Extracting Linearization Equations from Noisy Sources

This note was originally written under the name ``On the Security of HMFEv\u27\u27 and was submitted to PQCrypto 2018. The author was informed by the referees of his oversight of an eprint work of the same name by Hashimoto, see eprint article /2017/689/, that completely breaks HMFEv, rendering the result on HMFEv obsolete. Still, the author feels that the technique used here is interesting and that, at least in principal, this method could contribute to future cryptanalysis. Thus, with a change of title indicating the direction in which this work is leading, we present the original work with all of its oversights intact and with minimal correction (only references fixed). At PQCRYPTO 2017, a new multivariate digital signature based on Multi-HFE and utilizing the vinegar modifier was proposed. The vinegar modifier increases the Q-rank of the central map, preventing a direct application of the MinRank attack that defeated Multi-HFE. The authors were, therefore, confident enough to choose aggressive parameters for the Multi-HFE component of the central map (with vinegar variables fixed). Their analysis indicated that the security of the scheme depends on the sum of the number of variables $k$ over the extension field and the number $v$ of vinegar variables with the individual values being unimportant as long as they are not ``too small.\u27\u27 We analyze the consequences of this choice of parameters and derive some new attacks showing that the parameter $v$ must be chosen with care

Organs on chip approach: A tool to evaluate cancer-immune cells interactions

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a micro uidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative con rmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems

A phenomenological approach to normal form modeling: a case study in laser induced nematodynamics

An experimental setting for the polarimetric study of optically induced dynamical behavior in nematic liquid crystal films has allowed to identify most notably some behavior which was recognized as gluing bifurcations leading to chaos. This analysis of the data used a comparison with a model for the transition to chaos via gluing bifurcations in optically excited nematic liquid crystals previously proposed by G. Demeter and L. Kramer. The model of these last authors, proposed about twenty years before, does not have the central symmetry which one would expect for minimal dimensional models for chaos in nematics in view of the time series. What we show here is that the simplest truncated normal forms for gluing, with the appropriate symmetry and minimal dimension, do exhibit time signals that are embarrassingly similar to the ones found using the above mentioned experimental settings. The gluing bifurcation scenario itself is only visible in limited parameter ranges and substantial aspect of the chaos that can be observed is due to other factors. First, out of the immediate neighborhood of the homoclinic curve, nonlinearity can produce expansion leading to chaos when combined with the recurrence induced by the homoclinic behavior. Also, pairs of symmetric homoclinic orbits create extreme sensitivity to noise, so that when the noiseless approach contains a rich behavior, minute noise can transform the complex damping into sustained chaos. Leonid Shil'nikov taught us that combining global considerations and local spectral analysis near critical points is crucial to understand the phenomenology associated to homoclinic bifurcations. Here this helps us construct a phenomenological approach to modeling experiments in nonlinear dissipative contexts.Comment: 25 pages, 9 figure

A Data-driven Approach to Power System Dynamic State Estimation

State estimation is a key function in the supervisory control and planning of an electric power grid. Typically, the independent system operator (ISO) runs least-squares based static state estimation once every few minutes. Inherently, however, a power system is mostly in a transient state owing to load fluctuations, outages and network switching. In such a scenario, dynamic state estimation facilitates real-time monitoring and control of the system. Dynamic state estimation is implemented using Kalman filtering techniques. Popular estimators for nonlinear systems include the extended Kalman filter (EKF) and unscented Kalman filter (UKF). Practical implementation, however, is inhibited by the lack of an accurate system model and the high computational complexity of Kalman filtering methods. I address the former issue of model unavailability and rely instead on measurement data from phasor measurement units for dynamic state estimation (DSE). I build an estimator for DSE which uses only measurement and input information, and operates without knowledge of the underlying system model. The algorithm considered uses a Gaussian process (GP) approximation of the state transition and observation functions in the implementation of a UKF-based state estimation. I analyze the performance of the estimator for different scenarios using root mean squared (RMS) error as the metric. The estimator, when evaluated on the IEEE 14-bus test case, gives a minimum accuracy rate of over 94% over all considered scenarios

Simultaneous Tracking and Shape Estimation of Extended Objects

This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem and ii) the lack of statistical knowledge about possible measurement sources on the extended object

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin