arXiv subjects

Yoram Louzoun

Publications and source records attributed to Yoram Louzoun.

27 records · Page 2Linked to original sources

arXiv2019

Topological based classification of paper domains using graph convolutional networks

The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their class to predict this class. Moreover, combining topological information with information propagation improves classification accuracy on the standard CiteSeer and Cora paper classification task. Topological features and information propagation produce results almost as good as text-based classification, without no textual or content information. We propose to represent the topology and information propagation through a GCN with the neighboring training node classification as an input and the current node classification as output. Such a formalism outperforms state of the art methods.

cs.SI↗

arXiv2018

A combined network and machine learning approaches for product market forecasting

Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we develop a new approach based on combined network theory and machine learning to study the structure and operations of financial product markets. Our network links are based on the similarity of firms' products and are constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several features in our network can serve as good precursors of financial market risks. We then combine the network topology and machine learning methods to predict both successful and failed firms. We find that the forecasts made using our method are much better than other well-known regression techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrate the power of combining network theory and machine learning.

physics.soc-ph↗

arXiv2017

Non-invasive biomarkers of fetal brain development reflecting prenatal stress: an integrative multi-scale multi-species perspective on data collection and analysis

Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of 'fetal programming' is mediated by the effects of the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS). The HPA axis is a dynamic system regulating homeostasis, especially the stress response, and is highly sensitive to adverse early life experiences. We review the evidence for the effects of PS on fetal programming of the HPA axis and the ANS. We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of these programming effects. Such approach would identify adverse postnatal brain developmental trajectories, a prerequisite for designing therapeutic interventions. The multiple scales include the biomarkers reflecting changes in the brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS. We conclude that studies into PS effects must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive biomarkers.

q-bio.QM↗

arXiv2001

Volatility driven market in a generalised Lotka Voltera formalism

The Generalized Lotka Voltera (GLV) formalism has been introduced in order to explain the power law distributions in the individual wealth (w_i (t)) (Pareto law) and financial markets returns (fluctuations) (r) as a result of the auto-catalytic (multiplicative random) character of the individual capital dynamics. As long as the multiplicative random factor (lambda) is extracted from the same probability distribution for all the individuals, the exponent of the power laws turns out to be independent on the time variations of the average ( ). This explains also the stability over the past century of experimentally measured Pareto exponent. In contrast to the scaling properties of the single time ("unconditional") probability distributions, the (auto-)correlations between observables measured at different times are not correctly reproduced by the original GLV, if the variance (sigma^2) of $λ$ is time independent. In the GLV formalism the volatility (r^2) auto-correlations decay exponentially while the measurements in real markets indicate a power law with a very small exponent. We show in the present paper that by making the variance of the individual wealth changes (sigma^2) a function of the market volatility , one correctly reproduces the market volatility long range correlations. Moreover, we show that this non-trivial feedback loop between the market price volatility and the variance of the investors wealth leads to non-trivial patterns in the overall market trends. If the feedback is too strong, it may even endanger the market stability.

cond-mat↗

arXiv2000

HIV time hierarchy: Winning the war while, loosing all the battles

AIDS is the pandemic of our era. A disease that scares us not only because it is fatal but also because its insidious time course makes us all potential carriers long before it hands us our heads in a basket. The strange three stage dynamics of aids is also one of the major puzzles in describing the disease theoretically (1). Aids starts, like most diseases, in a peak of virus expression (2,3), which is practically wiped out by the immune system. However it then remains in the body at a low level of expression until later (some time years later) when there is an outbreak of the disease which terminally cripples the immune system causing death from various common pathogens. In this paper we show, using a microscopic simulation, that the time course of AIDS is determined by the interactions of the virus and the immune cells in the shape space of antigens and that it is the virus's ability to move more rapidly in this space (it's high mutability) that causes the time course and eventual 'victory' of the disease. These results open the way for further experimental and therapeutic conclusions in the ongoing battle with the HIV epidemic.

nlin.AO↗

arXiv2000

The Emergence of Spatial Complexity in the immune System

Biological systems, unlike physical or chemical systems, are characterized by the very inhomogeneous distribution of their components. The immune system, in particular, is notable for self-organizing its structure. Classically, the dynamics of natural systems have been described using differential equations. But, differential equation models fail to account for the emergence of large-scale inhomogeneities and for the influence of inhomogeneity on the overall dynamics of biological systems. Here, we show that a microscopic simulation methodology enables us to model the emergence of large-scale objects and to extend the scope of mathematical modeling in biology. We take a simple example from immunology and illustrate that the methods of classical differential equations and microscopic simulation generate contradictory results. Microscopic simulations generate a more faithful approximation of the reality of the immune system.

cond-mat.stat-mech↗

arXiv2000

Destruction of CD4 T Lymphocytes Alone Cannot Account for their Long-term Decrease in AIDS

Following previous models describing a quasi steady state (QSS) for the evolution of HIV infection and AIDS, we have developed a larger formalism simulating the long-term evolution of the QSS We show that the long-term evolution of AIDS cannot be explained by the destruction alone of CD4 T cells, either directly or indirectly. The destruction of CD4 T cells can lead only to a QSS with a lower concentration of CD4 T cells, but CD4 destruction cannot generate the sustained long-term decrease in T cells leading to AIDS. We here suggest some workable explanations.

math.DS↗

arXiv2000

Microscopic Discrete Proliferating Components Cause the Self-Organized Emergence of Macroscopic Adaptive Features in Biological Systems

We study the emergence of the collective spatio-temporal macroscopic properties of the immune system, by representing individually the elementary interactions between its microscopic components (antibodies, antigens, cytokines). The results of this detailed explicit analysis are compared with the traditional procedure of averaging over individuals and representing the collective dynamics in terms of densities that obey partial differential equations (PDE). The simulations show even for very simple elementary reactions the spontaneous emergence of localized complex structures. In turn the effective dynamics of these structures affects the average behavior of the system in a very decisive way: systems which would according to the differential equations approximation die, display in reality a very lively behavior. Our conclusions are supported both by explicit microscopic simulations and by analytic calculations. As the optimal method we propose a mixture of microscopic simulation (MS) systems describing each reaction separately, and average methods describing the average behavior of the agents.

nlin.AO↗

arXiv1999

The Importance of Being Discrete - Life Always Wins on the Surface

Many systems in chemistry, biology, finance and social sciences present emerging features which are not easy to guess from the elementary interactions of their microscopic individual components. In the past, the macroscopic behavior of such systems was modeled by assuming that the collective dynamics of microscopic components can be effectively described collectively by equations acting on spatially continuous density distributions. It turns out that quite contrary, taking into account the actual individual/discrete character of the microscopic components of these systems is crucial for explaining their macroscopic behavior. In fact, we find that in conditions in which the continuum approach would predict the extinction of all the population (respectively the vanishing of the invested capital or of the concentration of a chemical substance, etc), the microscopic granularity insures the emergence of macroscopic localized sub-populations with collective adaptive properties which allow their survival and development. In particular it is found that in 2 dimensions "life" (the localized proliferating phase) always prevails.

adap-org↗