The marketing innovation and the innovation technology in food industry enterprises

Growing competition in the world determines the importance of innovation technology between countries. High-tech markets encourage the inventors to apply their inventions to commercial project. Many new trends on the world markets depend on factors that generate the ideas and their capacity to be absorbed. Development of new technologies in foodservice is considered a luxury as the sector is comprised predominantly by small and medium size businesses that may not be able to afford the heavy costs involved. However, rapid advancements in information technology have allowed dedicated suppliers to foodservice businesses to develop such innovative products or services which help to be successful on the market. Such hardware or software developments enable food and beverage outlets to increase quality of product, productivity and profitability. This review attempts to provide in-depth discussion and enhance understanding on innovation technology in foodservice enterprises. An article was conducted of a detailed analysis for EU countries food service industry. Analysis shows that international chains have a very strong position in the quick service segment. Technology is developing at an ever-increasing pace and dramatically changes business models in the hospitality industry. In the new economy, organizations that have the ability to develop and adopt the invention in a short period of time and profitably apply it in all areas of business reach competitive advantage over the competition in time. The growing importance of innovation in function of achieving a sustainable competitive advantage determined a brand new concept and innovation classification. Nowadays, the term innovation means not only a significant improvement in process and product technology, but it refers more to the innovation process in the field of human resources, especially in marketing management. For the above mentioned reasons, the paper pays special attention to the marketing innovation analysis and the increasingly significant impact it has on the process of achieving sustainable competitive advantage. The main goal of this article is analysis the theoretical and practical contexts concerning marketing innovation, innovation technology in foodservice enterprises

Deep semi-supervised segmentation with weight-averaged consistency targets

Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also devise a method to solve the problems that arise when using traditional data augmentation strategies for segmentation tasks on our new training scheme.Comment: 8 pages, 1 figure, accepted for DLMIA/MICCA

Mirror Descent and Convex Optimization Problems With Non-Smooth Inequality Constraints

We consider the problem of minimization of a convex function on a simple set with convex non-smooth inequality constraint and describe first-order methods to solve such problems in different situations: smooth or non-smooth objective function; convex or strongly convex objective and constraint; deterministic or randomized information about the objective and constraint. We hope that it is convenient for a reader to have all the methods for different settings in one place. Described methods are based on Mirror Descent algorithm and switching subgradient scheme. One of our focus is to propose, for the listed different settings, a Mirror Descent with adaptive stepsizes and adaptive stopping rule. This means that neither stepsize nor stopping rule require to know the Lipschitz constant of the objective or constraint. We also construct Mirror Descent for problems with objective function, which is not Lipschitz continuous, e.g. is a quadratic function. Besides that, we address the problem of recovering the solution of the dual problem

Mathematical Modeling Links Pregnancy-Associated Changes and Breast Cancer Risk

Recent debate has concentrated on the contribution of bad luck to cancer development. The tight correlation between the number of tissue-specific stem cell divisions and cancer risk of the same tissue suggests that bad luck has an important role to play in tumor development, but the full extent of this contribution remains an open question. Improved understanding of the interplay between extrinsic and intrinsic factors at the molecular level is one promising route to identifying the limits on extrinsic control of tumor initiation, which is highly relevant to cancer prevention. Here, we use a simple mathematical model to show that recent data on the variation in numbers of breast epithelial cells with progenitor features due to pregnancy are sufficient to explain the known protective effect of full-term pregnancy in early adulthood for estrogen receptor-positive (ER+) breast cancer later in life. Our work provides a mechanism for this previously ill-understood effect and illuminates the complex influence of extrinsic factors at the molecular level in breast cancer. These findings represent an important contribution to the ongoing research into the role of bad luck in human tumorigenesis

Dynamic sampling schemes for optimal noise learning under multiple nonsmooth constraints

We consider the bilevel optimisation approach proposed by De Los Reyes, Sch\"onlieb (2013) for learning the optimal parameters in a Total Variation (TV) denoising model featuring for multiple noise distributions. In applications, the use of databases (dictionaries) allows an accurate estimation of the parameters, but reflects in high computational costs due to the size of the databases and to the nonsmooth nature of the PDE constraints. To overcome this computational barrier we propose an optimisation algorithm that by sampling dynamically from the set of constraints and using a quasi-Newton method, solves the problem accurately and in an efficient way

East African HIV care: depression and HIV outcomes.

Importance:Depression is a common co-morbidity for people living with HIV (PLWH) and is associated with elevated plasma HIV RNA levels. While depression correlates with deficits in antiretroviral (ARV) adherence, little data exist to inform the relationship between depression and HIV vial load more broadly. Objective:To examine the relationship between depression and viral load in the African Cohort Study (AFRICOS) independently of ARV adherence. Design:PLWH in Kenya, Uganda and Tanzania underwent screening for depression using the Center for Epidemiologic Studies Depression Scale (CESD) upon enrollment at AFRICOS HIV care sites. Setting:AFRICOS is an ongoing prospective longitudinal cohort study enrolling HIV-infected adults at HIV care centers including sites in Kenya, Tanzania and Uganda. These sites are administered by President's Emergency Plan For AIDS Relief programs. Participants:HIV+ individuals were eligible if they were at least 18 years old, receiving HIV care at the enrolling clinic and consented to data and specimen collection. Main outcome measure:CESD. Results:Among 2307 participants, 18-25% met the CESD threshold for depression. Depression was associated with decreased ARV adherence (OR 0.59, p =  0.01). Higher scores on three CESD items were significantly associated with 209-282% higher viral load, independently of ARV adherence among participants on ARVs ⩾6 months. Conclusions:PLWH had high prevalence of depression on the CESD. Diverse depression symptoms were independently associated with increases in viral load, underscoring the need for comprehensive treatment of depression

On representations of the feasible set in convex optimization

We consider the convex optimization problem $\min \{f(x) : g_j(x)\leq 0, j=1,...,m\}$ where $f$ is convex, the feasible set K is convex and Slater's condition holds, but the functions $g_j$ are not necessarily convex. We show that for any representation of K that satisfies a mild nondegeneracy assumption, every minimizer is a Karush-Kuhn-Tucker (KKT) point and conversely every KKT point is a minimizer. That is, the KKT optimality conditions are necessary and sufficient as in convex programming where one assumes that the $g_j$ are convex. So in convex optimization, and as far as one is concerned with KKT points, what really matters is the geometry of K and not so much its representation.Comment: to appear in Optimization Letter

Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints

In this paper we consider a class of optimization problems with a strongly convex objective function and the feasible set given by an intersection of a simple convex set with a set given by a number of linear equality and inequality constraints. A number of optimization problems in applications can be stated in this form, examples being the entropy-linear programming, the ridge regression, the elastic net, the regularized optimal transport, etc. We extend the Fast Gradient Method applied to the dual problem in order to make it primal-dual so that it allows not only to solve the dual problem, but also to construct nearly optimal and nearly feasible solution of the primal problem. We also prove a theorem about the convergence rate for the proposed algorithm in terms of the objective function and the linear constraints infeasibility.Comment: Submitted for DOOR 201

РОЗКРИТТЯ ІНФОРМАЦІЇ ПРО НАСЛІДКИ НАДЗВИЧАЙНИХ СИТУАЦІЙ У СИСТЕМІ БУХГАЛТЕРСЬКОГО ОБЛІКУ

Nowadays accounting statements is one of the most dynamic accounting method elements. First and foremost it is related to the correspondence of its subject-matter to the demands of different users willing to know economic conditions in reality changing rapidly. Thus, applying methods of economic analysis on the basis of accounting data it is possible to calculate a range of figures giving an opportunity to estimate both financial condition and economic potential of the enterprise in the future and the past. That is why to calculate such figures in the copes of accounting there should be information about emergency consequences cases affecting the production processes, product cost and further the enterprise’s financial results. This condition will allow estimate their influence on the financial figures and economic potential of the enterprise. The directions of financial statement’s expanding in content and form are articulated. Financial statement is improved in order to reflect information about emergency consequences through expanding factors in already existing chapters (as a part of other costs) and formation of the new chapter V «External and internal risks environmental impact» containing information about separation of emergency consequences from activity costs. Fragments of notes to the annual financial statement are developed giving an opportunity to estimate the impact of emergency consequences on noncurrent and current assets and the production process. Building specific information for internal use about emergency consequences is performed on the basis of justified directions of internal reporting formation and development of ways and mechanisms of their factors formation. It allowed to increase quality of information space of business activity management in emergency conditions. The use of suggestions in practice contributed to increasing the information space quality in users’ decision-making and developing a complex of measures to prevent and address the emergency consequences. In its turn analytical measures calculated on the basis of their facts will take into account a number of factors allowing to fully estimate the financial condition and economic potential.На сьогодні бухгалтерська звітність є одним з найбільш динамічних елементів методу обліку. Перш за все, це пов’язано з відповідністю його предметa вимогам різних користувачів, які бажають дізнатися, що економічні умови в реальності швидко змінюються. Тому, застосовуючи методи економічного аналізу на основі даних бухгалтерського обліку, можна розрахувати діапазон цифр, що дають можливість оцінити як фінансовий стан, так і економічний потенціал підприємства в майбутньому і минулому. Тому для обчислення таких цифр у справах бухгалтерського обліку повинна бути інформація про надзвичайні наслідки, що впливають на виробничі процеси, вартість продукції і фінансові результати підприємства. Ця умова дозволить оцінити їхній вплив на фінансові показники та економічний потенціал підприємства. Сформульовано напрями розширення фінансової звітності за змістом і формою. Фінансова звітність удосконалюється з метою відображення інформації про надзвичайні наслідки за рахунок розширення факторів у наявних главах (як частина інших витрат) і формування нової глави V «Зовнішні та внутрішні ризики впливу на навколишнє середовище», що містить інформацію про відокремлення надзвичайних наслідків від витрати на діяльність. Фрагменти приміток до річної фінансової звітності розроблено, що дає можливість оцінити вплив надзвичайних наслідків на необоротні та оборотні активи і виробничий процес. Побудова конкретної інформації для внутрішнього використання про надзвичайні наслідки здійснюється на основі обґрунтованих напрямів формування внутрішньої звітності та розроблення шляхів і механізмів формування їхніх факторів. Це дозволило підвищити якість інформаційного простору управління діловою діяльністю в надзвичайних умовах. Використання пропозицій на практиці сприяло підвищенню якості інформаційного простору при ухваленні рішень користувачами і розробленні комплексу заходів щодо запобігання надзвичайним наслідкам та їх ліквідації . У свою чергу, аналітичні заходи, розраховані на основі їхніх фактів, ураховуватимуть низку факторів, що дозволяють повністю оцінити фінансовий стан та економічний потенціал

Advances in low-memory subgradient optimization

One of the main goals in the development of non-smooth optimization is to cope with high dimensional problems by decomposition, duality or Lagrangian relaxation which greatly reduces the number of variables at the cost of worsening differentiability of objective or constraints. Small or medium dimensionality of resulting non-smooth problems allows to use bundle-type algorithms to achieve higher rates of convergence and obtain higher accuracy, which of course came at the cost of additional memory requirements, typically of the order of n2, where n is the number of variables of non-smooth problem. However with the rapid development of more and more sophisticated models in industry, economy, finance, et all such memory requirements are becoming too hard to satisfy. It raised the interest in subgradient-based low-memory algorithms and later developments in this area significantly improved over their early variants still preserving O(n) memory requirements. To review these developments this chapter is devoted to the black-box subgradient algorithms with the minimal requirements for the storage of auxiliary results, which are necessary to execute these algorithms. To provide historical perspective this survey starts with the original result of N.Z. Shor which opened this field with the application to the classical transportation problem. The theoretical complexity bounds for smooth and non-smooth convex and quasi-convex optimization problems are briefly exposed in what follows to introduce to the relevant fundamentals of non-smooth optimization. Special attention in this section is given to the adaptive step-size policy which aims to attain lowest complexity bounds. Unfortunately the non-differentiability of objective function in convex optimization essentially slows down the theoretical low bounds for the rate of convergence in subgradient optimization compared to the smooth case but there are different modern techniques that allow to solve non-smooth convex optimization problems faster then dictate lower complexity bounds. In this work the particular attention is given to Nesterov smoothing technique, Nesterov Universal approach, and Legendre (saddle point) representation approach. The new results on Universal Mirror Prox algorithms represent the original parts of the survey. To demonstrate application of non-smooth convex optimization algorithms for solution of huge-scale extremal problems we consider convex optimization problems with non-smooth functional constraints and propose two adaptive Mirror Descent methods. The first method is of primal-dual variety and proved to be optimal in terms of lower oracle bounds for the class of Lipschitz-continuous convex objective and constraints. The advantages of application of this method to sparse Truss Topology Design problem are discussed in certain details. The second method can be applied for solution of convex and quasi-convex optimization problems and is optimal in a sense of complexity bounds. The conclusion part of the survey contains the important references that characterize recent developments of non-smooth convex optimization