Naive Extension Research Articles

NeUDF: Learning Neural Unsigned Distance Fields With Volume Rendering.

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in the neural implicit rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF, that can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. To gain the flexibility of representing arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as surface representation. While a naive extension of SDF-based neural renderer cannot scale to UDF, we formalize the rules of neural volume rendering for open surface reconstruction (e.g., self-consistent, unbiased, occlusion-aware), and derive a dedicated rendering weight function specially tailored for UDF. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity. We extensively evaluate our method over a number of challenging datasets, including two typical open surface datasets MGN (Bhatnagar et al., 2019) and Deep Fashion 3D (Zhu et al., 2020). Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art methods in the task of multi-view surface reconstruction, especially for the complex shapes with open boundaries.

Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference

The inverse probability (IPW) and doubly robust (DR) estimators are often used to estimate the average causal effect (ATE), but are vulnerable to outliers. The IPW/DR median can be used for outlier-resistant estimation of the ATE, but the outlier resistance of the median is limited and it is not resistant enough for heavy contamination. We propose extensions of the IPW/DR estimators with density power weighting, which can eliminate the influence of outliers almost completely. The outlier resistance of the proposed estimators is evaluated through the unbiasedness of the estimating equations. Unlike the median-based methods, our estimators are resistant to outliers even under heavy contamination. Interestingly, the naive extension of the DR estimator requires bias correction to keep the double robustness even under the most tractable form of contamination. In addition, the proposed estimators are found to be highly resistant to outliers in more difficult settings where the contamination ratio depends on the covariates. The outlier resistance of our estimators from the viewpoint of the influence function is also favorable. Our theoretical results are verified via Monte Carlo simulations and real data analysis. The proposed methods were found to have more outlier resistance than the median-based methods and estimated the potential mean with a smaller error than the median-based methods.

Weak vs. strong breaking of integrability in interacting scalar quantum field theories

The recently proposed classification of integrability-breaking perturbations according to their strength is studied in the context of quantum field theories. Using random matrix methods to diagnose the resulting quantum chaotic behaviour, we investigate the \phi^4ϕ4 and \phi^6ϕ6 interactions of a massive scalar, by considering the crossover between Poissonian and Wigner-Dyson distributions in systems truncated to a finite-dimensional Hilbert space. We find that a naive extension of the scaling of crossover coupling with the volume observed in spin chains does not give satisfactory results for quantum field theory. Instead, we demonstrate that considering the scaling of the crossover coupling with the number of particles yields robust signatures, and is able to distinguish between the strengths of integrability breaking in the \phi^4ϕ4 and \phi^6ϕ6 quantum field theories.

Global GPBiCGstab(L) method for solving linear matrix equations

Global Krylov subspace methods are effective iterative solvers for large linear matrix equations. Several Lanczos-type product methods (LTPMs) for solving standard linear systems of equations have been extended to their global versions. However, the GPBiCGstab(L) method, which unifies two well-known LTPMs (i.e., BiCGstab(L) and GPBiCG methods), has been developed recently, and it has been shown that this novel method has superior convergence when compared to the conventional LTPMs. In the present study, we therefore extend the GPBiCGstab(L) method to its global version. Herein, we present not only a naive extension of the original GPBiCGstab(L) algorithm but also its alternative implementation. This variant enables the preconditioning technique to be applied stably and efficiently. Numerical experiments were performed, and the results demonstrate the effectiveness of the proposed global GPBiCGstab(L) method.

Contribution of collective excitations to third harmonic generation in two-band superconductors: The case of MgB2

Multiband superconductors can host collective excitations with marked differences with respect to their single-band counterpart. We first study the spectrum of collective amplitude fluctuations in a clean two-band superconductor, showing that the spectral weight of the Higgs mode rapidly deviates from the naive extension of the single band case as the interband coupling is turned on. These results are then used to critically analyze the nonlinear optical response in ${\mathrm{MgB}}_{2}$, providing an explanation for the apparently contradictory results of recent experiments, pointing towards a selective relevance either of the Leggett mode or of the amplitude fluctuations at twice the lower gap. By using exact numerical simulations and realistic estimates of disorder we compute the relative contribution of the quasiparticle, amplitude, and phase fluctuations to the nonlinear optical response. We show that at low pumping frequency only the resonance at twice the smaller gap emerges, as due to the BCS response, while the Leggett mode dominates only in a narrow range of higher pumping frequencies matching its low-temperature value. Our findings provide a fresh perspective on the potential of nonlinear THz spectroscopy to detect collective modes in other multiband systems, such as iron-based superconductors.

Scaling of Acceleration Statistics in High Reynolds Number Turbulence

The scaling of acceleration statistics in turbulence is examined by combining data from the literature with new data from well-resolved direct numerical simulations of isotropic turbulence, significantly extending the Reynolds number range. The acceleration variance at higher Reynolds numbers departs from previous predictions based on multifractal models, which characterize Lagrangian intermittency as an extension of Eulerian intermittency. The disagreement is even more prominent for higher-order moments of the acceleration. Instead, starting from a known exact relation, we relate the scaling of acceleration variance to that of Eulerian fourth-order velocity gradient and velocity increment statistics. This prediction is in excellent agreement with the variance data. Our Letter highlights the need for models that consider Lagrangian intermittency independent of the Eulerian counterpart.

Black holes and the swampland: the deep throat revelations

Multi-centered bubbling solutions are black hole microstate geometries that arise as smooth solutions of 5-dimensional mathcal{N} = 2 Supergravity. When these solutions reach the scaling limit, their resulting geometries develop an infinitely deep throat and look arbitrarily close to a black hole geometry. We depict a connection between the scaling limit in the moduli space of Microstate Geometries and the Swampland Distance Conjecture. The naive extension of the Distance Conjecture implies that the distance in moduli space between a reference point and a point approaching the scaling limit is set by the proper length of the throat as it approaches the scaling limit. Independently, we also compute a distance in the moduli space of 3-centre solutions, from the Kähler structure of its phase space using quiver quantum mechanics. We show that the two computations of the distance in moduli space do not agree and comment on the physical implications of this mismatch.

Irreducible components of exotic Springer fibres II : The exotic Robinson–Schensted algorithm

Kato's exotic nilpotent cone was introduced as a substitute for the ordinary nilpotent cone of type C with cleaner properties. The geometric Robinson-Schensted correspondence is obtained by parametrizing the irreducible components of the Steinberg variety (the conormal variety for the action of a semisimple group on two copies of its flag variety); in type A the bijection coincides with the classical Robinson-Schensted algorithm for the symmetric group. Here we give a combinatorial description of the bijection obtained by using the exotic nilpotent cone instead of ordinary type C nilpotent cone in the geometric Robinson-Schensted correspondence; we refer this as the "exotic Robinson-Schensted bijection". This is interesting from a combinatorial perspective, and not a naive extension of the type A Robinson-Schensted bijection.

Nonperturbative matching between equal-time and lightcone quantization

We investigate the nonperturbative relation between lightcone (LC) and standard equal-time (ET) quantization in the context of λϕ4 theory in d = 2. We discuss the perturbative matching between bare parameters and the failure of its naive nonperturbative extension. We argue that they are nevertheless the same theory nonperturbatively, and that furthermore the nonperturbative map between bare parameters can be extracted from ET perturbation theory via Borel resummation of the mass gap. We test this map by using it to compare physical quantities computed using numerical Hamiltonian truncation methods in ET and LC.

Conformal invariance from scale invariance in nonlinear sigma models

There exists a certain argument that in even dimensions, scale invariant quantum field theories are conformal invariant. We may try to extend the argument in $2n + \epsilon$ dimensions, but the naive extension has a small loophole, which indeed shows an obstruction in non-linear sigma models in $2+\epsilon$ dimensions. Even though it could have failed due to the loophole, we show that scale invariance does imply conformal invariance of non-linear sigma models in $2+\epsilon$ dimension from the seminal work by Perelman on the Ricci flow.

Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta

Purpose Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP. Design/methodology/approach Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta. Findings Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance. Research limitations/implications This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems. Practical implications The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing. Originality/value The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

Real monodromy action

The monodromy group is an invariant for parameterized systems of polynomial equations that encodes structure of the solutions over the parameter space. Since the structure of real solutions over real parameter spaces are of interest in many applications, real monodromy action is investigated here. A naive extension of monodromy action from the complex numbers to the real numbers is shown to be very restrictive. Therefore, we introduce a real monodromy structure which need not be a group but contains tiered characteristics about the real solutions over the parameter space. An algorithm is provided to compute the real monodromy structure. In addition, this real monodromy structure is applied to an example in kinematics which summarizes all the ways performing loops parameterized by leg lengths can cause a mechanism to change poses.

Argumentation Reasoning via Circumscription with Pyglaf

The fundamental mechanism that humans use in argumentation can be formalized in abstract argumentation frameworks. Many semantics are associated with abstract argumentation frameworks, each one consisting of a set of extensions, that is, a set of sets of arguments. Some of these semantics are based on preference relations that essentially impose to maximize or minimize some property. This paper presents the argumentation reasoner PYGLAF, which provides a uniform view of many semantics for abstract argumentation frameworks in terms of circumscription. Specifically, several computational problems of abstract argumentation frameworks are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription. Finally, grounded extensions are obtained in polynomial time by unit propagation, and acceptance problems are addressed by first checking cardinality optimal models of circumscribed theories, so that the naive extension enumeration is possibly avoided.

Slow-scale split-step tau-leap method for stiff stochastic chemical systems

Tau-leaping is a family of algorithms for the approximate simulation of the discrete state continuous time Markov chains. Motivation for the development of such methods can be found, for instance, in the fields of chemical kinetics and systems biology. It is known that the dynamical behavior of biochemical systems is often intrinsically stiff representing a serious challenge for their numerical approximation. The naive extension of stiff deterministic solvers to stochastic integration often yields numerical solutions with either impractically large relaxation times or incorrectly resolved covariance. In this paper, we propose a splitting heuristic which helps to resolve some of these issues. The proposed integrator contains a number of unknown parameters which are estimated for each particular problem from the moment equations of the corresponding linearized system. We show that this method is able to reproduce the exact mean and variance of the linear scalar test equation and demonstrates a good accuracy for the arbitrarily stiff systems at least in the linear case. The numerical examples for both linear and nonlinear systems are also provided and the obtained results confirm the efficiency of the considered splitting approach.

Collision-Free Encoding for Chance-Constrained Nonconvex Path Planning

The path planning methods based on nonconvex constrained optimization, such as mixed-integer linear programming (MILP), have found various important applications, ranging from unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to space vehicles. Moreover, their stochastic extensions have enabled risk-aware path planning, which explicitly limits the probability of failure to a user-specified bound. However, a major challenge of those path planning methods is constraint violation between discrete time steps. In the existing approach, a path is represented by a sequence of waypoints and the safety constraints (e.g., obstacle avoidance) are imposed on waypoints. Therefore, the trajectory between waypoints could violate the safety constraints. A naive continuous-time extension results in unrealistic computation cost. In this paper, we propose a novel approach to ensure constraint satisfaction between waypoints without employing a continuous-time formulation. The key idea is to enforce that the same inequality constraint is satisfied on any two adjacent time steps, under assumptions of polygonal obstacles and straight line trajectory between waypoints. The resulting problem encoding is MILP, which can be solved efficiently by commercial solvers. Thus, we also introduce novel extensions to risk-allocation path planners with improved scalability for real-world scenarios and run-time performance. While the proposed encoding approach is general, the particular emphasis of this paper is placed on the chance-constrained, nonconvex path-planning problem (CNPP). We provide extensive simulation results on CNPP to demonstrate the path safety and scalability of our encoding and related path planners.

A cutting-plane method to nonsmooth multiobjective optimization problems

The cutting-plane optimization methods rely on the idea that any subgradient of the objective function or the active/violated constraints defines a halfspace to be excluded from a set that contains an optimal solution: the localizing set. This algorithm converges towards a global minimum of any pseudoconvex subdifferentiable function. A naive extension for multiobjective optimization would be using simultaneously some subgradients of all objective functions for a given feasible point. However, as demonstrated in this paper, this approach can lead to a convergence towards non-optimal points. This paper introduces an optimization strategy for cutting-plane methods to cope with multiobjective problems without any scalarization procedure. The proposed strategy guarantees that its optimal solution is a Pareto Optimal solution of the original problem, which is also no worse than the starting point, and that any Pareto Optimal solution can be sampled. Moreover, the auxiliary problem is infeasible only if the original problem is also infeasible. The new strategy inherits the original theoretical guarantees of cutting planes methods and it can be applied to build other strategies.

Efficient Multispectral Facial Capture With Monochrome Cameras

We propose a variant to polarized gradient illumination facial scanning which uses monochrome instead of color cameras to achieve more efficient and higher-resolution results. In typical polarized gradient facial scanning, sub-millimeter geometric detail is acquired by photographing the subject in eight or more polarized spherical gradient lighting conditions made with white LEDs, and RGB cameras are used to acquire color texture maps of the subject's appearance. In our approach, we replace the color cameras and white LEDs with monochrome cameras and multispectral, colored LEDs, leveraging that color images can be formed from successive monochrome images recorded under different illumination colors. While a naive extension of the scanning process to this setup would require multiplying the number of images by number of color channels, we show that the surface detail maps can be estimated directly from monochrome imagery, so that only an additional n photographs are required, where n is the number of added spectral channels. We also introduce a new multispectral optical flow approach to align images across spectral channels in the presence of slight subject motion. Lastly, for the case where a capture system's white light sources are polarized and its multispectral colored LEDs are not, we introduce the technique of multispectral polarization promotion, where we estimate the cross- and parallel-polarized monochrome images for each spectral channel from their corresponding images under a full sphere of even, unpolarized illumination. We demonstrate that this technique allows us to efficiently acquire a full color (or even multispectral) facial scan using monochrome cameras, unpolarized multispectral colored LEDs, and polarized white LEDs.

Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C).

BackgroundChromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as a viable means of generating data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised.MethodsWe developed a computational simulation pipeline for metagenomic 3C and Hi-C sequencing to evaluate the accuracy of genomic reconstructions at, above, and below an operationally defined species boundary. We simulated datasets and measured accuracy over a wide range of parameters. Five clustering algorithms were evaluated (2 hard, 3 soft) using an adaptation of the extended B-cubed validation measure.ResultsWhen all genomes in a sample are below 95% sequence identity, all of the tested clustering algorithms performed well. When sequence data contains genomes above 95% identity (our operational definition of strain-level diversity), a naive soft-clustering extension of the Louvain method achieves the highest performance.DiscussionPreviously, only hard-clustering algorithms have been applied to metagenomic 3C and Hi-C data, yet none of these perform well when strain-level diversity exists in a metagenomic sample. Our simple extension of the Louvain method performed the best in these scenarios, however, accuracy remained well below the levels observed for samples without strain-level diversity. Strain resolution is also highly dependent on the amount of available 3C sequence data, suggesting that depth of sequencing must be carefully considered during experimental design. Finally, there appears to be great scope to improve the accuracy of strain resolution through further algorithm development.

Labeling uncertainty in multitarget tracking

In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as labeling. In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful.We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation.We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled)MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its naive extension, the labeled SIR M-SMC filter.

Mode specificity in the HCl + OH → Cl + H2O reaction: Polanyi's rules vs sudden vector projection model.

The dynamics and mode specificity of the HCl + OH → Cl + H2O reaction are investigated using a full-dimensional quantum dynamics method on an accurate global potential energy surface. It is shown that the vibrational excitation of the HCl reactant greatly enhances the reactivity while the OH vibrational excitation has little effect. The surprising HCl vibrational enhancement of this early barrier reaction contradicts a naive extension of Polanyi's rules, but can be explained by the sudden vector projection model, which attributes the promotional effect of the HCl vibration to its strong coupling with the reaction coordinate at the transition state. In addition, it is found that the fundamental and overtone excitations of the HCl reactant change the reaction mechanism from a direct barrier crossing process to a capture-like process.