Miracle-dz/UniFAR-FTUs huggingface dataset:
Real Robot Challenge: A Robotics Competition in the Cloud

Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designed and built robotic platforms that are hosted at MPI for Intelligent Systems and can be accessed remotely. Each platform consists of three robotic fingers that are capable of dexterous object manipulation. Users are able to control the platforms remotely by submitting code that is executed automatically, akin to a computational cluster. Using this setup, i) we host robotics competitions, where teams from anywhere in the world access our platforms to tackle challenging tasks ii) we publish the datasets collected during these competitions (consisting of hundreds of robot hours), and iii) we give researchers access to these platforms for their own projects.
Characterizing and Mitigating Telluric Absorption in Precise Radial Velocities. II. A Study of an M2-type Star

Telluric absorption lines impact the measuring of precise radial velocities (RVs) from high-resolution ground-based spectrographs. In this paper, we simulate the dependence of this impact on stellar spectral type and extend the work of the first paper in this series, which studied a G-type star, to a synthetic M-dwarf star. We quantify the bias in precise RV measurements in the visible and near-infrared (NIR) from the presence of tellurics in a simulated set of observations. We find that M-dwarf RVs are more impacted by tellurics compared to G-type stars. Specifically, for an M-dwarf star, tellurics can induce RV errors of up to 16 cm s−1 in the red optical and in excess of 220 cm s−1 in the NIR. For a G dwarf, the comparable RV systematics are 3 cm s−1 in the red optical and 240 cm s−1 in the NIR. We attribute this relative increase for M-dwarf stars to the increased concordance in wavelength between telluric lines and stellar Doppler information content. We compare the results of our simulation to data collected for Barnard’s star from the iSHELL spectrograph at the NASA Infrared Telescope Facility. This study was conducted as a follow-up to the NASA probe mission concept study EarthFinder.
New Insights into the Structure of Open Clusters in the Gaia Era

With the help of Gaia data, it is noted that in addition to the core components, there are low-density outer halo components in the extended region of open clusters. In order to study the extended structure beyond the core radius of the cluster (∼10 pc), based on Gaia Early Data Release 3, taking up to 50 pc as the searching radius, we use the pyUPMASK algorithm to redetermine the member stars of the open cluster within 1–2 kpc. We obtain the member stars of 256 open clusters, especially those located in the outer halo region of open clusters. Furthermore, we find that the radial density profile in the outer region for most open clusters deviates from the Kings profile. In order to better describe the internal and external structural characteristics of open clusters, we propose a two-component model: a core component with a King model distribution and an outer halo component with a logarithmic Gaussian distribution, and then suggest using four radii (r c , r t , r o , r e ) for describing the structure and distribution profile of star clusters, where r t and r e represent the boundaries of the core component and outer halo component, respectively. Finally, we provide a catalog of 256 clusters with structural parameters. In addition, our study shows the sizes of these radii are statistically linearly related, which indicates that the inner and outer regions of the cluster are interrelated and follow similar evolutionary processes. Further, we show that the structure of two components can be used to better trace the cluster evolution properties at different stages.
A new distance measurement and its application in K-Means Algorithm

K-Means clustering algorithm is one of the most commonly used clustering algorithms because of its simplicity and efficiency. K-Means clustering algorithm based on Euclidean distance only pays attention to the linear distance between samples, but ignores the overall distribution structure of the dataset (i.e. the fluid structure of dataset). Since it is difficult to describe the internal structure of two data points by Euclidean distance in high-dimensional data space, we propose a new distance measurement, namely, view-distance, and apply it to the K-Means algorithm. On the classical manifold learning datasets, S-curve and Swiss roll datasets, not only this new distance can cluster the data according to the structure of the data itself, but also the boundaries between categories are neat dividing lines. Moreover, we also tested the classification accuracy and clustering effect of the K-Means algorithm based on view-distance on some real-world datasets. The experimental results show that, on most datasets, the K-Means algorithm based on view-distance has a certain degree of improvement in classification accuracy and clustering effect.
Learning the Space of Deep Models

Embedding of large but redundant data, such as images or text, in a hierarchy of lower-dimensional spaces is one of the key features of representation learning approaches, which nowadays provide state-of-the-art solutions to problems once believed hard or impossible to solve. In this work 1, in a plot twist with a strong meta aftertaste, we show how trained deep models are as redundant as the data they are optimized to process, and how it is therefore possible to use deep learning models to embed deep learning models. In particular, we show that it is possible to use representation learning to learn a fixed-size, low-dimensional embedding space of trained deep models and that such space can be explored by interpolation or optimization to attain ready-to-use models. We find that it is possible to learn an embedding space of multiple instances of the same architecture and of multiple architectures. We address image classification and neural representation of signals, showing how our embedding space can be learnt so as to capture the notions of performance and 3D shape, respectively. In the Multi-Architecture setting we also show how an embedding trained only on a subset of architectures can learn to generate already-trained instances of architectures it never sees instantiated at training time.
Characterization of a large mass archaeological lead-based cryogenic detectors for the RES-NOVA experiment

One of the most energetic events in the Universe are core-collapse Supernovae (SNe), where almost all the star’s binding energy is released as neutrinos. These particles are direct probes of the processes occurring in the 1simone.quitadamo@gssi.it, luca.pattavina@lngs.infn. it Preprint submitted to Applied Radiation and Isotopes June 13, 2022 ar X iv :2 20 6. 05 11 6v 1 [ ph ys ic s. in sde t] 2 9 M ay 2 02 2 stellar core and provide unique insights into the gravitational collapse. RESNOVA will revolutionize how we detect neutrinos from astrophysical sources, by deploying the first ton-scale array of cryogenic detectors made from archaeological lead. Pb offers the highest neutrino interaction cross-section via coherent elastic neutrino-nucleus scattering (CEνNS). Such process will enable RES-NOVA to be equally sensitive to all neutrino flavors. For the first time, we propose to use archaeological Pb as sensitive target material in order to achieve an ultra-low background level in the region of interest (O(1keV)). All these features make possible the deployment of the first cmscale neutrino telescope for the investigation of astrophysical sources. In this contribution, we will characterize the radiopurity level and the performance of a small-scale proof-of-principle detector of RES-NOVA, consisting in a PbWO4 crystal made from archaeological-Pb operated as cryogenic detector.
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC

In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as exogenous signals with inexact bounds), where the Linear Time Invariant (LTI) models (vertices) captured by combinations of the scheduling parameters becomes wrong. We first propose to adopt a Moving Horizon Estimation (MHE) scheme to simultaneously estimate the convex combination vector and unmeasured states based on the observations and model matching error. To tackle the wrong LTI models used in both the MPC and MHE schemes, we then adopt a Policy Gradient (PG) Reinforcement Learning (RL) to learn both the estimator (MHE) and controller (MPC) so that the best closed-loop performance is achieved. The effectiveness of the proposed RL-based MHE/MPC design is demonstrated using an illustrative example.
Computation of high-frequency magnetoelastic waves in layered materials

The direct calculation of magnetoelastic wave dispersion in layered media is presented using an efficient, accurate computational technique. The governing, coupled equations for elasticity and magnetism, the Navier and Landau-Lifshitz equations, respectively, are linearized to form a quadratic eigenvalue problem that determines a complex web of wavenumber-frequency dispersion branches and their corresponding mode profiles. Numerical discretization of the eigenvalue problem via a spectral collocation method (SCM) is employed to determine the complete dispersion maps for both a single, finite-thickness magnetic layer and a finite magnetic-nonmagnetic double-layer. The SCM, previously used to study elastic waves in non-magnetic media, is fast, accurate, and adaptable to a variety of sample configurations and geometries. Emphasis is placed on the extremely high frequency regimes being accessed in ultrafast magnetism experiments. The dispersion maps and modes provide insight into how energy propagates through the coupled system, including how energy can be transferred between elastic- and magnetic-dominated waves as well as between different layers. The numerical computations for a single layer are further understood by a simplified analytical calculation in the high-frequency, exchange-dominated regime where the resonance condition required for energy exchange (an anticrossing) between quasi-elastic and quasi-magnetic dispersion branches is determined. Nonresonant interactions are shown to be well approximated by the dispersion of uncoupled elastic and magnetic waves. The methods and results provide fundamental theoretical tools to model and understand current and future magnetic devices powering spintronic innovation.
Multifidelity Reinforcement Learning with Control Variates

In many computational science and engineering applications, the output of a system of interest corresponding to a given input can be queried at different levels of fidelity with different costs. Typically, low-fidelity data is cheap and abundant, while high-fidelity data is expensive and scarce. In this work we study the reinforcement learning (RL) problem in the presence of multiple environments with different levels of fidelity for a given control task. We focus on improving the RL agent's performance with multifidelity data. Specifically, a multifidelity estimator that exploits the cross-correlations between the low- and high-fidelity returns is proposed to reduce the variance in the estimation of the state-action value function. The proposed estimator, which is based on the method of control variates, is used to design a multifidelity Monte Carlo RL (MFMCRL) algorithm that improves the learning of the agent in the high-fidelity environment. The impacts of variance reduction on policy evaluation and policy improvement are theoretically analyzed by using probability bounds. Our theoretical analysis and numerical experiments demonstrate that for a finite budget of high-fidelity data samples, our proposed MFMCRL agent attains superior performance compared with that of a standard RL agent that uses only the high-fidelity environment data for learning the optimal policy.
Efficient Quantum Circuit Design with a Standard Cell Approach

We design quantum circuits by using the standard cell approach borrowed from classical circuit design, which can speed-up the layout of circuits with a regular structure. Our standard cells are general and can be used for all types of quantum circuits: error-corrected or not. The standard cell approach enable the formulation of layout-aware routing algorithms. Herein, we use cubic standard cells for Toffoli gates and, starting from a 3D architecture, we design a multiplication circuit. We present evidence that, when compared with automatic routing methods, our layout-aware routers are significantly faster and achieve shallower 3D circuits (by at least 2.5x) and with a lower routing cost. Additionally, our co-design approach can be used to estimate the resources necessary for a quantum computation without using complex compilation methods. We conclude that standard cells, with the support of layout-aware routing, pave the way to very large scale methods for quantum circuit compilation.
Towards Target Sequential Rules

In many real-world applications, sequential rule mining (SRM) can provide prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that belong to high-frequency and high-confidence sequential rules. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on target sequential rules. Targeted sequential rule mining aims at mining the interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. This approach can further improve the efficiency of users in analyzing rules and reduce the consumption of data resources. In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining. Furthermore, we propose an efficient algorithm, called targeted sequential rule mining (TaSRM). Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of their running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
Development of a surrogate model for simplified neutronic calculations involved in the design stage of a thermonuclear fusion reactor

Several system codes have been developed since the eighties, with different objectives and appropriate architecture and level of development, aiming to explore possible operating condition ranges of a fusion power reactor. In some “system codes” technology/engineering assumptions/models (e.g. thermodynamic efficiency of coolant cycle, neutron multiplication coefficient, Tritium Breeding Ratio, radial built) are treated as input data inserted by the user and integrated in a main module essentially describing “plasma physics” aspects. In a first stage, these values come from previous studies on equivalent reactor concepts. Subsequent and more complete analyses with detailed models allow to confirm/deny these values and modify, if needed, for a second run and most likely several run, up to convergence. Some other codes consist of different specific modules (calculation tools) each one treating a separate aspects (e.g. physics, engineering, costing.) and integrated together in a common multiphysics calculation platform. Appropriate modules consistently calculate needed values using simplified models or surrogate models that enable an acceleration of the convergence of these systems codes. A system code based on this approach, SYCOMORE, is under development at CEA. The characteristics of the plasma and of the various reactor subsystems are addressed by various codes/models which are linked together via an integrated tokamak modelling platform. This platform allows creating a system design workflow by chaining the execution of the various modules. In this framework, this document describes a methodology developed to build the neutronic module of SYCOMORE: a surrogate model, based on neural network giving main neutronic parameters characterizing a fusion reactor (tokamak): tritium breeding ration (TBR), multiplication factor, and nuclear heating as a function of the reactor main geometrical parameters (major radius, elongation…), of the radial built, Li enrichment, blanket and shield thickness, etc. (3) In order to obtain a reliable surrogate model, a consistent database is needed. Simplified 1D and 2D neutronic calculation carried out with APOLLO2 (deterministic) and and TRIPOLI-4 (Montecarlo) codes codes are therefore used to fill the database. The URANIE platform is used to build the surrogate model from neutronic results. The simplified 1D and 2D models are validated against more detailed 3D Monte-Carlo model conducted with TRIPOLI-4. This methodology is devoted to helium cooled lithium lead (HCLL) blanket, but it could be applied to any breeder blanket concept provided that appropriate validation could be carried out.
A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models

Responsible use of machine learning requires that models be audited for undesirable properties. However, how to do principled auditing in a general setting has remained ill-understood. In this paper, we propose a formal learning-theoretic framework for auditing. We propose algorithms for auditing linear classiﬁers for feature sensitivity using label queries as well as different kinds of explanations
Empirical Bayes approach to Truth Discovery problems

When aggregating information from conflicting sources, one's goal is to find the truth. Most real-value \emph{truth discovery} (TD) algorithms try to achieve this goal by estimating the competence of each source and then aggregating the conflicting information by weighing each source's answer proportionally to her competence. However, each of those algorithms requires more than a single source for such estimation and usually does not consider different estimation methods other than a weighted mean. Therefore, in this work we formulate, prove, and empirically test the conditions for an Empirical Bayes Estimator (EBE) to dominate the weighted mean aggregation. Our main result demonstrates that EBE, under mild conditions, can be used as a second step of any TD algorithm in order to reduce the expected error.
Symbolic image detection using scene and knowledge graphs

Sometimes the meaning conveyed by images goes beyond the list of objects they contain; instead, images may express a powerful message to affect the viewers' minds. Inferring this message requires reasoning about the relationships between the objects, and general common-sense knowledge about the components. In this paper, we use a scene graph, a graph representation of an image, to capture visual components. In addition, we generate a knowledge graph using facts extracted from ConceptNet to reason about objects and attributes. To detect the symbols, we propose a neural network framework named SKG-Sym. The framework first generates the representations of the scene graph of the image and its knowledge graph using Graph Convolution Network. The framework then fuses the representations and uses an MLP to classify them. We extend the network further to use an attention mechanism which learn the importance of the graph representations. We evaluate our methods on a dataset of advertisements, and compare it with baseline symbolism classification methods (ResNet and VGG). Results show that our methods outperform ResNet in terms of F-score and the attention-based mechanism is competitive with VGG while it has much lower model complexity.
Zero-Shot Audio Classification using Image Embeddings

Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typically requires manual labeling which is expensive and time-consuming. Zero-shot learning models are capable of classifying the unseen concepts by utilizing their semantic information. The present study introduces image embeddings as side information on zero-shot audio classification by using a nonlinear acoustic-semantic projection. We extract the semantic image representations from the Open Images dataset and evaluate the performance of the models on an audio subset of AudioSet using semantic information in different domains; image, audio, and textual. We demonstrate that the image embeddings can be used as semantic information to perform zero-shot audio classification. The experimental results show that the image and textual embeddings display similar performance both individually and together. We additionally calculate the semantic acoustic embeddings from the test samples to provide an upper limit to the performance. The results show that the classification performance is highly sensitive to the semantic relation between test and training classes and textual and image embeddings can reach up to the semantic acoustic embeddings when the seen and unseen classes are semantically similar.
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially the return-on-investment (ROI) constraint. ROIs change non-monotonically during the sequential bidding process, and often induce a see-saw effect between constraint satisfaction and objective optimization. While some existing approaches show promising results in static or mildly changing ad markets, they fail to generalize to highly dynamic ad markets with ROI constraints, due to their inability to adaptively balance constraints and objectives amidst non-stationarity and partial observability. In this work, we specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, our method exploits an indicator-augmented reward function free of extra trade-off parameters and develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework to adaptively control the constraint-objective trade-off in non-stationary ad markets. Extensive experiments on a large-scale industrial dataset with two problem settings reveal that CBRL generalizes well in both in-distribution and out-of-distribution data regimes, and enjoys superior learning efficiency and stability.
Self-Supervised Deep Subspace Clustering with Entropy-norm

Auto-Encoder based deep subspace clustering (DSC) is widely used in computer vision, motion segmentation and image processing. However, it suffers from the following three issues in the self-expressive matrix learning process: the first one is less useful information for learning self-expressive weights due to the simple reconstruction loss; the second one is that the construction of the self-expression layer associated with the sample size requires high-computational cost; and the last one is the limited connectivity of the existing regularization terms. In order to address these issues, in this paper we propose a novel model named Self-Supervised deep Subspace Clustering with Entropy-norm (S$^{3}$CE). Specifically, S$^{3}$CE exploits a self-supervised contrastive network to gain a more effetive feature vector. The local structure and dense connectivity of the original data benefit from the self-expressive layer and additional entropy-norm constraint. Moreover, a new module with data enhancement is designed to help S$^{3}$CE focus on the key information of data, and improve the clustering performance of positive and negative instances through spectral clustering. Extensive experimental results demonstrate the superior performance of S$^{3}$CE in comparison to the state-of-the-art approaches.