Miracle-dz/UniFAR-FTUs huggingface dataset:
CGS-Net: Aggregating Colour, Geometry and Semantic Features for Large-Scale Indoor Place Recognition

—We describe an approach to large-scale indoor place recognition that aggregates low-level colour and geometric features with high-level semantic features. We use a deep learning network that takes in RGB point clouds and extracts local features with ﬁve 3-D kernel point convolutional (KPConv) layers. We speciﬁcally train the KPConv layers on the semantic segmentation task to ensure that the extracted local features are semantically meaningful. Then, feature maps from all the ﬁve KPConv layers are concatenated together and fed into the NetVLAD layer to generate the global descriptors. The approach is trained and evaluated using a large-scale indoor place recognition dataset derived from the ScanNet dataset, with a test set comprising 3,608 point clouds generated from 100 different rooms. Comparison with a traditional feature based method and three state-of-the-art deep learning methods demonstrate that the approach signiﬁcantly outperforms all four methods, achieving, for example, a top-3 average recall rate of 75% compared with 41% for the closest rival method.
Online Bayesian Optimization for the SECAR Recoil Mass Separator

S. A. Miskovich , 2, 3, ∗ F. Montes, 3 G. P. A. Berg, 5 J. Blackmon, K. A. Chipps, M. Couder, 5 C. M. Deibel, K. Hermansen , 2, 3 A. A. Hood, R. Jain, 2, 3 T. Ruland, H. Schatz, 2, 3 M. S. Smith, P. Tsintari, and L. Wagner 2, 3 Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, MI 48824 The Joint Institute for Nuclear Astrophysics, Michigan State University, East Lansing, MI 48824 Department of Physics, University of Notre Dame, Notre Dame, IN 46556 The Joint Institute for Nuclear Astrophysics, University of Notre Dame, Notre Dame, IN 46556 Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803 Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 Cyclotron Institute, Texas A&M University, College Station, TX 77843 Department of Physics, Central Michigan University, Mt Pleasant, MI 48859 (Dated: January 2022)
Retinal Vessel Segmentation with Pixel-Wise Adaptive Filters

Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast. Previous methods generally refine segmentation results by cascading multiple deep networks, which are time-consuming and inefficient. In this paper, we propose two novel methods to address these challenges. First, we devise a light-weight module, named multi-scale residual similarity gathering (MRSG), to generate pixel-wise adaptive filters (PA-Filters). Different from cascading multiple deep networks, only one PA-Filter layer can improve the segmentation results. Second, we introduce a response cue erasing (RCE) strategy to enhance the segmentation accuracy. Experimental results on the DRIVE, CHASE_DB1, and STARE datasets demonstrate that our proposed method outperforms state-of-the-art methods while maintaining a compact structure. Code is available at https://github.com/Limingxing00/Retinal-Vessel-Segmentation-ISBI2022.
A Bibliometric Perspective of Social Science Scientific Communities of Pakistan and India

Social network analysis is becoming one of the fundamental topics in graph theory and community detection is one of the important sub fields of social networks. Authors in scientific communities collaborate with each other. These collaborations in research groups are made for various purposes, e.g. to achieve a common goal, for knowledge sharing, improve quality of research etc. Collaboration among authors of a community can be measured via network map. In this study, we will use research publication data from the field of social science to identify collaboration networks among social science research communities of India and Pakistan. We have used Scopus database to extract information of social science journals for both countries India and Pakistan. Study of this data is significant as both countries have common social issues and many of common social values. Research groups of both countries with the same cultural background and having same social issues are more likely to work in related areas. Authors from these countries also work with each other for common social issues. For this study, we have compared data of research publications from the field of social science in India and Pakistan. Based on different collaborations maps among authors and countries like co-authorship network analysis, citation network, source citation network analysis, we have compared collaboration of both communities. Although communities of both countries are facing many common social issues, but still, research communities from both countries are not likely to directly work with each other. Based on collaboration maps, it is identified that collaboration among both countries is weaker as compared to other countries like USA, UK, and Canada etc. Quality of research publications for both countries is also measured in this study and based on these measurements, we have seen performance of both communities. Based on published data of both communities, it was measured that quality of Indian research publication is better as compared to Pakistan. Publications of Indian articles are mostly in Q2 ranks journals, while most of Pakistan’s research publications are in Q4 rank journals.
Detecting distributional differences in labeled sequence data with application to tropical cyclone satellite imagery

Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this question, we propose a new nonparametric test of association between a time series of images and a series of binary event labels. We ask whether there is a difference in distribution between (dependent but identically distributed) 24-h sequences of images preceding an event versus a non-event. By rewriting the statistical test as a regression problem, we leverage neural networks to infer modes of structural evolution of TC convection that are representative of the lead-up to rapid intensity change events. Dependencies between nearby sequences are handled by a bootstrap procedure that estimates the marginal distribution of the label series. We prove that type I error control is guaranteed as long as the distribution of the label series is well-estimated, which is made easier by the extensive historical data for binary TC event labels. We show empirical evidence that our proposed method identifies archetypes of infrared imagery associated with elevated rapid intensification risk, typically marked by deep or deepening core convection over time. Such results provide a foundation for improved forecasts of rapid intensification.
A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting

Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we formulate and implement a multi‐model ensemble Kalman filter (MM‐EnKF) based on this framework. The MM‐EnKF can combine multiple model ensembles for both DA and forecasting in a flow‐dependent manner; it uses adaptive model error estimation to provide matrix‐valued weights for the separate models and the observations. We apply this methodology to various situations using the Lorenz96 model for illustration purposes. Our numerical experiments include multiple models with parametric error, different resolved scales, and different fidelities. The MM‐EnKF results in significant error reductions compared to the best model, as well as to an unweighted multi‐model ensemble, with respect to both probabilistic and deterministic error metrics.
Tsetlin Machine for Solving Contextual Bandit Problems

This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.
Unified Characterization and Precoding for Non-Stationary Channels

Modern wireless channels are increasingly dense and mobile making the channel highly non-stationary. The time-varying distribution and the existence of joint interference across multiple degrees of freedom (e.g., users, antennas, frequency and symbols) in such channels render conventional precoding sub-optimal in practice, and have led to historically poor characterization of their statistics. The core of our work is the derivation of a high-order generalization of Mercer’s Theorem to decompose the non-stationary channel into constituent fading sub-channels (2-D eigenfunctions) that are jointly orthogonal across its degrees of freedom. Consequently, transmitting these eigenfunctions with optimally derived coefficients eventually mitigates any interference across these dimensions and forms the foundation of the proposed joint spatio-temporal precoding. The precoded symbols directly reconstruct the data symbols at the receiver upon demodulation, thereby significantly reducing its computational burden, by alleviating the need for any complementary decoding. These eigenfunctions are paramount to extracting the second-order channel statistics, and therefore completely characterize the underlying channel. Theory and simulations show that such precoding leads to >104× BER improvement (at 20dB) over existing methods for non-stationary channels.
Microphysics of Relativistic Collisionless Electron-ion-positron Shocks

We perform particle-in-cell simulations to elucidate the microphysics of relativistic weakly magnetized shocks loaded with electron-positron pairs. Various external magnetizations σ ≲ 10−4 and pair-loading factors Z ± ≲ 10 are studied, where Z ± is the number of loaded electrons and positrons per ion. We find the following: (1) The shock becomes mediated by the ion Larmor gyration in the mean field when σ exceeds a critical value σ L that decreases with Z ±. At σ ≲ σ L the shock is mediated by particle scattering in the self-generated microturbulent fields, the strength and scale of which decrease with Z ±, leading to lower σ L. (2) The energy fraction carried by the post-shock pairs is robustly in the range between 20% and 50% of the upstream ion energy. The mean energy per post-shock electron scales as E¯e∝Z±+1−1 . (3) Pair loading suppresses nonthermal ion acceleration at magnetizations as low as σ ≈ 5 × 10−6. The ions then become essentially thermal with mean energy E¯i , while electrons form a nonthermal tail, extending from E∼Z±+1−1E¯i to E¯i . When σ = 0, particle acceleration is enhanced by the formation of intense magnetic cavities that populate the precursor during the late stages of shock evolution. Here, the maximum energy of the nonthermal ions and electrons keeps growing over the duration of the simulation. Alongside the simulations, we develop theoretical estimates consistent with the numerical results. Our findings have important implications for models of early gamma-ray burst afterglows.
Neural dual contouring

We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per grid cell and one quad for each grid edge intersection, a natural and efficient structure for reproducing sharp features. However, rather than computing vertex locations and edge crossings with hand-crafted functions that depend directly on difficult-to-obtain surface gradients, NDC uses a neural network to predict them. As a result, NDC can be trained to produce meshes from signed or unsigned distance fields, binary voxel grids, or point clouds (with or without normals); and it can produce open surfaces in cases where the input represents a sheet or partial surface. During experiments with five prominent datasets, we find that NDC, when trained on one of the datasets, generalizes well to the others. Furthermore, NDC provides better surface reconstruction accuracy, feature preservation, output complexity, triangle quality, and inference time in comparison to previous learned (e.g., neural marching cubes, convolutional occupancy networks) and traditional (e.g., Poisson) methods. Code and data are available at https://github.com/czq142857/NDC.
Uniform tail estimates and $ L^p( {\mathbb{R}}^N) $-convergence for finite-difference approximations of nonlinear diffusion equations

<p style='text-indent:20px;'>We obtain new equitightness and <inline-formula><tex-math id="M2">\begin{document}$ C([0, T];L^p( {\mathbb{R}}^N)) $\end{document}</tex-math></inline-formula>-convergence results for finite-difference approximations of generalized porous medium equations of the form</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \partial_tu-\mathfrak{L}[\varphi(u)] = g\qquad\text{in }~~ {\mathbb{R}}^N\times(0, T) , $\end{document} </tex-math></disp-formula></p><p style='text-indent:20px;'>where <inline-formula><tex-math id="M3">\begin{document}$ \varphi: {\mathbb{R}}\to {\mathbb{R}} $\end{document}</tex-math></inline-formula> is continuous and nondecreasing, and <inline-formula><tex-math id="M4">\begin{document}$ \mathfrak{L} $\end{document}</tex-math></inline-formula> is a local or nonlocal diffusion operator. Our results include slow diffusions, strongly degenerate Stefan problems, and fast diffusions above a critical exponent. These results improve the previous <inline-formula><tex-math id="M5">\begin{document}$ C([0, T];L_ \text{loc}^p( {\mathbb{R}}^N)) $\end{document}</tex-math></inline-formula>-convergence obtained in a series of papers on the topic by the authors. To have equitightness and global <inline-formula><tex-math id="M6">\begin{document}$ L^p( {\mathbb{R}}^N) $\end{document}</tex-math></inline-formula>-convergence, some additional restrictions on <inline-formula><tex-math id="M7">\begin{document}$ \mathfrak{L} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M8">\begin{document}$ \varphi $\end{document}</tex-math></inline-formula> are needed. Most commonly used symmetric operators <inline-formula><tex-math id="M9">\begin{document}$ \mathfrak{L} $\end{document}</tex-math></inline-formula> are still included: the Laplacian, fractional Laplacians, and other generators of symmetric Lévy processes with some fractional moment. We also discuss extensions to nonlinear possibly strongly degenerate convection-diffusion equations. </p>
Rapid loss of Arctic sea ice

Several environmental tipping points and self-reinforcing feedback loops are still disregarded within the frequently used climate models. Thus, existing climate models are not very representative for providing projections of the conditions after the actual environmental tipping points are triggered. In this contribution, we discuss the presence of the Arctic sea-ice loss tipping point and its influence on global warming. Our literature search and PIOMAS sea volume observation data based projection indicate the significant acceleration of the sea-ice loss (which leads to a year around and permanent ice-free Arctic) in near future.
Geometrically Higher Order Unfitted Space-Time Methods for PDEs on Moving Domains

In this paper, we propose new geometrically unfitted space-time Finite Element methods for partial differential equations posed on moving domains of higher order accuracy in space and time. As a model problem, the convection-diffusion problem on a moving domain is studied. For geometrically higher order accuracy, we apply a parametric mapping on a background space-time tensor-product mesh. Concerning discretisation in time, we consider discontinuous Galerkin, as well as related continuous (Petrov-)Galerkin and Galerkin collocation methods. For stabilisation with respect to bad cut configurations and as an extension mechanism that is required for the latter two schemes, a ghost penalty stabilisation is employed. The article puts an emphasis on the techniques that allow to achieve a robust but higher order geometry handling for smooth domains. We investigate the computational properties of the respective methods in a series of numerical experiments. These include studies in different dimensions for different polynomial degrees in space and time, validating the higher order accuracy in both variables.
Robust Audio Anomaly Detection

We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data. The presented approach doesn't assume the presence of labeled anomalies in the training dataset and uses a novel deep neural network architecture to learn the temporal dynamics of the multivariate time series at multiple resolutions while being robust to contaminations in the training dataset. The temporal dynamics are modeled using recurrent layers augmented with attention mechanism. These recurrent layers are built on top of convolutional layers allowing the network to extract features at multiple resolutions. The output of the network is an outlier robust probability density function modeling the conditional probability of future samples given the time series history. State-of-the-art approaches using other multiresolution architectures are contrasted with our proposed approach. We validate our solution using publicly available machine sound datasets. We demonstrate the effectiveness of our approach in anomaly detection by comparing against several state-of-the-art models.
FRL: Federated Rank Learning

Federated learning (FL) allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data among each other. FL is unfortunately susceptible to poisoning by malicious clients who aim to hamper the accuracy of the commonly trained model through sending malicious model updates during FL’s training process. We argue that the key factor to the success of poisoning attacks against existing FL systems is the large space of model updates available to the clients, allowing malicious clients to search for the most poisonous model updates, e.g., by solv-ing an optimization problem. To address this, we propose F ederated R ank L earning (FRL). FRL reduces the space of client updates from model parameter updates (a continuous space of ﬂoat numbers) in standard FL to the space of parameter rankings (a discrete space of integer values). To be able to train the global model using parameter ranks (instead of parameter weights), FRL leverage ideas from recent supermasks training mechanisms. Speciﬁcally, FRL clients rank the parameters of a randomly initialized neural network (pro-vided by the server) based on their local training data. The FRL server uses a voting mechanism to aggregate the parameter rankings submitted by clients in each training epoch to generate the global ranking of the next training epoch. Intuitively, our voting-based aggregation mechanism pre-vents poisoning clients from making signiﬁcant adversarial modiﬁcations to the global model, as each client will have a single vote! We demonstrate the robustness of FRL to poisoning through analytical proofs and experimentation. We also show FRL’s high communication efﬁciency. Our experiments demonstrate the superiority of FRL in real-world FL settings. In particular, (1) FRL is substantially more robust to poisoning attacks than state-of-the-art robust aggregation algorithms; (2) FRL achieves performances similar to the
Finite element modelling of central leader and vase shape cherry trees

Central leader and vase form tree models were built using Finite Element Modelling (FEM). Their main characteristics were chosen to be the same. To get comparable results to real values, acceleration versus time curves of the two types of real trees were processed using FFT method to determine their natural frequencies. The natural frequencies measured on real trees and calculated for the models have shown good similarity. The models were virtually exposed to the effect of horizontal forced vibration in the frequency range of 0–20 Hz. Acceleration-frequency curves were calculated and drawn to find the best frequency values for the highest accelerations and also to see their differences in the limb. For the same purpose, the direction of shaking was also changed. It was found that for the central leader limb shape multidirectional shaking would bring uniform detachment, while for the vase form trees, even the unidirectional shakers are appropriate. Real trees were also shaken and their acceleration-frequency curves were compared with the values of the FEMs. The resultant good similarity proves the ability of the models. The acceleration values achieved in the vase form models were much higher than for the central leader type. The acceleration-frequency curve of the shaker unit can be used to find the best frequency for shaking.
Do Patient-Reported Outcome Measures Tell Us the Full Story?

Patient-reported outcome measures (PROMs) play a central role in clinical research and patient care driven most likely by the need to provide more patient-centered care (McClimans, 2010). In the nursing context, there are examples of PROMs Kotronoulas et al. (2018), Basch et al. (2016), Kyte et al. (2016) although there is scant discussion on the theory underpinning their development and use and whether they provide a true picture of the individual’s experience of their health status and/or quality of life? When completing a PROM can we say we are providing the patient with a voice and treating them as individuals who create and follow their own individual life plans? In this editorial, I propose an alternative approach and argue that the numeric score, derived from the PROM and representing an individual’s health status or health-related quality of life, is limited due to their illness narrative being lost along the way.