Chaotic memristor

We suggest and experimentally demonstrate a chaotic memory resistor (memristor). The core of our approach is to use a resistive system whose equations of motion for its internal state variables are similar to those describing a particle in a multi-well potential. Using a memristor emulator, the chaotic memristor is realized and its chaotic properties are measured. A Poincar\'{e} plot showing chaos is presented for a simple nonautonomous circuit involving only a voltage source directly connected in series to a memristor and a standard resistor. We also explore theoretically some details of this system, plotting the attractor and calculating Lyapunov exponents. The multi-well potential used resembles that of many nanoscale memristive devices, suggesting the possibility of chaotic dynamics in other existing memristive systems.Comment: Applied Physics A (in press

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics

Preface to foundations of information/decision fusion with applications to engineering problems

In engineering design, it was shown by von Neumann that a reliable system can be built using unreliable components by employing simple majority rule fusers. If error densities are known for individual pattern recognizers then an optimal fuser was shown to be implementable as a threshold function. Many applications have been developed for distributed sensor systems, sensor-based robotics, face recognition, decision fusion, recognition of handwritten characters, and automatic target recognition. Recently, information/decision fusion has been recognized as an independently growing field with its own principles and methods. While some of the fusion problems in engineering systems could be solved by applying existing results from other domains, many others require original approaches and solutions. In turn, these new approaches would lead to new applications in other areas. There are two paradigms at the extrema of the spectrum of the information/decision methods: (i) Fusion as Problem: In certain applications, fusion is explicitly specified in the problem statement. Particularly in robotics applications, many researchers realized the fundamental limitations of single sensor systems, thereby motivating the deployment of multiple sensors. In more general engineering applications, similar sensors are employed for fault tolerance, while in several others, different sensor modalities are required to achieve the given task. In these scenarios, fusion methods have to be first designed to solve the problem at hand. (ii) Fusion as Solution: In many instances (e.g., DNA analysis), a number of different solutions to a particular problem already exist. Often these solutions can be combined to obtain solutions that outperform any individual one. The area of forecasting is a good example of such paradigm. Although fusion is not explicitly specified in these problems, it is used as an ingredient of the solution

A generic sensor fusion problem: Classification and function estimation

Abstract. A generic fusion problem is studied for multiple sensors whose outputs are probabilistically related to their inputs according to unknown distributions. Sensor measurements are provided as iid input-output samples, and an empirical risk minimization method is described for designing fusers with distribution-free performance bounds. The special cases of isolation and projective fusers for classifiers and function estimators, respectively, are described in terms of performance bounds. The isolation fusers for classifiers are probabilistically guaranteed to perform at least as good as the best classifier. The projective fusers for function estimators are probabilistically guaranteed to perform at least as good as the best subset of estimators.