Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm

Cue-Pin-Select, a Secure and Usable Offline Password Scheme

People struggle to invent safe passwords for many of their typical online activities. This leads to a variety of security problems when they use overly simple passwords or reuse them multiple times with minor modifications. Having different passwords for each service generally requires password managers or memorable (but weak) passwords, introducing other vulnerabilities [10, 18]. Recent research [14, 6] has offered multiple alternatives but those require either rote mem-orization [8] or computation on a physical device [23, 7]. This paper presents the Cue-Pin-Select password family scheme, which uses simple mental operations (counting and character selection) to create a password from a passphrase and the name of the service the password is targeted for. It needs little memorization to create and retrieve passwords, and requires no assistance from any physical device. It is durable and adaptable to different password requirements. It is secure against known threat models, including against adversaries with stolen passwords. A usability test shows the successes of users in real-life conditions over four days

Метод решения задачи оптимальной стратегии для производственно-финансовой модели фирмы

The paper deals with the investigation of optimum policy on a production-financial model of a firm. A method for formation of an optimum strategy which is based on the solution of an optimum control problem is proposed in the paper. For a special class of functions a cheap credit situation is studied, an analysis of the corresponding optimum control problem is carried out and it is shown that an optimum solution satisfies a main principle.Статья посвящена исследованию оптимальной политики одной производственно-финансовой модели фирмы. Предложен метод построения оптимальной стратегии, основанный на решении задачи оптимального управления. Для специального класса функций изучена ситуация дешевого кредита, проведен анализ соответствующей задачи оптимального управления и показано, что оптимальное решение удовлетворяет магистральному принципу

ОПТИМИЗАЦИЯ ЛИНЕЙНЫХ ГИБРИДНЫХ СИСТЕМ УПРАВЛЕНИЯ

A linear optimal control problem for a hybrid system is solved. The Cauchy formula is derived with its help the initial problem is reduced to a special linear programming.Решается линейная задача оптимального управления гибридными системами. Выводится формула Коши, с помощью которой исходная задача сводится к специальной задаче линейного программировани

О ПОСТРОЕНИИ ОПТИМАЛЬНОЙ ПРОГРАММЫ ДЛЯ ОДНОГО КЛАССА ГИБРИДНЫХ СИСТЕМ

A formula for presenting solutions for a hybrid control system containing  continuous and discrete parts has been developed in the paper.  The paper shows  that a problem pertaining to development of optimal programs is reduced to the linear programming problem which may be solved with the help of the known methods.Получена формула представления решений для гибридной системы управления, содержащей непрерывную и дискретную части. Показано, что задача построения оптимальных программ сводится к задаче линейного программирования, которая может быть решена известными методами

ПРИНЦИП ε-МАКСИМУМА В ЛИНЕЙНОЙ ЗАДАЧЕ ОПТИМАЛЬНОГО УПРАВЛЕНИЯ ГИБРИДНОЙ СИСТЕМОЙ

The paper reveals necessary and sufficient conditions of optimality and sub-optimality of the programs for a hybrid system in the class of discrete control actions. The conditions are formulated in the support terms of  the initial problem. Получены необходимые и достаточные условия оптимальности и субоптимальности программ для гибридной системы в классе дискретных управляющих воздействий. Условия формулируются в терминах опоры исходной задачи