Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage the DTN to perform different optimization tasks (e.g., Traffic Engineering, Network Planning). Deep Reinforcement Learning (DRL) showed a high performance when applied to solve network optimization problems. In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior. However, DRL scales poorly with the problem size and complexity. In this paper, we explore the use of Evolutionary Strategies (ES) to train DRL agents for solving a routing optimization problem. The experimental results show that ES achieved a training time speed-up of 128 and 6 for the NSFNET and GEANT2 topologies respectively.
Exoplanet Characterization using Conditional Invertible Neural Networks

The characterization of an exoplanet's interior is an inverse problem, which requires statistical methods such as Bayesian inference in order to be solved. Current methods employ Markov Chain Monte Carlo (MCMC) sampling to infer the posterior probability of planetary structure parameters for a given exoplanet. These methods are time consuming since they require the calculation of a large number of planetary structure models. To speed up the inference process when characterizing an exoplanet, we propose to use conditional invertible neural networks (cINNs) to calculate the posterior probability of the internal structure parameters. cINNs are a special type of neural network which excel in solving inverse problems. We constructed a cINN using FrEIA, which was then trained on a database of $5.6\cdot 10^6$ internal structure models to recover the inverse mapping between internal structure parameters and observable features (i.e., planetary mass, planetary radius and composition of the host star). The cINN method was compared to a Metropolis-Hastings MCMC. For that we repeated the characterization of the exoplanet K2-111 b, using both the MCMC method and the trained cINN. We show that the inferred posterior probability of the internal structure parameters from both methods are very similar, with the biggest differences seen in the exoplanet's water content. Thus cINNs are a possible alternative to the standard time-consuming sampling methods. Indeed, using cINNs allows for orders of magnitude faster inference of an exoplanet's composition than what is possible using an MCMC method, however, it still requires the computation of a large database of internal structures to train the cINN. Since this database is only computed once, we found that using a cINN is more efficient than an MCMC, when more than 10 exoplanets are characterized using the same cINN.
A General, Evolution-Inspired Reward Function for Social Robotics

The field of social robotics will likely need to depart from a paradigm of designed behaviours and imitation learning and adopt modern reinforcement learning (RL) methods to enable robots to interact fluidly and efficaciously with humans. In this paper, we present the Social Reward Function as a mechanism to provide (1) a real-time, dense reward function necessary for the deployment of RL agents in social robotics, and (2) a standardised objective metric for comparing the efficacy of different social robots. The Social Reward Function is designed to closely mimic those genetically endowed social perception capabilities of humans in an effort to provide a simple, stable and culture-agnostic reward function. Presently, datasets used in social robotics are either small or significantly out-of-domain with respect to social robotics. The use of the Social Reward Function will allow larger in-domain datasets to be collected close to the behaviour policy of social robots, which will allow both further improvements to reward functions and to the behaviour policies of social robots. We believe this will be the key enabler to developing efficacious social robots in the future.
Mixed Variational Finite Elements for Implicit, General-Purpose Simulation of Deformables

We propose and explore a new, general-purpose method for the implicit time integration of elastica. Key to our approach is the use of a mixed variational principle. In turn its finite element discretization leads to an efficient alternating projections solver with a superset of the desirable properties of many previous fast solution strategies. This framework fits a range of elastic constitutive models and remains stable across a wide span of timestep sizes, material parameters (including problems that are quasi-static and approximately rigid). It is efficient to evaluate and easily applicable to volume, surface, and rods models. We demonstrate the efficacy of our approach on a number of simulated examples across all three codomains.
From Explanations to Segmentation: Using Explainable AI for Image Segmentation

The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision. In this work, we address this by following an indirect solution. We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation from the output of the Layer-wise Relevance Propagation (LRP) explaining the decision of a classification network. We show that we achieve similar results compared to an established U-Net segmentation architecture, while the generation of the training data is significantly simplified. The proposed method can be trained in a weakly supervised fashion, as the training samples must be only labeled on image-level, at the same time enabling the output of a segmentation mask. This makes it especially applicable to a wider range of real applications where tedious pixel-level labelling is often not possible.
The Impact of Removing Head Movements on Audio-Visual Speech Enhancement

This paper investigates the impact of head movements on audio-visual speech enhancement (AVSE). Although being a common conversational feature, head movements have been ignored by past and recent studies: they challenge today’s learning-based methods as they often degrade the performance of models that are trained on clean, frontal, and steady face images. To alleviate this problem, we propose to use robust face frontalization (RFF) in combination with an AVSE method based on a variational auto-encoder (VAE) model. We briefly describe the basic ingredients of the proposed pipeline and we perform experiments with a recently released audio-visual dataset. In the light of these experiments, and based on three standard metrics, namely STOI, PESQ and SI-SDR, we conclude that RFF improves the performance of AVSE by a considerable margin.1
Deep Reference Priors: What is the best way to pretrain a model?

What is the best way to exploit extra data—be it unlabeled data from the same task, or labeled data from a related task—to learn a given task? This paper formalizes the question using the theory of reference priors. Reference priors are objective, uninformative Bayesian priors that maximize the mutual information between the task and the weights of the model. Such priors enable the task to maximally affect the Bayesian posterior, e.g., reference priors depend upon the number of samples available for learning the task and for very small sample sizes, the prior puts more probability mass on low-complexity models in the hypothesis space. This paper presents the first demonstration of reference priors for medium-scale deep networks and image-based data. We develop generalizations of reference priors and demonstrate applications to two problems. First, by using unlabeled data to compute the reference prior, we develop new Bayesian semi-supervised learning methods that remain effective even with very few samples per class. Second, by using labeled data from the source task to compute the reference prior, we develop a new pretraining method for transfer learning that allows data from the target task to maximally affect the Bayesian posterior. Empirical validation of these methods is conducted on image classification datasets. Code is available at https://github.com/grasp-lyrl/deep reference priors.
A Cheap Bootstrap Method for Fast Inference

The bootstrap is a versatile inference method that has proven powerful in many statistical problems. However, when applied to modern large-scale models, it could face substantial computation demand from repeated data resampling and model fitting. We present a bootstrap methodology that uses minimal computation, namely with a resample effort as low as one Monte Carlo replication, while maintaining desirable statistical guarantees. We present the theory of this method that uses a twisted perspective from the standard bootstrap principle. We also present generalizations of this method to nested sampling problems and to a range of subsampling variants, and illustrate how it can be used for fast inference across different estimation problems.
Empirical complexity of comparator-based nearest neighbor descent

A Java parallel streams implementation of the $K$-nearest neighbor descent algorithm is presented using a natural statistical termination criterion. Input data consist of a set $S$ of $n$ objects of type V, and a Function>, which enables any $x \in S$ to decide which of $y, z \in S\setminus\{x\}$ is more similar to $x$. Experiments with the Kullback-Leibler divergence Comparator support the prediction that the number of rounds of $K$-nearest neighbor updates need not exceed twice the diameter of the undirected version of a random regular out-degree $K$ digraph on $n$ vertices. Overall complexity was $O(n K^2 \log_K(n))$ in the class of examples studied. When objects are sampled uniformly from a $d$-dimensional simplex, accuracy of the $K$-nearest neighbor approximation is high up to $d = 20$, but declines in higher dimensions, as theory would predict.
Absolute measurements of state-to-state rotational energy transfer between CO and 
H2
 at interstellar temperatures

Hamza Labiad, Martin Fournier, Laura A. Mertens, 2 Alexandre Faure, David Carty, Thierry Stoecklin, Piotr Jankowski, Krzysztof Szalewicz, Sébastien D. Le Picard, and Ian R. Sims ∗ Univ Rennes, CNRS, IPR (Institut de Physique de Rennes) UMR 6251, F-35000 Rennes, France Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States Université de Grenoble Alpes, CNRS, IPAG, F-38000 Grenoble, France Durham University, Joint Quantum Centre Durham-Newcastle, Departments of Physics and Chemistry, Lower Mountjoy, South Road, Durham DH1 3LE, UK Institut des Sciences Moléculaires, Université de Bordeaux, F-33405 Talence, France Faculty of Chemistry, Nicolaus Copernicus University in Torun, Gagarina 7, PL-87-100 Torun, Poland Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA (Dated: February 2, 2022)
Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra

—Low-rank approximation of images via singular value decomposition is well-received in the era of big data. However, singular value decomposition (SVD) is only for order-two data, i.e., matrices. It is necessary to ﬂatten a higher order input into a matrix or break it into a series of order-two slices to tackle higher order data such as multispectral images and videos with the SVD. Higher order singular value decomposition (HOSVD) extends the SVD and can approximate higher order data using sums of a few rank-one components. We consider the problem of generalizing HOSVD over a ﬁnite-dimensional commutative algebra. This algebra, referred to as t-algebra, generalizes the ﬁeld of complex numbers. The elements of the algebra, called t-scalars, are ﬁxed-sized arrays of complex numbers. To obtain higher-performance versions, one can generalize matrices and tensors over t-scalars and then extend many canonical matrix and tensor algorithms, including HOSVD. The generalization of HOSVD is called THOSVD. An alternating algorithm can further improve its performance in approximating multiway data. THOSVD also uniﬁes a wide range of principal component analysis algorithms. To exploit the potential of generalized algorithms using t-scalars for approximating images, we use a pixel neighbor- hood strategy to convert each pixel to a “deeper-order” t-scalar. Experiments on publicly available images show that the generalized algorithm over t-scalars, namely THOSVD, compares favorably with its canonical counterparts.
Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction

We consider feature representation learning problem of molecular graphs. Graph Neural Networks have been widely used in feature representation learning of molecular graphs. However, most existing methods deal with molecular graphs individually while neglecting their connections, such as motif-level relationships. We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue. In particular, we build a heterogeneous motif graph that contains motif nodes and molecular nodes. Each motif node corresponds to a motif extracted from molecules. Then, we propose a Heterogeneous Motif Graph Neural Network (HM-GNN) to learn feature representations for each node in the heterogeneous motif graph. Our heterogeneous motif graph also enables effective multi-task learning, especially for small molecular datasets. To address the potential efﬁ-ciency issue, we propose to use an edge sampler, which can signiﬁcantly reduce computational resources usage. The experimental results show that our model consistently outperforms previous state-of-the-art models. Under multi-task settings, the promising performances of our methods on combined datasets shed light on a new learning paradigm for small molecular datasets. Finally, we show that our model achieves similar performances with signiﬁcantly less computational resources by using our edge sampler.
Relative Transformation Estimation Based on Fusion of Odometry and UWB Ranging Data

In this article, we study the problem of estimating the four-degree-of-freedom (3-D position and heading) robot-to-robot relative frame transformation using onboard odometry and interrobot distance measurements. First, we present a theoretical analysis of the problem, namely, the derivation and interpretation of the Cramèr–Rao lower bound, the Fisher information matrix, and its determinant. Second, we propose optimization-based solutions, including a quadratically constrained quadratic programming (QCQP) formulation and its semidefinite programming (SDP) relaxation. Third, based on the theoretical results, we can detect singular configurations as well as measure the uncertainty of each individual parameter. We perform extensive simulations and real-life experiments with aerial robots to show that the proposed QCQP and SDP methods can outperform state-of-the-art approaches, especially in geometrically poor or large measurement noise conditions. In general, the QCQP method provides the best results at the expense of computational time, while the SDP method runs much faster and is sufficiently accurate in most cases.
Improving parametric neural networks for high-energy physics (and beyond)

Signal-background classification is a central problem in high-energy physics, that plays a major role for the discovery of new fundamental particles. A recent method—the parametric neural network (pNN)—leverages multiple signal mass hypotheses as an additional input feature to effectively replace a whole set of individual classifiers, each providing (in principle) the best response for the corresponding mass hypothesis. In this work we aim at deepening the understanding of pNNs in light of real-world usage. We discovered several peculiarities of parametric networks, providing intuition, metrics, and guidelines to them. We further propose an alternative parametrization scheme, resulting in a new parametrized neural network architecture: the AffinePNN; along with many other generally applicable improvements, like the balanced training procedure. Finally, we extensively and empirically evaluate our models on the HEPMASS dataset, along its imbalanced version (called HEPMASS-IMB) we provide here for the first time, to further validate our approach. Provided results are in terms of the impact of the proposed design decisions, classification performance, and interpolation capability, as well.
High-performance, additively-manufactured atomic spectroscopy apparatus for portable quantum technologies.

We demonstrate a miniaturised and highly robust system for performing Doppler-free spectroscopy on thermal atomic vapour for three frequencies as required for cold atom-based quantum technologies. The application of additive manufacturing techniques, together with efficient use of optical components, produce a compact, stable optical system, with a volume of 0.089 L and a weight of 120 g. The device occupies less than a tenth of the volume of, and is considerably lower cost than, conventional spectroscopic systems, but also offers excellent stability against environmental disturbances. We characterise the response of the system to changes in environmental temperature between 7 and 35 ∘C and exposure to vibrations between 0 - 2000 Hz, finding that the system can reliably perform spectroscopic measurements despite substantial vibrational noise and temperature changes. Our results show that 3D-printed optical systems are an excellent solution for portable quantum technologies.
Evidence for Super-Polynomial Advantage of QAOA over Unstructured Search

We compare the performance of several variations of the Quantum Alternating Operator Ansatz (QAOA) on constrained optimization problems. Speciﬁcally, we study the Clique, Ring, and Grover mixers as well as the traditional objective value and recently introduced threshold-based phase separators. These are studied through numerical simulation on k -Densest Subgraph, Maximum k - Vertex Cover, and Maximum Bisection problems of size up to n = 18 on Erd˝os-Renyi graphs. We show that only one of these QAOA variations, the Clique mixer with objective value phase separator, outperforms Grover-style unstructured search, with a potentially super-polynomial advantage.
A Possible Alignment Between the Orbits of Planetary Systems and their Visual Binary Companions

Astronomers do not have a complete picture of the effects of wide-binary companions (semimajor axes greater than 100 au) on the formation and evolution of exoplanets. We investigate these effects using new data from Gaia Early Data Release 3 and the Transiting Exoplanet Survey Satellite mission to characterize wide-binary systems with transiting exoplanets. We identify a sample of 67 systems of transiting exoplanet candidates (with well-determined, edge-on orbital inclinations) that reside in wide visual binary systems. We derive limits on orbital parameters for the wide-binary systems and measure the minimum difference in orbital inclination between the binary and planet orbits. We determine that there is statistically significant difference in the inclination distribution of wide-binary systems with transiting planets compared to a control sample, with the probability that the two distributions are the same being 0.0037. This implies that there is an overabundance of planets in binary systems whose orbits are aligned with those of the binary. The overabundance of aligned systems appears to primarily have semimajor axes less than 700 au. We investigate some effects that could cause the alignment and conclude that a torque caused by a misaligned binary companion on the protoplanetary disk is the most promising explanation.
Can Population III stars be major origins of both merging binary black holes and extremely metal poor stars?

Population (Pop) III stars, first stars, or metal-free stars are made of primordial gas. We have examined if they can be dominant origins of merging binary black holes (BHs) and extremely metal-poor stars. The abundance pattern of EMP stars is helpful to trace back the properties of Pop III stars. We have confirmed previous arguments that the observed BH merger rate needs Pop III star formation efficiency 10 times larger than theoretically predicted values, while the cosmic reionization history still permits such a high Pop III star formation efficiency. On the other hand, we have newly found that the elemental abundance pattern of EMP stars only allows the Pop III initial mass function with the minimum mass of ∼ 15−27 M . In other words, the minimum mass must not deviate largely from the critical mass below and above which Pop III stars leave behind neutron stars and BHs, respectively. Pop III stars may be still a dominant origin of merging binary BHs but our study has reduced the allowed parameter space under a hypothesis that EMP stars are formed from primordial gas mixed with Pop III supernova ejecta.
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration

Policy gradient (PG) estimation becomes a challenge when we are not allowed to sample with the target policy but only have access to a dataset generated by some unknown behavior policy. Conventional methods for off-policy PG estimation often suffer from either significant bias or exponentially large variance. In this paper, we propose the double Fitted PG estimation (FPG) algorithm. FPG can work with an arbitrary policy parameterization, assuming access to a Bellman-complete value function class. In the case of linear value function approximation, we provide a tight finite-sample upper bound on policy gradient estimation error, that is governed by the amount of distribution mismatch measured in feature space. We also establish the asymptotic normality of FPG estimation error with a precise covariance characterization, which is further shown to be statistically optimal with a matching Cramer-Rao lower bound. Empirically, we evaluate the performance of FPG on both policy gradient estimation and policy optimization, using either softmax tabular or ReLU policy networks. Under various metrics, our results show that FPG significantly outperforms existing off-policy PG estimation methods based on importance sampling and variance reduction techniques.