The alldifferent Constraint: A Survey

The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several solving strategies have been designed for this constraint. In this paper we give both a practical overview and an abstract comparison of these different strategies.Comment: 12 pages, 3 figures, paper accepted at the 6th Annual workshop of the ERCIM Working Group on Constraint

Decomposition Based Search - A theoretical and experimental evaluation

In this paper we present and evaluate a search strategy called Decomposition Based Search (DBS) which is based on two steps: subproblem generation and subproblem solution. The generation of subproblems is done through value ranking and domain splitting. Subdomains are explored so as to generate, according to the heuristic chosen, promising subproblems first. We show that two well known search strategies, Limited Discrepancy Search (LDS) and Iterative Broadening (IB), can be seen as special cases of DBS. First we present a tuning of DBS that visits the same search nodes as IB, but avoids restarts. Then we compare both theoretically and computationally DBS and LDS using the same heuristic. We prove that DBS has a higher probability of being successful than LDS on a comparable number of nodes, under realistic assumptions. Experiments on a constraint satisfaction problem and an optimization problem show that DBS is indeed very effective if compared to LDS.Comment: 16 pages, 8 figures. LIA Technical Report LIA00203, University of Bologna, 200

Conjunctions of Among Constraints

Many existing global constraints can be encoded as a conjunction of among constraints. An among constraint holds if the number of the variables in its scope whose value belongs to a prespecified set, which we call its range, is within some given bounds. It is known that domain filtering algorithms can benefit from reasoning about the interaction of among constraints so that values can be filtered out taking into consideration several among constraints simultaneously. The present pa- per embarks into a systematic investigation on the circumstances under which it is possible to obtain efficient and complete domain filtering algorithms for conjunctions of among constraints. We start by observing that restrictions on both the scope and the range of the among constraints are necessary to obtain meaningful results. Then, we derive a domain flow-based filtering algorithm and present several applications. In particular, it is shown that the algorithm unifies and generalizes several previous existing results.Comment: 15 pages plus appendi

Constraint Programming for LNG Ship Scheduling and Inventory Management

Abstract We propose a constraint programming approach for the optimization of inventory routing in the liquefied natural gas industry. We present two constraint programming models that rely on a disjunctive scheduling representation of the problem. We also propose an iterative search heuristic to generate good feasible solutions for these models. Computational results on a set of largescale test instances demonstrate that our approach can find better solutions than existing approaches based on mixed integer programming, while being 4 to 10 times faster on average

Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading system. It was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the observers (o1, o2 and o3) ($\kappa_{o1,dl}=0.81, \kappa_{o2,dl}=0.53, \kappa_{o3,dl}=0.40$) than the observers amongst each other ($\kappa_{o1,o2}=0.58, \kappa_{o1,o3}=0.50, \kappa_{o2,o3}=0.42$) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers ($\kappa_{o1,dl}=0.77, \kappa_{o2,dl}=0.75, \kappa_{o3,dl}=0.70$) as the observers amongst each other ($\kappa_{o1,o2}=0.77, \kappa_{o1,o3}=0.75, \kappa_{o2,o3}=0.72$). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade

Continuity of care for patients with de novo metastatic cancer during the COVID-19 pandemic:A population-based observational study

During the COVID-19 pandemic recommendations were made to adapt cancer care. This population-based study aimed to investigate possible differences between the treatment of patients with metastatic cancer before and during the pandemic by comparing the initial treatments in five COVID-19 periods (weeks 1–12 2020: pre-COVID-19, weeks 12–20 2020: 1st peak, weeks 21–41 2020: recovery, weeks 42–53 2020: 2nd peak, weeks 1–20 2021: prolonged 2nd peak) with reference data from 2017 to 2019. The proportion of patients receiving different treatment modalities (chemotherapy, hormonal therapy, immunotherapy or targeted therapy, radiotherapy primary tumor, resection primary tumor, resection metastases) within 6 weeks of diagnosis and the time between diagnosis and first treatment were compared by period. In total, 74,208 patients were included. Overall, patients were more likely to receive treatments in the COVID-19 periods than in previous years. This mainly holds for hormone therapy, immunotherapy or targeted therapy and resection of metastases. Lower odds were observed for resection of the primary tumor during the recovery period (OR 0.87; 95% CI 0.77–0.99) and for radiotherapy on the primary tumor during the prolonged 2nd peak (OR 0.84; 95% CI 0.72–0.98). The time from diagnosis to the start of first treatment was shorter, mainly during the 1st peak (average 5 days, p &lt;.001). These findings show that during the first 1.5 years of the COVID-19 pandemic, there were only minor changes in the initial treatment of metastatic cancer. Remarkably, time from diagnosis to first treatment was shorter. Overall, the results suggest continuity of care for patients with metastatic cancer during the pandemic.</p

Combining Symmetry Breaking and Global Constraints

Abstract. We propose a new family of constraints which combine together lexicographical ordering constraints for symmetry breaking with other common global constraints. We give a general purpose propagator for this family of constraints, and show how to improve its complexity by exploiting properties of the included global constraints.

Population-based impact of COVID-19 on incidence, treatment, and survival of patients with pancreatic cancer

Background: The COVID-19 pandemic has put substantial strain on the healthcare system of which the effects are only partly elucidated. This study aimed to investigate the impact on pancreatic cancer care. Methods: All patients diagnosed with pancreatic cancer between 2017 and 2020 were selected from the Netherlands Cancer Registry. Patients diagnosed and/or treated in 2020 were compared to 2017–2019. Monthly incidence was calculated. Patient, tumor and treatment characteristics were analyzed and compared using Chi-squared tests. Survival data was analyzed using Kaplan–Meier and Log-rank tests. Results: In total, 11019 patients were assessed. The incidence in quarter (Q)2 of 2020 was comparable with that in Q2 of 2017–2019 (p = 0.804). However, the incidence increased in Q4 of 2020 (p = 0.031), mainly due to a higher incidence of metastatic disease (p = 0.010). Baseline characteristics, surgical resection (15% vs 16%; p = 0.466) and palliative systemic therapy rates (23% vs 24%; p = 0.183) were comparable. In 2020, more surgically treated patients received (neo)adjuvant treatment compared to 2017–2019 (73% vs 67%; p = 0.041). Median overall survival was comparable (3.8 vs 3.8 months; p = 0.065). Conclusion: This nationwide study found a minor impact of the COVID-19 pandemic on pancreatic cancer care and outcome. The Dutch health care system was apparently able to maintain essential care for patients with pancreatic cancer

Impact of tissue adhesives on the prevention of anastomotic leakage of colonic anastomoses: an in vivo study

Background: Tissue adhesives (TA) may be useful to strengthen colorectal anastomoses, thereby preventing anastomotic leakage (AL). Previous studies have identified cyanoacrylate (CA) TAs as the most promising colonic anastomotic sealants. This stud