Miracle-dz/UniFAR-FTUs huggingface dataset:
Pebbles versus planetesimals: the case of Trappist-1

We present a study into the formation of planetary systems around low mass stars similar to Trappist-1, through the accretion of either planetesimals or pebbles. The aim is to determine if the currently observed systems around low mass stars could favour one scenario over the other. To determine these differences, we ran numerous N-body simulations, coupled to a thermally evolving viscous 1D disc model, and including prescriptions for planet migration, photoevaporation, and pebble and planetesimal dynamics. We mainly examine the differences between the pebble and planetesimal accretion scenarios, but we also look at the influences of disc mass, size of planetesimals, and the percentage of solids locked up within pebbles. When comparing the resulting planetary systems to Trappist-1, we find that a wide range of initial conditions for both the pebble and planetesimal accretion scenarios can form planetary systems similar to Trappist-1, in terms of planet mass, periods, and resonant configurations. Typically these planets formed exterior to the water iceline and migrated in resonant convoys into the inner region close to the central star. When comparing the planetary systems formed through pebble accretion to those formed through planetesimal accretion, we find a large number of similarities, including average planet masses, eccentricities, inclinations, and period ratios. One major difference between the two scenarios was that of the water content of the planets. When including the effects of ablation and full recycling of the planets’ envelope with the disc, the planets formed through pebble accretion were extremely dry, whilst those formed through planetesimal accretion were extremely wet. If the water content is not fully recycled and instead falls to the planets’ core, or if ablation of the water is neglected, then the planets formed through pebble accretion are extremely wet, similar to those formed through planetesimal accretion. Should the water content of the Trappist-1 planets be determined accurately, this could point to a preferred formation pathway for planetary systems, or to specific physics that may be at play.
Enhanced 3D convolutional networks for crowd counting

Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting. However, when dealing with video datasets, CNN-based methods still process each video frame independently, thus ignoring the powerful temporal information between consecutive frames. In this work, we propose a novel architecture termed as "temporal channel-aware" (TCA) block, which achieves the capability of exploiting the temporal interdependencies among video sequences. Specifically, we incorporate 3D convolution kernels to encode local spatio-temporal features. Furthermore, the global contextual information is encoded into modulation weights which adaptively recalibrate channel-aware feature responses. With the local and global context combined, the proposed block enhances the discriminative ability of the feature representations and contributes to more precise results in diverse scenes. By stacking TCA blocks together, we obtain the deep trainable architecture called enhanced 3D convolutional networks (E3D). The experiments on three benchmark datasets show that the proposed method delivers state-of-the-art performance. To verify the generality, an extended experiment is conducted on a vehicle dataset TRANCOS and our approach beats previous methods by large margins.
Taming unbalanced training workloads in deep learning with partial collective operations

Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synchronous Stochastic Gradient Descent (SGD) achieves good accuracy for a wide variety of tasks, but relies on global synchronization to accumulate the gradients at every training step. In this paper, we propose eager-SGD, which relaxes the global synchronization for decentralized accumulation. To implement eager-SGD, we propose to use two partial collectives: solo and majority. With solo allreduce, the faster processes contribute their gradients eagerly without waiting for the slower processes, whereas with majority allreduce, at least half of the participants must contribute gradients before continuing, all without using a central parameter server. We theoretically prove the convergence of the algorithms and describe the partial collectives in detail. Experiments are conducted on a variety of neural networks and datasets. The results on load-imbalanced environments show that eager-SGD achieves 2.64 X speedup (ResNet-50 on ImageNet) over the asynchronous centralized SGD, and achieves 1.29 X speedup (ResNet-50 on ImageNet) and 1.27X speedup (LSTM on UCF101) over the state-of-the-art synchronous decentralized SGDs, without losing accuracy.
The permutation entropy and its applications on fire tests data

Based on the data gained from a full-scale experiment, the order/disorder characteristics of the compartment fire temperatures are analyzed. Among the known permutation/encoding type entropies used to analyze time series, we look for those that fit better in the fire phenomena. The literature in its major part does not focus on time series with data collected during full-scale fire experiments, therefore we do not only perform our analysis and report the results, but also discuss methods, algorithms, the novelty of our entropic approach and details behind the scene. The embedding dimension selection in the complexity evaluation is also discussed. Finally, more research directions are proposed.
Automorphisms of the generalised Thompson's group $T_{n,r}$

The recent paper "The further chameleon groups of Richard Thompson and Graham Higman: automorphisms via dynamics for the Higman groups $G_{n,r}$" of Bleak, Cameron, Maissel, Navas and Olukoya (BCMNO) characterises the automorphisms of the Higman-Thompson groups $G_{n,r}$ as the specific subgroup of the rational group $\mathcal{R}_{n,r}$ of Grigorchuk, Nekrashevych and Suchanski{\u i}'s consisting of those elements which have the additional property of being bi-synchronizing. In this article, we extend the arguments of BCMNO and characterise the automorphism group of $T_{n,r}$ as a subgroup of $\mathrm{Aut}{G_{n,r}}$. We then show that the groups $\mathrm{Out}{T_{n,r}}$ can be identified with subgroups of the group $\mathrm{Out}{T_{n,n-1}}$. Extending results of Brin and Guzman, we show that the groups $\mathrm{Out}{T_{n,r}}$, for $n>2$, are all infinite and contain an isomorphic copy of Thompson's group $F$. For $X \in \{T,G\}$, we study the groups $\mathrm{Out}{X_{n,r}}$ and show that these fit in a lattice structure where $\mathrm{Out}{X_{n,1}} \unlhd \mathrm{Out}{X_{n,r}}$ for all $1 \le r \le n-1$ and $\mathrm{Out}{X_{n,r}} \unlhd \mathrm{Out}{X_{n,n-1}}$. This gives a partial answer to a question in BCMNO concerning the normal subgroup structure of $\mathrm{Out}{G_{n,n-1}}$. Furthermore, we deduce that for $1\le j,d \le n-1$ such that $d = \gcd(j, n-1)$, $\mathrm{Out}{X_{n,j}} = \mathrm{Out}{X_{n,d}}$ extending a result of BCMNO for the groups $G_{n,r}$ to the groups $T_{n,r}$. We give a negative answer to the question in BCMNO which asks whether or not $\mathrm{Out}{G_{n,r}} \cong \mathrm{Out}{G_{n,s}}$ if and only if $\gcd(n-1,r) = \gcd(n-1,s)$. We conclude by showing that the groups $T_{n,r}$ have the $R_{\infty}$ property extending the result of Burillo, Matucci and Ventura and, independently, Gon{\c c}alves and Sankaran, for Thompson's group $T$.
A Mask-RCNN Baseline for Probabilistic Object Detection

The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability-based Detection Quality (PDQ), on a new synthetic image dataset. We present our submission to the challenge, a fine-tuned version of Mask-RCNN with some additional post-processing. Our method, submitted under username pammirato, is currently second on the leaderboard with a score of 21.432, while also achieving the highest spatial quality and average overall quality of detections. We hope this method can provide some insight into how detectors designed for mean average precision (mAP) evaluation behave under PDQ, as well as a strong baseline for future work.
Maximum Approximated Likelihood Estimation

Empirical economic research frequently applies maximum likelihood estimation in cases where the likelihood function is analytically intractable. Most of the theoretical literature focuses on maximum simulated likelihood (MSL) estimators, while empirical and simulation analyzes often find that alternative approximation methods such as quasi-Monte Carlo simulation, Gaussian quadrature, and integration on sparse grids behave considerably better numerically. This paper generalizes the theoretical results widely known for MSL estimators to a general set of maximum approximated likelihood (MAL) estimators. We provide general conditions for both the model and the approximation approach to ensure consistency and asymptotic normality. We also show specific examples and finite-sample simulation results.
Frequency selection in a gravitationally stretched capillary jet in the jetting regime

A capillary jet falling under the effect of gravity continuously stretches while thinning downstream. We report here the effect of external periodic forcing on such a spatially varying jet in the jetting regime. Surprisingly, the optimal forcing frequency producing the most unstable jet is found to be highly dependent on the forcing amplitude. Taking benefit of the one-dimensional Eggers & Dupont (J. Fluid Mech., vol. 262, 1994, pp. 205–221) equations, we investigate the case through nonlinear simulations and linear stability analysis. In the local framework, the WKBJ (Wentzel–Kramers–Brillouin–Jeffreys) formalism, established for weakly non-parallel flows, fails to capture the nonlinear simulation results quantitatively. However, in the global framework, the resolvent analysis, supplemented by a simple approximation of the required response norm inducing breakup, is shown to correctly predict the optimal forcing frequency at a given forcing amplitude and the resulting jet breakup length. The results of the resolvent analysis are found to be in good agreement with those of the nonlinear simulations.
Transactive Energy System: Market-Based Coordination of Distributed Energy Resources.

Distributed energy resources (DER) provide significant value for renewable energy integration in modern power grids. However, unlocking this value requires complex design and coordination. This paper focuses on the emerging {\em transactive energy systems}, which draw tools and principles from economics to design the coordination strategies for DERs. The concept of transactive energy system broadly captures a huge body of literature, and many of them are closely related but fundamentally different. This gives rise to the following questions: how to formally compare different transactive energy systems and their proposed approaches? How to choose the right transactive energy system to formulate a given problem? What tools are available in the literature for each class of transactive energy systems? In this paper, we answer these questions by synthesizing a unifying framework for a large class of problems studied in the literature. The framework consists of preferences, control decision, information structure and solution concept. These elements are important in identifying and distinguishing various transactive energy systems in the literature. We employ the proposed framework to analyze a few important class of transactive energy systems. Their connections and differences are discussed, and available tools for each class of problems are surveyed.
Variable jet Lorentz factors can explain soft self-absorbed radio spectra of accreting black holes

We study the effect of variable jet bulk Lorentz factors, i.e. either jet acceleration or deceleration, on partially synchrotron self-absorbed radio spectra from cores of radio-loud active galactic nuclei and black hole binaries in the hard state. In about a half of quasars and radio galaxies, their core radio spectra are observed to be soft, i.e. have the spectral index of α < 0. If they are emitted by jets with constant Lorentz factors, that softness implies deposition of large amounts of energy at large distances from the centre. We show here that such soft spectra can be explained without that energetic requirement by emission of jets with the Doppler factor increasing with the distance. This can happen for either jet acceleration or deceleration, depending on the jet viewing angle. We find our model can explain the quiescent radio to X-ray spectra of the BL Lac objects Mrk 421 and Mrk 501.
Almost‐Rigidity of Frameworks

We extend the mathematical theory of rigidity of frameworks (graphs embedded in d‐dimensional space) to consider nonlocal rigidity and flexibility properties. We provide conditions on a framework under which (I) as the framework flexes continuously it must remain inside a small ball, a property we call “almost‐rigidity”; (II) any other framework with the same edge lengths must lie outside a much larger ball; (III) if the framework deforms by some given amount, its edge lengths change by a minimum amount; (IV) there is a nearby framework that is prestress stable, and thus rigid. The conditions can be tested efficiently using semidefinite programming. The test is a slight extension of the test for prestress stability of a framework, and gives analytic expressions for the radii of the balls and the edge length changes. Examples illustrate how the theory may be applied in practice, and we provide an algorithm to test for rigidity or almost‐rigidity. We briefly discuss how the theory may be applied to tensegrities. © 2020 Wiley Periodicals LLC.
Many-body dynamical localization and thermalization

We show that a quantum dynamical localization effect can be observed in a generic thermalization process of two weakly-coupled chaotic subsystems. Specifically, our model consists of the minimal experimentally relevant subsystems that exhibit chaos, which are 3-site Bose-Hubbard units. Due to the high dimensionality of the composite 6-site system, the quantum localization effect is weak and can not be resolved merely by the breakdown of quantum-to-classical correspondence. Instead, we adopt an intrinsic definition of localization as the memory of initial conditions, that is not related to the underlying classical dynamics. We discuss the dynamics in the chaotic sea, and in the vicinity of the mobility edge, beyond which ergodization is suppressed.
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems – predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as 'cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions. We employ four baselines – first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement – whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC.
THE UNIT MAP OF THE ALGEBRAIC SPECIAL LINEAR COBORDISM SPECTRUM

Abstract In the joint work with Elmanto, Hoyois, Khan and Sosnilo, we computed infinite $\mathbb{P}^{1}$ -loop spaces of motivic Thom spectra using the technique of framed correspondences. This result allows us to express non-negative $\mathbb{G}_{m}$ -homotopy groups of motivic Thom spectra in terms of geometric generators and relations. Using this explicit description, we show that the unit map of the algebraic special linear cobordism spectrum induces an isomorphism on $\mathbb{G}_{m}$ -homotopy sheaves.
Computation of Jacobi sums of order l2 and 2l2 with prime l

In this article, we present fast computational algorithms for Jacobi sums of orders l^2 and 2l^2 with odd prime l. Cyclotomic numbers play a crucial rule in implementing these algorithms. To increase efficiency, we present explicit expressions for Jacobi sums of orders l^2 and 2l^2 in terms of the minimum number of cyclotomic numbers of orders l^2 and 2l^2 respectively, which will be utilized in implementing the algorithms. To validate these expressions, we implement two additional algorithms which demonstrate the minimality of cyclotomic numbers required for the determination of all Jacobi sums of orders l^2 and 2l^2 respectively.
A review on deep reinforcement learning for fluid mechanics: An update

In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning techniques has increased at fast pace, leading to a growing bibliography on the topic. Due to its ability to solve complex decision-making problems, deep reinforcement learning has especially emerged as a valuable tool to perform flow control, but recent publications also advertise the great potential for other applications, such as shape optimization or microfluidics. The present work proposes an exhaustive review of the existing literature and is a follow-up to our previous review on the topic. The contributions are regrouped by the domain of application and are compared together regarding algorithmic and technical choices, such as state selection, reward design, time granularity, and more. Based on these comparisons, general conclusions are drawn regarding the current state-of-the-art, and perspectives for future improvements are sketched.
A Study on Angular Based Embedding Learning for Text-independent Speaker Verification

Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization. The embeddings learned by softmax are not discriminative enough for open-set verification tasks. Angular based embedding learning target can achieve such discriminativeness by optimizing angular distance and adding margin penalty. We apply several different popular angular margin embedding learning strategies in this work and explicitly compare their performance on Voxceleb speaker recognition dataset. Observing the fact that encouraging inter-class separability is important when applying angular based embedding learning, we propose an exclusive inter-class regularization as a complement for angular based loss. We verify the effectiveness of these methods for learning a discriminative embedding space on ASV task with several experiments. These methods together, we manage to achieve an impressive result with 16.5% improvement on equal error rate (EER) and 18.2% improvement on minimum detection cost function comparing with baseline softmax systems.
3-D Positioning and Environment Mapping for mmWave Communication Systems

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths, large antenna arrays, and increased densities of base stations allow for their unparalleled delay and angular resolution. Combining localization into communications and designing simultaneous localization and mapping (SLAM) algorithms are challenging problems. This study considers the joint position and velocity estimation and environment mapping problem in a three-dimensional mmWave CRAN architecture. We first embed cooperative localization into communications and establish the joint estimation and mapping model with hybrid delay and angle measurements. Then, we propose a closed-form weighted least square (WLS) solution for the joint estimation and mapping problems. The proposed WLS estimator is proven asymptotically unbiased and confirmed by simulations as effective in achieving the Cramer-Rao lower bound (CRLB) and the desired decimeter-level accuracy. Furthermore, we propose a WLS-Net-based SLAM algorithm by embedding neural networks into the proposed WLS estimators to replace the linear approximations. The solution possesses both powerful learning ability of the neural network and robustness of the proposed geometric model, and the ensemble learning is applied to further improve positioning accuracy. A public ray-tracing dataset is used in the simulations to test the performance of the WLS-Net-based SLAM algorithm, which is proven fast and effective in attaining the centimeter-level accuracy.
Chlorine-bearing molecules in molecular absorbers at intermediate redshifts

We use observations of chlorine-bearing species in molecular absorbers at intermediate redshifts to investigate chemical properties and 35Cl/37Cl isotopic ratios in the absorbing sightlines. Chloronium (H2Cl+) is detected along three independent lines of sight in the z = 0.89 and z = 0.68 molecular absorbers located in front of the lensed quasars PKS 1830−211 and B 0218+357, respectively. Hydrogen chloride (HCl) was observed only toward PKS 1830−211, and is found to behave differently from H2Cl+. It is detected in one line of sight with an abundance ratio [H2Cl+] / [HCl] ∼1, but remains undetected in the other, more diffuse, line of sight, with a ratio [H2Cl+] / [HCl] > 17. The absorption profiles of these two chlorine-bearing species are compared to other species and discussed in terms of the physical properties of the absorbing gas. Our findings are consistent with the picture emerging from chemical models where different species trace gas with different molecular hydrogen fraction. The 35Cl/37Cl isotopic ratios are measured in the different lines of sight and are discussed in terms of stellar nucleosynthesis.