An Efficient Local Search for Partial Latin Square Extension Problem

A partial Latin square (PLS) is a partial assignment of n symbols to an nxn grid such that, in each row and in each column, each symbol appears at most once. The partial Latin square extension problem is an NP-hard problem that asks for a largest extension of a given PLS. In this paper we propose an efficient local search for this problem. We focus on the local search such that the neighborhood is defined by (p,q)-swap, i.e., removing exactly p symbols and then assigning symbols to at most q empty cells. For p in {1,2,3}, our neighborhood search algorithm finds an improved solution or concludes that no such solution exists in O(n^{p+1}) time. We also propose a novel swap operation, Trellis-swap, which is a generalization of (1,q)-swap and (2,q)-swap. Our Trellis-neighborhood search algorithm takes O(n^{3.5}) time to do the same thing. Using these neighborhood search algorithms, we design a prototype iterated local search algorithm and show its effectiveness in comparison with state-of-the-art optimization solvers such as IBM ILOG CPLEX and LocalSolver.Comment: 17 pages, 2 figure

Polynomial Time Construction for Spatially Balanced Latin Squares

In this paper we propose a construction that generates spatially balanced Latin squares (SBLSs) in polynomial time. These structures are central to the design of agronomic experiments, as they avoid biases that are otherwise unintentionally introduced due to spatial auto-correlation. Previous approaches were able to generate SBLSs of order up to 35 and required about two weeks of computation. Our algorithm runs in O(n2) and generates SBLSs of arbitrary order n where 2n + 1 is prime. For example, this algorithm generates a SBLS of order 999 in a fraction of a second.National Science Foundation (NSF Expeditions in Computing award for Computational Sustainability, grant 0832782; NSF IIS award, grant 0514429), Intelligent Information Systems Institute, Cornell University (Air Force O ce of Scienti c Research, AFOSR, grant FA9550-04-1-0151), Natural Sciences and Engineering Research Council of Canada (NSERC

Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility

The COVID-19 pandemic presents an unprecedented clinical and healthcare challenge for the many medical researchers who are attempting to prevent its worldwide spread. It also presents a challenge for statisticians involved in designing appropriate sampling plans to estimate the crucial parameters of the pandemic. These plans are necessary for monitoring and surveillance of the phenomenon and evaluating health policies. In this respect, we can use spatial information and aggregate data regarding the number of verifed infections (either hospitalized or in compulsory quarantine) to improve the standard two-stage sampling design broadly adopted for studying human populations. We present an optimal spatial sampling design based on spatially balanced sampling techniques. We prove its relative performance analytically in comparison to other competing sampling plans, and we also study its properties through a series of Monte Carlo experiments. Considering the optimal theoretical properties of the proposed sampling plan and its feasibility, we discuss suboptimal designs that approximate well optimality and are more readily applicable

Distant Healing Techniques and Distant Intercessory Prayer – A Tentative Scientific Conciliation

Currently there is a lack of a universally accepted theory that would constitute the base for the DH paradigm, and some fundamental issues about the mechanisms of DH remain non-responded. Even so, there is sparse documentation that intentions of one person can remotely influence mental and body functions of another person. With the available data, it becomes difficult to formulate an opinion about the validity of such techniques in healthcare. The question of DH may be put under the frame "Is the glass half empty or half full?". People who look at the issue of DH and see a half empty glass usually raise these points: Scientific evidence of benefit is poor, from scarce studies, many of them with methodological limitations; There is a lack of a coherent theory aligned to the ordinary reality based upon Newtonian science; Practical obstacles for healthcare include high variability of outcomes and low relevance of clinical effects. People who look at the issue of DH and see a half full glass usually raise these points: the positive results from some serious and well designed researches may indicate a possible hidden reality; emerging understanding of the mind and its non-local properties may explain the gap of distance; commitment to the patients\u27 claims for a humanistic, comprehensive and integrative healthcare. We may cite two poles of ignorance and the balanced position related to the discussion of DH. The first pole is the obstinate skepticism (arrogant and prejudiced attachment to materialism), that denies the full half. The opposite pole is the naive mysticism (unrealistic trust on paranormal potentialities), that denies the empty half. The balanced position is called here the option for the open-minded scientificism. Some opportunities of advancement in this field would arise from these points: new and adequate research designs complying with limitations of the phenomenon; the progressive consolidation of a new, post-materialist scientific paradigm; optimizing the efficacy of the phenomenon knowing better its interfering factors

Policy and Place: A Spatial Data Science Framework for Research and Decision-Making

abstract: A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation. The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.Dissertation/ThesisDoctoral Dissertation Geography 201

Design of a Transceive Coil Array for Parallel Imaging at 9.4T

The main goal of this thesis is to design and develop a transmit/receive (transceive) coil array for small animal imaging at 9.4T. The goal is achieved by following basic RF design principles with a methodical construction approach and demonstrating viable applications. As operational frequencies increase linearly with higher static fields, the wavelength approaches the size of the sample being imaged. The resulting standing wave mode deteriorates image homogeneity. Fortunately, with multi-channel coil arrays, the produced Bi field can be tailored to produce a homogeneous excitation in the region of interest, thus overcoming the so called dielectric resonance effect. We examined a solution to achieve a higher level of Bx homogeneity and we compared the improvement of RF wavelength effects reduction against the results obtained with a similar-sized conventional birdcage coil. An additional benefit of this design lies in the fact that the use of multiple receiving coil elements is necessary for the implementation of fast imaging acquisition techniques such as parallel imaging. This is possible because the distinct element sensitivities are used to reconstruct conventional images from undersampled (or accelerated) data. The greatest advantage of parallel imaging is thus the reduction of total acquisition time. In functional MRI (fMRI), single-shot EPI is one of the standard imaging technique. Unfortunately, EPI suffers from significant limitations, precisely because all of the data is acquired following a single RF excitation. As a result EPI images can manifest artifacts and blurring due to susceptibility mismatch, off-resonance effects and reduced signal at the edges of k-space. Fortunately, parallel imaging can be used to decrease such unwanted effects by reducing the total k-space data acquired. Presented in this thesis is the logical progression of the construction of a transceive coil from surface coil fundamentals to high field applications such as field focusing and parallel imaging techniques

Accurate molecular imaging of small animals taking into account animal models, handling, anaesthesia, quality control and imaging system performance

Small-animal imaging has become an important technique for the development of new radiotracers, drugs and therapies. Many laboratories have now a combination of different small-animal imaging systems, which are being used by biologists, pharmacists, medical doctors and physicists. The aim of this paper is to give an overview of the important factors in the design of a small animal, nuclear medicine and imaging experiment. Different experts summarize one specific aspect important for a good design of a small-animal experiment