arXiv subjects

Luciano da F. Costa

Publications and source records attributed to Luciano da F. Costa.

At least 73 records · Page 4Linked to original sources

arXiv2019

Distance-Based Network Partitioning

A new method for identifying communities in networks is proposed. Reference nodes, either selected using a priory information about the network or according to relevant node measurements, are obtained so as to indicate putative communities. Distance vectors between each network node and the reference nodes are then used for defining a coordinate system representing the community structure of the network at many different scales. For modular networks, the distribution of nodes in this space often results in a well-separated clustered structure, with each cluster corresponding to a community. One interesting feature of the reported methodology for community finding is that the coordinate system defined by the seeds allows an intuitive and direct interpretation of the situation of each node with respect to the considered communities. The potential of the method is illustrated with respect to a community detection benchmark, a spatial network model and to city streets networks.

cs.SI↗

arXiv2019

Numerical Frequency Transfer Function Analysis of a Leaky Integrate-and-Fire Neuron

This work reports a transfer function-based approach to characterizing the operation of single neuronal cells in terms of the instantaneous frequency of the input and output signals. We adopt the leaky integrate-and-fire model. The transfer relationship is obtained by performing successive numeric-computational simulations and statistical regressions. Several interesting results are reported, including the identification and characterization of linearity in the transfer relationship, as well as the identification of regions in the parameter space characterized by sharper transfer functions. These properties can be used to facilitate simulations under certain circumstances and to validate the model by comparison with biological results.

q-bio.NC↗

arXiv2019

How Coupled are Mass Spectrometry and Capillary Electrophoresis?

The understanding of how science works can contribute to making scientific development more effective. In this paper, we report an analysis of the organization and interconnection between two important issues in chemistry, namely mass spectrometry (MS) and capillary electrophoresis (CE). For that purpose, we employed science of science techniques based on complex networks. More specifically, we considered a citation network in which the nodes and connections represent papers and citations, respectively. Interesting results were found, including a good separation between some clusters of articles devoted to instrumentation techniques and applications. However, the papers that describe CE-MS did not lead to a well-defined cluster. In order to better understand the organization of the citation network, we considered a multi-scale analysis, in which we used the information regarding sub-clusters. Firstly, we analyzed the sub-cluster of the first article devoted to the coupling between CE and MS, which was found to be a good representation of its sub-cluster. The second analysis was about the sub-cluster of a seminal paper known to be the first that dealt with proteins by using CE-MS. By considering the proposed methodologies, our paper paves the way for researchers working with both techniques, since it elucidates the knowledge organization and can therefore lead to better literature reviews.

cs.DL↗

arXiv2019

Interdisciplinary Relationships Between Biological and Physical Sciences

Several interdisciplinary areas have appeared at the interface between biological and physical sciences. In this work, we suggest a complex network-based methodology for analyzing the interrelationships between some of these interdisciplinary areas, including Bioinformatics, Computational Biology, Biochemistry, among others. This approach has been applied over respective data derived from Wikipedia. Related reviews from the scientific literature are also considered as a reference, yielding a respective bipartite hypergraph which can be used to gain insights about the interrelationships underlying the considered interdisciplinary areas. Several interesting results are obtained, including greater interconnection between the considered interdisciplinary areas with biological than with physical sciences. A good agreement was also found between the network obtained from Wikipedia and the interrelationships revealed by the literature reviews. At the same time, the former network was found to exhibit more intricate relationships than in the hypergraph derived from the literature review.

cs.DL↗

arXiv2018

Characterization and space embedding of directed graphs and social networks through magnetic Laplacians

Though commonly found in the real world, directed networks have received relatively less attention from the literature in which concerns their topological and dynamical characteristics. In this work, we develop a magnetic Laplacian-based framework that can be used for studying directed complex networks. More specifically, we introduce a specific heat measurement that can help to characterize the network topology. It is shown that, by using this approach, it is possible to identify the types of several networks, as well as to infer parameters underlying specific network configurations. Then, we consider the dynamics associated with the magnetic Laplacian as a means of embedding networks into a metric space, allowing the identification of mesoscopic structures in artificial networks or unravel the polarization on political blogosphere. By defining a coarse-graining procedure in this metric space, we show how to connect the specific heat measurement and the positions of nodes in this space.

cs.SI↗

arXiv2018

Intelligent Complex Networks

The present work addresses the issue of using complex networks as artificial intelligence mechanisms. More specifically, we consider the situation in which puzzles, represented as complex networks of varied types, are to be assembled by complex network processing engines of diverse structures. The puzzle pieces are initially distributed on a set of nodes chosen according to different criteria, including degree and eigenvector centrality. The pieces are then repeatedly copied to the neighboring nodes. The provision of buffering of different sizes are also investigated. Several interesting results are identified, including the fact that BA-based assembling engines tend to provide the fastest solutions. It is also found that the distribution of pieces according to the eigenvector centrality almost invariably leads to the best performance. Another result is that using the buffer sizes proportional to the degree of the respective nodes tend to improve the performance.

physics.soc-ph↗

arXiv2018

Malleability of complex networks

Most complex networks are not static, but evolve along time. Given a specific configuration of one such changing network, it becomes a particularly interesting issue to quantify the diversity of possible unfoldings of its topology. In this work, we suggest the concept of malleability of a network, which is defined as the exponential of the entropy of the probabilities of each possible unfolding with respect to a given configuration. We calculate the malleability with respect to specific measurements of the involved topologies. More specifically, we identify the possible topologies derivable from a given configuration and calculate some topological measurement of them (e.g. clustering coefficient, shortest path length, assortativity, etc.), leading to respective probabilities being associated to each possible measurement value. Though this approach implies some level of degeneracy in the mapping from topology to measurement space, it still paves the way to inferring the malleability of specific network types with respect to given topological measurements. We report that the malleability, in general, depends on each specific measurement, with the average shortest path length and degree assortativity typically leading to large malleability values. The maximum malleability was observed for the Wikipedia network and the minimum for the Watts-Strogatz model.

physics.soc-ph↗

arXiv2018

Can Spatiality Promote Diversity?

Real-world dynamics running on networks can be characterized in terms of their respective diversity, or heterogeneity of state values. Spatial networks can be understood as networks exhibiting limited small world characteristics. In the present work we argue that network spatiality can enhance the diversity of respectively unfolding dynamics. This also means that the small world property tends to reduce diversity. We illustrate this conjecture by simulating one type of Sznajd dynamics at the transient regime on Watts-Strogatz networks with varying rewiring levels. The obtained results show a marked reduction of state diversity as spatiality is replaced by the small world property.

physics.soc-ph↗

arXiv2018

Pattern Recognition Approach to Violin Shapes of MIMO database

Since the landmarks established by the Cremonese school in the 16th century, the history of violin design has been marked by experimentation. While great effort has been invested since the early 19th century by the scientific community on researching violin acoustics, substantially less attention has been given to the statistical characterization of how the violin shape evolved over time. In this paper we study the morphology of violins retrieved from the Musical Instrument Museums Online (MIMO) database -- the largest freely accessible platform providing information about instruments held in public museums. From the violin images, we derive a set of measurements that reflect relevant geometrical features of the instruments. The application of Principal Component Analysis (PCA) uncovered similarities between violin makers and their respective copyists, as well as among luthiers belonging to the same family lineage, in the context of historical narrative. Combined with a time-windowed approach, thin plate splines visualizations revealed that the average violin outline has remained mostly stable over time, not adhering to any particular trends of design across different periods in music history.

stat.AP↗

arXiv2018

Paragraph-based complex networks: application to document classification and authenticity verification

With the increasing number of texts made available on the Internet, many applications have relied on text mining tools to tackle a diversity of problems. A relevant model to represent texts is the so-called word adjacency (co-occurrence) representation, which is known to capture mainly syntactical features of texts.In this study, we introduce a novel network representation that considers the semantic similarity between paragraphs. Two main properties of paragraph networks are considered: (i) their ability to incorporate characteristics that can discriminate real from artificial, shuffled manuscripts and (ii) their ability to capture syntactical and semantic textual features. Our results revealed that real texts are organized into communities, which turned out to be an important feature for discriminating them from artificial texts. Interestingly, we have also found that, differently from traditional co-occurrence networks, the adopted representation is able to capture semantic features. Additionally, the proposed framework was employed to analyze the Voynich manuscript, which was found to be compatible with texts written in natural languages. Taken together, our findings suggest that the proposed methodology can be combined with traditional network models to improve text classification tasks.

cs.CL↗

arXiv2018

Principal Component Analysis: A Natural Approach to Data Exploration

Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, in an intuitive and accessible manner, the basic principles underlying PCA and its applications. Next, we present a systematic, though no exclusive, survey of some representative works illustrating the potential of PCA applications to a wide range of areas. An experimental investigation of the ability of PCA for variance explanation and dimensionality reduction is also developed, which confirms the efficacy of PCA and also shows that standardizing or not the original data can have important effects on the obtained results. Overall, we believe the several covered issues can assist researchers from the most diverse areas in using and interpreting PCA.

cs.CE↗

arXiv2018

Linearity Analysis of the Common Collector Amplifier, or Emitter Follower

A recently introduced Early modeling of transistors is applied to the study of the common collector amplifier (or emitter follower), an important type of electronic circuit typically employed as buffer, being characterized by near unit voltage gain, high input resistance, and low output resistance. The Early equivalent model is applied to derive a simple representation that is simple and yet capable of incorporating the transistor non-linearities implied by the Early effect. Mathematical expressions are obtained describing completely the circuit operation in terms of currents and voltages, allowing accurate estimation of the average voltage gain, total harmonic distortion (THD), and average input and output resistances. Prototypes of small signal silicon transistors of types NPN and PNP obtained in a previous work are used to discuss the respectively implied properties of the common collector transistor. In addition to confirming the importance of the trade-off of current gain for the desired properties, it is also shown that sub-optimal performance can be obtained in case the base and emitter resistances are not properly chosen. Even so, the limited current gain implied by real-world NPN and PNP small signal silicon devices implies some performance constraints. In particular, it has been observed that the THD tends to be larger for PNP devices than NPN counterparts with the same average current gain. The obtained results pave the way not only to complementary analytical studies, but also provide guidance for design and implementation of improved common collector configurations.

physics.app-ph↗

arXiv2018

An Early Modeling Approach to Digital Electronics

An Early modeling approach of transistors characterized by simplicity and accuracy in representing intrinsic non-linearities is applied to the characterization of propagation delay and level transition switching properties of NPN and PNP small signal transistors. Eight types of devices were considered, each represented by 5 samples taken from the same lot, totaling 20 NPN and 20 PNP transistors. Four switching time measurements were experimentally obtained, and the transistors also had their Early parameters $V_a$ (the Early voltage) and $s$ (a proportionality constant such that $R_o = 1/tan(s I_B)$ accurately estimated by using an experimental-numeric procedure that involves Hough transform accumulation in order to identify the crossing of the base current ($I_B$) indexed characteristic isolines, yielding the respective $V_a$. The timing measurements exhibited strong positive Pearson correlations when taken pairwise. When these measurements were compared individually to the respective Early parameters, no significant Pearson correlation was obtained. However, a strong relationship was observed between the product of the two Early parameters and the ratio between the fall and rise time. A Pearson correlation coefficient of 0.78 was observed between these variables in the case of NPN devices. This suggests that transistors with larger average current gain tend to have more similar rise and fall times. The different relationship observed for PNP devices (Pearson 0.41) suggests some intrinsic difference in the way the Early parameters influence the rise and fall times of small signal transistors.

physics.app-ph↗

arXiv2018

The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks

Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, network's nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics considering a single node as the "network brain". Such brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. Surprisingly, the results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on the topology, search strategy and localization of the network brain.

cs.SI↗

arXiv2018

Analysis of the Common Emitter Amplifier Taking into Account Transistor Non-Linearity

The operation of a typical common emitter amplifier, including negative feedback, is studied taking into account the non-linearity characteristic of real-world transistors. This has been accomplished by employing a recently proposed Early modeling approach, which allowed the analytical equations to be obtained describing the current and voltage behavior in the adopted common emitter circuit. Average and dispersion (coefficient of variation) of the current and voltage gains can then be calculated and used to characterize the common emitter amplification while reflecting the transistor non-linearity. Several interesting results were obtained, including the fact that the negative feedback provided by the emitter resistance is not capable of completely eliminating effects of parameters differences exhibited by transistors. Importantly, transistors with larger Early voltage $V_a$ magnitudes tended to provide significantly enhanced linearity even when substantial negative feedback is used. These results motivates customized design, implementation and application approaches taking into account the parameters of the available devices.

physics.ins-det↗

arXiv2018

On the Effects of Resistive and Reactive Loads on Signal Amplification

The effects of reactive loads into amplification is studied. A simplified common emitter circuit configuration was adopted and respective time-independent and time-dependent voltage and current equations were obtained. As phasor analysis cannot be used because of the non-linearity, the voltage at the capacitor was represented in terms of the respective integral, implying a numerical approach. The effect of purely resistive loads was investigated first, and it was shown that the fanned structure of the transistor isolines can severely distort the amplification, especially for $V_a$ small and $s$ large. The total harmonic distortion was found not to depend on $V_a$, being determined by $s$ and the load resistance $R$. An expression was obtained for the current gain in terms of the base current and it was shown that it decreases in an almost perfectly linearly fashion with $I_B$. Remarkably, no gain variation, and hence perfectly linear amplification, is obtained when $R=0$, provided maximum power dissipation limits are not exceeded. Capacitive loads imply the detachment of the circuit trajectory from a straight line to an "ellipsoidal"-like loop. This implies a gain asymmetry along upper or lower arcs of this loop. By using the time-dependent circuit equations, it was possible to show numerically and by an analytical approximation that, at least for the adopted circuit and parameter values, the asymmetry induced by capacitive loads is not substantial. However, capacitive loads will imply lag between the output voltage and current and, hence, low-pass filtering. It was shown that smaller $V_a$ and larger $s$ can substantially reduce the phase lag, but at the cost of severe distortion.

eess.SP↗

arXiv2018

Towards a Simple, and Yet Accurate, Transistor Equivalent Circuit and Its Application to the Analysis and Design of Discrete and Integrated Electronic Circuits

Transistors are the cornerstone of modern electronics. Yet, their relatively complex characteristics, allied with often observed great parameter variation, remain a challenge for discrete and integrated electronics. Much of transistor research and applications have relied on transistor models, as well as respective equivalent circuits, to be employed for circuit analysis and simulations. Here, a simple and yet accurate transistor equivalent circuit is derived, based on the Early effect, which involves only the voltage $V_a$ and a companion parameter $s$. Equations are obtained for currents and voltages in a common-emitter circuit, allowing the derivation of respective gain functions. These functions are found to exhibit interesting mathematical structure, with gain values varying almost linearly with the base current, allowing the gains to be well characterized in terms of their average and variation values. These results are applied to deriving a prototypic Early space summarizing the characteristics of transistors, enriched with recently experimentally obtained prototypes of NPN and PNP silicon BJTs and alloy germanium transistors. Though a trade-off between gain and linearity is revealed, a band characterized by small values of $V_a$ stands out when aiming at both high gain and low distortion. The Early equivalent model was used also for studying the stability of circuits under voltage supply oscillations, as well as parallel combinations of transistors. In the former case, it was verified that more traditional approaches assuming constant current gain can yield stability factors that deviate substantially from those derived for the more accurate Early approach. The equivalent circuit obtained for parallel combinations of transistors was shown also to closely follow the Early formulation.

physics.app-ph↗

arXiv2018

Note: Distance-Based Network Partitioning

A new method for identifying soft communities in networks is proposed. Reference nodes, either selected using a priori information about the network or according to relevant node measurements, are obtained. Distance vectors between each network node and the reference nodes are then used for defining a multidimensional coordinate system representing the community structure of the network at many different scales. For modular networks, the distribution of nodes in this space often results in a well-separated clustered structure, with each cluster corresponding to a community. The potential of the method is illustrated with respect to a spatial network model and the Zachary's karate club network.

physics.soc-ph↗