Coloring vertices of a graph or finding a Meyniel obstruction

A Meyniel obstruction is an odd cycle with at least five vertices and at most one chord. A graph is Meyniel if and only if it has no Meyniel obstruction as an induced subgraph. Here we give a O(n^2) algorithm that, for any graph, finds either a clique and coloring of the same size or a Meyniel obstruction. We also give a O(n^3) algorithm that, for any graph, finds either aneasily recognizable strong stable set or a Meyniel obstruction

The travelling preacher, projection, and a lower bound for the stability number of a graph

AbstractThe coflow min–max equality is given a travelling preacher interpretation, and is applied to give a lower bound on the maximum size of a set of vertices, no two of which are joined by an edge

Machine Learning-Based Side-Channel Analysis on the Advanced Encryption Standard

Hardware security is essential in keeping sensitive information private. Because of this, it’s imperative that we evaluate the ability of cryptosystems to withstand cutting edge attacks. Doing so encourages the development of countermeasures and new methods of data protection as needed. In this thesis, we present our findings of an evaluation of the Advanced Encryption Standard, particularly unmasked and masked AES-128, implemented in software on an STM32F415 microcontroller unit (MCU), against machine learning-based side-channel analysis (MLSCA). 12 machine learning classifiers were used in combination with a side-channel leakage model in the context of four scenarios: profiling one device and key and attacking the same device with the same key, profiling one device and key and attacking a different device with the same key, profiling one device and key and attacking the same device with a different key, and profiling one device and key and attacking a different device with a different key. We found that unmasked AES-128 can be very vulnerable to this form of attack and that masking can be applied as a countermeasure to successfully prevent attacks in 2 out of the 4 tested scenarios. In addition to providing our experimental results on the following pages, we also plan to release a public GitHub repository with all of our collected side-channel data along with sample analysis code shortly after the time of writing this. We hope that doing so will allow for complete reproducibility of our results and encourage future research without the need for purchasing hardware equipment

Modified Dark Matter: Relating Dark Energy, Dark Matter and Baryonic Matter

Modified dark matter (MDM) is a phenomenological model of dark matter, inspired by gravitational thermodynamics. For an accelerating Universe with positive cosmological constant ($\Lambda$), such phenomenological considerations lead to the emergence of a critical acceleration parameter related to $\Lambda$. Such a critical acceleration is an effective phenomenological manifestation of MDM, and it is found in correlations between dark matter and baryonic matter in galaxy rotation curves. The resulting MDM mass profiles, which are sensitive to $\Lambda$, are consistent with observational data at both the galactic and cluster scales. In particular, the same critical acceleration appears both in the galactic and cluster data fits based on MDM. Furthermore, using some robust qualitative arguments, MDM appears to work well on cosmological scales, even though quantitative studies are still lacking. Finally, we comment on certain non-local aspects of the quanta of modified dark matter, which may lead to novel non-particle phenomenology and which may explain why, so far, dark matter detection experiments have failed to detect dark matter particles

Privacy policies: Are they meeting users\u27 needs?

This paper examines the web site privacy policies of 200 web sites, 100 of the most popular as well as 100 random web sites. It examines the extent at which these privacy policies comprehensively define a company\u27s data collection and dissemination policies. Such policies are important in creating trust between a company and its customers

Testing Modified Dark Matter with Galaxy Clusters: Does Dark Matter know about the Cosmological Constant?

We discuss the possibility that the cold dark matter mass profiles contain information on the cosmological constant, and that such information constrains the nature of cold dark matter (CDM). We call this approach Modified Dark Matter (MDM). In particular, we examine the ability of MDM to explain the observed mass profiles of 13 galaxy clusters. Using general arguments from gravitational thermodynamics, we provide a theoretical justification for our MDM mass profile and successfully compare it to the NFW mass profiles both on cluster and galactic scales. Our results suggest that indeed the CDM mass profiles contain information about the cosmological constant in a non-trivial way

Distributed Approximation of Maximum Independent Set and Maximum Matching

We present a simple distributed $\Delta$-approximation algorithm for maximum weight independent set (MaxIS) in the $\mathsf{CONGEST}$ model which completes in $O(\texttt{MIS}(G)\cdot \log W)$ rounds, where $\Delta$ is the maximum degree, $\texttt{MIS}(G)$ is the number of rounds needed to compute a maximal independent set (MIS) on $G$, and $W$ is the maximum weight of a node. %Whether our algorithm is randomized or deterministic depends on the \texttt{MIS} algorithm used as a black-box. Plugging in the best known algorithm for MIS gives a randomized solution in $O(\log n \log W)$ rounds, where $n$ is the number of nodes. We also present a deterministic $O(\Delta +\log^* n)$-round algorithm based on coloring. We then show how to use our MaxIS approximation algorithms to compute a $2$-approximation for maximum weight matching without incurring any additional round penalty in the $\mathsf{CONGEST}$ model. We use a known reduction for simulating algorithms on the line graph while incurring congestion, but we show our algorithm is part of a broad family of \emph{local aggregation algorithms} for which we describe a mechanism that allows the simulation to run in the $\mathsf{CONGEST}$ model without an additional overhead. Next, we show that for maximum weight matching, relaxing the approximation factor to ($2+\varepsilon$) allows us to devise a distributed algorithm requiring $O(\frac{\log \Delta}{\log\log\Delta})$ rounds for any constant $\varepsilon>0$. For the unweighted case, we can even obtain a $(1+\varepsilon)$-approximation in this number of rounds. These algorithms are the first to achieve the provably optimal round complexity with respect to dependency on $\Delta$