725 research outputs found

    Balanced Allocations: A Simple Proof for the Heavily Loaded Case

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    We provide a relatively simple proof that the expected gap between the maximum load and the average load in the two choice process is bounded by (1+o(1))log‚Ā°log‚Ā°n(1+o(1))\log \log n, irrespective of the number of balls thrown. The theorem was first proven by Berenbrink et al. Their proof uses heavy machinery from Markov-Chain theory and some of the calculations are done using computers. In this manuscript we provide a significantly simpler proof that is not aided by computers and is self contained. The simplification comes at a cost of weaker bounds on the low order terms and a weaker tail bound for the probability of deviating from the expectation

    Scheduling Packets with Values and Deadlines in Size-bounded Buffers

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    Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm has been provided for this model (Azar and Levy. SWAT 2006), but no offline algorithms have been known yet. In this paper, We study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature. These offline algorithms help us understand the model better in designing online algorithms.Comment: 7 page

    Improved algorithms for online load balancing

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    We consider an online load balancing problem and its extensions in the framework of repeated games. On each round, the player chooses a distribution (task allocation) over KK servers, and then the environment reveals the load of each server, which determines the computation time of each server for processing the task assigned. After all rounds, the cost of the player is measured by some norm of the cumulative computation-time vector. The cost is the makespan if the norm is L‚ąěL_\infty-norm. The goal is to minimize the regret, i.e., minimizing the player's cost relative to the cost of the best fixed distribution in hindsight. We propose algorithms for general norms and prove their regret bounds. In particular, for L‚ąěL_\infty-norm, our regret bound matches the best known bound and the proposed algorithm runs in polynomial time per trial involving linear programming and second order programming, whereas no polynomial time algorithm was previously known to achieve the bound.Comment: 16 pages; typos correcte

    Upaya Keluarga Untuk Mencegah Penularan Dalam Perawatan Anggota Keluarga Dengan Tb Paru

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    Indonesia merupakan negara keempat dengan insiden kasus terbanyak untuk tuberkulosis (TB) paru didunia..Penelitian ini menggunakan desain kualitatif dengan pendekatan case study research, bertujuan untuk memberikan penjelasan tentang upaya keluarga untuk mencegah penularan dalam perawatan anggota keluarga dengan TB Paru. Dari hasil analisa data, didapatkan tiga tema dan tujuh subtema yaitu: (1) Modifikasi lingkungan dengan subtema modifikasi ventilasi yang memadai dan menjaga kebersihan. (2) Upaya memutus transmisi penyakit dengan subtema membuang dahak, pengunaan masker, dan menutup saat batuk. (3) Konsumsi obat dan kontrol rutin ke puskesmas dengan subtema pemantauan dari keluarga dalam minum obat (PMO), serta kontrol rutin ke Puskesmas.Berdasarkan hasil penelitian ini diharapkan Puskesmas dapat menambah dan memodifikasi program penanggulangan tuberkulosis (TB). Selain itu perlu dilakukan pengawasan secara berkala atau kunjungan rumah secara rutin untuk memantau pengobatan dan pencegahan penularan Tuberkulosis (TB) yang dilakukan keluarga di rumah

    Wear Minimization for Cuckoo Hashing: How Not to Throw a Lot of Eggs into One Basket

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    We study wear-leveling techniques for cuckoo hashing, showing that it is possible to achieve a memory wear bound of log‚Ā°log‚Ā°n+O(1)\log\log n+O(1) after the insertion of nn items into a table of size CnCn for a suitable constant CC using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically, showing that it significantly improves on the memory wear performance for classic cuckoo hashing and linear probing in practice.Comment: 13 pages, 1 table, 7 figures; to appear at the 13th Symposium on Experimental Algorithms (SEA 2014

    Leveraging data mining techniques to understand drivers of obesity

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    Abstract‚ÄĒSubstantial research has been carried out to explain\ud the effects of economic variables on obesity, typically\ud considering only a few factors at a time, using parametric\ud linear regression models. Recent studies have made a significant\ud contribution by examining economic factors affecting body\ud weight using the Behavioral Risk Factor Surveillance System\ud data with 27 state-level variables for a period of 20 years (1990-\ud 2010). As elsewhere, the authors solely focus on individual\ud effects of potential drivers of obesity than critical interactions\ud among the drivers. We take some steps to extend the literature\ud and gain a deeper understanding of the drivers of obesity.\ud We employ state-of-the-art data mining techniques to uncover\ud critical interactions that may exist among drivers of obesity\ud in a data-driven manner. The state-of-the-art techniques reveal\ud several complex interactions among economic and behavioral\ud factors that contribute to the rise of obesity. Lower levels of\ud obesity, measured by a body mass index (BMI), belong to\ud female individuals who exercise outside work, enjoy higher\ud levels of education and drink less alcohol. The highest level of\ud obesity, in contrast, belongs to those who fail to exercise outside\ud work, smoke regularly, consume more alcohol and come from\ud lower income groups. These and other complementary results\ud suggest that it is the joint complex interactions among various\ud behavioral and economic factors that gives rise to obesity or\ud lowers it; it is not simply the presence or absence of individual\ud factor

    Managing Climate Risk

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    At the heart of the traditional approach to strategy in the climate change dilemma lies the assumption that the global community, by applying a set of powerful analytical tools, can predict the future of climate change accurately enough to choose a clear strategic direction for it. We claim that this approach might involve underestimating uncertainty in order to lay out a vision of future events sufficiently precise to be captured in a discounted cost flow analysis in integrated assessment models. However, since the future of climate change is truly uncertain, this approach might at best be marginally helpful and at worst downright dangerous: underestimating uncertainty can lead to strategies that do not defend the world against unexpected and sometimes even catastrophic threats. Another danger lies on the other extreme: if the global community can not find a strategy that works under traditional analysis or if uncertainties are too large that clear messages are absent, they may abandon the analytical rigor of their planning process altogether and base their decisions on good instinct and consensus of some future process that is easy to agree upon. In this paper, we try to outline a system to derive strategic decisions under uncertainty for the climate change dilemma. What follows is a framework for determining the level of uncertainty surrounding strategic decisions and for tailoring strategy to that uncertainty. Our core argument is that a robust strategy towards climate change involves the building of a technological portfolio of mitigation and adaptation measures that includes sufficient opposite technological positions to the underlying baseline emission scenarios given the uncertainties of the entire physical and socioeconomic system in place. In the case of mitigation, opposite technological positions with the highest leverage are particular types of sinks. A robust climate risk management portfolio can only work when the opposite technological positions are readily available when needed and therefore have to be prepared in advance. It is precisely the flexibility of these technological options which has to be quantified under the perspective of the uncertain nature of the underlying system and compared to the cost of creating these options, rather than comparing their cost with expected losses in a net present value type analysis. We conclude that climate policy - especially under the consideration of the precautionary principle - would look much different if uncertainties would be taken explicitly into account
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