Mia Persson

Towards more efficient infection and fire fighting

The firefighter problem models the situation where an infection, a computer virus, an idea or fire etc. is spreading through a network and the goal is to save as many as possible nodes of the network through targeted vaccinations. The number of nodes that can be vaccinated at a single time-step is typically one, or more generally O(1). In a non-standard model, the so called spreading model, the vaccinations also spread in contrast to the standard model. Our main results are concerned with general graphs in the spreading model. We provide a very simple exact 2O(√n log n)-time algorithm. In the special case of trees, where the standard and spreading model are equivalent, our algorithm is substantially simpler than that exact subexponential algorithm for trees presented in (Cai et al. 2008). On the other hand, we show that the firefighter problem on weighted directed graphs in the spreading model cannot be approximated within a constant factor better than 1−1/e unless NP ⊆ DTIME(nO(log log n)). We also present several results in the standard model. We provide approximation algorithms for planar graphs in case when at least two vaccinations can be performed at a time-step. We also derive trade-offs between approximation factors for polynomial-time solutions and the time complexity of exact or nearly exact solutions for instances of the firefighter problem for the so called directed layered graphs.

Impacts of smoking on endocrine treatment response in a prospective breast cancer cohort

Background:The association between smoking and breast cancer prognosis remains unclear. The purpose of this study was to investigate whether preoperative smoking was associated with prognosis in different treatment groups.Methods:This population-based cohort consisted of 1065 breast cancer patients without preoperative treatment included between 2002 and 2012 in Lund, Sweden. Smoking status was examined in relation to patient and tumour characteristics, and prognosis in different treatment groups.Results:At the preoperative visit, 21.0% smoked. Median follow-up time was 5.1 years. Overall, in the 1016 patients included in the survival analyses, there was no significant association between smoking and risk of breast cancer events (adjusted hazard ratio (adjHR): 1.45; 95% confidence interval (CI): 0.95–2.20). For the 309 aromatase inhibitor (AI)-treated patients ⩾50 years with oestrogen receptor-positive (ER+) tumours, smoking was associated with risk of breast cancer events (adjHR: 2.97; 95% CI: 1.44–6.13), distant metastasis (adjHR: 4.19; 95% CI: 1.81–9.72), and death (adjHR: 3.52; 95% CI: 1.59–7.81). Smoking was not associated with breast cancer events or distant metastasis in other treatment groups.Conclusions:Preoperative smoking was only associated with an increased risk for breast cancer events and distant metastasis in AI-treated patients. If confirmed, smoking status should be taken into consideration when selecting an endocrine therapy.

Competitive exploration of rectilinear polygons

Exploring a polygon with robots when the robots do not have knowledge of the surroundings can be viewed as an online problem. Typical for online problems is that decisions must be made based on past events without complete information about the future. In our case the robots do not have complete information about the environment. Competitive analysis can be used to measure the performance of methods solving online problems. The competitive ratio of such a method is the ratio between the methods performance and the performance of the best method having full knowledge of the future. We prove constant competitive strategies and lower bounds for exploring a simple rectilinear polygon in the L1 metric.

Competitive Online Clique Clustering

Clique clustering is the problem of partitioning a graph into cliques so that some objective function is optimized. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The objective here is to maintain a clustering the never deviates too far in the objective function compared to the optimal solution. We give a constant competitive upper bound for online clique clustering, where the objective function is to maximize the number of edges inside the clusters. We also give almost matching upper and lower bounds on the competitive ratio for online clique clustering, where we want to minimize the number of edges between clusters. In addition, we prove that the greedy method only gives linear competitive ratio for these problems.

Iterative merging heuristics for correlation clustering

A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyse the approximation and complexity aspects of this heuristic and its three simple deterministic or random refinements.

A new improved distributed e-healthcare system based on open standards for depression treatment

The use of open standards to promote responsible and available design in Internet-based healthcare systems is examined by using theoretical notations and recent research results from the literature. Moreover, empirical data will be gathered from a case study on an e-healthcare system for depression treatment. Our contribution is to initiate a discussion aiming at increasing the awareness on appropriate conceptual models for Internet-based depression treatment. Moreover, an up-to-date e-healthcare system, based on open standards and implemented with requirements of an Internet-based system treating patients suffering from depressions, is proposed.

Approximate clustering of incomplete fingerprints

We study the problem of clustering fingerprints with at most p missing values (CMV(p) for short) naturally arising in oligonucleotide fingerprinting, which is an efficient method for characterizing DNA clone libraries. We show that already CMV(2) is NP-hard. We also show that a greedy algorithm yields a min(1+lnn,2+plnl) approximation for CMV(p), and can be implemented to run in O(nl2^p) time. We also introduce other variants of the problem of clustering incomplete fingerprints based on slightly different optimization criteria and show that they can be approximated in polynomial time with ratios 2^2^p^-^1 and 2(1-12^2^p), respectively.

Bounds for Semi-disjoint Bilinear Forms in a Unit-Cost Computational Model

We study the complexity of the so called semi-disjoint bilinear forms over different semi-rings, in particular the n-dimensional vector convolution and \(n\times n\) matrix product. We consider a powerful unit-cost computational model over the ring of integers allowing for several additional operations and generation of large integers. We show the following dichotomy for such a powerful model: while almost all arithmetic semi-disjoint bilinear forms have the same asymptotic time complexity as that yielded by naive algorithms, matrix multiplication, the so called distance matrix product, and vector convolution can be solved in a linear number of steps. It follows in particular that in order to obtain a non-trivial lower bounds for these three basic problems one has to assume restrictions on the set of allowed operations and/or the size of used integers.

The Snow Team Problem

We study several problems of clearing subgraphs by mobile agents in digraphs. The agents can move only along directed walks of a digraph and, depending on the variant, their initial positions may be pre-specified. In general, for a given subset \(\mathcal {S}\) of vertices of a digraph D and a positive integer k, the objective is to determine whether there is a subgraph \(H=(\mathcal {V}_H,\mathcal {A}_H)\) of D such that (a) \(\mathcal {S}\subseteq \mathcal {V}_H\), (b) H is the union of k directed walks in D, and (c) the underlying graph of H includes a Steiner tree for \(\mathcal {S}\). We provide several results on parameterized complexity and hardness of the problems.

Abstract P5-08-29: Tamoxifen or aromatase inhibitors: Should smoking status impact on selection of endocrine therapy in breast cancer patients?

Introduction: The association between smoking and breast cancer prognosis has been investigated in several studies but remains unclear. To our knowledge, no study has investigated whether the response to different endocrine treatments differs between smokers and non-smokers. Smoking can suppress aromatase activity, but also increase inflammation, which may lead to higher activity. The aim was to investigate whether preoperative smoking was associated with risk of breast cancer events in endocrine-treated patients. Patients and methods: This population based cohort consisted of 1026 female breast cancer patients with invasive tumors and no preoperative treatment who were enrolled in an ongoing prospective cohort study at Skane University Hospital in Lund between October 2002 and June 2012. Pre- and postoperative questionnaires regarding lifestyle factors, including smoking status, and treatments were completed. Information on tumor characteristics, treatments, and dates for new breast cancer events or deaths was obtained from pathology reports, patients9 charts and population registers. A breast cancer event was defined as local or regional recurrence, contralateral breast cancer, or distant metastasis. Results: For the survival analyses, two patients were excluded due to missing information on smoking and eight patients were excluded due to metastatic spread within 0.3 years of inclusion, leaving 1016 patients of which 206 (20.3%) reported preoperative smoking. Less than 1% of the 810 preoperative non-smokers reported smoking at either the 3-6-months or 1-year postoperative visits, while about 10% of the patients who smoked preoperatively reported not to smoke during the follow-up visits. Thus, the majority of the patients did not switch smoking status. Patients were followed for up to 11 years (median 5.1 years for patients still at risk). Overall, there was no significant association between smoking and risk of breast cancer events (adjusted hazard ratio (HR) 1.45: 95% CI 0.95-2.20; P=0.08) adjusted for patient and tumor characteristics. For the 408 tamoxifen-treated patients aged 50 years or older with estrogen receptor positive tumors, smoking was not significantly associated with risk for early events (adjusted HR 1.58: 95% CI 0.76-3.30; P=0.22). However, for the 309 aromatase inhibitor-treated patients aged 50 years or older with estrogen receptor positive tumors, smoking was significantly associated with a 3-fold increased risk of breast cancer events (adjusted HR 2.97: 95% CI 1.44-6.12; P=0.003). Some patients had been treated with sequential tamoxifen and aromatase inhibitor therapy. Conclusions: Preoperative smoking was associated with a significantly increased risk for breast cancer events among patients treated with aromatase inhibitors, but not among tamoxifen-treated patients. If confirmed, smoking status should be taken into consideration when selecting endocrine therapy. Citation Format: Jernstrom H, Persson M, Simonsson M, Markkula A, Rose C, Ingvar C. Tamoxifen or aromatase inhibitors: Should smoking status impact on selection of endocrine therapy in breast cancer patients?. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-08-29.