How Differs Outsourcing of Core and Supportive Activities in Slovenian SMEs

Companies are facing increasing and faster changes on the market, including globalization, shortening of life cycles, complexity of products, intensive technological development. Achieving and maintaining competitiveness can be done with different strategies and tools. One of them is also outsourcing. Outsourcing is widespread throughout the world and represents a commonly accepted business practice. An important feature of outsourcing is that it is determined by several characteristics at the same time. Since outsourcing is a multidimensional phenomenon, we examined it’s most important and distinctive dimensions, including share of outsourcing particular activity, duration of the outsourcing contract, and number, size and location of outsourcing providers. For the purpose of research we have examined which activities are companies outsourcing and divide them on core and supportive activities. On this ground we studied differences in particular characteristics between those two groups of activities. The paper is based on empirical study of 154 Slovenian SMEs concluded in 2008. Its findings are presented in the paper.outsourcing, activities, dimensions, SMEs

(Near) Optimal Adaptivity Gaps for Stochastic Multi-Value Probing

Consider a kidney-exchange application where we want to find a max-matching in a random graph. To find whether an edge e exists, we need to perform an expensive test, in which case the edge e appears independently with a known probability p_e. Given a budget on the total cost of the tests, our goal is to find a testing strategy that maximizes the expected maximum matching size. The above application is an example of the stochastic probing problem. In general the optimal stochastic probing strategy is difficult to find because it is adaptive - decides on the next edge to probe based on the outcomes of the probed edges. An alternate approach is to show the adaptivity gap is small, i.e., the best non-adaptive strategy always has a value close to the best adaptive strategy. This allows us to focus on designing non-adaptive strategies that are much simpler. Previous works, however, have focused on Bernoulli random variables that can only capture whether an edge appears or not. In this work we introduce a multi-value stochastic probing problem, which can also model situations where the weight of an edge has a probability distribution over multiple values. Our main technical contribution is to obtain (near) optimal bounds for the (worst-case) adaptivity gaps for multi-value stochastic probing over prefix-closed constraints. For a monotone submodular function, we show the adaptivity gap is at most 2 and provide a matching lower bound. For a weighted rank function of a k-extendible system (a generalization of intersection of k matroids), we show the adaptivity gap is between O(k log k) and k. None of these results were known even in the Bernoulli case where both our upper and lower bounds also apply, thereby resolving an open question of Gupta et al. [Gupta et al., 2017]

Robust Algorithms for the Secretary Problem

In classical secretary problems, a sequence of n elements arrive in a uniformly random order, and we want to choose a single item, or a set of size K. The random order model allows us to escape from the strong lower bounds for the adversarial order setting, and excellent algorithms are known in this setting. However, one worrying aspect of these results is that the algorithms overfit to the model: they are not very robust. Indeed, if a few "outlier" arrivals are adversarially placed in the arrival sequence, the algorithms perform poorly. E.g., Dynkin’s popular 1/e-secretary algorithm is sensitive to even a single adversarial arrival: if the adversary gives one large bid at the beginning of the stream, the algorithm does not select any element at all. We investigate a robust version of the secretary problem. In the Byzantine Secretary model, we have two kinds of elements: green (good) and red (rogue). The values of all elements are chosen by the adversary. The green elements arrive at times uniformly randomly drawn from [0,1]. The red elements, however, arrive at adversarially chosen times. Naturally, the algorithm does not see these colors: how well can it solve secretary problems? We show that selecting the highest value red set, or the single largest green element is not possible with even a small fraction of red items. However, on the positive side, we show that these are the only bad cases, by giving algorithms which get value comparable to the value of the optimal green set minus the largest green item. (This benchmark reminds us of regret minimization and digital auctions, where we subtract an additive term depending on the "scale" of the problem.) Specifically, we give an algorithm to pick K elements, which gets within (1-ε) factor of the above benchmark, as long as K ≥ poly(ε^{-1} log n). We extend this to the knapsack secretary problem, for large knapsack size K. For the single-item case, an analogous benchmark is the value of the second-largest green item. For value-maximization, we give a poly log^* n-competitive algorithm, using a multi-layered bucketing scheme that adaptively refines our estimates of second-max over time. For probability-maximization, we show the existence of a good randomized algorithm, using the minimax principle. We hope that this work will spur further research on robust algorithms for the secretary problem, and for other problems in sequential decision-making, where the existing algorithms are not robust and often tend to overfit to the model.ISSN:1868-896

Nanoscale Optical Trapping: A Review

© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Optical trapping is the craft of manipulating objects with light. Decades after its first inception in 1970, the technique has become a powerful tool for ultracold-atom physics and manipulation of micron-sized particles. Yet, optical trapping of objects at the intermediate—nanoscale—range is still beyond full grasp. This matters because the nanometric realm is where several promising advances, from mastering single-molecule experiments in biology, to fabricating hybrid devices for nanoelectronics and photonics, as well as testing fundamental quantum phenomena in optomechanics, are anticipated to produce impactful breakthroughs. After a comprehensive, theoretical introduction to the phenomenon of optical trapping, this review delves into assessing the current state-of-the-art for optical manipulation of objects at the nanoscale. Emphasis is put on presenting the challenges that coalesced into driving the field to its current development, as well as discussing the outstanding barriers, which might lead to future advancements in the field

Combining weak and strong lensing in cluster potential reconstruction

We propose a method for recovering the two-dimensional gravitational potential of galaxy clusters which combines data from weak and strong gravitational lensing. A first estimate of the potential from weak lensing is improved at the approximate locations of critical curves. The method can be fully linearised and does not rely on the existence and identification of multiple images. We use simulations to show that it recovers the surface-mass density profiles and distributions very accurately, even if critical curves are only partially known and if their location is realistically uncertain. We further describe how arcs at different redshifts can be combined, and how deviations from weak lensing can be included.Comment: 9 pages, 5 figures, A&A in press, changes to match the accepted versio

Efficient characterization of blinking quantum emitters from scarce data sets via machine learning

Single photon emitters are core building blocks of quantum technologies, with established and emerging applications ranging from quantum computing and communication to metrology and sensing. Regardless of their nature, quantum emitters universally display fluorescence intermittency or photoblinking: interaction with the environment can cause the emitters to undergo quantum jumps between on and off states that correlate with higher and lower photoemission events, respectively. Understanding and quantifying the mechanism and dynamics of photoblinking is important for both fundamental and practical reasons. However, the analysis of blinking time traces is often afflicted by data scarcity. Blinking emitters can photo-bleach and cease to fluoresce over time scales that are too short for their photodynamics to be captured by traditional statistical methods. Here, we demonstrate two approaches based on machine learning that directly address this problem. We present a multi-feature regression algorithm and a genetic algorithm that allow for the extraction of blinking on/off switching rates with >85% accuracy, and with >10x less data and >20x higher precision than traditional methods based on statistical inference. Our algorithms effectively extend the range of surveyable blinking systems and trapping dynamics to those that would otherwise be considered too short-lived to be investigated. They are therefore a powerful tool to help gain a better understanding of the physical mechanism of photoblinking, with practical benefits for applications based on quantum emitters that rely on either mitigating or harnessing the phenomenon

Suppression of Spectral Diffusion by Anti-Stokes Excitation of Quantum Emitters in Hexagonal Boron Nitride

Solid-state quantum emitters are garnering a lot of attention due to their role in scalable quantum photonics. A notable majority of these emitters, however, exhibit spectral diffusion due to local, fluctuating electromagnetic fields. In this work, we demonstrate efficient Anti-Stokes (AS) excitation of quantum emitters in hexagonal boron nitride (hBN), and show that the process results in the suppression of a specific mechanism responsible for spectral diffusion of the emitters. We also demonstrate an all-optical gating scheme that exploits Stokes and Anti-Stokes excitation to manipulate spectral diffusion so as to switch and lock the emission energy of the photon source. In this scheme, reversible spectral jumps are deliberately enabled by pumping the emitter with high energy (Stokes) excitation; AS excitation is then used to lock the system into a fixed state characterized by a fixed emission energy. Our results provide important insights into the photophysical properties of quantum emitters in hBN, and introduce a new strategy for controlling the emission wavelength of quantum emitters