95 research outputs found
Whole-Page Optimization and Submodular Welfare Maximization with Online Bidders
In the context of online ad serving, display ads may appear on different types of webpages, where each page includes several ad slots and therefore multiple ads can be shown on each page. The set of ads that can be assigned to ad slots of the same page needs to satisfy various prespecified constraints including exclusion constraints, diversity constraints, and the like. Upon arrival of a user, the ad serving system needs to allocate a set of ads to the current webpage respecting these per-page allocation constraints. Previous slot-based settings ignore the important concept of a page and may lead to highly suboptimal results in general. In this article, motivated by these applications in display advertising and inspired by the submodular welfare maximization problem with online bidders, we study a general class of page-based ad allocation problems, present the first (tight) constant-factor approximation algorithms for these problems, and confirm the performance of our algorithms experimentally on real-world datasets.
A key technical ingredient of our results is a novel primal-dual analysis for handling free disposal, which updates dual variables using a “level function” instead of a single level and unifies with previous analyses of related problems. This new analysis method allows us to handle arbitrarily complicated allocation constraints for each page. Our main result is an algorithm that achieves a 1 &minus frac 1 e &minus o(1)-competitive ratio. Moreover, our experiments on real-world datasets show significant improvements of our page-based algorithms compared to the slot-based algorithms.
Finally, we observe that our problem is closely related to the submodular welfare maximization (SWM) problem. In particular, we introduce a variant of the SWM problem with online bidders and show how to solve this problem using our algorithm for whole-page optimization.postprin
Buyback Problem - Approximate matroid intersection with cancellation costs
In the buyback problem, an algorithm observes a sequence of bids and must
decide whether to accept each bid at the moment it arrives, subject to some
constraints on the set of accepted bids. Decisions to reject bids are
irrevocable, whereas decisions to accept bids may be canceled at a cost that is
a fixed fraction of the bid value. Previous to our work, deterministic and
randomized algorithms were known when the constraint is a matroid constraint.
We extend this and give a deterministic algorithm for the case when the
constraint is an intersection of matroid constraints. We further prove a
matching lower bound on the competitive ratio for this problem and extend our
results to arbitrary downward closed set systems. This problem has applications
to banner advertisement, semi-streaming, routing, load balancing and other
problems where preemption or cancellation of previous allocations is allowed
Online Independent Set Beyond the Worst-Case: Secretaries, Prophets, and Periods
We investigate online algorithms for maximum (weight) independent set on
graph classes with bounded inductive independence number like, e.g., interval
and disk graphs with applications to, e.g., task scheduling and spectrum
allocation. In the online setting, it is assumed that nodes of an unknown graph
arrive one by one over time. An online algorithm has to decide whether an
arriving node should be included into the independent set. Unfortunately, this
natural and practically relevant online problem cannot be studied in a
meaningful way within a classical competitive analysis as the competitive ratio
on worst-case input sequences is lower bounded by .
As a worst-case analysis is pointless, we study online independent set in a
stochastic analysis. Instead of focussing on a particular stochastic input
model, we present a generic sampling approach that enables us to devise online
algorithms achieving performance guarantees for a variety of input models. In
particular, our analysis covers stochastic input models like the secretary
model, in which an adversarial graph is presented in random order, and the
prophet-inequality model, in which a randomly generated graph is presented in
adversarial order. Our sampling approach bridges thus between stochastic input
models of quite different nature. In addition, we show that our approach can be
applied to a practically motivated admission control setting.
Our sampling approach yields an online algorithm for maximum independent set
with competitive ratio with respect to all of the mentioned
stochastic input models. for graph classes with inductive independence number
. The approach can be extended towards maximum-weight independent set by
losing only a factor of in the competitive ratio with denoting
the (expected) number of nodes
Advances on Matroid Secretary Problems: Free Order Model and Laminar Case
The most well-known conjecture in the context of matroid secretary problems
claims the existence of a constant-factor approximation applicable to any
matroid. Whereas this conjecture remains open, modified forms of it were shown
to be true, when assuming that the assignment of weights to the secretaries is
not adversarial but uniformly random (Soto [SODA 2011], Oveis Gharan and
Vondr\'ak [ESA 2011]). However, so far, there was no variant of the matroid
secretary problem with adversarial weight assignment for which a
constant-factor approximation was found. We address this point by presenting a
9-approximation for the \emph{free order model}, a model suggested shortly
after the introduction of the matroid secretary problem, and for which no
constant-factor approximation was known so far. The free order model is a
relaxed version of the original matroid secretary problem, with the only
difference that one can choose the order in which secretaries are interviewed.
Furthermore, we consider the classical matroid secretary problem for the
special case of laminar matroids. Only recently, a constant-factor
approximation has been found for this case, using a clever but rather involved
method and analysis (Im and Wang, [SODA 2011]) that leads to a
16000/3-approximation. This is arguably the most involved special case of the
matroid secretary problem for which a constant-factor approximation is known.
We present a considerably simpler and stronger -approximation, based on reducing the problem to a matroid secretary
problem on a partition matroid
Packing Returning Secretaries
We study online secretary problems with returns in combinatorial packing
domains with candidates that arrive sequentially over time in random order.
The goal is to accept a feasible packing of candidates of maximum total value.
In the first variant, each candidate arrives exactly twice. All arrivals
occur in random order. We propose a simple 0.5-competitive algorithm that can
be combined with arbitrary approximation algorithms for the packing domain,
even when the total value of candidates is a subadditive function. For
bipartite matching, we obtain an algorithm with competitive ratio at least
for growing , and an algorithm with ratio at least
for all . We extend all algorithms and ratios to arrivals
per candidate.
In the second variant, there is a pool of undecided candidates. In each
round, a random candidate from the pool arrives. Upon arrival a candidate can
be either decided (accept/reject) or postponed (returned into the pool). We
mainly focus on minimizing the expected number of postponements when computing
an optimal solution. An expected number of is always
sufficient. For matroids, we show that the expected number can be reduced to
, where is the minimum of the ranks of matroid and
dual matroid. For bipartite matching, we show a bound of , where
is the size of the optimum matching. For general packing, we show a lower
bound of , even when the size of the optimum is .Comment: 23 pages, 5 figure
Active Re-identification Attacks on Periodically Released Dynamic Social Graphs
Active re-identification attacks pose a serious threat to privacy-preserving
social graph publication. Active attackers create fake accounts to build
structural patterns in social graphs which can be used to re-identify
legitimate users on published anonymised graphs, even without additional
background knowledge. So far, this type of attacks has only been studied in the
scenario where the inherently dynamic social graph is published once. In this
paper, we present the first active re-identification attack in the more
realistic scenario where a dynamic social graph is periodically published. The
new attack leverages tempo-structural patterns for strengthening the adversary.
Through a comprehensive set of experiments on real-life and synthetic dynamic
social graphs, we show that our new attack substantially outperforms the most
effective static active attack in the literature by increasing the success
probability of re-identification by more than two times and efficiency by
almost 10 times. Moreover, unlike the static attack, our new attack is able to
remain at the same level of effectiveness and efficiency as the publication
process advances. We conduct a study on the factors that may thwart our new
attack, which can help design graph anonymising methods with a better balance
between privacy and utility
Building data warehouses in the era of big data: an approach for scalable and flexible big data warehouses
During the last few years, the concept of Big Data Warehousing gained significant attention from the scientific community, highlighting the need to make design changes to the traditional Data Warehouse (DW) due to its limitations, in order to achieve new characteristics relevant in Big Data contexts (e.g., scalability on commodity hardware, real-time performance, and flexible storage). The state-of-the-art in Big Data Warehousing reflects the young age of the concept, as well as ambiguity and the lack of common approaches to build Big Data Warehouses (BDWs). Consequently, an approach to design and implement these complex systems is of major relevance to business analytics researchers and practitioners. In this tutorial, the design and implementation of BDWs is targeted, in order to present a general approach that researchers and practitioners can follow in their Big Data Warehousing projects, exploring several demonstration cases focusing on system design and data modelling examples in areas like smart cities, retail, finance, manufacturing, among others
Solving Multi-choice Secretary Problem in Parallel: An Optimal Observation-Selection Protocol
The classical secretary problem investigates the question of how to hire the
best secretary from candidates who come in a uniformly random order. In
this work we investigate a parallel generalizations of this problem introduced
by Feldman and Tennenholtz [14]. We call it shared -queue -choice
-best secretary problem. In this problem, candidates are evenly
distributed into queues, and instead of hiring the best one, the employer
wants to hire candidates among the best persons. The quotas are
shared by all queues. This problem is a generalized version of -choice
-best problem which has been extensively studied and it has more practical
value as it characterizes the parallel situation.
Although a few of works have been done about this generalization, to the best
of our knowledge, no optimal deterministic protocol was known with general
queues. In this paper, we provide an optimal deterministic protocol for this
problem. The protocol is in the same style of the -solution for the
classical secretary problem, but with multiple phases and adaptive criteria.
Our protocol is very simple and efficient, and we show that several
generalizations, such as the fractional -choice -best secretary problem
and exclusive -queue -choice -best secretary problem, can be solved
optimally by this protocol with slight modification and the latter one solves
an open problem of Feldman and Tennenholtz [14].
In addition, we provide theoretical analysis for two typical cases, including
the 1-queue 1-choice -best problem and the shared 2-queue 2-choice 2-best
problem. For the former, we prove a lower bound of
the competitive ratio. For the latter, we show the optimal competitive ratio is
while previously the best known result is 0.356 [14].Comment: This work is accepted by ISAAC 201
Selênio como suplemento para bovinos intoxicados cronicamente por Pteridium sp. no Espirito Santo. 2017.
Pteridiumsp.(samambaia) é uma planta responsável por diversos quadros de intoxicação em animais e seres humanos. Em bovinos, um dos quadros comuns na região sul do Espírito Santo é a hematúria enzoótica bovina (HEB) que não possui tratamento. Assim, o objetivo do presente trabalho foi determinar os efeitos do selênio associado a vitamina E como suplemento em animais intoxicados cronicamente pelo Pteridium sp. Foram selecionados 21 animais intoxicados cronicamente pela planta e com HEB. Os animais foram examinados clinicamente e foi realizada a coleta da urina para a confirmação da hematúria. O delineamento experimental foi feito em quatro grupos divididos ao acaso (controle soro fisiológico; tratamento 1 0,05 mg/Kg do suplemento;tratamento20,10mg/Kgdosuplemento;tratamento30,20mg/Kgdo suplemento). Foi feita a suplementação parenteral, via intramuscular, uma vez por semana, durante 13 semanas. Quinzenalmente os animais foram avaliados clinicamente e foram coletadas amostras de sangue para dosagem do selêniosérico. A análise de selênio foi feita nos momentos inicial, antes da suplementação com selênio (M0), após quatro semanas de tratamento (M4), após oito semanas (M8) e após 12 semanas (M12), pelo método de espectrofotometria de absorção atômica. Utilizou-seaanálisedevariância(ANOVA)seguidadotestedeTukeya5%.Verificou-se que houve maior ganho de peso dos animais tratados com selênio em relação ao grupocontrolee,também,entreosgrupos.Aintensidadedahematúriareduziuapartir da sexta semana e houve diferença significativa entre os grupos tratados e o grupo controle, assim como entre os grupos. Houve diferença significativa da concentração sérica de selênio entre os tratamentos. Assim, conclui-se que o selênio associado a vitaminaEcomosuplementoparabovinosintoxicadoscronicamenteporPteridiumsp. no Espirito Santo com quadro de HEB teve efeito dose dependente sobre a melhora doquadroclínicocausandoreduçãodaintensidadedehematúriaeaumentodoganho de pes
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Metabolic adaptation drives arsenic trioxide resistance in acute promyelocytic leukemia.
Acquired genetic mutations can confer resistance to arsenic trioxide (ATO) in the treatment of acute promyelocytic leukemia (APL). However, such resistance-conferring mutations are rare and do not explain most disease recurrence seen in the clinic. We have generated stable ATO-resistant promyelocytic cell lines that are also less sensitive to ATRA and the combination of ATO and ATRA compared to the sensitive cell line. Characterization of these in-house generated resistant cell lines showed significant differences in immunophenotype, drug transporter expression, anti-apoptotic protein dependence, and PML-RARA mutation. Gene expression profiling revealed prominent dysregulation of the cellular metabolic pathways in these ATO resistant APL cell lines. Glycolytic inhibition by 2-DG was sufficient and comparable to the standard of care (ATO) in targeting the sensitive APL cell line. 2-DG was also effective in the in vivo transplantable APL mouse model; however, it did not affect the ATO resistant cell lines. In contrast, the resistant cell lines were significantly affected by compounds targeting the mitochondrial respiration when combined with ATO, irrespective of the ATO resistance-conferring genetic mutations or the pattern of their anti-apoptotic protein dependency. Our data demonstrate that the addition of mitocans in combination with ATO can overcome ATO resistance. We further show that this combination has the potential in the treatment of non-M3 AML and relapsed APL. The translation of this approach in the clinic needs to be explored further
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