What Europe Knows and Thinks About Algorithms Results of a Representative Survey. Bertelsmann Stiftung eupinions February 2019

We live in an algorithmic world. Day by day, each of us is affected by decisions that algorithms make for and about us – generally without us being aware of or consciously perceiving this. Personalized advertisements in social media, the invitation to a job interview, the assessment of our creditworthiness – in all these cases, algorithms already play a significant role – and their importance is growing, day by day. The algorithmic revolution in our daily lives undoubtedly brings with it great opportunities. Algorithms are masters at handling complexity. They can manage huge amounts of data quickly and efficiently, processing it consistently every time. Where humans reach their cognitive limits, find themselves making decisions influenced by the day’s events or feelings, or let themselves be influenced by existing prejudices, algorithmic systems can be used to benefit society. For example, according to a study by the Expert Council of German Foundations on Integration and Migration, automotive mechatronic engineers with Turkish names must submit about 50 percent more applications than candidates with German names before being invited to an in-person job interview (Schneider, Yemane and Weinmann 2014). If an algorithm were to make this decision, such discrimination could be prevented. However, automated decisions also carry significant risks: Algorithms can reproduce existing societal discrimination and reinforce social inequality, for example, if computers, using historical data as a basis, identify the male gender as a labor-market success factor, and thus systematically discard job applications from woman, as recently took place at Amazon (Nickel 2018)

Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains

We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size. While the detailed analysis was only performed for quadratic functions, we provide an explicit asymptotic expansion of the moments of the averaged SGD iterates that outlines the dependence on initial conditions, the effect of noise and the step-size, as well as the lack of convergence in the general (non-quadratic) case. For this analysis, we bring tools from Markov chain theory into the analysis of stochastic gradient. We then show that Richardson-Romberg extrapolation may be used to get closer to the global optimum and we show empirical improvements of the new extrapolation scheme

DNA ANALYSIS USING GRAMMATICAL INFERENCE

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA. An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm. Testing shows that the accuracy of inferred languages for components of DNA are consistently accurate. By using the proposed algorithm languages are inferred for coding DNA with average conditional probability over 80%. This reveals that languages for components of DNA can be inferred and are useful independent of the process that created them. These languages can then be analyzed or used for other tasks in computational biology. To illustrate potential applications of regular grammars for DNA components, an inferred language for exon sequences is applied as post processing to Hidden Markov exon prediction to reduce the number of wrong exons detected and improve the specificity of the model significantly

Distribution System Voltage Management and Optimization for Integration of Renewables and Electric Vehicles: Research Gap Analysis

California is striving to achieve 33% renewable penetration by 2020 in accordance with the state’s Renewable Portfolio Standard (RPS). The behavior of renewable resources and electric vehicles in distribution systems is creating constraints on the penetration of these resources into the distribution system. One such constraint is the ability of present-­‐‑day voltage management methodologies to maintain proper distribution system voltage profiles in the face of higher penetrations of PV and electric vehicle technologies. This white paper describes the research gaps that have been identified in current Volt/VAR Optimization and Control (VVOC) technologies, the emerging technologies which are becoming available for use in VVOC, and the research gaps which exist and must be overcome in order to realize the full promise of these emerging technologies

Development and evaluation of clustering techniques for finding people

Typically in a large organisation much expertise and knowledge is held informally within employees' own memories. When employees leave an organisation many documented links that go through that person are broken and no mechanism is usually available to overcome these broken links. This match making problem is related to the problem of finding potential work partners in a large and distributed organisation. This paper reports a comparative investigation into using standard information retrieval techniques to group employees together based on their webpages. This information can, hopefully, be subsequently used to redirect broken links to people who worked closely with a departed employee or used to highlight people, say indifferent departments, who work on similar topics. The paper reports the design and positive results of an experiment conducted at Risø National Laboratory comparing four different IR searching and clustering approaches using real users' web pages

Deterministic Partial Differential Equation Model for Dose Calculation in Electron Radiotherapy

Treatment with high energy ionizing radiation is one of the main methods in modern cancer therapy that is in clinical use. During the last decades, two main approaches to dose calculation were used, Monte Carlo simulations and semi-empirical models based on Fermi-Eyges theory. A third way to dose calculation has only recently attracted attention in the medical physics community. This approach is based on the deterministic kinetic equations of radiative transfer. Starting from these, we derive a macroscopic partial differential equation model for electron transport in tissue. This model involves an angular closure in the phase space. It is exact for the free-streaming and the isotropic regime. We solve it numerically by a newly developed HLLC scheme based on [BerCharDub], that exactly preserves key properties of the analytical solution on the discrete level. Several numerical results for test cases from the medical physics literature are presented.Comment: 20 pages, 7 figure