1,720,985 research outputs found
Deep Learning the Functional Renormalization Group
Get PDFWe perform a data-driven dimensionality reduction of the scale-dependent
4-point vertex function characterizing the functional Renormalization Group
(fRG) flow for the widely studied two-dimensional Hubbard model on the
square lattice. We demonstrate that a deep learning architecture based on a
Neural Ordinary Differential Equation solver in a low-dimensional latent space
efficiently learns the fRG dynamics that delineates the various magnetic and
-wave superconducting regimes of the Hubbard model. We further present a
Dynamic Mode Decomposition analysis that confirms that a small number of modes
are indeed sufficient to capture the fRG dynamics. Our work demonstrates the
possibility of using artificial intelligence to extract compact representations
of the 4-point vertex functions for correlated electrons, a goal of utmost
importance for the success of cutting-edge quantum field theoretical methods
for tackling the many-electron problem.Comment: 6 pages, 5 figure
An O(n log n) algorithm for finding dissimilar strings
No full textLet be a finite alphabet and . A string is said to be -dissimilar to , if no length substring of is equal to any length substring of . We present an algorithm which on input and an integer outputs an integer and such that:
- is -dissimilar to .
- There does not exist a string of length which is dissimilar to .Technical report LCSR-TR-26
A Similarity-based Normative Framework for Bio-plausible Neural Nets
Get PDFIn the last decade, Artificial Neural Nets (ANNs), rebranded as Deep Learning, have
revolutionized the field of Artificial Intelligence. While these neural nets have their origin
in analogy with the neural networks in the brain, in many ways they are trained in ways
that are very different from how real neurons learn. For example, to date there is no
satisfactory biologically plausible mechanism for backpropagation, the workhorse for
training ANNs.
Motivated by this gap, we have looked at alternative normative approaches to neural
networks that could give rise to more plausible learning rules. One such approach, which
works rather well for representation learning problems, is based on similarity matching
or kernel alignment. In this approach, one demands that similar sensory inputs produce
similar neural activities. From this rather limited constraint, one can give rise to interesting
neural networks performing many common unsupervised learning tasks. I will illustrate,
in particular, the case of representing continuous manifolds like spatial information. Here
, this approach produces representations very much like place cells in the hippocampus.
Consequences of our theory and its relations to some experiments would be discussed.
Time permitting, I would touch upon the role of similarity matching in current work in
ANNs as well.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Extracting sparse signals from high-dimensional data: a statistical mechanics approach
No full textSparse reconstruction algorithms aim to retrieve high-dimensional sparse signals from a limited amount of measurements under suitable conditions. As the number of variables go to infinity, these algorithms exhibit sharp phase transition boundaries where the sparse retrieval breaks down. Several sparse reconstruction algorithms are formulated as optimization problems. Few of the prominent ones among these have been analyzed in the literature by statistical mechanical methods. The function to be optimized plays the role of energy. The treatment involves finite temperature replica mean-field theory followed by the zero temperature limit. Although this approach has been successful in reproducing the algorithmic phase transition boundaries, the replica trick and the non-trivial zero temperature limit obscure the underlying reasons for the failure of the algorithms. In this thesis, we employ the ``cavity method" to give an alternative derivation of the phase transition boundaries, working directly in the zero-temperature limit. This approach provides insight into the origin of the different terms in the mean field self-consistency equations. The cavity method naturally generates a local susceptibility which leads to an identity that clearly indicates the existence of two phases. The identity also gives us a novel route to the known parametric expressions for the phase boundary of the Basis Pursuit algorithm and to the new ones for the Elastic Net. These transitions being continuous (second order), we explore the scaling laws and critical exponents that are uniquely determined by the nature of the distribution of the density of the nonzero components of the sparse signal. Not only is the phase boundary of the Elastic Net different from that of the Basis Pursuit, we show that the critical behavior of the two algorithms are from different universality classes.Ph.D.Includes bibliographical referencesby Mohammad Ramezanal
Data driven approach towards quantifying dynamics in biological systems
No full textIn this thesis, we develop data driven formalism for analysing features that govern dynamics in biological systems. We first consider the social amoeba Dictyostelium discoideum as a model organism. D. discoideum cells form protrusions and migrate via cytoskeletal reorganization driven by coordinated waves of actin polymerization and depolymerization. Assembly and disassembly of actin filaments are regulated by a complex network of biochemical reactions, exhibiting sensitivity to external physical cues such as stiffness, composition, and surface topography of the extracellular matrix. The dynamics and topology of actin waves in moving D. discoideum cells are affected by the nanotopography of the cell surface and the presence of the external electric field. In the first part of this study, we employ machine learning techniques to predict the type of the extracellular environment by observing actin waves either frame-by-frame or over a period of time, and identify key visual features that help classify cell motion by the microenvironment type. In the second part, we focus on learning dynamical rules with the goal of predicting dynamics from experimental observations of signaling in biological systems. In recent years, Dynamic Mode Decomposition (DMD), along with Koopman Operator Theory, has been used as a data-driven technique to understand complex dynamics in high-dimensional systems. We reformulate DMD using kernel methods and provide a formalism to estimate Koopman operator from observed data, which can be used to predict temporal evolution of dynamical systems. We showcase some applications of our formalism on both synthetic and real datasets. We expect our computational approach to be useful in many settings where non-trivial collective dynamics is observed.Ph.D.Includes bibliographical reference
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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