Likelihood Consensus and Its Application to Distributed Particle Filtering

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measurements of all sensors---using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem

Coplanar Repeats by Energy Minimization

This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization. The proposed energy functional combines several features that model how planes with coplanar repeats are projected into images and captures global interactions between different coplanar repeat groups and scene planes. An inference framework based on a recent variant of $\alpha$-expansion is described and fast convergence is demonstrated. We compare the proposed method to two widely-used geometric multi-model fitting methods using a new dataset of annotated images containing multiple scene planes with coplanar repeats in varied arrangements. The evaluation shows a significant improvement in the accuracy of rectifications computed from coplanar repeats detected with the proposed method versus those detected with the baseline methods.Comment: 14 pages with supplemental materials attache

The Interplay of Structure and Dynamics in the Raman Spectrum of Liquid Water over the Full Frequency and Temperature Range

While many vibrational Raman spectroscopy studies of liquid water have investigated the temperature dependence of the high-frequency O-H stretching region, few have analyzed the changes in the Raman spectrum as a function of temperature over the entire spectral range. Here, we obtain the Raman spectra of water from its melting to boiling point, both experimentally and from simulations using an ab initio-trained machine learning potential. We use these to assign the Raman bands and show that the entire spectrum can be well described as a combination of two temperature-independent spectra. We then assess which spectral regions exhibit strong dependence on the local tetrahedral order in the liquid. Further, this work demonstrates that changes in this structural parameter can be used to elucidate the temperature dependence of the Raman spectrum of liquid water and provides a guide to the Raman features that signal water ordering in more complex aqueous systems

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and store a longer computational graph. This cost prevents scaling them to larger network architectures. We present EvoGrad, a new approach to meta-learning that draws upon evolutionary techniques to more efficiently compute hypergradients. EvoGrad estimates hypergradient with respect to hyperparameters without calculating second-order gradients, or storing a longer computational graph, leading to significant improvements in efficiency. We evaluate EvoGrad on three substantial recent meta-learning applications, namely cross-domain few-shot learning with feature-wise transformations, noisy label learning with Meta-Weight-Net and low-resource cross-lingual learning with meta representation transformation. The results show that EvoGrad significantly improves efficiency and enables scaling meta-learning to bigger architectures such as from ResNet10 to ResNet34.Comment: Accepted at NeurIPS 202

On the integral characterization of principal solutions for half-linear ODE

We discuss a new integral characterization of principal solutions for half-linear differential equations, introduced in the recent paper of S. Fisnarova and R. Marik, Nonlinear Anal. 74 (2011), 6427-6433. We study this characterization in the framework of the existing results and we show when this new integral characterization with a parameter $\alpha$ is equivalent with two extremal cases of the integral characterization used in the literature. We illustrate our results on the Euler and Riemann-Weber differential equations

Synthesis of Terminal Ribose Analogues of Adenosine 5′-Diphosphate Ribose as Probes for the Transient Receptor Potential Cation Channel TRPM2

TRPM2 (transient receptor potential cation channel, subfamily M, member 2) is a nonselective cation channel involved in the response to oxidative stress and in inflammation. Its role in autoimmune and neurodegenerative diseases makes it an attractive pharmacological target. Binding of the nucleotide adenosine 5′-diphosphate ribose (ADPR) to the cytosolic NUDT9 homology (NUDT9H) domain activates the channel. A detailed understanding of how ADPR interacts with the TRPM2 ligand binding domain is lacking, hampering the rational design of modulators, but the terminal ribose of ADPR is known to be essential for activation. To study its role in more detail, we designed synthetic routes to novel analogues of ADPR and 2′-deoxy-ADPR that were modified only by removal of a single hydroxyl group from the terminal ribose. The ADPR analogues were obtained by coupling nucleoside phosphorimidazolides to deoxysugar phosphates. The corresponding C2″-based analogues proved to be unstable. The C1″- and C3″-ADPR analogues were evaluated electrophysiologically by patch-clamp in TRPM2-expressing HEK293 cells. In addition, a compound with all hydroxyl groups of the terminal ribose blocked as its 1″-β-O-methyl-2″,3″-O-isopropylidene derivative was evaluated. Removal of either C1″ or C3″ hydroxyl groups from ADPR resulted in loss of agonist activity. Both these modifications and blocking all three hydroxyl groups resulted in TRPM2 antagonists. Our results demonstrate the critical role of these hydroxyl groups in channel activation.</p