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The present author continued his work on the study of the control parametrization technique for solution of the distributed optimization problems. Proposed was a new method of control parametrization resembling that for information compression used in the JPEG format to reduce by the factor of several times the count of parameters describing the control without an appreciable deterioration in closeness to the optimum. Withdrawn were formulas required for application of the proposed approach to control parametrization in combination with the numerical methods of finite-dimensional optimization for solution of the mathematical programming problem approximating the original optimal control problem. The results of numerical experiments were described and analyzed in detail.

Every day more and more the world produces, stores, and analyzes millions of dollars, be they telecommunications, medicine, companies, management, and people, among others. In this context, computational simulations are an important ally both for the analysis and for the creation of new methodologies that aid in the management of different types of data. The objective of this research is improving the transmission of content in wireless telecommunication systems, being proposed in the simulation environment a precoding process of bits based on the application of technique discrete events in the signal before the modulation process. The signal transmission on the channel occurs in the discrete domain with the implementation of discrete entities in the process of bit generation. In this way, the methodology developed was named CBEDE (coding of bits for entities by means of discrete events) that implements a proposal based on discrete events in a wireless telecommunication system. For this, the advanced modulation format DBPSK for signal transmission in an AWGN channel was considered. The results show improved memory utilization, related to information compression, in addition to a low level of abstraction, which facilitates the use of the CBEDE methodology in several areas.

Creating Strong AI means to develop artificial intelligence to the point where the machine’s intellectual capability is in a way equal to a human’s. Science is definitely one of the summits of human intelligence, the other being the art. Scientific research consists in creating hypotheses that are limited applicability models (methods) implying lossy information compression. In this article, we show that this paradigm is not unique to the science and is common to the most developed areas of human activities, like business and engineering. Thus, we argue, a Strong AI should possess a capability to build such models. Still, the known tests to confirm the human-level AI do not address this consideration. Based on the above we suggest a series of six tests of rising complexity to check if AI have achieved the human-level intelligence (Explanation, Problem-setting, Refutation, New phenomenon prediction, Business creation, Theory creation), five of which are new to the AGI literature.

The article discusses one of the methods of information compression - creating a word cloud that allows you to determine the most common subtopics of the text. Services are given that make it possible to compose a word cloud, the compression method itself is compared with another method of compressing scientific information - compiling an annotation. It is determined that the cloud, in contrast to the annotation, represents more generalized information, in visual form, the annotation contains more facts, numerical data, information about the results of the research. В статье рассматривается один из способов компрессии информации — создание облака слов, который позволяет определить наиболее общие субтемы текста. Даются сервисы, позволяющие составлять облако слов, сам метод компрессии сравнивается с другим методом сжатия научной информации — составлением аннотации. Определяется, что облако, в отличие от аннотации, представляет информацию более обобщенную, в визуальной форме, аннотация содержит в себе больше фактов, числовых данных, информации о результатах исследования.

Music is a language of emotions and music emotional recognition has been addressed by different disciplines (e.g., psychology, cognitive science and musicology). Nowadays, the music fruition mechanism is evolving, focusing on the music content. In this work, a framework for processing, classification and clustering of songs on the basis of their emotional contents, is explained. On one hand, the main emotional features are extracted after a pre-processing phase where both Sparse Modeling and Independent Component Analysis based methodologies are applied. The approach makes it possible to summarize the main sub-tracks of an acoustic music song (e.g., information compression and filtering) and to extract the main features from these parts (e.g., music instrumental features). On the other hand, a system for music emotion recognition based on Machine Learning and Soft Computing techniques is introduced. One user can submit a target song, representing his conceptual emotion, and obtain a playlist of audio songs with similar emotional content. In the case of classification, a playlist is retrieved from songs belonging to the same class. In the other case, the playlist is suggested by the system exploiting the content of the audio songs and it could also contains songs of different classes. Experimental results are proposed to show the performance of the developed framework.

International audience; This paper describes a novel perspective on the foundations of mathematics: how mathematics may be seen to be largely about`information compression (IC) via the matching and unication of patterns' (ICMUP). That is itself a novel approach to IC, couched in terms of non-mathematical primitives, as is necessary in any investigation of the foundations of mathematics. This new perspective on the foundations of mathematics reects the idea that, as an aid to human thinking, mathematics is likely to be consonant with much evidence for the importance of IC in human learning, perception, and cognition. This perspective on the foundations of mathematics has grown out of a long-term programme of research developing the SP Theory of Intelligence and its realisation in the SP Computer Model, a system in which a generalised version of ICMUPthe powerful concept of SP-multiple-alignmentplays a central role. The paper shows with an example how mathematics, without any special provision, may achieve compression of information. Then it describes examples showing how variants of ICMUP may be seen in widely-used structures and operations in mathematics. Examples are also given to show how several aspects of the mathematics-related disciplines of logic and computing may be understood as ICMUP. Also discussed is the intimate relation between IC and concepts of probability, with arguments that there are advantages in approaching AI, cognitive science, and concepts of probability via ICMUP. Also discussed is how the close relation between IC and concepts of probability relates to the established view that some parts of mathematics are intrinsically probabilistic, and how that * Dr Gerry Wol, BA (Cantab) PhD (Wales) CEng MBCS MIEEE, ORCID ID 0000-0002-4624-8904. CognitionResearch.org, Menai Bridge, UK; Visiting Researcher at Coventry University ; jgw@cognitionresearch.org; +44 (0) 1248 712962; +44 (0) 7746 290775; Skype: gerry.wol; Web: www.cognitionresearch.org. 1 latter view may be reconciled with the all-or-nothing,`exact', forms of calculation or inference that are familiar in mathematics and logic. There are many potential benets and applications of the mathematics-as-IC perspective .

The QBIT theory is an attempt toward solving the problem of consciousness in the light of Quantum mechanics, Biology, Information theory, and Thermodynamics. “Internal representation” is a key concept in the QBIT theory of consciousness. An internal representation is defined as a pack of information (within a cognitive system) that represents an external stimulus.The QBIT theory suggests that when robustness of an internal representation exceeds a certain threshold, a conscious experience (or a quale) is generated. In this paper, the concept of internal representation and its relation with consciousness is explored.

In this paper, we design semantic based routing algorithms for IoT service discovery. First, we consider the model for describing the IoT services and the corresponding discovery queries so that service lookup can be performed effectively. Second, we consider the limited memory space of many IoT devices and introduce our summarization algorithm for routing information compression to make sure that the routing table at each node can satisfy the space limit imposed by the node without needing to throw away useful routing information. Third, we develop a novel two-level coding mechanism to effectively code IoT service capabilities to enable summarization and further reduce routing table size. We experimentally compare various coding approaches and the results show that our scheme requires less memory and imposes lower volume of network traffic compared to existing approaches.

The increase in frequency and scale of natural disasters is the typical negative examples of the global climate change and the change of the human living environment in cities. The damage caused by natural disasters in particular including human and physical damage is directly linked to the safety and properties of citizens. Besides, the damage may occur to SOC facilities directly or indirectly. The SOC facilities damaged by disasters cause infringement of citizens safety rights. Therefore, a plan to provide prompt and effective risk map information by linking a 3D disaster information display system, which handles the information of the damage that may occur to SOC facilities such as bridges, beams, and dams at the time of disasters, with an on-site assistance application is suggested in this study. The prompt provision of risk map information is defined as a dynamic expression technology in this study. Also, disaster information is processed and compressed with a module developed to be linked organically to a system that displays disaster information, a proliferation system that can use the information on site, and a DB system that constructs a relationship with the information. Based on the module, the effective disaster information compression plan will be prepared, and the prompt information transmission system will be secured in the future.

Community detection in uni-partite single-relational networks which contain only one type of nodes and edges has been extensively studied in the past decade. However, many real-world systems are naturally described as multi-partite multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose an information compression based method for detecting communities in such networks. Specifically, based on the minimum description length (MDL) principle, we propose a quality function for evaluating partitions of a multi-partite multi-relational network into communities, and develop a heuristic algorithm for optimizing the quality function. We demonstrate that our method outperforms the state-of-the-art techniques in both synthetic and real-world networks.

This paper discusses compression methods focused on data transmission over communication channels. The characteristics of different algorithms for different types of incoming data are analyzed. The purpose of this study is to evaluate the speed of operation of each of the compression algorithms for different types of information and different compression parameters, on the basis of the obtained results to make recommendations for the application of compression methods in systems critical to the performance of the algorithm. Based on the results of the analysis, the methods of compression that can be used in communication channels are selected: LZW, LZH, Vitter and matrix. The practical research of the selected methods on different information flows (text, graphics, measurement data, combined data) was carried out, their comparative analysis was performed. Research has highlighted compression methods that give the most optimal results in each case. Comparative evaluation of algorithms for different parameters is made, the possibility of data compression implementation in systems running in real time is analyzed. Based on the results of the study, recommendations are made for the application of particular compression methods in specific conditions.

Considering the vast amount of degrees of freedom that the Central Nervous System holds, synergies, understood as building blocks led by the linear combination of multiple muscles, a novel hypothesis to understand the generation of movement. This project suggests a Deep Learning approach to build two predictive kinematic model based on muscles and synergies. Both models will be built with Convolutional Neural networks, whose inputs will be Transcranial Magnetic Stimulations and the outputs, the evoked muscle responses monitored through Electromyography. By comparing the predictions from both models, the results evinced that synergies propose a more robust model to understand movement control than direct connections to muscles, even though they are not able to fully characterize the movement by themselves. El Sistema Nervioso Central concentra una gran cantidad de grados de libertad a la hora de generar movimiento. Por eso, la inclusión de las sinergias, entendidas como elementos básicos caracterizados por la combinación lineal de múltiples músculos, es una hipótesis innovadora para entender la concepción del movimiento. Este proyecto sugiere una estrategia basada en Deep Learning con el propósito de elaborar dos modelos predictivos que expliquen el movimiento basados en músculos y sinergias. Ambos modelos se construirán mediante Redes Neuronales Convolucionales, cuyas entradas serán Estimulaciones Magnéticas Transcraneanas y las salidas, las respuestas evocadas a los músculos y monitoreadas con Electromiografía. La comparativa de los resultados obtenidos demuestra que las sinergias proponen un modelo más robusto para entender el control del movimiento que las conexiones directas a los músculos, aunque no son capaces de caracterizar el movimiento por si mismas. El Sistema Nerviós Central concentra una gran quantitat de graus de llibertat a l'hora de generar moviments. Per això, la inclusió de les sinèrgies, enteses com elements bàsics caracteritzats per la combinació lineal de múltiples músculs, és una hipòtesi innovadora per entendre la concepció del moviment. Aquest projecte suggereix una estratègia basada en Deep Learning per tal d'elaborar dos models predictius que expliquin el moviment basat en músculs i sinèrgies. Ambdós models es construiran mitjançant Xarxes Neuronals Convolucionals, les entrades de les quals seran Estimulacions Magnètiques Transcranial i les sortides, les respostes evocades als músculs i monitoritzades amb Electromiografia. La comparativa dels resultats obtinguts demostra que les sinèrgies proposen un model més robust per entendre el control del moviment que les connexions directes als músculs, encara que no són capaces de caracteritzar el moviment per elles soles.

Music has an extraordinary ability to evoke emotions. Nowadays, the music fruition mechanism is evolving, focusing on the music content. In this work, a novel approach for agglomerating songs on the basis of their emotional contents, is introduced. The main emotional features are extracted after a pre-processing phase where both Sparse Modeling and Independent Component Analysis based methodologies are applied. The approach makes it possible to summarize the main sub-tracks of an acoustic music song (e.g., information compression and filtering) and to extract the main features from these parts (e.g., music instrumental features). Experiments are presented to validate the proposed approach on collections of real songs.

The book "Architects of Intelligence" by Martin Ford presents conversations about AI between the author and people who are influential in the field. This paper discusses issues described in the book in relation to features of the "SP System", meaning the "SP Theory of Intelligence" and its realisation in the "SP Computer Model", both outlined in an appendix. The SP System has the potential to solve most of the problems in AI described in the book, and some others. Strengths and potential of the SP System, which in many cases contrast with weaknesses of deep neural networks (DNNs), include the following: the system exhibits a more favourable combination of simplicity and expressive or descriptive power than, arguably, any alternatives; the system has strengths and long-term potential in pattern recognition; the system appears to be free of the tendency of DNNs to make large and unexpected errors in recognition; the system has strengths and potential in unsupervised learning, including grammatical inference; the SP Theory of Intelligence provides a theoretically coherent basis for generalisation and the avoidance of under-or over-generalisations; that theory of generalisation may help driverless cars avoid accidents; the system, unlike DNNs, can achieve learning from a single occurrence or experience; the system, unlike DNNs, has relatively tiny demands for computational resources and volumes of data, with potentially much higher speeds in learning; the system, unlike most DNNs, has strengths in transfer learning; the system, unlike DNNs, provides transparency in the representation of knowledge and an audit trail for all its processing; the system has strengths and potential in the processing of natural language; as a by-product of its design, the system exhibits several different kinds of probabilistic reasoning; the system has strengths and potential in commonsense reasoning and the representation of commonsense knowledge; other strengths of the SP System are in information compression, biological validity, scope for adaptation, and freedom from catastrophic forgetting. Despite the importance of motivations and emotions, no attempt has been made in the SP research to investigate these areas.

Commonsense reasoning (CSR) and commonsense knowledge (CSK) (together abbreviated as CSRK) are areas of study concerned with problems which are trivially easy for adults but which are challenging for artificial systems. This paper describes how the SP System -- meaning the "SP Theory of Intelligence" and its realisation in the "SP Computer Model" -- has strengths and potential in several aspects of CSRK. Some shortcomings of the system in that area may be overcome with planned future developments. A particular strength of the SP System is that it shows promise as an overarching theory for four areas of relative success with CSRK problems -- described by other authors -- which have been developed without any integrative theory. How the SP System may help to solve four other kinds of CSRK problem is described: 1) how the strength of evidence for a murder may be influenced by the level of lighting of the murder as it was witnessed; 2) how people may arrive at the commonly-accepted interpretation of phrases like "water bird"; 3) the interpretation of the horse's head scene in "The Godfather" film; and how the SP System may help to resolve the reference of an ambiguous pronoun in sentences in the format of a 'Winograd schema'. Also described is why a fifth CSRK problem -- modelling how a chef may crack an egg into a bowl -- is beyond the capabilities of the SP System as it is now and how those deficiencies may be overcome via planned developments of the system.

International audience; Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working‐memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed that compressibility can be used to predict working‐memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems.

In the process of using Beidou short message to communicate the message in real time, there will be a problem of limited message length. To solve this problem, a method of information compression is proposed in this paper1. This method uses Chinese segmentation algorithm to cut Beidou short message into dictionary index code, and then compress algorithm to get the final message encoding. The experimental results show that this compression method shows good compression performance in Beidou short message compression and provides a reliable compression transmission method for Beidou short message2.

We show that estimating productivity premia for internationalization modes requires information compression when firms can choose from many modes. Using a unique database of European firms we illustrate the different approaches and suggest that researchers should deliberately choose from them.

Este trabajo presenta una técnica de descomposición de señales bioacústicas empleando como funciones base dos familias de polinomios ortogonales. Esta técnica está orientada a implementarse en dispositivos de bajas capacidades de cómputo, como redes de sensores inalámbricos, por lo tanto debe ser rápida y efi ciente, además de permitir una alta compresión de la información y una buena calidad en la reconstrucción. En este artículo se explica el fundamento de la descomposición usando familias de polinomios ortogonales, se muestran el procedimiento empleado para alcanzar la resolución adecuada, que permite una mayor compresión de la información y una mejor calidad en la reconstrucción de la señal. Por último, se muestran los resultados obtenidos al aplicar el método a funciones generadas artifi cialmente y a registros sonoros del páramo de Chingaza (Cundinamarca) suministrados por el instituto Alexander von Humboldt, cuyos resultados se comparan con la transformada de Fourier tradicional.

Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.

У роботі досліджується метод стиснення цифрових відео-файлів за допомогою дискретного вейвлет-перетворення в орієнтованому базисі, а також порівняння його конкурентоспроможності серед декількох інших вейвлетів. Поставлені задачі вирішувалися шляхом проведення теоретичних та експериментальних досліджень. Використано алгоритми дискретного вейвлет-перетворення, зокрема Хаара, Добеші та перетворення в орієнтованому базисі. В результаті проведення дослідження отримано зображення та відео, стиснені з різними налаштуваннями втрат якості, та на основі їх параметрів побудовано порівняльні характеристики. На основі порівняльних характеристик сформовано рекомендації щодо вибору типу перетворення і проведена оцінка сумісності вейвлет-перетворення з іншими методами стиснення відео. Створено програмну модель алгоритму дискретного вейвлет-перетворення зображень та відео на основі вейвлетів Хаара, Добеші та вейвлету в орієнтованому базисі. This work researches a method of compressing digital video files using discrete wavelet transformation in an oriented basis, as well as comparison of its competitiveness amongst several other wavelets. The tasks were solved by conducting theoretical and experimental studies. Methods of discrete wavelet transforms, in particular, Haar, Daubechies and transformations in an oriented basis are used. As a result of the research, images and videos compressed with various quality loss settings were obtained and, based on their parameters, comparative characteristics were constructed. Recommendations on the choice of the type of transformation were formed based on the comparative characteristics and the compatibility of the wavelet transform with other methods of video compression is evaluated. A software model of an algorithm for discrete wavelet transform of images and video is constructed on the basis of Haar, Daubechies and wavelets in an oriented basis. Research showed that wavelet transorm in an oriented basis possesses best quality/compression ratio amongst all reviewed wavelets when it comes to the raw compression of visual data, which makes discrete wavelet-transform in oriented basis show great potential for further testing. However, it was also shown that this particular wavelet is ill-suited to use in conjunction with modern image/video compression methods due to the mismatch in their multiplicity. Also, with the combined use of discrete wavelet-transform in oriented basis and PNG format, the best compression results were obtained between the four wavelets. Discrete wavelet-transform in oriented basis has 1.5% better quality / compression ratio than Haar transform, but loses 5.6% at speed. As for compatibility with other compression methods, Haar is 5.21% more compatible with PNG than discrete wavelet-transform in oriented basis, 4.2% better with JPEG and 41.2% is better when used in conjunction with MPEG. In most cases, the discrete wavelet-transform in oriented basis with multiplicity of 3 is the better choice than Haar transform except JPEG, where Daubechies wavelets are between Haar and discrete wavelet-transform in oriented basis in regards to the compatibility criterion. Studies also found that a discrete wavelet-transform in oriented basis with 3 filters was poorly compatible with JPEG and MPEG compression formats because they were developed specifically on the basis of discrete cosine transform and function more effectively with Haar transfrom, which both possess conversion multiplicity equal to 2. The research results are of interest for the development and optimization of digital video compression algorithms based on the discrete wavelet transform in oriented basis. Further research may include but is not limited to attuning and modifying existing compression standards, namely JPEG and MPEG, to fit discrete wavelet transform in oriented basis and fully utilize it’s potential. В работе исследуется метод сжатия цифровых видеофайлов с помощью дискретного вейвлет преобразования в ориентированном базисе, а также сравнение его конкурентоспособности среди нескольких других вейвлетов. Поставленные задачи решались путем проведения теоретических и экспериментальных исследований. Использованы методы дискретного вейвлет-преобразования, в частности, Хаара, Добеши и преобразования в ориентированном базисе. В результате проведения исследования получены изображения и видео, сжатые с различными настройками потерь качества, и на основе их параметров построено сравнительные характеристики. На основе сравнительных характеристик сформированы рекомендации по выбору типа преобразования и проведена оценка совместимости вейвлет-преобразования с другими методами сжатия видео. Построено программная модель алгоритма дискретного вейвлет-преобразования изображений и видео на основе вейвлетов Хаара, Добеши и вейвлета в ориентированном базисе.

In natural environments with many options of uncertain value, one faces a difficult tradeoff between exploiting familiar, valuable options or searching for better alternatives. Reinforcement learning models of this exploration/exploitation dilemma typically modulate the rate of exploratory choices or preferentially sample uncertain options. The extent to which such models capture human behavior remains unclear, in part because they do not consider the constraints on remembering what is learned. Using reinforcement-based timing as a motivating example, we show that selectively maintaining high-value actions compresses the amount of information to be tracked in learning, as quantified by Shannon's entropy. In turn, the information content of the value representation controls the balance between exploration (high entropy) and exploitation (low entropy). Selectively maintaining preferred action values while allowing others to decay renders the choices increasingly exploitative across learning episodes. To adjudicate among alternative maintenance and sampling strategies, we developed a new reinforcement learning model, StrategiC ExPloration/ExPloitation of Temporal Instrumental Contingencies (SCEPTIC). In computational studies, a resource-rational selective maintenance approach was as successful as more resource-intensive strategies. Furthermore, human behavior was consistent with selective maintenance; information compression was most pronounced in subjects with superior performance and non-verbal intelligence, and in learnable vs. unlearnable contingencies. Cognitively demanding uncertainty-directed exploration recovered a more accurate representation in simulations with no foraging advantage and was strongly unsupported in our human study.

This paper addresses the problem of balanced, redundant indexing of media information. Our goal is to partition and distribute the search index, taking advantage of the distributed systems properties: balanced load across nodes, redundancy on node down and efficient node usage under concurrent querying. We follow an information compression approach to solve this problem and propose to represent data with overcomplete codebooks, where each document is represented by only a few codewords and an indexing node is responsible for several codewords. Quantization algorithms are designed to fit the original data as best as possible, leading to bias towards codewords that fit the principal directions of data. In this paper, we propose the balanced KSVD (B-KSVD) algorithm, that distributes the allocation of data across a balanced number of codewords, according to the global distribution of data. Indexing experiments showed that B-KSVD can achieve 38% 1-recall by inspecting only 1% of the full index, distributed over 10 partitions. Traditional methods based on k-means need to either use larger codebooks or to inspect a larger portion of the index to achieve the same retrieval performance.

We developed and implemented a fully automated method to perform X-ray photoelectron spectroscopy (XPS) spectral analysis based on the active Shirley method and information criteria. Our method searches a large number of initial fitting models by changing the degree of smoothing, and then optimizes the peak parameters and background parameters to obtain a large number of fitting results. The goodness of those optimized models is ranked using information criteria. As a result of applying this algorithm to measured XPS spectra, we found that, using the Bayesian information criterion (BIC), a simple model with reasonably good agreement and a moderate number of peaks was selected. The model selected by the BIC was close to the result of peak fitting performed by XPS analysis experts., Journal of Electron Spectroscopy and Related Phenomena. 239 (2020) 146903

Motivation: Progress in protein biology depends on the reliability of results from a handful of computational techniques, structural alignments being one. Recent reviews have highlighted substantial inconsistencies and differences between alignment results generated by the ever-growing stock of structural alignment programs. The lack of consensus on how the quality of structural alignments must be assessed has been identified as the main cause for the observed differences. Current methods assess structural alignment quality by constructing a scoring function that attempts to balance conflicting criteria, mainly alignment coverage and fidelity of structures under superposition. This traditional approach to measuring alignment quality, the subject of considerable literature, has failed to solve the problem. Further development along the same lines is unlikely to rectify the current deficiencies in the field. Results: This paper proposes a new statistical framework to assess structural alignment quality and significance based on lossless information compression. This is a radical departure from the traditional approach of formulating scoring functions. It links the structural alignment problem to the general class of statistical inductive inference problems, solved using the information-theoretic criterion of minimum message length. Based on this, we developed an efficient and reliable measure of structural alignment quality, I-value. The performance of I-value is demonstrated in comparison with a number of popular scoring functions, on a large collection of competing alignments. Our analysis shows that I-value provides a rigorous and reliable quantification of structural alignment quality, addressing a major gap in the field. Availability: http://lcb.infotech.monash.edu.au/I-value Contact: arun.konagurthu@monash.edu Supplementary information: Online supplementary data are available at http://lcb.infotech.monash.edu.au/I-value/suppl.html

This contribution presents the rationale, basis, and potential for continuous and event-driven online chromatic acoustic monitoring of transformers and their tap changers. Illustrations are given from a feasibility project for monitoring transformers at several substation sites in the Manchester and Stoke regions of the U.K. Examples of monitoring outputs are presented in a top-down order of information compression, representing an approach that is tailorable to substation operators' information requirements for tracking and tracing overall and individual tap change quality. It is concluded that the approach is feasible for trend sampling and continuous monitoring regimes. Ongoing work includes the evaluation of overall transformer acoustic behavior patterns and individual internal tap-change elements. The monitoring is being rolled out to 50 transformers.

The existence of reduced scalability and delivery leads to the development of scalable routing by contact information compression. The previous work dealt with the result of consistent analysis in the performance of DTN hierarchical routing (DHR). It increases as the source to destination distance increases with decreases in the routing performance. This paper focuses on improving the scalability and delivery through contact information compression algorithm and also addresses the problem of power awareness routing to increase the lifetime of the overall network. Thus implementing the contact information compression (CIC) algorithm the estimated shortest path (ESP) is detected dynamically. The scalability and release are more improved during multipath multicasting, which delivers the information to a collection of target concurrently in a single transmission from the source, Comment: 5 pages and 3 figures

Simple span tasks are tasks commonly used to measure short-term memory, while complex span tasks are usually considered typical measures of working memory. Because complex span tasks were designed to create a concurrent task, the average span is usually lower (4 ± 1items) than in simple span tasks (7±2 items). One possible reason for measuring higher spansduring simple span tasks is that participants can take profit of the spare time between the stimuli to detect, and recode regularities in the stimulus series (in the absence of a concurrent task), and such regularities can be used to pack a few stimuli into 4 ± 1 chunks. Our main hypothesis was that information compression in immediate memory is an excellent indicator for studying the relationship between immediate-memory capacity and fluid intelligence. The idea is that both depend on the efficiency of information processing, and more precisely, on the interaction between storage and processing. We developed various span tasks measuringa chunking capacity, in which compressibility of memoranda was estimated using different algorithmic complexity metrics. The results showed that compressibility can be used to predictworking-memory performance, and that fluid intelligence is well predicted by the ability to compress information.We conclude that the ability to compress information in working memoryis the reason why both manipulation and retention of information are linked to intelligence.; Les tâches d’empan simples sont classiquement utilisées pour évaluer la mémoire à court terme, tandis que les tâches d’empan complexes sont généralement considérées comme spécifiques de la mémoire de travail. Par construction, les tâches d’empan complexes étant plus difficiles,en raison de l’utilisation d’une tâche concurrente, l’empan moyen est généralement plus faible (4 ± 1 items) que dans les tâches d’empan simples (7 ± 2 items). Une raison possible de cet écart est que sans tâche concurrente, les participants peuvent tirer profit du temps libre entre les stimuli pour détecter et recoder des régularités présentes dans la série de stimuli afin de regrouper les stimuli en 4 ± 1 chunks. Notre hypothèse principale est que la compression de l’information en mémoire immédiate est un indicateur pertinent pour étudier la relation entre la capacité de mémoire immédiate et l’intelligence fluide. L’idée est que les deux dépendent de la qualité du traitement de l’information, autrement dit, selon nos hypothèses, d’une interaction entre traitement et stockage. Nous avons développé plusieurs tâches de mesure d’empan de chunks dans lesquelles la compressibilité de l’information a été estimée en utilisant différentes mesures de complexité algorithmique. Les résultats ont montré que la compressibilité peut être utilisée pour prédire la performance en mémoire de travail, et que la capacité à compresser l’information est un bon prédicteur de l’intelligence fluide. Nous concluons que la capacité à compresser l’information en mémoire de travail est la raison pour laquelle à la fois traitement et stockage de l’information sont liés à l’intelligence.