Online Makespan Minimization with Parallel Schedules

In online makespan minimization a sequence of jobs $\sigma = J_1,..., J_n$ has to be scheduled on $m$ identical parallel machines so as to minimize the maximum completion time of any job. We investigate the problem with an essentially new model of resource augmentation. Here, an online algorithm is allowed to build several schedules in parallel while processing $\sigma$. At the end of the scheduling process the best schedule is selected. This model can be viewed as providing an online algorithm with extra space, which is invested to maintain multiple solutions. The setting is of particular interest in parallel processing environments where each processor can maintain a single or a small set of solutions. We develop a (4/3+\eps)-competitive algorithm, for any 0<\eps\leq 1, that uses a number of 1/\eps^{O(\log (1/\eps))} schedules. We also give a (1+\eps)-competitive algorithm, for any 0<\eps\leq 1, that builds a polynomial number of (m/\eps)^{O(\log (1/\eps) / \eps)} schedules. This value depends on $m$ but is independent of the input $\sigma$. The performance guarantees are nearly best possible. We show that any algorithm that achieves a competitiveness smaller than 4/3 must construct $\Omega(m)$ schedules. Our algorithms make use of novel guessing schemes that (1) predict the optimum makespan of a job sequence $\sigma$ to within a factor of 1+\eps and (2) guess the job processing times and their frequencies in $\sigma$. In (2) we have to sparsify the universe of all guesses so as to reduce the number of schedules to a constant. The competitive ratios achieved using parallel schedules are considerably smaller than those in the standard problem without resource augmentation

Greedy D-Approximation Algorithm for Covering with Arbitrary Constraints and Submodular Cost

This paper describes a simple greedy D-approximation algorithm for any covering problem whose objective function is submodular and non-decreasing, and whose feasible region can be expressed as the intersection of arbitrary (closed upwards) covering constraints, each of which constrains at most D variables of the problem. (A simple example is Vertex Cover, with D = 2.) The algorithm generalizes previous approximation algorithms for fundamental covering problems and online paging and caching problems

Role of the νg9/2 orbital in the development of collectivity in the A≈60 region: The case of Co 61

An extensive study of the level structure of Co61 has been performed following the complex Mg26(Ca48,2α4npγ)Co61 reaction at beam energies of 275, 290, and 320 MeV using Gammasphere and the Fragment Mass Analyzer (FMA). The low-spin structure is discussed within the framework of shell-model calculations using the GXPF1A effective interaction. Two quasirotational bands consisting of stretched-E2 transitions have been established up to spins I=41/2 and (43/2), and excitation energies of ∼17 and ∼20 MeV, respectively. These are interpreted as signature partners built on a neutron ν(g9/2)2 configuration coupled to a proton πp3/2 state, based on cranked shell model (CSM) calculations and comparisons with observations in neighboring nuclei. In addition, four ΔI=1 bands were populated to high spin, with the yrast dipole band interpreted as a possible candidate for the shears mechanism, a process seldom observed thus far in this mass region

Periprosthetic Infection: Major Cause of Early Failure of Primary and Revision Total Knee Arthroplasty

Revision total knee arthroplasty (RTKA) represents an effective treatment for failed TKA, but with less favorable outcomes. Considering the technical complexity and economic burden of RTKA procedures, it is mandatory to investigate current mechanisms and predictors for RTKA failure. The objective of this study is to evaluate the survivorship and determine the predominant causes of failure of RTKA. A total of 146 patients undergoing RTKA between 2003 and 2013 were identified from the institutional database. Revision was defined as surgery in which the whole prostheses (inlay and both femoral and tibial components) required exchange. Median follow-up was 6.3 ± 2.7 years (range: 2.2-10). Patient demographics, year of primary implantation, reasons for revision surgery, implant type, pain, knee mobility, systemic or local postoperative complications, and treatment of the complications were recorded and evaluated. Infection was a major cause of failure followed by aseptic loosening, instability, pain, malalignment, and inlay wear. Following RTKA, Knee Society Score (KSS) (knee score and functional score) demonstrated a significant improvement (p &lt; 0.05). No significant difference in flexion, extension deficit, and KSS was detected between aseptic and septic primary TKAs preoperatively and following first RTKA. Reinfection rate of the septic primary TKAs was 5%. Infection was the major cause of a second revision, reaching as high as 50% in all cases. The results of this study support that septic failure of a primary TKA is likely to occur within the first 2 years following implantation. Septic failure of primary TKA does not influence survival of the revision prosthesis. © 2019 Georg Thieme Verlag. All rights reserved

Bi-objective approximation scheme for makespan and reliability optimization on uniform parallel machines

We study the problem of scheduling independent tasks on a\ud set of related processors which have a probability of failure governed by\ud an exponential law. We are interested in the bi-objective analysis, namely\ud simultaneous optimization of the makespan and the reliability. We show\ud that this problem can not be approximated by a single schedule. A sim-\ud ilar problem has already been studied leading to a ¯ 1 -approximation\ud ¸\ud 2,\ud algorithm (i.e. for any fixed value of the makespan, the obtained solution\ud is optimal on the reliability and no more than twice the given makespan).\ud We provide an algorithm which has a much lower complexity. This solu-\ud tion is finally used to derive a (2 + , 1)-approximation of the Pareto set\ud of the problem, for any &gt; 0.\u

Online Scheduling with Bounded Migration

Consider the classical online scheduling problem where jobs that arrive one by one are assigned to identical parallel machines with the objective of minimizing the makespan. We generalize this problem by allowing the current assignment to be changed whenever a new job arrives, subject to the constraint that the total size of moved jobs is bounded by times the size of the arriving job. Our mai

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict ordinal 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by fusing a Deep Learning model of diffusion-weighted imaging images and clinical information from the acute period. A total of 640 acute ischemic stroke patients who underwent magnetic resonance imaging within 1 to 7 days poststroke and had 90-day mRS follow-up data were randomly divided into 70% (n=448) for model training, 15% (n=96) for validation, and 15% (n=96) for internal testing. Additionally, external testing on a cohort from Lausanne University Hospital (n=280) was performed to further evaluate model generalization. Accuracy for ordinal mRS, accuracy within ±1 mRS category, mean absolute prediction error, and determination of unfavorable outcome (mRS score &gt;2) were evaluated for clinical only, imaging only, and 2 fused clinical-imaging models. The fused models demonstrated superior performance in predicting ordinal mRS score and unfavorable outcome in both internal and external test cohorts when compared with the clinical and imaging models. For the internal test cohort, the top fused model had the highest area under the curve of 0.92 for unfavorable outcome prediction and the lowest mean absolute error (0.96 [95% CI, 0.77-1.16]), with the highest proportion of mRS score predictions within ±1 category (79% [95% CI, 71%-88%]). On the external Lausanne University Hospital cohort, the best fused model had an area under the curve of 0.90 for unfavorable outcome prediction and outperformed other models with an mean absolute error of 0.90 (95% CI, 0.79-1.01), and the highest percentage of mRS score predictions within ±1 category (83% [95% CI, 78%-87%]). A Deep Learning-based imaging model fused with clinical variables can be used to predict 90-day stroke outcome with reduced subjectivity and user burden

Life History and Ecology of Coyotes in the Mid-Atlantic States: A Summary of the Scientifi c Literature

Relatively little information has been published on Coyotes in the eastern United States, particularly in the mid-Atlantic region, the last area of the contiguous US to be colonized by Coyotes. Increases in eastern Coyote distribution and abundance have been documented, and concerns about their impact on wildlife and livestock are growing. Information from published and unpublished manuscripts, theses, dissertations, and state wildlife agency records in the mid-Atlantic region were examined and synthesized. This review provides a comprehensive summary of Coyote ecology in the mid-Atlantic for natural resource managers and researchers