The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

All-optical coherent population trapping with defect spin ensembles in silicon carbide

Divacancy defects in silicon carbide have long-lived electronic spin states and sharp optical transitions, with properties that are similar to the nitrogen-vacancy defect in diamond. We report experiments on 4H-SiC that investigate all-optical addressing of spin states with the zero-phonon-line transitions. Our magneto-spectroscopy results identify the spin $S=1$ structure of the ground and excited state, and a role for decay via intersystem crossing. We use these results for demonstrating coherent population trapping of spin states with divacancy ensembles that have particular orientations in the SiC crystal.Comment: 28 page document: Pages 1-14 main text (with 3 figures); pages 15-28 supplementary information (with 5 figues). v2 has minor correction

A Network Topology Approach to Bot Classification

Automated social agents, or bots, are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%

Improving DRAM Performance by Parallelizing Refreshes with Accesses

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. However, there are two shortcomings of per-bank refresh. First, the per-bank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations show that our mechanisms improve system performance and energy efficiency compared to state-of-the-art refresh policies and the benefit increases as DRAM density increases.Comment: The original paper published in the International Symposium on High-Performance Computer Architecture (HPCA) contains an error. The arxiv version has an erratum that describes the error and the fix for i

Benefit Plan Design and Prescription Drug Utilization Among Asthmatics: Do Patient Copayments Matter?

Objective: The ratio of controller to reliever medication use has been proposed as a measure of treatment quality for asthma patients. In this study we examine the effects of plan level mean out-of-pocket asthma medication patient copayments and other features of benefit plan design on the use of controller medications alone, controller and reliever medications (combination therapy), and reliever medications alone. Methods: 1995-2000 MarketScan claims data were used to construct plan-level out-of-pocket copayment and physician/practice prescriber preference variables for asthma medications. Separate multinomial logit models were estimated for patients in fee-for-service (FFS) and non-FFS plans relating benefit plan design features, physician/practice prescribing preferences, patient demographics, patient comorbidities and county-level income variables to patient-level asthma treatment patterns. Results: We find that the controller reliever ratio rose steadily over 1995-2000, along with out-of-pocket payments for asthma medications, which rose more for controllers than for relievers. However, after controlling for other variables, plan level mean out-of-pocket copayments were not found to have a statistically significant influence upon patient-level asthma treatment patterns. On the other hand, physician practice prescribing patterns strongly influenced patient level treatment patterns. Conclusions: There is no strong statistical evidence that higher levels of out-of-pocket copayments for prescription drugs influence asthma treatment patterns. However, physician/practice prescribing preferences influence patient treatment.

Stabilizing nuclear spins around semiconductor electrons via the interplay of optical coherent population trapping and dynamic nuclear polarization

We experimentally demonstrate how coherent population trapping (CPT) for donor-bound electron spins in GaAs results in autonomous feedback that prepares stabilized states for the spin polarization of nuclei around the electrons. CPT was realized by excitation with two lasers to a bound-exciton state. Transmission studies of the spectral CPT feature on an ensemble of electrons directly reveal the statistical distribution of prepared nuclear spin states. Tuning the laser driving from blue to red detuned drives a transition from one to two stable states. Our results have importance for ongoing research on schemes for dynamic nuclear spin polarization, the central spin problem and control of spin coherence.Comment: 5 pages, 4 figure