Scan Integration as a Labeling Problem

Integration is a crucial step in the reconstruction of complete 3D surface model from multiple scans. Ever-present registration errors and scanning noise make integration a nontrivial problem. In this paper, we propose a novel method for multi-view scan integration where we solve it as a labelling problem. Unlike previous methods, which have been based on various merging schemes, our labelling-based method is essentially a selection strategy. The overall surface model is composed of surface patches from selected input scans. We formulate the labelling via a higher-order Markov Random Field (MRF) which assigns a label representing an index of some input scan to every point in a base surface. Using a higherorder MRF allows us to more effectively capture spatial relations between 3D points. We employ belief propagation to infer this labelling and experimentally demonstrate that this integration approach provides significantly improved integration via both qualitative and quantitative comparisons

Mesh saliency via spectral processing

We propose a novel method for detecting mesh saliency, a perceptuallybased measure of the importance of a local region on a 3D surface mesh. Our method incorporates global considerations by making use of spectral attributes of the mesh, unlike most existing methods which are typically based on local geometric cues. We first consider the properties of the log- Laplacian spectrum of the mesh. Those frequencies which show differences from expected behaviour capture saliency in the frequency domain. Information about these frequencies is considered in the spatial domain at multiple spatial scales to localise the salient features and give the final salient areas. The effectiveness and robustness of our approach are demonstrated by comparisons to previous approaches on a range of test models. The benefits of the proposed method are further evaluated in applications such as mesh simplification, mesh segmentation and scan integration, where we show how incorporating mesh saliency can provide improved results

Accurately Estimating Rigid Transformations in Registration using a Boosting-Inspired Mechanism

Feature extraction and matching provide the basis of many methods for object registration, modeling, retrieval, and recognition. However, this approach typically introduces false matches, due to lack of features, noise, occlusion, and cluttered backgrounds. In registration, these false matches lead to inaccurate estimation of the underlying transformation that brings the overlapping shapes into best possible alignment. In this paper, we propose a novel boosting-inspired method to tackle this challenging task. It includes three key steps: (i) underlying transformation estimation in the weighted least squares sense, (ii) boosting parameter estimation and regularization via Tsallis entropy, and (iii) weight re-estimation and regularization via Shannon entropy and update with a maximum fusion rule. The process is iterated. The final optimal underlying transformation is estimated as a weighted average of the transformations estimated from the latest iterations, with weights given by the boosting parameters. A comparative study based on real shape data shows that the proposed method outperforms four other state-of-the-art methods for evaluating the established point matches, enabling more accurate and stable estimation of the underlying transformation

Extreme magnetic field-boosted superconductivity

Applied magnetic fields underlie exotic quantum states, such as the fractional quantum Hall effect and Bose-Einstein condensation of spin excitations. Superconductivity, on the other hand, is inherently antagonistic towards magnetic fields. Only in rare cases can these effects be mitigated over limited fields, leading to reentrant superconductivity. Here, we report the unprecedented coexistence of multiple high-field reentrant superconducting phases in the spin-triplet superconductor UTe2. Strikingly, we observe superconductivity in the highest magnetic field range identified for any reentrant superconductor, beyond 65 T. These extreme properties reflect a new kind of exotic superconductivity rooted in magnetic fluctuations and boosted by a quantum dimensional crossover

Uncertainty Analysis in Population-Based Disease Microsimulation Models

Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models

Developmental differences in the influence of phonological similarity on spoken word processing in Mandarin Chinese.

The developmental trajectory of spoken word recognition has been well established in Indo-European languages, but to date remains poorly characterized in Mandarin Chinese. In this study, typically developing children (N=17; mean age 10; 5) and adults (N=17; mean age 24) performed a picture-word matching task in Mandarin while we recorded ERPs. Mismatches diverged from expectations in different components of the Mandarin syllable; namely, word-initial phonemes, word-final phonemes, and tone. By comparing responses to different mismatch types, we uncovered evidence suggesting that both children and adults process words incrementally. However, we also observed key developmental differences in how subjects treated onset and rime mismatches. This was taken as evidence for a stronger influence of top-down processing on spoken word recognition in adults compared to children. This work therefore offers an important developmental component to theories of Mandarin spoken word recognition

Evidence for partial melt in the crust beneath Mt. Paektu (Changbaishan), Democratic People's Republic of Korea and China

Mt. Paektu (also known as Changbaishan) is an enigmatic volcano on the border between the Democratic People's Republic of Korea (DPRK) and China. Despite being responsible for one of the largest eruptions in history, comparatively little is known about its magmatic evolution, geochronology, or underlying structure. We present receiver function results from an unprecedented seismic deployment in the DPRK. These are the first estimates of the crustal structure on the DPRK side of the volcano and, indeed, for anywhere beneath the DPRK. The crust 60 km from the volcano has a thickness of 35 km and a bulk $V_\text{P}$/$V_\text{S}$ of 1.76, similar to that of the Sino-Korean craton. The $V_\text{P}$/$V_\text{S}$ ratio increases ~20 km from the volcano, rising to >1.87 directly beneath the volcano. This shows that a large region of the crust has been modified by magmatism associated with the volcanism. Such high values of $V_\text{P}$/$V_\text{S}$ suggest that partial melt is present in the crust beneath Mt. Paektu. This region of melt represents a potential source for magmas erupted in the last few thousand years and may be associated with an episode of volcanic unrest observed between 2002 and 2005.This work was supported by the Richard Lounsbery Foundation. The UK seismic instruments and data management facilities were provided under loan number 976 by SEIS-UK at the University of Leicester. The facilities of SEIS-UK are supported by the NERC under Agreement R8/H10/64. J.O.S.H. was supported by an NERC Fellowship NE/I020342/1