900 research outputs found
Least space-time first scheduling algorithm : scheduling complex tasks with hard deadline on parallel machines
Both time constraints and logical correctness are essential to real-time systems and failure to specify and observe a time constraint may result in disaster. Two orthogonal issues arise in the design and analysis of real-time systems: one is the specification of the system, and the semantic model describing the properties of real-time programs; the other is the scheduling and allocation of resources that may be shared by real-time program modules.
The problem of scheduling tasks with precedence and timing constraints onto a set of processors in a way that minimizes maximum tardiness is here considered. A new scheduling heuristic, Least Space Time First (LSTF), is proposed for this NP-Complete problem. Basic properties of LSTF are explored; for example, it is shown that (1) LSTF dominates Earliest-Deadline-First (EDF) for scheduling a set of tasks on a single processor (i.e., if a set of tasks are schedulable under EDF, they are also schedulable under LSTF); and (2) LSTF is more effective than EDF for scheduling a set of independent simple tasks on multiple processors.
Within an idealized framework, theoretical bounds on maximum tardiness for scheduling algorithms in general, and tighter bounds for LSTF in particular, are proven for worst case behavior. Furthermore, simulation benchmarks are developed, comparing the performance of LSTF with other scheduling disciplines for average case behavior.
Several techniques are introduced to integrate overhead (for example, scheduler and context switch) and more realistic assumptions (such as inter-processor communication cost) in various execution models. A workload generator and symbolic simulator have been implemented for comparing the performance of LSTF (and a variant -- LSTF+) with that of several standard scheduling algorithms.
LSTF\u27s execution model, basic theories, and overhead considerations have been defined and developed. Based upon the evidence, it is proposed that LSTF is a good and practical scheduling algorithm for building predictable, analyzable, and reliable complex real-time systems.
There remain some open issues to be explored, such as relaxing some current restrictions, discovering more properties and theorems of LSTF under different models, etc. We strongly believe that LSTF can be a practical scheduling algorithm in the near future
Multiply robust estimation for causal survival analysis with treatment noncompliance
Comparative effectiveness research with randomized trials or observational
studies frequently addresses a time-to-event outcome and can require unique
considerations in the presence of treatment noncompliance. Motivated by the
challenges in addressing noncompliance in the ADAPTABLE pragmatic trial, we
develop a multiply robust estimator to estimate the principal survival causal
effects under the principal ignorability and monotonicity assumption. The
multiply robust estimator involves several working models including that for
the treatment assignment, the compliance strata, censoring, and time to event
of interest. We demonstrate that the proposed estimator is consistent even if
one, and sometimes two, of the working models are incorrectly specified. We
further contribute sensitivity analysis strategies for investigating the
robustness of the multiply robust estimator under violation of two
identification assumptions specific to noncompliance. We implement the multiply
robust method in the ADAPTABLE trial to evaluate the effect of low- versus
high-dose aspirin assignment on patients' death and hospitalization from
cardiovascular diseases, and further obtain the causal effect estimates when
the identification assumptions fail to hold. We find that, comparing to
low-dose assignment, assignment to the high-dose leads to differential effects
among always high-dose takers, compliers, and always low-dose takers. Such
treatment effect heterogeneity contributes to the null intention-to-treatment
effect, and suggests that policy makers should design personalized strategies
based on potential compliance patterns to maximize treatment benefits to the
entire study population
On Investigating the Conservative Property of Score-Based Generative Models
Existing Score-based Generative Models (SGMs) can be categorized into
constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their
parameterization approaches. CSGMs model probability density functions as
Boltzmann distributions, and assign their predictions as the negative gradients
of some scalar-valued energy functions. On the other hand, USGMs employ
flexible architectures capable of directly estimating scores without the need
to explicitly model energy functions. In this paper, we demonstrate that the
architectural constraints of CSGMs may limit their modeling ability. In
addition, we show that USGMs' inability to preserve the property of
conservativeness may lead to degraded sampling performance in practice. To
address the above issues, we propose Quasi-Conservative Score-based Generative
Models (QCSGMs) for keeping the advantages of both CSGMs and USGMs. Our
theoretical derivations demonstrate that the training objective of QCSGMs can
be efficiently integrated into the training processes by leveraging the
Hutchinson trace estimator. In addition, our experimental results on the
CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets validate the effectiveness of
QCSGMs. Finally, we justify the advantage of QCSGMs using an example of a
one-layered autoencoder
N′-(3,4-Dimethoxybenzylidene)acetohydrazide
The asymmetric unit of the title compound, C11H14N2O3, contains two independent molecules with close conformations; the C=N—N—C torsion angle is 176.4 (1)° in both molecules. In the crystal, intermolecular N—H⋯O and C—H⋯O hydrogen bonds link the molecules into chains running along the [01] direction
Abnormal magnetoresistance behavior in Nb thin film with rectangular antidot lattice
Abnormal magnetoresistance behavior is found in superconducting Nb films
perforated with rectangular arrays of antidots (holes). Generally
magnetoresistance were always found to increase with increasing magnetic field.
Here we observed a reversal of this behavior for particular in low temperature
or current density. This phenomenon is due to a strong 'caging effect' which
interstitial vortices are strongly trapped among pinned multivortices.Comment: 4 pages, 2 figure
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