising the generator, which makes our framework suitable for large amounts of unpaired training data. By exploiting this framework, we can achieve face synthesis between multiple domains using some paired data together with a large training database without ground truth target faces. Experimental results demonstrate our framework achieves great performances on qualitative and quantitative evaluations under both artificial and uncontrolled environments, and our framework has competitive performances in single translation compared with specialized methods for translation between two specific domains.

With the introduction of artificial intelligence (AI), current deep learning-driven change detection methods generally regard changes as a type of specific land cover information and try to detect them simply by using existing semantic labeling models. In practice, the changes that occur on different land cover categories may appear completely different for remote sensing images, making it difficult to detect multiple categories of changes with an end-to-end model. In addition, training such networks requires a large amount of prelabeled references, which are labor-intensive and time-consuming. Motivated by this observation, we integrate dual learning algorithm and disentangled representation theory to develop a novel approach, named content-invariant dual learning (CiDL), for either supervised or unsupervised change detection in remote sensing images. In our framework, two opposite Y-shaped networks, each of which consists of two encoders and one decoder, are introduced to translate bitemporal images from their original domains to each others, where their intrinsic content features are retained, while their style features are consistent. By training our hybrid framework, even without references, this method learns a category-wise cross-domain translation to suppress the discrepancies in paired unchanged regions, and meanwhile highlight those in paired changed regions. The experimental results on two typical change detection data sets and the comparison with other state-of-the-art deep learning-driven methods verify the effectiveness and competitiveness of our proposed CiDL.

Since the human body is non-rigid, actions captured in different views always involve action occlusion and information loss. Recently, view-variation-related human action recognition is still a challenging problem. To address the problem, we propose a View Transformation Network (VTN) that realizes the view normalization by transforming arbitrary-view action samples to a base view to seek for a view-invariant representation. an attention learning module is designed to learn a co-attention for action samples of different views, that contributes to output a similar feature representation to erase the view diversity in different views. Extensive and fair evaluations are performed on the UESTC varying-view RGB-D dataset, the NTU RGB-D 60 dataset, and the NTU RGB-D 120 dataset, where three evaluation types,~\ie X-subject, X-view, and A-view recognition, are performed. Experiments illustrate that our VTN model achieves outstanding performance.

Software rejuvenation was born to fix operating system faults by periodically refreshing the run-time code and data. This mechanism has been extended to protect control systems from cyber-attacks. This letter proposes a software rejuvenation design method in discrete-time where invariant sets for the safety and mission controllers are designed to schedule the timing of software refreshes. To compute a minimal robust positively invariant (min-RPI) set and the bounded time between software refreshes to ensure system safety, an LP based approach is proposed for stable and unstable systems. Finally, the designed approach is illustrated by the case study of a simulated lab-scale microgrid.

Person re-identification (ReID) aims at matching identities across disjoint cameras. Its fundamental difficulty lies in associating images across individual cameras, where a key clue, i.e., identity appearance, is prone to the environmental factors of cameras and, consequently, subject to distinct image distributions due to the environmental differences between cameras. To associate images from training cameras, ReID methods strongly demand expensive inter-camera annotations for learning the relations between the distribution of these cameras, yet trained models are still not guaranteed to transfer well to unseen cameras. This problem significantly limits the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we guarantee an invariant input distribution independent of all cameras. Thus, the training and testing procedures are always conducted under the same input distribution. This alignment brings three benefits. First, ReID models enjoy better abilities to generalize across testing scenarios with unseen cameras and transfer across multiple training sets. Second, it makes better use of intra-camera annotations, which have been undervalued before due to the lack of cross-camera information. Ideally, the cost of inter-camera annotations can be largely reduced. Third, cross-modality tasks can be better defined through aligning visible/infrared cameras’ distributions. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach.

In the development of autonomous ground vehicles (AGVs), guaranteeing vehicle driving safety is a major concern. Among various aspects that need to be thoughtfully considered for driving safety, vehicle stability is one of the most fundamental and important factors. In this paper, to describe a guaranteed vehicle stability control problem, a new time-varying control-dependent invariant set is introduced. Correspondingly, the concept of a time-varying control-dependent barrier function (CDBF) is proposed. The proposed time-varying CDBF is more general than conventional control barrier functions (CBF), since we additionally consider invariant sets that can be time-varying and control-dependent, which will have broader applications. Then, using the proposed framework, we design a vehicle stability control algorithm, which guarantees that the vehicle states are always kept in the time-varying and control-dependent lateral stability regions. Finally, the correctness and effectiveness of the proposed theory and control method are verified and discussed through illustrative simulation results of high-speed J-turn and double lane change maneuvers for an AGV.

Person Re-identification (ReID) is an important yet challenging task in computer vision. Due to diverse background clutters, variations of viewpoints and body poses, it is far from being solved. How to extract discriminative and robust features invariant to background clutters is one of the core problems. In this paper, we first introduce a set of binary segmentation masks to construct synthetic RGB-Mask pairs as inputs, and then design a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions. Moreover, we propose a novel region-level triplet loss to guide the features learning, i.e., pulling the features from the full image and body region close, whereas pushing the features from backgrounds away. To learn the similarities from multiple features of the proposed MGCAM, we further introduce the instance-level two-stream metric co-learning (TSMCL) to help learn pair-wise relations between features from not only different regions but also different instances. TSMCL could help learn more compact features across full and body streams, enhancing the performance of MGCAM. We evaluate the proposed method on four public datasets, including MARS, Market-1501, CUHK03, and DukeMTMC-reID. Extensive experiments show that the proposed method is effective and achieves satisfying results.

Visual place recognition (VPR) in environments subject to extreme appearance variation due to changing weather, illumination or seasons is a challenging task. Recent works have shown that features learned from CNNs can achieve promising performance. However, most of the existing methods concentrate so much on the image itself that they neglect the architecture of the network, especially different filters that may carry more meaningful information. In this paper, we develop a learnable feature map filtering (FMF) module constrained by triplet loss to re-calibrate the weight of the individual feature map. In this way, specific feature maps that encode invariant characteristics of location are extracted. Moreover, to make full use of the rich global mutual information that resides in the sample set, we propose an influence-based graph attention network (IB-GAT) with a verification subnet to better incorporate the relations among samples during the training process. Different from conventional GAT approaches, IB-GAT enables feature nodes to attend over the influence of other nodes instead of the original feature. Thus refined features with more discriminative power could be generated. Extensive experiments have been conducted on six public VPR datasets with varying appearances. Ablation analysis verifies the potential efficacy of the FMF module and the IB-GAT components. The experimental results also demonstrate that the proposed methods can achieve better performance than the current state of the art.

Reinforcement learning(RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary RL research is to discover how to learn with less data. Previous work has shown that domain information can be successfully used to shape the reward; by adding additional reward information, the agent can learn with much less data. Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered. While suchpotential-based reward shaping(PBRS) holds promise, it is limited by the need for a well-defined potential function. Ideally, we would like to be able to take arbitrary advice from a human or other agent and improve performance without affecting the optimal policy. The recently introduceddynamic potential-based advice(DPBA) was proposed to tackle this challenge by predicting the potential function values as part of the learning process. However, this article demonstrates theoretically and empirically that, while DPBA can facilitate learning with good advice, it does in fact alter the optimal policy. We further show that when adding the correction term to “fix” DPBA it no longer shows effective shaping with good advice. We then present a simple method calledpolicy invariant explicit shaping(PIES) and show theoretically and empirically that PIES can use arbitrary advice, speed-up learning, and leave the optimal policy unchanged.

This paper claims that, to the extent that temporal direction figures in physics at all, it is found there as part of the extra-scientific language science employs. The asymmetry between “before” and “after” is not captured by the mathematics of any theory, nor can it be derived from the laws of any theory. This, I argue, is true even of theories whose laws are not time reversal invariant. Recognizing that physics does not yield temporal direction but receives it from the background in which physics develops and operates does not, however, expose any hitherto unknown limitations or deficiencies of physics. The claim is not about physics, but about metaphysical stances regarding physics, specifically, physicalism, which requires from physics to deliver more than it does, or should. Once the place of temporal direction in physics is understood, infamous difficulties can be addressed in a novel way. Issues emerging from the 2nd law of thermodynamics, such as the minimum problem, and the worry that the law generates outlandish predictions, are removed. A side benefit is the recognition that the past hypothesis is superfluous. A final conclusion of the paper is that the relationship between physics and everyday language, far from being a source of difficulties, is healthy and beneficial to both.

Memory allocation and deallocation are the fundamental operations of embedded operating systems, which have been extensively used in many safety critical systems. The correctness of the operations is of paramount importance because their failure could incur severe consequences. While the system is running, the memory state can easily grow to a gigantic amount, which means that it is impossible to verify the huge memory states one by one. Therefore, it is a challenge how to verify the correctness of running memory state of the system. In this article, we propose a novel memory state verification method based on inductive and deductive reasoning. First, we abstract the memory state as a list of memory blocks, which will transform in memory operations. Second, we construct the generic model based on the transition function of the memory management and summarize the invariant properties of the memory state. Third, we use the inductive method to calculate the changes between the memory states, and verify that the memory state of the system always satisfy the global properties. All the proofs are implemented in the interactive theorem prover Coq. On the basis of our proposed model, we verify the correctness of a two-level segregated fit (TLSF) algorithm through some extensions, and we also apply this method to verify the correctness of the memory state of the embedded system at runtime.

Noisy intermediate scale quantum (NISQ) computers are becoming a reality thanks to the recent advances made by researchers who physically build such systems. In order to execute corresponding quantum algorithms (usually provided in terms of quantum circuits), certain physical constraints in the architectures need to be satisfied. More precisely, physical constraints restrict the possible interactions between qubits which frequently result in cases where qubits which are supposed to interact in a quantum circuit are not allowed to interact on the physical device. Thus far, this is addressed by dedicated methods that map the logical quantum circuit to a physical realization and satisfy the constraints by inserting further operations. This leads to additional costs which harm the fidelity of the circuit and, hence, urgently need to be avoided. Unfortunately, current state-of-the-art approaches for this mapping process take the existing architectures as invariant and only try to reduce the number of additionally needed operations. In contrast, (slight) changes in the, respectively, given architectures (which still keep the underlying physical constraints satisfied) might be possible and may allow for even better (i.e., less costly) mappings. But this potential has not been investigated yet. In this work, we explore this potential. More precisely, we introduce several schemes for generating alternative coupling graphs (and, by this, quantum computing architectures) that still might be able to satisfy physical constraints but, at the same time, allow for a more efficient realization of the desired quantum functionality. Evaluations confirm the potential of those alternative coupling graphs and demonstrate that they can reduce the mapping overhead by up to 60% in the best case and up to almost 40% on average.

In this paper, we present the first general solution to the automatic reconfiguration problem of timed discrete-event systems. We extend the recursive forcible backtracking approach which had been already solved the automatic reconfiguration problem of untimed discrete-event systems. In particular, we first solve the timed centralized reconfiguration problem using a specific timed eligibility set. Then, we study the identity between the solutions to an arbitrary timed centralized reconfiguration problem and its corresponding decentralized version. It turns out that the solutions to both cases are identical to each other. So, the solution obtained by the proposed theory is interestingly invariant to systematic distributions., 2020 24th International Conference on System Theory, Control and Computing (ICSTCC)

This paper describes the design and verification of a copy elimination and memory deallocation scheme for a compiler that translates the verification-friendly programming language Whiley into efficient C code. We use a combination of static analysis and runtime flags to replace array copies with reference assignments when possible. Formal proofs have been carried out, both manually and with the automatic theorem prover Boogie, to show that the generated code satisfies a crucial invariant, which is needed to show the absence of invalid memory access errors and memory leaks. Our benchmark results show that the compiler effectively reduces the time complexity of LZ77 decompression from $$O(n^2)$$ to the theoretically lowest possible level $$O(n\log n)$$ while avoiding all memory leaks.

This is a study of adverbs in nine typologically divergent Austronesian languages of Taiwan, Atayal, Bunun, Favorlang, Kavalan, Puyuma, Rukai, Saisiyat, Thao, and Tsou. There are only a few adverbs in each of these languages. The form of an adverb is usually invariant and its position in a sentence is relatively free. On the contrary, the form of a verb usually varies and its position in the sentence is usually fixed. Since the function of an adverb is to modify a verb, it may not occur without a verb in a sentence, whereas a true verb may occur without any other verb. Many adverbial concepts in Chinese and English, such as ‘all’, ‘only’, ‘often’, and ‘again’, are expressed using verbs that manifest different foci and take aspect markers. When these words function as the main verb in the sentence, they may attract bound personal pronouns in many Austronesian languages of Taiwan. However, there are a few genuine adverbs in each of these languages. It varies from language to language whether a certain lexical item functions as a verb or adverb.

The article is devoted to dynamic transformational processes within contemporary English minitexts (small-format texts). The paper reflects the findings of a research that encompasses 500 advertising abstracts to works of fiction. Transformational dynamics is disclosed in terms of linguosynergetics based on a dynamic-and-systemic representation of evolutionary developments in book abstract discourse. Particularly, the conventional invariant discourse features are considered; non-systemic stochastic elements are revealed in such texts; abstract discourse ability of generating senses which exceed mechanic addition of text units meanings, i.e. functional synergism, is under analysis. The authors argue that fluctuations are observed on the lexical, structural and contextual levels of text organisation. The analysis of the data obtained leads to the conclusion that the system features of book abstracts are currently undergoing transformation of their pragmasemantic parameters, which are moving towards those of purely advertising discourse. Today’s trend to the reduction of the verbal communication volume shows that the prospect for further investigation of minitexts (including book abstract discourse) lies in the elaboration of the pragmasemantic synergism concept, with the said discourse feature being of special significance to the texts aimed at compact incorporation of considerable contents.

The emerging fifth generation technology has attracted the attention of many researchers and developers. It is designed to connect everyone and everything together including machines, vehicles, objects, and devices. One of the fifth generation research topics that are being investigated is the modelling of the wireless communication channel. In this paper, Third Generation Partnership Project channel models are studied and its variant Three-Dimensional Third Generation Partnership Project model is examined. This model is complex and not easy to use due to the employment of a large number of parameters. For these reasons, a new simplified method for parameter estimation of the three-dimensional model of third generation partnership project based on estimation of signal parameters via rotational invariant techniques algorithm is proposed. The simplified model is less complex than the three-dimensional model of third generation partnership project and is found to perform better in terms of computational time while maintaining similar results.

We present a method for feedback motion planning of systems with unknown dynamics which provides probabilistic guarantees on safety, reachability, and goal stability. To find a domain in which a learned control-affine approximation of the true dynamics can be trusted, we estimate the Lipschitz constant of the difference between the true and learned dynamics, and ensure the estimate is valid with a given probability. Provided the system has at least as many controls as states, we also derive existence conditions for a one-step feedback law which can keep the real system within a small bound of a nominal trajectory planned with the learned dynamics. Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal. We demonstrate our approach by planning using learned models of a 6D quadrotor and a 7DOF Kuka arm. We show that a baseline which plans using the same learned dynamics without considering the error bound or the existence of the feedback law can fail to stabilize around the plan and become unsafe., Comment: Accepted at RA-L and ICRA 2021. Craig Knuth and Glen Chou contributed equally to this work

Motivation Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources. Results Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. Availability and implementation The software is publicly available at https://github.com/uci-cbcl/SAILER. Supplementary information Supplementary data are available at Bioinformatics online.

Fault diagnosis based on data-driven methods are widely investigated when enough supervised samples of the target machine are available to build a reliable model. However, the labeled samples in practical operated machine are usually scarce and difficult to collect. If the model is built based on the sufficient labeled samples from different source machines, the diagnosis performance will degenerate owing to the domain discrepancy. To solve this issue, in this article, transfer learning (TL) is proposed by leveraging knowledge learned from source domain to target domain. While TL methods for fault diagnosis have been actively studied, most of them focus on learning from a single source. Since the labeled samples can come from multiple domains, more general diagnosis knowledge can be learned, which is beneficial to the prediction for the target domain. Therefore, a new TL approach based on multisource domain adaptation is proposed. A multiadversarial learning strategy is utilized for obtaining feature representations, which are invariant to the multiple domain shifts and discriminative for the learning goal at the same time. Extensive experimental analysis on four different bearing datasets is performed to illustrate the effectiveness and advantage of the proposed method.

The object of research is the shadow economy as structural component (invariant) of economic security of state on example of Ukraine. The level of shadowing of Ukrainian economy requires improvement of both: the system of indicators and mechanism of influence on the level of shadowing on the basis of using of fundamental constants of mathematics of harmony and principle of structural harmony of systems. Under research, fundamental provisions and methods of the theory of safety studies, economic theory, theories of systems and structural harmony of systems, institutionalism, transdisciplinary nature were used. The theme of management of the system of economic security of the state is investigated from various scientific positions of classical science. At the same time, practically ignores the theory of synergetic, which is the synthesis of transdisciplinary approach, price approach, classical theory of systems, structural harmony of systems, theory of fractals and attractors and other components of theory of complexity. Obviously, any system has metric characteristics inherent. The most widespread math constant found in nature is the Phidias number (Golden section). Meаsure is the most important component of all and any knowledge, without it any description of processes and phenomena will be incorrect. The law of measures, or the law of proportional distribution with its derivatives can be defined as law of projecting and organizing sustainable systems at all levels of hierarchical management. The structure of the system is the most conservative characteristic of it as opposed to the state of the system. The properties of the system as a whole are determined not only the properties of its individual elements, but also the properties of the structure of the system as whole. They depend on the existence of the system. The existence of the system retains these parameters in the process of adapting the system to external conditions and thus maintains the existence of the system itself. Matching the metric quality system of economic security of the state and obtaining a conclusion on its stability and abilities to self-organization under the conditions of shadowing of the economy can be carried out by comparison. It is carried out using attractors representing a recurrent number of gold sections: 0.500 ...; 0.618 ...; 0.682 ...; 0.725, as well as – distractors: 0.5698 ...; 0.6540 ...; 0.7053. An integral indicator characterizing the state of any system is the relative information entropy by Shannon. Entropy testing of the system (calculation of relative information entropy) is a logical component of the innovative model of system research.

Automatic target detection plays a significant role during war operations. The concept behind automatic target detection in war is military object recognition from the captured images. For object recognition in the given image, convolutional neural network (CNN) architectures are used efficiently. However, CNN suffers from a problem of location invariant and their performance depends mainly on the size of the training set. Generally, the available training data will be in less proportion for military objects due to its operational and security issues. Due to these two issues of CNN, the performance of CNN may degrade abruptly. To address military object recognition, a relatively new neural network architecture based on capsule network (CapsNet) is introduced in this paper. A variant of CapsNet called the multi-level CapsNet framework is projecting in this paper for efficient military object recognition under the case of a small training dataset. The validation of the introduced framework is done on a dataset of military objects collected from the Internet. The dataset contains particularly five different military objects and similar civil ones. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional support vector machines and transfer learning-based CNNs.

The paper presents a review regarding the current methods of evaluating, ranking, and rating academic economics journals in Russia and other states – the USA, the EU countries and China. In order to do this, the authors introduce a common typology of analytical methods which includes bibliometric, peer review, network (invariant), hybrid, and consolidated algorithms for evaluating the quality of journals. The article shows that the differentiation of periodicals can be performed using quantitative and qualitative approaches. The authors explore the usage of so-called institutional filters, which are the rules for regulating the academic sphere via specialized tools for assessing the academic economics journals. The findings reveal that the trend established in the beginning of the 21st century concerning the application of international databases’ formal metrics in science policy hides a lot of threats. Among them status losses by some Russian academic economics journals maintaining high academic standards, which are not covered by international databases, as well as various consequences of manipulating practices and setting of obviously unrealistic targets can be named. The authors outline the conditions for the efficient usage of formal academic economics journals metrics as institutional filters. The paper proves that current analytical methods for evaluating the periodicals quality allow to carry out their reasonable and unbiased ranking for research needs of Russian economists.

Learning a cross-domain representation from labeled source domains to unlabeled target domains is an important research problem in representation learning. Despite the success of traditional adversarial methods, they proposed to align features from each domain only while neglecting the importance of labels, when fooling a special domain discriminator network. Thus, the discriminator of these approaches merely distinguishes whether the generated features are in-domain or not, which may lead to less class-discriminative features. In this paper, by considering the joint distributions of features and labels in both domains, we present Feature and Label Adversarial Networks (FLAN). As a result, FLAN can generate more discriminative features in both domains. Experimental results on standard unsupervised domain adaptation benchmarks have demonstrated that FLAN can outperform the state-of-art domain invariant representation learning methods.

Movement is the primary reason for the existence of many electrical machines. Modeling movement can often be very expensive. In some circumstances where the moving object is invariant in the direction of motion, this approach can be simplified using methods, such as the Minkowski transform. In the general case, where the moving object is not as above, many solutions of nonlinear equations by time-stepping techniques can be required. Some methods developed in the past have stood the test of time and others have proved to be less useful.

This paper examines games with non-neutral option labels (such as “A”, “B”, “A”, “A”) and finds surprisingly invariant behaviour across games. The behaviour closely resembles the choices people make when they have to bet on one of the options in individual lotteries. An option’s ‘representativeness’ (lack of distinguishing features) and ‘reachability’ (physical centrality, salience, and valence) determine choice behaviour in both the lotteries and the highly strategic games. There is no evidence of people best-responding to others’ betting(-like) behaviour. This is in line with the idea that once people decide that strategic reasoning would not take them any further, they pick an alternative as if they were betting on one of their ‘current best-responses’. The findings explain the well-documented seeker advantage in hide-and-seek games, as well as why participants often display behaviour that could be exploited by others. On top, they help understand why in national lotteries, people also tend to bet on identical subsets of the available numbers.

Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditions alike. Finally, we present means to extend the perceptive radius of the vehicle by using it in cooperation with an autonomous drone, which provides aerial reconnaissance. Performance of the proposed system has been evaluated in a series of experiments conducted in real-world conditions., Comment: 8 pages, 11 figures, submitted to IEEE ICRA and RA-L 2021, under review

Recognising individuals through their voice requires listeners to form an invariant representation of the speaker’s identity, immune to episodic changes that may occur between encounters. We conducted two experiments to investigate to what extent within-speaker stimulus variability influences different behavioural indices of implicit and explicit identity recognition memory, using short sentences with semantically neutral content. In Experiment 1, we assessed how speaker recognition was affected by changes in prosody (fearful to neutral, and vice versa in a between-group design) and speech content. Results revealed that, regardless of encoding prosody, changes in prosody, independent of content, or changes in content, when prosody was kept unchanged, led to a reduced accuracy in explicit voice recognition. In contrast, both groups exhibited the same pattern of response times (RTs) for correctly recognised speakers: faster responses to fearful than neutral stimuli, and a facilitating effect for same-content stimuli only for neutral sentences. In Experiment 2, we investigated whether an invariant representation of a speaker’s identity benefitted from exposure to different exemplars varying in emotional prosody (fearful and happy) and content ( Multi condition), compared to repeated presentations of a single sentence ( Uni condition). We found a significant repetition priming effect (i.e., reduced RTs over repetitions of the same voice identity) only for speakers in the Uni condition during encoding, but faster RTs when correctly recognising old speakers from the Multi, compared to the Uni, condition. Overall, our findings confirm that changes in emotional prosody and/or speech content can affect listeners’ implicit and explicit recognition of newly familiarised speakers.

Vehicle reidentification, aiming at identifying vehicles across images, has drawn a lot of attention and has made significant achievements in recent years. However, vehicle reidentification remains a challenging task caused by severe appearance changes due to different orientations. In practice, the result of reidentification is greatly influenced by the pose of vehicles, and we call this influence as a pose barrier problem. One way to address the pose barrier problem is to train a feature representation that is invariant for various vehicle poses. To this end, we present pose robust features (PRFs) that contains two components: 1) pose-invariant features (PIFs) and 2) pose discriminative features (PDFs). On the one hand, PIF is the expert in exploring the overall characteristic of vehicles. When training PIF, we adopt an identity classifier as well as an orientation classifier. In addition, an adversarial loss is deployed in the PIF network. On the other hand, we design a PDF network, which has a similar architecture to the PIF network but can distinguish the difference between local details. The difference between PDF and PIF is that the network of training PDF does not apply the adversarial loss. Finally, by combining PIF and PDF, PRF has the advantages of the two features and can alleviate the influence of the pose barrier problem. Experiments are conducted on the VeRi-776 and VehicleID data sets. We show that PIF and PDF are complementary and that PRF produces competitive performance compared with state-of-the-art approaches.

This paper aims at the safety-critical control synthesis of network systems such that the satisfaction of the safety constraints can be guaranteed. To handle the large state dimension of such systems, an assume-guarantee contract is used to break the large synthesis problem into smaller subproblems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that robust control invariant sets (RCIs) for the subsystems can be composed to form a robust control invariant set for the whole network system under a valid assume-guarantee contract. An epigraph algorithm is proposed to solve for a contract that is valid, ---an approach that has linear complexity for sparse networks, which leads to a robust control invariant set for the whole network system. Implemented with control barrier function (CBF), the state of each subsystem is guaranteed to stay within the safe set. Furthermore, we propose a contingency tube Model Predictive Control approach based on the RCI, which is capable of handling severe contingencies, including topology changes of the network. A power grid example is used to demonstrate the proposed method. The simulation result includes both set point control and contingency recovery, and the safety constraint is always satisfied., arXiv admin note: text overlap with arXiv:1810.10636

Given the significant role of universities in economic growth and social progress as well as the increasing demand for greater transparency regarding the use of public money, a valid assessment of university performance has become crucial for various stakeholders, including government, industry, funding agencies, and society at large. Contemporary assessments still focus solely on universities’ properties, thereby failing to capture their network relations. To overcome this limitation, this paper proposes a multilayer network-based method to measure the embeddedness of universities in collaboration and mobility networks. This method has several advantages: first, it is relevant for HEIs’ core missions, introducing a new dimension complementary to the existing rankings; second, it is size invariant; and last but not least, it is fully transparent. The proposed multilayer network approach enables the integration of further networks, which creates opportunities for a more comprehensive assessment of universities’ performance in achieving their core missions.

Measures of happiness are increasingly being used throughout the social sciences. While these measures have attracted numerous types of criticisms, a crucial aspect of these measures has been left largely unexplored—their calibration. Using Eran Tal’s recently developed notion of calibration we argue first that the prospect of continued calibration of happiness measures is crucial for the science of happiness, and second, that continued calibration of happiness measures faces a particular problem—The Two Unknowns Problem. The Two Unknowns Problem relies on the claim that individuals are necessarily a part of the measurement apparatus in first person measures of happiness, and the claim that we have no reason to believe that the evaluation standards people employ are invariant across individuals and time. We argue that calibrating happiness measures therefore involves solving an equation with two unknowns—an individual’s degree of happiness, and their evaluation standards—which is, generally, not possible. Third, we consider two possible escape routes from this problem and we suggest that the most promising route requires yet unexplored empirical and theoretical work on linking happiness to behavioral or neural evidence.

The article deals with Sergei Bortkiewicz form, structure and functional experiments on sonata cycle in Sonata for Violin and Piano op. 26. The date of creation of the work is clarified. The article provides an overview of the history of development of the sonata cycle genre invariant. Comprehensive analysis of the work by Bortkiewicz provides tracing influences of West-European and Russian composing traditions. Different approaches to the interpretation of the composer's idea are presented, which allows to explain the specific construction of the cycle. Key words: violin sonata, sonata cycle, interpretation of the genre invariant, music by Sergei Bortkiewicz.

We demonstrate the power of the simulation-based invariant verification technique through two case studies in which it is formally verified that two mutual exclusion protocols, MCS protocol and Anderson protocol, enjoy the mutual exclusion property by the simulation-based invariant verification technique. We initially attempted formally verifying that the two protocols enjoy the property by the induction-based invariant verification technique. We successfully completed the formal proof for MCS protocol by the simulation-based invariant verification technique earlier than the one by the induction-based invariant verification technique even though we started the latter earlier than the former. We did not successfully complete the formal proof for Anderson protocol by the induction-based invariant verification technique as of the time of this paper submission. We define a variant of simulation, “observably equivalent simulations,” and prove some theorems on them to conduct the MCS case study. Given a state machine $M$ , another state machine $M'$ constructed by introducing auxiliary variables into $M$ observably equivalently simulates $M$ . In this sense, $M'$ can be regarded as an abstract version of $M$ .

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The locativity category has philosophical and linguistic interpretations. Linguistics regards locativityas a category encompassing the entire spectrum of spatial meanings represented by such main variations as static, dynamics, transitivity. The category under consideration has been reflected on the sentence level of the structural submodel Sub + Praed + Adv loc with its attached invariant semantics ‘carrier of procedural characteristic + procedural characteristic + spatial boundaries of procedural characteristic’. Formation of differential meanings as specific representatives of invariant content plane is done through all constituent positions –subject, predicate, and near-predicate. The complex locative mechanism is reflected by sentences with the following denotative situations: ‘somebody (something) is residing somewhere’, ‘something (somebody) is heading some-where with registering spatial coordinates –start, finish, both start and finish simultaneously’, ‘something (somebody) is heading somewhere without registering spatial coordinates’. Locative conceptualization mechanism is complex, with its important marker being a locative reference point related to the real roundabouts of somebody or something.

This article focuses on the image of the Russian professors in The Eccentric University (2008), a novel by Stanislav Rakús. Based on previous research, the author presents a short survey of Russian images in Slovak Literature in the late nineteenth — early twentieth-first centuries, whose peculiarities are rooted in the history of interaction between the two Slavic nations. Thus, the early twentieth century idealistic image of the Russian was built on the basis of Russian literature. The first images of Russians based on personal experience were created by Czechoslovak legionnaires as a result of interaction with Russians between 1917 and 1920, while after World War II, they were presented through a dichotomy “brother — suppressor”, to be changed into more dynamic ones by the early twenty-first century. The Eccentric University, which the author approaches from the perspective of the academic novel and part of Rakъs’s academic trilogy, enlarges the list of literary images of the Russians in Slovak literature. The author analyses images of Maria Petrovna Golovčikova, a female professor of Russian nineteenth-century literature, and Alexandr Kirillovič Ћuprej, a professor of Russian literature, both immigrant scholars at a Slovak university in the 1950s. The author maintains that through these images, Rakъs addresses not only Slovak Russophilic stereotypes historically embedded in nineteenth-century literary images of Russians, but also gender and immigrant stereotypes that circulate in contemporary Slovak culture. The author concludes that an ironic portrayal of Russian professors is directed at the cultural memory activation, which together with other features typical for both campus and academic novel adds to the new (Rakús’s) invariant of the “university novel”., Статья посвящена образу русского профессора в романе «Эксцентричный университет» (2008) словацкого писателя Станислава Ракуса. На основании существующих исследований в этой области предлагается краткий обзор эволюции образа русского в словацкой литературе с конца XIX — до начала ХХI в., которая была обусловалена историей взаимоотношений обоих народов. Так, в начале ХХ в. источником образа русских в словацкой литературе была классическая русская литература, в результате чего сформировался достаточно идеалистический образ русского человека. Опыт взаимодействия с русскими в 1917–1920 гг. позволил словацким легионерам, среди которых были и писатели, выработать более реалистичный образ русских. После Второй мировой войны литературный образ русских характеризовался дихотомией «брат – угнетатель», в то время как более объемные, динамичные образы русских начали появляться в словацкой литературе в последние десятилетия. Роман «Эксцентричный университет», являю­щийся частью академической трилогии Станислава Ракуса, рассматривается в статье как университетский роман, который привносит новые черты в образы русских персонажей, существующие в словацкой литературе. Проанализированы образы двух русских преподавателей-эмигрантов в одном из словацких университетов середины ХХ в.: Марии Петровны Головчиковой (литературоведа) и Александра Кирилловича Жупрея (языковеда). Автор утверждает, что с помощью этих образов Станислав Ракус обращается к проблеме стереотипного словацкого русофильства, сформировавшегося под влиянием русской литературы ХIХ в., а также и к стереотипам, связанным с эмиграционным и гендерным дисскурсом в современной словацкой культуре. Ироничный образ русских, созданный Станиславом Ракусом, по мнению автора, направлен на активацию культурной памяти, что, в свою очередь, вместе с соединением в одном романе типичных черт университетского (ориентированного на жизнь студентов) и академического (ориентированного на жизнь профессоров) романа предлагает новый инвариант университетской прозы.

Data-driven fault classification methods are receiving great attention as they can be applied to many real-world applications. However, they work under the assumption that training data and testing data are drawn from the same distribution. Practical scenarios have varying operating conditions, which results in a domain-shift problem that significantly deteriorates the diagnosis performance. Recently, domain adaptation (DA) has been explored to address the domain-shift problem by transferring the knowledge from labeled source domain (e.g., source working condition) to unlabeled target domain (e.g., target working condition). Yet, all the existing methods are working under single-source single-target (1S1T) settings. Hence, a new model needs to be trained for each new target domain. This shows limited scalability in handling multiple working conditions since different models should be trained for different target working conditions, which is clearly not a viable solution in practice. To address this problem, we propose a novel adversarial multiple-target DA (AMDA) method for single-source multiple-target (1SmT) scenario, where the model can generalize to multiple-target domains concurrently. Adversarial adaptation is applied to transform the multiple-target domain features to be invariant from the single-source-domain features. This leads to a scalable model with a novel capability of generalizing to multiple-target domains. Extensive experiments on two public datasets and one self-collected dataset have demonstrated that the proposed method outperforms state-of-the-art methods consistently. Our source codes and data are available at https://github.com/mohamedr002/AMDA .

This paper presents a robust economic Model Predictive Control (EMPC) formulation for discrete-time uncertain nonlinear systems. The proposed controller not only ensures that the closed-loop system is robust to disturbances, but also ensures that the economic performance does not deteriorate in the presence of the disturbances. The key idea is to have the controller track a robust control invariant subset of the state space with specified economic properties at all times, and within the zone optimize the process economics. To this end, we introduce the notion of risk factor in the controller design and provide an algorithm to determine the economic zone to be tracked. The risk factor determines the conservativeness of the controller. Our proposed controller is computationally less demanding as it only makes use of the system model without disturbances. A nonlinear CSTR example is presented to demonstrate the performance of the proposed formulation.

Searchable symmetric encryption (SSE) enables users to make confidential queries over always encrypted data while confining information disclosure to pre-defined leakage profiles. Despite the well-understood performance and potentially broad applications of SSE, recent leakage-abuse attacks (LAAs) are questioning its real-world security implications. They show that a passive adversary with certain prior information of a database can recover queries by exploiting the legitimately admitted leakage. While several countermeasures have been proposed, they are insufficient for either security, i.e., handling only specific leakage like query volume, or efficiency, i.e., incurring large storage and bandwidth overhead. We aim to fill this gap by advancing the understanding of LAAs from a fundamental algebraic perspective. Our investigation starts by revealing that the index matrices of a plaintext database and its encrypted image can be linked by linear transformation. The invariant characteristics preserved under the transformation encompass and surpass the information exploited by previous LAAs. They allow one to unambiguously link encrypted queries with corresponding keywords, even with only partial knowledge of the database. Accordingly, we devise a new powerful attack and conduct a series of experiments to show its effectiveness. In response, we propose a new security notion to thwart LAAs in general, inspired by the principle of local differential privacy (LDP). Under the notion, we further develop a practical countermeasure with tunable privacy and efficiency guarantee. Experiment results on representative real-world datasets show that our countermeasure can reduce the query recovery rate of LAAs, including our own.

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous non-convolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner., Submitted to the IEEE Transactions on Neural Networks and Learning Systems

Within the framework of solving the problem of the formation of the given modern professional competencies among students of pedagogical specialties, the issue of the importance of methodological culture as one of the leading components of pedagogical activity and the basis for the formation of professional competencies is considered. From the standpoint of including the process of upbringing a methodological culture in a stable self-sustaining structure of pedagogical culture, the solution of the problem of designing a methodology for the formation of methodical culture in the educational SMART-paradigm is presented. The purpose of the described research: to highlight the features and develop a model for the formation of the methodological culture of future teachers studying in the direction of training “Pedagogical education”. When clarifying the definition of the concept of “methodical culture” for the development of a methodology, the idea of enrichment was proposed, which is implemented in a “three-dimensional” model along the following “planes”: design by a teacher – joint design of a methodology, design – joint assessment and reflection of the process and results of design and application of teaching methods. It is these “planes” (directions) that define the synergistic content of the selected components of the methodological system: motivational, value, target, diagnostic, cognitive, operational-activity and reflexive-evaluative. The specified three-dimensional feature of the model, on the one hand, determines the universality of the invariant component of the projected methodological training system, on the other hand, it allows enriching the methodology of forming a methodological culture for specific students. The presented model can be used to build a methodological system for the training of students at the master’s level with the targeted highlighting of the importance of the formation of methodological culture.

The materials of the article substantiate the urgency of solving the problem of identifying meaningful links in electronic text documents in order to further compare their content and improve the operation of plagiarism detection systems. An important step is to assess the reliability of the formed formal models. Therefore, the aim of this article is to study the algorithm of automatic analysis of logic and linguistic models of electronic text documents for the reproduction of textual information, which combines the basic properties of the text and its components. The logic and linguistic model of a text document reflects the main relationships between structural components; it is an ordered quadruple and an array of logic and linguistic models of sentences of natural language, which are included in the text. The author proposes several ways of restoration of textual information, starting from the structure of the logic and linguistic model of an electronic text document, which contains a linguistic and semantic-syntactic component. The article describes the schemes of text information recovery, it chooses the combined method, which provides the analysis of semantic-syntactic component in parallel with the analysis of the text base, in particular, its components - a set of sentences containing connections between logic and linguistic models of sentences of the text within electronic text document. It has been developed an algorithm for recovery of textual information presented in the form of a formal logic and linguistic model of an electronic text document, and there have been described the stages of abovementioned algorithm. All steps of the algorithm are demonstrated on the example of analysis of a specific given logic and linguistic model of a fragment of an electronic text document. The author conducted experiments on the restoration of textual information for scientific style texts. During experiments, it was revealed, that the main factors influencing the restoration of textual information include the removal of homonymy, as well as different interpretations of synonymous constructions and invariant forms of logic and linguistic models of sentences of natural language.

The purpose of the article is to study the concept of a virtual image created in the artist’s imagination and its correspondence to the material invariant. The research methodology based on a system approach to the analysis of the virtual dimension of art. The analytical method is applied to the study of philosophical, art, history and cultural literature for a topic. The theoretical and conceptual method is to characterise the research vocabulary; the comparative-typological method is to find out the correspondence of the virtual image, which is formed in the imagination of a person, with the recovered material artefacts. The article covers Plato’s concept “on the specifics of creating images and simulacra”, Thomas Aquinas’ regulation “on the category of virtuality” and Serhii Bezklubenko’s (2015) concept of “psychological ultrastructure of art”. The scientific novelty of the study is to research the differences between the virtual image and its material invariant, using the examples of the works of Ludwig van Beethoven, Leonardo da Vinci, and Rembrandt van Rijn. Conclusions. We have proven that the art piece making begins with the formation of a virtual image (concept statement) in the imagination of the creator and ends with the materialised form of the original plan. The art piece making is a complex psychological ultrastructure of art starting from the time when its image is born in the artist’s imagination to its embodiment (materialisation) into a certain product, and then the search for its completion in the minds of consumers. Using the examples of well-known artworks, we have learnt that the end image often is out of keeping the original virtual concept.