Miracle-dz/UniFAR-FTUs huggingface dataset:
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method

Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines a local reference frame (LRF) for every point using its nearest neighbors and finds local attributes. Next, R-PointHop obtains local-to-global hierarchical features by point downsampling, neighborhood expansion, attribute construction and dimensionality reduction steps. Thus, point correspondences are built in hierarchical feature space using the nearest neighbor rule. Afterwards, a subset of salient points with good correspondence is selected to estimate the 3D transformation. The use of the LRF allows for invariance of the hierarchical features of points with respect to rotation and translation, thus making R-PointHop more robust at building point correspondence, even when the rotation angles are large. Experiments are conducted on the 3DMatch, ModelNet40, and Stanford Bunny datasets, which demonstrate the effectiveness of R-PointHop for 3D point cloud registration. R-PointHop’s model size and training time are an order of magnitude smaller than those of deep learning methods, and its registration errors are smaller, making it a green and accurate solution. Our codes are available on GitHub (https://github.com/pranavkdm/R-PointHop).
IPHC emittance-meters: design and development

The Institut Pluridisciplinaire Hubert Curien (IPHC) of Strasbourg, which celebrates its 15th year in 2021, is composed of four departments. Each of these departments comes from a different scientific horizon such as eco-physiology, chemistry, subatomic research and medical imaging. IPHC was created with the ambition of having different competences to develop high-level multidisciplinary programs with the basis of scientific instrumentation. Beam diagnostics is one of the main fields that has been intensively investigated during all these years within the team of the Instrumentation of Accelerators. This paper focuses on one of its major achievements, the Allison emittance-meter, developed in the framework of SPIRAL2, MYRRHA and FAIR projects.
Boundary Proposal Network for Two-Stage Natural Language Video Localization

We aim to address the problem of Natural Language Video Localization (NLVL) — localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e.g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchor-free approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal two-stage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visual-language fusion layer is proposed to jointly model the multi-modal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BPNet on three challenging NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.
Prospects of newly detecting nearby star-forming galaxies by the Cherenkov Telescope Array

Prospects of the Cherenkov Telescope Array (CTA) for the study of very high energy gammaray emission from nearby star-forming galaxies are investigated. In the previous work, we constructed a model to calculate luminosity and energy spectrum of pion-decay gamma-ray emission produced by cosmic-ray interaction with the interstellar medium (ISM), from four physical quantities of galaxies [star formation rate (SFR), gas mass, stellar mass, and effective radius]. The model is in good agreement with the observed GeV–TeV emission of several nearby galaxies. Applying this model to nearby galaxies that are not yet detected in TeV (mainly from the KINGFISH catalog), their hadronic gamma-ray luminosities and spectra are predicted. We identify galaxies of the highest chance of detection by CTA, including NGC 5236, M33, NGC 6946, and IC 342. Concerning gamma-ray spectra, NGC 1482 is particularly interesting because our model predicts that this galaxy is close to the calorimetric limit and its gamma-ray spectral index in GeV–TeV is close to that of cosmic-ray protons injected into ISM. Therefore this galaxy may be detectable by CTA even though its GeV flux is below the Fermi Large Area Telescope sensitivity limit. In the TeV regime, most galaxies are not in the calorimetric limit, and the predicted TeV flux is lower than that assuming a simple relation between the TeV luminosity and SFR of M82 and NGC 253, typically by a factor of 15. This means that a more sophisticated model beyond the calorimetric limit assumption is necessary to study TeV emission from star-forming galaxies.
String-like collective atomic motion in the interfacial dynamics of nanoparticles

The exploding field of nanotechnology is largely driven by the significant physical and chemical property changes of nanoparticles (NPs) and other nanostructures in comparison to bulk materials and by an increasing capacity for measuring these property changes and synthetically tuning these property changes by controlling particle and feature size, along with surface chemistry. Given the large size-dependent shifts of NP melting temperatures Tm, we can expect at least some of these NP property changes to be associated with an alteration in the NP atomic mobility as the particle size and surface chemistry are varied. Since recent electron microscopy studies on metal NPs of interest in heterogeneous catalysis (e.g., fuel cells, carbon nanotube growth) have often indicated a high NP interfacial mobility and have suggested the relevance of this phenomenon to catalysis, we performed molecular dynamics (MD) simulations for a range of NP sizes in the catalytically relevant temperature range with a focus on quantifying the NP interfacial dynamics. Our illustrative computations were performed for Ni NPs because these particles have been considered in fundamental studies of both fuel cell catalysis and carbon nanotubes growth. Instead of a simple fluid layer on the NP surface, we find a prevalence of string-like collective atomic motions where the geometrical nature of these collective excitations is found to be quantitatively like the collective atomic motions found in glass-forming liquids and in the grain boundary dynamics of polycrystalline materials. We illustrate our new perspective on NP interfacial dynamics by showing metal atom additives (Ag and Pt) alter the length of the string excitations (decreasing and increasing the average length, respectively), as in previous studies of molecular and NP additives in glass-forming polymer liquids.
A note on asymptotic behavior of critical
Galton–Watson processes with immigration

In this somewhat didactic note we give a detailed alternative proof of the known result due to Wei and Winnicki (1989) which states that under second order moment assumptions on the offspring and immigration distributions the sequence of appropriately scaled random step functions formed from a critical Galton-Watson process with immigration (starting from not necessarily zero) converges weakly towards a squared Bessel process. The proof of Wei and Winnicki (1989) is based on infinitesimal generators, while we use limit theorems for random step processes towards a diffusion process due to Isp\'any and Pap (2010). This technique was already used in Isp\'any (2008), where he proved functional limit theorems for a sequence of some appropriately normalized nearly critical Galton-Watson processes with immigration starting from zero, where the offspring means tend to its critical value 1. As a special case of Theorem 2.1 in Isp\'any (2008) one can get back the result of Wei and Winnicki (1989) in the case of zero initial value. In the present note we handle non-zero initial values with the technique used in Isp\'any (2008), and further, we simplify some of the arguments in the proof of Theorem 2.1 in Isp\'any (2008) as well.
Modelling Behavioural Diversity for Learning in Open-Ended Games

Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissors). Yet, there is a lack of rigorous treatment for defining diversity and constructing diversity-aware learning dynamics. In this work, we offer a geometric interpretation of behavioural diversity in games and introduce a novel diversity metric based on determinantal point processes (DPP). By incorporating the diversity metric into best-response dynamics, we develop diverse fictitious play and diverse policy-space response oracle for solving normal-form games and open-ended games. We prove the uniqueness of the diverse best response and the convergence of our algorithms on two-player games. Importantly, we show that maximising the DPP-based diversity metric guarantees to enlarge the gamescape -- convex polytopes spanned by agents' mixtures of strategies. To validate our diversity-aware solvers, we test on tens of games that show strong non-transitivity. Results suggest that our methods achieve at least the same, and in most games, lower exploitability than PSRO solvers by finding effective and diverse strategies.
MWA and ASKAP observations of atypical radio-halo-hosting galaxy clusters: Abell 141 and Abell 3404

Abstract We report on the detection of a giant radio halo in the cluster Abell 3404 as well as confirmation of the radio halo observed in Abell 141 (with linear extents $\sim\! 770$ and $\sim\! 850$ kpc, respectively). We use the Murchison Widefield Array, the Australian Square Kilometre Array Pathfinder, and the Australia Telescope Compact Array to characterise the emission and intervening radio sources from $\sim100$ to 1 000 MHz; power law models are fit to the spectral energy distributions with spectral indices $\alpha_{88}^{1\,110} = -1.66 \pm 0.07$ and $\alpha_{88}^{943} = -1.06 \pm 0.09$ for the radio halos in Abell 3404 and Abell 141, respectively. We find strong correlation between radio and X-ray surface brightness for Abell 3404 but little correlation for Abell 141. We note that each cluster has an atypical morphology for a radio-halo-hosting cluster, with Abell 141 having been previously reported to be in a pre-merging state, and Abell 3404 is largely relaxed with only minor evidence for a disturbed morphology. We find that the radio halo powers are consistent with the current radio halo sample and $P_\nu$–M scaling relations, but note that the radio halo in Abell 3404 is an ultra-steep–spectrum radio halo (USSRH) and, as with other USSRHs lies slightly below the best-fit $P_{1.4}$–M relation. We find that an updated scaling relation is consistent with previous results and shifting the frequency to 150 MHz does not significantly alter the best-fit relations with a sample of 86 radio halos. We suggest that the USSRH halo in Abell 3404 represents the faint class of radio halos that will be found in clusters undergoing weak mergers.
Free-form Design of Discrete Architectural Surfaces by use of Circle Packing

This paper presents an efficient approach for the conceptual design of architectural surfaces which are composed of triangular panels. In the free-form design of discrete architectural surfaces, the Gaussian curvature plays an important role not only aesthetically but also in terms of stiffness and constructability. However, designing a surface manually with specific Gaussian curvatures can be a time-consuming task. We propose a method to find a triangulated surface with user-specified Gaussian curvatures (not limited to constant Gaussian curvatures) and boundary vertex positions. In addition, the conformal class of the final design can be specified; that is, the user has control over the shape (the corner angles) of each triangular panel. The panels could be encouraged to form a regular tessellation or kept close to those of the initial design. The controllability of the conformal class suppresses possible distortion of the panels, resulting in higher structural performance and aesthetics. Our method relies on the idea in computational conformal geometry called circle packing. In this line of research, the discrete Ricci flow has been widely used for surface modelling. However, it is not trivial to incorporate constraints such as boundary locations and convexity of the spanned surface, which are essential to architectural applications. We propose a perturbation of the discrete Ricci energy and develop a least-squares-based optimisation scheme to address these problems with an open-source implementation available online.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

In 5G Ultra-Dense Networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of KGA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time. INDEX TERMS 5G, backhaul network capacity, gateway location problem, heuristic, machine learning, small cells, ultra-dense networks.
Simeon - Secure Federated Machine Learning Through Iterative Filtering

Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware. A global model is distributed to clients, who perform training, and submit their newly-trained model to be aggregated into a superior model. However, federated learning systems are vulnerable to interference from malicious learning agents who may desire to prevent training or induce targeted misclassification in the resulting global model. A class of Byzantine-tolerant aggregation algorithms has emerged, offering varying degrees of robustness against these attacks, often with the caveat that the number of attackers is bounded by some quantity known prior to training. This paper presents Simeon: a novel approach to aggregation that applies a reputation-based iterative filtering technique to achieve robustness even in the presence of attackers who can exhibit arbitrary behaviour. We compare Simeon to state-of-the-art aggregation techniques and find that Simeon achieves comparable or superior robustness to a variety of attacks. Notably, we show that Simeon is tolerant to sybil attacks, where other algorithms are not, presenting a key advantage of our approach.
Convection in a mushy layer along a vertical heated wall

Abstract Motivated by the mushy zones of sea ice, volcanoes and icy moons of the outer solar system, we perform a theoretical and numerical study of boundary-layer convection along a vertical heated wall in a bounded ideal mushy region. The mush is comprised of a porous and reactive binary alloy with a mixture of saline liquid in a solid matrix, and is studied in the near-eutectic approximation. Here, we demonstrate the existence of four regions and study their behaviour asymptotically. Starting from the bottom of the wall, the four regions are (i) an isotropic corner region; (ii) a buoyancy dominated vertical boundary layer; (iii) an isotropic connection region; and (iv) a horizontal boundary layer at the top boundary with strong gradients of pressure and buoyancy. Scalings from numerical simulations are consistent with the theoretical predictions. Close to the heated wall, the convection in the mushy layer is similar to a rising buoyant plume abruptly stopped at the top, leading to increased pressure and temperature in the upper region, whose impact is discussed as an efficient melting mechanism.
Magnetically switchable spin-wave retarder with 
90∘
 antiferromagnetic domain wall

Polarization, denoting the precession direction with respect to the background magnetization, is an intrinsic degree of freedom of spin wave. Using magnetic textures to control the spin wave polarization is fundamental and indispensable toward reprogrammable polarization-based magnonics. Here we show that due to the intrinsic cubic anisotropy, a 90◦ antiferromagnetic domain wall naturally acts as a spin wave retarder (wave-plate). Moreover, for a 90◦ domain wall pair developed by introducing a second domain in a homogenous antiferromagnetic wire, the sign of retarding effect can be flipped by simply switching the magnetization direction of the intermediate domain.
Fast Antenna and Beam Switching Method for mmWave Handsets With Hand Blockage

Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme data speeds and capacity. However, mmWave comes with challenges related to significantly high path loss and susceptibility to blockage. Particularly, when mmWave communication is applied to a mobile terminal device, communication can be frequently broken because of rampant hand blockage. Although a number of mobile phone companies have suggested configuring multiple sets of antenna modules at different locations on a mobile phone to circumvent this problem, identifying an optimal antenna module and a beam pair by simultaneously opening multiple sets of antenna modules causes the problem of excessive power consumption and device costs. In this study, a fast antenna and beam switching method termed Fast-ABS is proposed. In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules. As such, unmasked antenna modules and their corresponding beam pairs can be rapidly selected for switching to avoid the problem of poor quality or disconnection of communications caused by hand blockage. Thorough analysis and extensive simulations, which include the derivation of relevant Cramér-Rao lower bounds, show that the performance of Fast-ABS is close to that of an oracle solution that can instantaneously identify the best beam of other antenna modules even in complex multipath scenarios. Furthermore, Fast-ABS is implemented on a software defined radio and integrated into a 5G New Radio physical layer. Over-the-air experiments reveal that Fast-ABS can achieve efficient and seamless connectivity despite hand blockage.
Monte Carlo Scene Search for 3D Scene Understanding

We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data. More exactly, we propose a modification of the Monte Carlo Tree Search (MCTS) algorithm to retrieve objects and room layouts from noisy RGB-D scans. While MCTS was developed as a game-playing algorithm, we show it can also be used for complex perception problems. Our adapted MCTS algorithm has few easy-to-tune hyperparameters and can optimise general losses. We use it to optimise the posterior probability of objects and room layout hypotheses given the RGB-D data. This results in an analysis-by-synthesis approach that explores the solution space by rendering the current solution and comparing it to the RGB-D observations. To perform this exploration even more efficiently, we propose simple changes to the standard MCTS’ tree construction and exploration policy. We demonstrate our approach on the ScanNet dataset. Our method often retrieves configurations that are better than some manual annotations, especially on layouts.
Reconsidering Representation Alignment for Multi-view Clustering

Aligning distributions of view representations is a core component of today’s state of the art models for deep multi-view clustering. However, we identify several drawbacks with naïvely aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model’s ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model’s ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets1.
Convergence analysis of stochastic higher-order majorization–minimization algorithms

Majorization-minimization schemes are a broad class of iterative methods targeting general optimization problems, including nonconvex, nonsmooth and stochastic. These algorithms minimize successively a sequence of upper bounds of the objective function so that along the iterations the objective value decreases. We present a stochastic higher-order algorithmic framework for minimizing the average of a very large number of sufficiently smooth functions. Our stochastic framework is based on the notion of stochastic higher-order upper bound approximations of the finite-sum objective function and minibatching. We derive convergence results for nonconvex and convex optimization problems when the higher-order approximation of the objective function yields an error that is p times differentiable and has Lipschitz continuous p derivative. More precisely, for general nonconvex problems we present asymptotic stationary point guarantees and under Kurdyka-Lojasiewicz property we derive local convergence rates ranging from sublinear to linear. For convex problems with uniformly convex objective function we derive local (super)linear convergence results for our algorithm. Compared to existing stochastic (first-order) methods, our algorithm adapts to the problem's curvature and allows using any batch size. Preliminary numerical tests support the effectiveness of our algorithmic framework.