Adiabatic Quantum Graph Matching with Permutation Matrix Constraints

Matching problems on 3D shapes and images are challenging as they are frequently formulated as combinatorial quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard. In this work, we address such problems with emerging quantum computing technology and propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware. We investigate several ways to inject permutation matrix constraints in a quadratic unconstrained binary optimization problem which can be mapped to quantum hardware. We focus on obtaining a sufficient spectral gap, which further increases the probability to measure optimal solutions and valid permutation matrices in a single run. We perform our experiments on the quantum computer D-Wave 2000Q (2^11 qubits, adiabatic). Despite the observed discrepancy between simulated adiabatic quantum computing and execution on real quantum hardware, our reformulation of permutation matrix constraints increases the robustness of the numerical computations over other penalty approaches in our experiments. The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures, which opens up multiple new directions for solving matching problems in 3D computer vision and graphics

QuAnt: Quantum Annealing with Learnt Couplings

Modern quantum annealers can find high-quality solutions to combinatorialoptimisation objectives given as quadratic unconstrained binary optimisation(QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computervision remains challenging and currently requires problem-specific analyticalderivations. Moreover, such explicit formulations impose tangible constraintson solution encodings. In stark contrast to prior work, this paper proposes tolearn QUBO forms from data through gradient backpropagation instead of derivingthem. As a result, the solution encodings can be chosen flexibly and compactly.Furthermore, our methodology is general and virtually independent of thespecifics of the target problem type. We demonstrate the advantages of learntQUBOs on the diverse problem types of graph matching, 2D point cloud alignmentand 3D rotation estimation. Our results are competitive with the previousquantum state of the art while requiring much fewer logical and physicalqubits, enabling our method to scale to larger problems. The code and the newdataset will be open-sourced.<br

Q-{M}atch: {I}terative Shape Matching via Quantum Annealing

Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density. A promising research direction is to tackle such quadratic optimization problems over binary variables with quantum annealing, which allows for some problems a more efficient search in the solution space. Unfortunately, enforcing the linear equality constraints in QAPs via a penalty significantly limits the success probability of such methods on currently available quantum hardware. To address this limitation, this paper proposes Q-Match, i.e., a new iterative quantum method for QAPs inspired by the alpha-expansion algorithm, which allows solving problems of an order of magnitude larger than current quantum methods. It implicitly enforces the QAP constraints by updating the current estimates in a cyclic fashion. Further, Q-Match can be applied iteratively, on a subset of well-chosen correspondences, allowing us to scale to real-world problems. Using the latest quantum annealer, the D-Wave Advantage, we evaluate the proposed method on a subset of QAPLIB as well as on isometric shape matching problems from the FAUST dataset

Developing User‐Friendly Habitat Suitability Tools from Regional Stream Fish Survey Data

We developed user‐friendly fish habitat suitability tools (plots) for fishery managers in Michigan; these tools are based on driving habitat variables and fish population estimates for several hundred stream sites throughout the state. We generated contour plots to show patterns in fish biomass for over 60 common species (and for 120 species grouped at the family level) in relation to axes of catchment area and low‐flow yield (90% exceedance flow divided by catchment area) and also in relation to axes of mean and weekly range of July temperatures. The plots showed distinct patterns in fish habitat suitability at each level of biological organization studied and were useful for quantitatively comparing river sites. We demonstrate how these plots can be used to support stream management, and we provide examples pertaining to resource assessment, trout stocking, angling regulations, chemical reclamation of marginal trout streams, indicator species, instream flow protection, and habitat restoration. These straightforward and effective tools are electronically available so that managers can easily access and incorporate them into decision protocols and presentations.Received April 9, 2010; accepted November 8, 2010Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141005/1/nafm0041.pd

Predicting Future Changes in Muskegon River Watershed Game Fish Distributions under Future Land Cover Alteration and Climate Change Scenarios

Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool‐ and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n‐dimensional niches for particular species.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141570/1/tafs0396.pd

NMDAR1 autoantibodies amplify behavioral phenotypes of genetic white matter inflammation: a mild encephalitis model with neuropsychiatric relevance

Encephalitis has an estimated prevalence of ≤0.01%. Even with extensive diagnostic work-up, an infectious etiology is identified or suspected in <50% of cases, suggesting a role for etiologically unclear, noninfectious processes. Mild encephalitis runs frequently unnoticed, despite slight neuroinflammation detectable postmortem in many neuropsychiatric illnesses. A widely unexplored field in humans, though clearly documented in rodents, is genetic brain inflammation, particularly that associated with myelin abnormalities, inducing primary white matter encephalitis. We hypothesized that “autoimmune encephalitides” may result from any brain inflammation concurring with the presence of brain antigen-directed autoantibodies, e.g., against N-methyl-D-aspartate-receptor NR1 (NMDAR1-AB), which are not causal of, but may considerably shape the encephalitis phenotype. We therefore immunized young female Cnp−/− mice lacking the structural myelin protein 2′-3′-cyclic nucleotide 3′-phosphodiesterase (Cnp) with a “cocktail” of NMDAR1 peptides. Cnp−/− mice exhibit early low-grade inflammation of white matter tracts and blood–brain barrier disruption. Our novel mental-time-travel test disclosed that Cnp−/− mice are compromised in what–where–when orientation, but this episodic memory readout was not further deteriorated by NMDAR1-AB. In contrast, comparing wild-type and Cnp−/− mice without/with NMDAR1-AB regarding hippocampal learning/memory and motor balance/coordination revealed distinct stair patterns of behavioral pathology. To elucidate a potential contribution of oligodendroglial NMDAR downregulation to NMDAR1-AB effects, we generated conditional NR1 knockout mice. These mice displayed normal Morris water maze and mental-time-travel, but beam balance performance was similar to immunized Cnp−/−. Immunohistochemistry confirmed neuroinflammation/neurodegeneration in Cnp−/− mice, yet without add-on effect of NMDAR1-AB. To conclude, genetic brain inflammation may explain an encephalitic component underlying autoimmune conditions

Parents’ intention to get vaccinated and to have their child vaccinated against COVID-19: cross-sectional analyses using data from the KUNO-Kids health study

A COVID-19 vaccine can be an important key for mitigating the spread of the pandemic, provided that it is accepted by a sufficient proportion of the population. This study investigated parents’ intention to get vaccinated and to have one’s child vaccinated against COVID-19. In May 2020, 612 parents participating with their child in the KUNO-Kids health study completed an online survey. Multivariable logistic regression models were calculated to analyze predictors of intention to vaccinate. Fifty-eight percent of parents intended to get vaccinated against COVID-19, and 51% intended to have their child vaccinated. Significant predictors for the intention to get vaccinated and for having the child vaccinated included stronger parental confidence in one’s knowledge about prevention measures and lower beliefs that policy measures were exaggerated. Conclusion: COVID-19 vaccination hesitancy was considerable in our sample of parents in Germany. However, our study revealed some potentially modifiable factors which should be addressed by a comprehensive and tailored communication and education strategy

PREDICTING THE SUMMER TEMPERATURE OF SMALL STREAMS IN SOUTHWESTERN WISCONSIN 1

One of the biggest challenges in managing cold water streams in the Midwest is understanding how stream temperature is controlled by the complex interactions among meteorologic processes, channel geometry, and ground water inflow. Inflow of cold ground water, shade provided by riparian vegetation, and channel width are the most important factors controlling summer stream temperatures. A simple screening model was used to quantitatively evaluate the importance of these factors and guide management decisions. The model uses an analytical solution to the heat transport equation to predict steady-state temperature throughout a stream reach. The model matches field data from four streams in southwestern Wisconsin quite well (typically within 1°C) and helps explain the observed warming and cooling trends along each stream reach. The distribution of ground water inflow throughout a stream reach has an important influence on stream temperature, and springs are especially effective at providing thermal refuge for fish. Although simple, this model provides insight into the importance of ground water and the impact different management strategies, such as planting trees to increase shade, may have on summer stream temperature.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74032/1/j.1752-1688.2005.tb03714.x.pd