Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalizable Episodic Memory for Deep Reinforcement Learning

Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning. However, little effort is put into the continuous domain, where a state is never visited twice, and previous episodic methods fail to efficiently aggregate experience across trajectories. To address this problem, we propose Generalizable Episodic Memory (GEM), which effectively organizes the state-action values of episodic memory in a generalizable manner and supports implicit planning on memorized trajectories. GEM utilizes a double estimator to reduce the overestimation bias induced by value propagation in the planning process. Empirical evaluation shows that our method significantly outperforms existing trajectory-based methods on various MuJoCo continuous control tasks. To further show the general applicability, we evaluate our method on Atari games with discrete action space, which also shows a significant improvement over baseline algorithms.
Level-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement

—The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i.e. style feature , and the feature representing the invariant semantic content, i.e. content feature . Previous methods separate content feature apart by utilizing it to classify haze image during the training process. However, in this paper we recognize the incompleteness of the content-style disentanglement in such technical routine. The ﬂawed style feature entangled with content information inevitably leads the ill-rendering of the haze images. To address, we propose a self-supervised style regression via stochastic linear interpolation to reduce the content information in style feature. The ablative experiments demonstrate the disentangling completeness and its superiority in level-aware haze image synthesis. Moreover, the generated haze data are applied in the testing generalization of vehicle detectors. Further study between haze-level and detection performance shows that haze has obvious impact on the generalization of the vehicle detectors and such performance degrading level is linearly correlated to the haze-level, which, in turn, validates the effectiveness of the proposed method.
Optical properties of elongated conducting grains

Extremely elongated, conducting dust particles (also known as metallic “needles” or “whiskers”) are seen in carbonaceous chondrites and in samples brought back from the Itokawa asteroid. Their formation in protostellar nebulae and subsequent injection into the interstellar medium have been demonstrated, both experimentally and theoretically. Metallic needles have been suggested to explain a wide variety of astrophysical phenomena, ranging from the mid-infrared interstellar extinction at ∼ 3–8μm to the thermalization of starlight to generate the cosmic microwave background. To validate (or invalidate) these suggestions, an accurate knowledge of the optics (e.g., the amplitude and the wavelength dependence of the absorption cross sections) of metallic needles is crucial. Here we calculate the absorption cross sections of iron needles of various aspect ratios over a wide wavelength range, by exploiting the discrete dipole approximation, the most powerful technique for rigorously calculating the optics of irregular or nonspherical grains. Our calculations support the earlier findings that the antenna theory and the Rayleigh approximation, which are often taken to approximate the optical properties of metallic needles, are indeed inapplicable.
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets

Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users’ age. The objective of this study was to develop and evaluate a method that automatically identifies the exact age of users based on self-reports in their tweets. Our end-to-end automatic natural language processing (NLP) pipeline, ReportAGE, includes query patterns to retrieve tweets that potentially mention an age, a classifier to distinguish retrieved tweets that self-report the user’s exact age (“age” tweets) and those that do not (“no age” tweets), and rule-based extraction to identify the age. To develop and evaluate ReportAGE, we manually annotated 11,000 tweets that matched the query patterns. Based on 1000 tweets that were annotated by all five annotators, inter-annotator agreement (Fleiss’ kappa) was 0.80 for distinguishing “age” and “no age” tweets, and 0.95 for identifying the exact age among the “age” tweets on which the annotators agreed. A deep neural network classifier, based on a RoBERTa-Large pretrained transformer model, achieved the highest F1-score of 0.914 (precision = 0.905, recall = 0.942) for the “age” class. When the age extraction was evaluated using the classifier’s predictions, it achieved an F1-score of 0.855 (precision = 0.805, recall = 0.914) for the “age” class. When it was evaluated directly on the held-out test set, it achieved an F1-score of 0.931 (precision = 0.873, recall = 0.998) for the “age” class. We deployed ReportAGE on a collection of more than 1.2 billion tweets, posted by 245,927 users, and predicted ages for 132,637 (54%) of them. Scaling the detection of exact age to this large number of users can advance the utility of social media data for research applications that do not align with the predefined age groupings of extant binary or multi-class classification approaches.
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification

Recent works in neural network veriﬁcation show that cheap incomplete veriﬁers such as CROWN, based upon bound propagations, can effectively be used in Branch-and-Bound (BaB) methods to accelerate complete veriﬁcation, achieving signiﬁcant speedups compared to expensive linear programming (LP) based techniques. However, they cannot fully handle the per-neuron split constraints introduced by BaB like LP veriﬁers do, leading to looser bounds and hurting their veri-ﬁcation efﬁciency. In this work, we develop β - CROWN, a new bound propagation based method that can fully encode per-neuron splits via opti-mizable parameters β . When the optimizable parameters are jointly optimized in intermediate layers, β -CROWN has the potential of producing better bounds than typical LP veriﬁers with neuron split constraints, while being efﬁciently parallelizable on GPUs. Applied to the complete veriﬁcation setting, β -CROWN is close to three orders of magnitude faster than LP-based BaB methods for robustness veriﬁcation, and also over twice faster than state-of-the-art GPU-based complete veri-ﬁers with similar timeout rates. By terminating BaB early, our method can also be used for incomplete veriﬁcation. Compared to the state-of-the-art semideﬁnite-programming (SDP) based veri-ﬁer
Yielding, shear banding, and brittle failure of amorphous materials

Widespread processes in nature and technology are governed by the dynamical transition whereby a material in an initially solid-like state, whether soft or hard, then yields. Major unresolved questions concern whether any material will yield smoothly and gradually (“ductile” behaviour) or fail abruptly and catastrophically (“brittle” behaviour); the roles of sample annealing, disorder and shear band formation in the onset of yielding and failure; and, most importantly from a practical viewpoint, whether any impending catastrophic failure can be predicted before it happens. We address these questions by studying theoretically the yielding of slowly sheared athermal amorphous materials, within a minimal mesoscopic lattice elastoplastic description. Our contributions are fourfold. First, we elucidate whether yielding will be ductile or brittle, for any given level of sample annealing prior to shear. For highly annealed samples, we ﬁnd brittle yielding for all samples sizes. For poorly annealed samples we uncover an important dependence on the size of the sample of material being sheared, with ductile yielding for small samples, and brittle yielding only for large system sizes. Second, we show that yielding comprises two distinct stages: a pre-failure stage, in which small levels of strain heterogeneity slowly accumulate within the material, followed by a catastrophic brittle failure event, in which a shear band quickly propagates across the sample via a cooperating line of (individually) localised plastic events. Third, we provide an exact expression for the slowly growing level of strain heterogeneity in the pre-failure stage, expressed in terms of the macroscopically measured stress-strain curve and the sample size, and in excellent agreement with our simulation results. Fourth, we elucidate the basic mechanism via which a shear band then nucleates, in terms of the onset of cooperativity between plastic events. We furthermore provide an expression for the probability distribution of shear strains at which failure occurs, expressed in terms of the sample size and the disorder inherent in the sample, as determined by the degree of annealing prior to shear. Importantly, this indicates a possible route to predicting impending sudden material
Firms’ challenges and social responsibilities during Covid-19: A Twitter analysis

This paper offers insights on the major issues and challenges firms face in the Covid-19 pandemic and their concerns for Corporate Social Responsibility (CSR) themes. To do so, we investigate large Italian firms’ discussions on Twitter in the first nine months of the pandemic. Specifically, we ask: How is firms’ Twitter discussion developing during the Covid-19 pandemic? Which CSR dimensions and topics do firms discuss? To what extent do they resonate with the public? We downloaded Twitter posts by the accounts of large Italian firms, and we built the bipartite network of accounts and hashtags. Using an entropy-based null model as a benchmark, we projected the information contained in the network into the accounts layers, identifying a network of accounts. We find that the network is composed of 13 communities and accounts at the core of the network focus on environmental sustainability, digital innovation, and safety. Firms’ ownership type does not seem to influence the conversation. While the relevance of CSR hashtags and stakeholder engagement is relatively small, peculiarities arise in some communities. Overall, our paper highlights the contribution of online social networks and complex networks methods for management and strategy research, showing the role of online social media in understanding firms’ issues, challenges, and responsibilities, with common narratives naturally emerging from data.
The monochlorine fluorides (ClFn) and their anions (ClFn-) n = 1-7: structures and energetics

Predictions of molecular structure and energies have been made for each of the members in the ClF n /ClF n - (n = 1-7) series of molecules using density functional theory (DFT). The three different types of prediction for electron affinities determined in this work are: the adiabatic electron affinity (EAad), the vertical electron affinity (EAvert), and the vertical detachment energy (VDE) of the anion. Also reported are the first Cl-F dissociation energies for both the neutral and the anion (D CIFn ). Self-consistent Kohn-Sham orbitals were obtained using four different functional forms and a double-ζ plus polarization (DZP) basis set. When diffuse s- and p-type functions were added to the basis set, the anions were stabilized significantly with respect to the neutrals, and the electron affinity values were increased significantly. Overall, the best agreement with experimental structures was obtained with the DZP++ BHLYP method based upon Becke's half-and-half exchange functional and the Lee-Yang-Parr co.
Generalized Contrastive Optimization of Siamese Networks for Place Recognition

Visual place recognition is a challenging task in computer vision and a key component of camera-based localization and navigation systems. Recently, Convolutional Neural Networks (CNNs) achieved high results and good generalization capabilities. They are usually trained using pairs or triplets of images labeled as either similar or dissimilar, in a binary fashion. In practice, the similarity between two images is not binary, but continuous. Furthermore, training these CNNs is computationally complex and involves costly pair and triplet mining strategies. We propose a Generalized Contrastive loss (GCL) function that relies on image similarity as a continuous measure, and use it to train a siamese CNN. Furthermore, we present three techniques for automatic annotation of image pairs with labels indicating their degree of similarity, and deploy them to re-annotate the MSLS, TB-Places, and 7Scenes datasets. We demonstrate that siamese CNNs trained using the GCL function and the improved annotations consistently outperform their binary counterparts. Our models trained on MSLS outperform the state-of-the-art methods, including NetVLAD, NetVLAD-SARE, AP-GeM and Patch-NetVLAD, and generalize well on the Pittsburgh30k, Tokyo 24/7, RobotCar Seasons v2 and Extended CMU Seasons datasets. Furthermore, training a siamese network using the GCL function does not require complex pair mining. We release the source code at https://github.com/marialeyvallina/generalized_contrastive_loss.
Partial Meissner effect measurement by a superconducting magnetic flux lens

Magnetic flux trapping in the Meissner transition of superconducting radio frequency cavities can substantially increase dissipation which impacts cryogenic costs and necessitates expensive magnetic shielding. Recent findings point at the material preparation and the cool down dynamics at the vortex state as the main ways to counteract flux trapping. Most of the related measurements are performed on the cavity, with risk of results being impacted by the cool down specifics of each facility. We demonstrate an alternative experiment in which flux expulsion is quantified from sheet material, with samples conduction cooled in a stand-alone instrument designed to collimate expelled flux at controlled cool down conditions. A series of tests reliably and reproducibly relate the amount of expelled flux to the cooling rate, the velocity of the superconducting front and the spatial temperature gradient. The results are in line with tests performed on bare cavities and show that expulsion of trapped flux improves with the spatial temperature gradient, independent on the cooling rate. Our concept offers means to control the dynamics of the Meissner effect and can be used for material qualification prior to cavity fabrication.
Saddle Point Method for Transient Processes in Waveguides

A modification of the saddle point method is proposed for computation of nonstationary wave processes (pulses) in waveguides. The dispersion diagram of the waveguide is continued analytically. A set of possible saddle points on the dispersion diagram is introduced. A method of checking whether the particular saddle points contribute terms to the field decomposition is proposed. A classification of the waveguides based on the topology of the set of possible saddle points is outlined.
A primal-dual flow for affine constrained convex optimization

We introduce a novel primal-dual flow for affine constrained convex optimization problems. As a modification of the standard saddle-point system, our primal-dual flow is proved to possess the exponential decay property, in terms of a tailored Lyapunov function. Then two primal-dual methods are obtained from numerical discretizations of the continuous model, and global nonergodic linear convergence rate is established via a discrete Lyapunov function. Instead of solving the subproblem of the primal variable, we apply the semi-smooth Newton iteration to the subproblem with respect to the multiplier, provided that there are some additional properties such as semi-smoothness and sparsity. Especially, numerical tests on the linearly constrained l 1 -l 2 minimization and the total-variation based image denoising model have been provided.
XDO: A Double Oracle Algorithm for Extensive-Form Games

Policy Space Response Oracles (PSRO) is a reinforcement learning (RL) algorithm for two-player zero-sum games that has been empirically shown to find approximate Nash equilibria in large games. Although PSRO is guaranteed to converge to an approximate Nash equilibrium and can handle continuous actions, it may take an exponential number of iterations as the number of information states (infostates) grows. We propose Extensive-Form Double Oracle (XDO), an extensive-form double oracle algorithm for two-player zero-sum games that is guaranteed to converge to an approximate Nash equilibrium linearly in the number of infostates. Unlike PSRO, which mixes best responses at the root of the game, XDO mixes best responses at every infostate. We also introduce Neural XDO (NXDO), where the best response is learned through deep RL. In tabular experiments on Leduc poker, we find that XDO achieves an approximate Nash equilibrium in a number of iterations an order of magnitude smaller than PSRO. Experiments on a modified Leduc poker game and Oshi-Zumo show that tabular XDO achieves a lower exploitability than CFR with the same amount of computation. We also find that NXDO outperforms PSRO and NFSP on a sequential multidimensional continuous-action game. NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games. Experiment code is available at https://github.com/indylab/nxdo.
Ab initio
 calculations of electric dipole moments of light nuclei

In any finite system, the presence of a non-zero permanent electric dipole moment (EDM) would indicate CP violation beyond the small violation predicted in the Standard Model. Here, we use the ab initio no-core shell model (NCSM) framework to theoretically investigate the magnitude of the nuclear EDM. We calculate EDMs of several light nuclei using chiral twoand three-body interactions and a PT-violating Hamiltonian based on a one-meson-exchange model. We present a benchmark calculation for He, as well as results for the more complex nuclei Li, Be, B, C, N, and F. Our results suggest that different nuclei can be used to probe different terms of the PT violating interaction. These calculations allow us to suggest which nuclei may be good candidates in the search for a measurable permanent electric dipole moment.
Calibrated and Partially Calibrated Semi-Generalized Homographies

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
Graph and wreath products in topological full groups of full shifts

We prove that the topological full group JXK of a two-sided full shift X = Σ contains every right-angled Artin group (also called a graph group). More generally, we show that the family of subgroups with “linear look-ahead” is closed under graph products. We show that the lamplighter group Z2 ≀ Z embeds in JXK, and conjecture that it does not embed in JXK with linear look-ahead. Generalizing the lamplighter group, we show that whenever G acts with “unique moves” (or at least “move-Aithfully”), we have A ≀ G ≤ JXK for finite abelian groups A. We show that free products of finite and cyclic groups act with unique moves. We show that Z does not admit move-Aithful actions, and conjecture that Z2 ≀ Z 2 does not embed in JXK at all. We show that topological full groups of all infinite nonwandering sofic shifts have the same subgroups, and that this set of groups is closed under commensurability. The group JXK embeds in the higher-dimensional Thompson group 2V, so it follows that 2V contains all RAAGs, refuting a conjecture of Belk, Bleak and Matucci.
Design and Analysis of Bipartite Experiments Under a Linear Exposure-response Model

A bipartite experiment consists of one set of units being assigned treatments and another set of units for which we measure outcomes. The two sets of units are connected by a bipartite graph, governing how the treated units can affect the outcome units. The bipartite framework naturally arises in marketplace experiments where, for example, experimenters may seek to investigate the effect of discounting goods on buyer behavior. In this paper, we consider estimation of the average total treatment effect in the bipartite experimental framework under a linear exposure-response model. We introduce the Exposure Reweighted Linear (ERL) estimator, and show that the estimator is unbiased, consistent and asymptotically normal, provided that the bipartite graph is sufficiently sparse. To facilitate inference, we introduce an unbiased and consistent estimator of the variance of the ERL point estimator. In addition, we introduce a cluster-based design, Exposure-Design, that uses heuristics to increase the precision of the ERL estimator by realizing a desirable exposure distribution. Finally, we demonstrate the application of the described methodology to marketplace experiments using a publicly available Amazon user-item review dataset. The full version of the paper is available at: https://arxiv.org/abs/2103.06392.
Statistics of 700 Individually Studied W UMa Stars

We present a statistical study of the largest bibliographic compilation of stellar and orbital parameters of W UMa stars derived by light-curve synthesis with Roche models. The compilation includes nearly 700 individually investigated objects from over 450 distinct publications. Almost 70% of this sample is comprised of stars observed in the past decade that have not been considered in previous statistical studies. We estimate the ages of the cataloged stars, model the distributions of their periods, mass ratios, temperatures, and other quantities, and compare them with the data from the Catalina Real-Time Transient Survey, LAMOST, and Gaia archives. As only a small fraction of the sample has radial-velocity curves, we examine the reliability of the photometric mass ratios in totally and partially eclipsing systems and find that totally eclipsing W UMa stars with photometric mass ratios have the same parameter distributions as those with spectroscopic mass ratios. Most of the stars with reliable parameters have mass ratios below 0.5 and orbital periods shorter than 0.5 days. Stars with longer periods and temperatures above 7000 K stand out as outliers and should not be labeled W UMa binaries. The collected data are available as an online database at https://wumacat.aob. rs.