72 research outputs found
Approximation Algorithms for Computing Maximin Share Allocations
We study the problem of computing maximin share allocations, a recently introduced fairness notion. Given a set of n agents and a set of goods, the maximin share of an agent is the best she can guarantee to herself, if she is allowed to partition the goods in any way she prefers, into n bundles, and then receive her least desirable bundle. The objective then is to find a partition, where each agent is guaranteed her maximin share. Such allocations do not always exist, hence we resort to approximation algorithms. Our main result is a 2/3-approximation that runs in polynomial time for any number of agents and goods. This improves upon the algorithm of Procaccia and Wang (2014), which is also a 2/3-approximation but runs in polynomial time only for a constant number of agents. To achieve this, we redesign certain parts of the algorithm in Procaccia and Wang (2014), exploiting the construction of carefully selected matchings in a bipartite graph representation of the problem. Furthermore, motivated by the apparent difficulty in establishing lower bounds, we undertake a probabilistic analysis. We prove that in randomly generated instances, maximin share allocations exist with high probability. This can be seen as a justification of previously reported experimental evidence. Finally, we provide further positive results for two special cases arising from previous works. The first is the intriguing case of three agents, where we provide an improved 7/8-approximation. The second case is when all item values belong to {0, 1, 2}, where we obtain an exact algorith
Approximate Sampling and Counting of Graphs with Near-Regular Degree Intervals
The approximate uniform sampling of graphs with a given degree sequence is a well-known, extensively studied problem in theoretical computer science and has significant applications, e.g., in the analysis of social networks. In this work we study an extension of the problem, where degree intervals are specified rather than a single degree sequence. We are interested in sampling and counting graphs whose degree sequences satisfy the degree interval constraints. A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed. In this work, we provide the first fully polynomial almost uniform sampler (FPAUS) as well as the first fully polynomial randomized approximation scheme (FPRAS) for sampling and counting, respectively, graphs with near-regular degree intervals, in which every node has a degree from an interval not too far away from a given . In order to design our FPAUS, we rely on various state-of-the-art tools from Markov chain theory and combinatorics. In particular, we provide the first non-trivial algorithmic application of a breakthrough result of Liebenau and Wormald (2017) regarding an asymptotic formula for the number of graphs with a given near-regular degree sequence. Furthermore, we also make use of the recent breakthrough of Anari et al. (2019) on sampling a base of a matroid under a strongly log-concave probability distribution. As a more direct approach, we also study a natural Markov chain recently introduced by Rechner, Strowick and M\"uller-Hannemann (2018), based on three simple local operations: Switches, hinge flips, and additions/deletions of a single edge. We obtain the first theoretical results for this Markov chain by showing it is rapidly mixing for the case of near-regular degree intervals of size at most one
Rapid mixing of the switch Markov chain for strongly stable degree sequences and 2-class joint degree matrices
The switch Markov chain has been extensively studied as the most natural Markov Chain Monte Carlo approach for sampling graphs with prescribed degree sequences. We use comparison arguments with other, less natural but simpler to analyze, Markov chains, to show that the switch chain mixes rapidly in two different settings. We first study the classic problem of uniformly sampling simple undirected, as well as bipartite, graphs with a given degree sequence. We apply an embedding argument, involving a Markov chain defined by Jerrum and Sinclair (TCS, 1990) for sampling graphs that almost have a given degree sequence, to show rapid mixing for degree sequences satisfying strong stability, a notion closely related to P-stability. This results in a much shorter proof that unifies the currently known rapid mixing results of the switch chain and extends them up to sharp characterizations of P-stability. In particular, our work resolves an open problem posed by Greenhill (SODA, 2015).Secondly, in order to illustrate the power of our approach, we study the problem of uniformly sampling graphs for which, in addition to the degree sequence, a joint degree distribution is given. Although the problem was formalized over a decade ago, and despite its practical significance in generating synthetic network topologies, small progress has been made on the random sampling of such graphs. The case of a single degree class reduces to sampling of regular graphs, but beyond this almost nothing is known. We fully resolve the case of two degree classes, by showing that the switch Markov chain is always rapidly mixing. Again, we first analyze an auxiliary chain for strongly stable instances on an augmented state space and then use an embedding argument.</p
Comparing approximate relaxations of envy-freeness
In fair division problems with indivisible goods it is well known that one cannot have any guarantees for the classic fairness notions of envy-freeness and proportionality. As a result, several relaxations have been introduced, most of which in quite recent works. We focus on four such notions, namely envy-freeness up to one good (EF1), envy-freeness up to any good (EFX), maximin share fairness (MMS), and pairwise maximin share fairness (PMMS). Since obtaining these relaxations also turns out to be problematic in several scenarios, approximate versions of them have been considered. In this work, we investigate further the connections between the four notions mentioned above and their approximate versions. We establish several tight, or almost tight, results concerning the approximation quality that any of these notions guarantees for the others, providing an almost complete picture of this landscape. Some of our findings reveal interesting and surprising consequences regarding the power of these notions, e.g., PMMS and EFX provide the same worst-case guarantee for MMS, despite PMMS being a strictly stronger notion than EFX. We believe such implications provide further insight on the quality of approximately fair solutions
Optimising outcomes for complex trauma survivors: assessing the motivators, barriers and enablers for implementing trauma informed practice within a multidisciplinary health setting
Background: Complex trauma is a significant public health issue with detrimental health, interpersonal and psychological impacts, which can impede client recovery and result in multiple representations. ‘Trauma Informed Practice’ (TIP) is an evidence-based model which ensures safe and effective services for clients and staff. This study examines health professional’s use of TIP, and the motivators, enablers and barriers to implementation in a multidisciplinary setting. Methods: A mixed methods study with 24 front-line clinicians and managers within a community health setting in Australia. A purpose designed, expert validated TIP checklist was completed, followed by semi-structured focus groups. Survey data was reported using descriptive statistics. Focus group data was digitally recorded, transcribed and thematically analysed. Results: Ten key factors were identified motivating, restricting or enabling TIP implementation. Seven were organisational factors including supportive and informed management, flexibility of service models, levels of service demands, resource availability, education opportunities, good client outcomes, and reporting requirements. Philosophical approach, team orientation, and vicarious trauma/stress management were three individual professional factors. Critically, alignment in two ways was necessary for successful implementation, that is: in knowledge and understanding across organisational role levels - clinician, manager and executive; and, in professional philosophy and team orientation of individual clinicians. Conclusion: Providing TIP is essential for ensuring optimum client outcomes for trauma survivors and for maintaining workforce wellbeing. Although the increasing uptake to TIP is evident within the health setting, further attention is required to address the tension between service models focused on efficiently servicing whole populations and those attuned to effectively meeting the needs of high risk groups. A complex strategy to unite therapeutic and managerial goals is necessary if client, professional and organisational needs are to be effectively met
Truthful Allocation Mechanisms Without Payments: Characterization and Implications on Fairness
We study the mechanism design problem of allocating a set of indivisible
items without monetary transfers. Despite the vast literature on this very
standard model, it still remains unclear how do truthful mechanisms look like.
We focus on the case of two players with additive valuation functions and our
purpose is twofold. First, our main result provides a complete characterization
of truthful mechanisms that allocate all the items to the players. Our
characterization reveals an interesting structure underlying all truthful
mechanisms, showing that they can be decomposed into two components: a
selection part where players pick their best subset among prespecified choices
determined by the mechanism, and an exchange part where players are offered the
chance to exchange certain subsets if it is favorable to do so. In the
remaining paper, we apply our main result and derive several consequences on
the design of mechanisms with fairness guarantees. We consider various notions
of fairness, (indicatively, maximin share guarantees and envy-freeness up to
one item) and provide tight bounds for their approximability. Our work settles
some of the open problems in this agenda, and we conclude by discussing
possible extensions to more players.Comment: To appear in the 18th ACM Conference on Economics and Computation
(ACM EC '17
Code smells survival analysis in web apps
Web applications are heterogeneous, both in their target platform (split across client and server sides) and on the formalisms they are built with, usually a mixture of programming and formatting languages. This heterogeneity is perhaps an explanation why software evolution of web applications (apps) is a poorly addressed topic in the literature. In this paper we focus on web apps built with PHP, the most widely used server-side programming language.
We analyzed the evolution of 6 code smells in 4 web applications, using the survival analysis technique. Since code smells are symptoms of poor design, it is relevant to study their survival, that is, how long did it take from their introduction to their removal. It is obviously desirable to minimize their survival.
In our analysis we split code smells in two categories: scattered smells and localized smells, since we expect the former to be more harmful than the latter. Our results provide some evidence that the survival of PHP code smells depends on their spreadness.
We have also analyzed whether the survival curve varies in the long term, for the same web application. Due to the increasing awareness on the potential harm-fulness of code smells, we expected to observe a reduction in the survival rate in the long term. The results show that there is indeed a change, for all applications except one, which lead us to consider that other factors should be analyzed in the future, to explain the phenomenon.info:eu-repo/semantics/acceptedVersio
On Budget-Feasible Mechanism Design for Symmetric Submodular Objectives
We study a class of procurement auctions with a budget constraint, where an
auctioneer is interested in buying resources or services from a set of agents.
Ideally, the auctioneer would like to select a subset of the resources so as to
maximize his valuation function, without exceeding a given budget. As the
resources are owned by strategic agents however, our overall goal is to design
mechanisms that are truthful, budget-feasible, and obtain a good approximation
to the optimal value. Budget-feasibility creates additional challenges, making
several approaches inapplicable in this setting. Previous results on
budget-feasible mechanisms have considered mostly monotone valuation functions.
In this work, we mainly focus on symmetric submodular valuations, a prominent
class of non-monotone submodular functions that includes cut functions. We
begin first with a purely algorithmic result, obtaining a
-approximation for maximizing symmetric submodular functions
under a budget constraint. We view this as a standalone result of independent
interest, as it is the best known factor achieved by a deterministic algorithm.
We then proceed to propose truthful, budget feasible mechanisms (both
deterministic and randomized), paying particular attention on the Budgeted Max
Cut problem. Our results significantly improve the known approximation ratios
for these objectives, while establishing polynomial running time for cases
where only exponential mechanisms were known. At the heart of our approach lies
an appropriate combination of local search algorithms with results for monotone
submodular valuations, applied to the derived local optima.Comment: A conference version appears in WINE 201
Visual Personal Familiarity in Amnestic Mild Cognitive Impairment
BACKGROUND: Patients with amnestic mild cognitive impairment are at high risk for developing Alzheimer's disease. Besides episodic memory dysfunction they show deficits in accessing contextual knowledge that further specifies a general concept or helps to identify an object or a person. METHODOLOGY/PRINCIPAL FINDINGS: Using functional magnetic resonance imaging, we investigated the neural networks associated with the perception of personal familiar faces and places in patients with amnestic mild cognitive impairment and healthy control subjects. Irrespective of stimulus type, patients compared to control subjects showed lower activity in right prefrontal brain regions when perceiving personally familiar versus unfamiliar faces and places. Both groups did not show different neural activity when perceiving faces or places irrespective of familiarity. CONCLUSIONS/SIGNIFICANCE: Our data highlight changes in a frontal cortical network associated with knowledge-based personal familiarity among patients with amnestic mild cognitive impairment. These changes could contribute to deficits in social cognition and may reduce the patients' ability to transition from basic to complex situations and tasks
- …