Miracle-dz/UniFAR-FTUs huggingface dataset:
Know Thyself: Transferable Visuomotor Control Through Robot-Awareness

Training visuomotor robot controllers from scratch on a new robot typically requires generating large amounts of robot-speciﬁc data. Could we leverage data previously collected on another robot to reduce or even completely remove this need for robot-speciﬁc data? We propose a “robot-aware” solution paradigm that exploits readily available robot “self-knowledge” such as proprioception, kinematics, and camera calibration to achieve this. First, we learn modular dynamics models that pair a transferable, robot-agnostic world dynamics module with a robot-speciﬁc, analytical robot dynamics module. Next, we set up visual planning costs that draw a distinction between the robot self and the world. Our experiments on tabletop manipulation tasks in simulation and on real robots demonstrate that these plug-in improvements dramatically boost the transferability of visuomotor controllers, even permitting zero-shot transfer onto new robots for the very ﬁrst time. Project website: https://hueds.github. io/rac/
Epistemic Neural Networks

Intelligence relies on an agent's knowledge of what it does not know. This capability can be assessed based on the quality of joint predictions of labels across multiple inputs. In principle, ensemble-based approaches produce effective joint predictions, but the computational costs of training large ensembles can become prohibitive. We introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty. With an epinet, conventional neural networks outperform very large ensembles, consisting of hundreds or more particles, with orders of magnitude less computation. The epinet does not fit the traditional framework of Bayesian neural networks. To accommodate development of approaches beyond BNNs, such as the epinet, we introduce the epistemic neural network (ENN) as an interface for models that produce joint predictions.
QuicSDN: Transitioning from TCP to QUIC for Southbound Communication in SDNs

In Software-Defined Networks (SDNs), the control plane and data plane communicate for various purposes, such as applying configurations and collecting statistical data. While various methods have been proposed to reduce the overhead and enhance the scalability of SDNs, the impact of the transport layer protocol used for southbound communication has not been investigated. Existing SDNs rely on TCP (and TLS) to enforce reliability and security. In this paper, we show that the use of TCP imposes a considerable overhead on southbound communication, identify the causes of this overhead, and demonstrate how replacing TCP with QUIC can enhance the performance of this communication. We introduce the quicSDN architecture, enabling southbound communication in SDNs via the QUIC protocol. We present a reference architecture based on the standard, most widely used protocols by the SDN community and show how the controller and switch are revamped to facilitate this transition. We compare, both analytically and empirically, the performance of quicSDN versus the traditional SDN architecture and confirm the superior performance of quicSDN.
Decentralized federated learning of deep neural networks on non-iid data

We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting. More specifically, we study decentralized federated learning, a peer-to-peer setting where data is distributed among many clients and where there is no central server to orchestrate the training. In real world scenarios, the data distributions are often heterogeneous between clients. Therefore, in this work we study the problem of how to efficiently learn a model in a peer-to-peer system with non-iid client data. We propose a method named Performance-Based Neighbor Selection (PENS) where clients with similar data distributions detect each other and cooperate by evaluating their training losses on each other's data to learn a model suitable for the local data distribution. Our experiments on benchmark datasets show that our proposed method is able to achieve higher accuracies as compared to strong baselines.
A Tree Search Approach for Maximum-Likelihood Decoding of Reed-Muller Codes

A low-complexity tree search approach is presented that is equivalent to the maximum-likelihood (ML) decoding of Reed-Muller (RM) codes. The proposed approach generates a bit-flipping tree that is traversed to find the ML decoding result by performing successive-cancellation decoding after each node visit. A depth-first search (DFS) and a breadth-first search (BFS) scheme are developed and a log-likelihood-ratio-based bit-flipping metric is utilized to avoid redundant node visits in the tree. Several enhancements to the proposed algorithm are presented to further reduce the number of node visits. Simulation results confirm that the BFS scheme provides a lower average number of node visits than the existing tree search approach to decode RM codes.
ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles

A key component in autonomous driving is the ability of the self-driving car to understand, track and predict the dynamics of the surrounding environment. Although there is significant work in the area of object detection, tracking and observations prediction, there is no prior work demonstrating that raw observations prediction can be used for motion planning and control. In this paper, we propose ObserveNet Control, which is a vision-dynamics approach to the predictive control problem of autonomous vehicles. Our method is composed of a i) deep neural network able to confidently predict future sensory data on a time horizon of up to $\text{10}\,s$ and ii) a temporal planner designed to compute a safe vehicle state trajectory based on the predicted sensory data. Given the vehicle's historical state and sensing data in the form of Lidar point clouds, the method aims to learn the dynamics of the observed driving environment in a self-supervised manner, without the need to manually specify training labels. The experiments are performed both in simulation and real-life, using CARLA and RovisLab's AMTU mobile platform as a 1:4 scaled model of a car. We evaluate the capabilities of ObserveNet Control in aggressive driving contexts, such as overtaking maneuvers or side cut-off situations, while comparing the results with a baseline Dynamic Window Approach (DWA) and two state-of-the-art imitation learning systems, that is, Learning by Cheating (LBC) and World on Rails (WOR).
Detection of Double Compression in MPEG-4 Videos Using Refined Features-based CNN

Double compression is accompanied by various types of video manipulation and its traces can be exploited to determine whether a video is a forgery. This Letter presents a convolutional neural network for detecting double compression in MPEG-4 videos. Through analysis of the intra-coding process, we utilize two reﬁned features for capturing the subtle artifacts caused by double compression. The discrete cosine transform (DCT) histogram feature effectively detects the change of statistical characteristics in DCT coefﬁcients and the parameter-based feature is utilized as auxiliary information to help the network learn double compression artifacts. When compared with state-of-the-art networks and forensic method, the results show that the proposed approach achieves a higher performance.
Path Integrals for the Attribution of Model Uncertainties

Enabling interpretations of model uncertainties is of key importance in Bayesian machine learning applications. Often, this requires to meaningfully attribute predictive uncertainties to source features in an image, text or categorical array. However, popular attri-bution methods are particularly designed for classiﬁcation and regression scores. In order to explain uncertainties, state of the art alternatives commonly procure counterfactual feature vectors, and proceed by making direct comparisons. In this paper, we leverage path integrals to attribute uncertainties in Bayesian diﬀeren-tiable models. We present a novel algorithm that relies on in-distribution curves connecting a feature vector to some counterfactual counterpart, and we retain desirable properties of interpretability methods. We validate our approach on benchmark image data sets with varying resolution, and show that it sig-niﬁcantly simpliﬁes interpretability over the existing alternatives.
Nonlinear optical effects on the atom-field interaction based on the nonlinear coherent states approach

In this paper, to study the effects of a nonlinear medium on the atom-field interaction, we use the nonlinear coherent states approach. For this purpose, we choose the two-mode cross-Kerr as the our nonlinear optical phenomena and with the use of it's algebra, we show that it can be described equivalently by a deformed oscillator algebra and also, by a deformed (su(2)) algebra. Then, we construct the associated coherent states and investigate their statistical properties. After that, as an example of applicability of the constructed two-mode nonlinear coherent states, we investigate the nonlinear effects of the medium on the dynamics of atom-field interaction within the framework of the coherent states. By using the time-dependent Schr\"odinger equation, we first obtain the atom-field state and then study the effect of the nonlinear medium on the occupation probabilities of the atomic levels. In the following, we consider the relation between the revival time of the atomic occupation probabilities and the nonlinear parameter of the medium. Then, to study the nonlinear effects on the dynamical properties of the cavity field, we consider photon distribution, correlation function, Mandel parameters of the field, the von Neumann entropy and the squeezing. Particularly, the nonlinearity of the media on the nonclassical properties of two modes is clarified.
X-ray bounds on cooling, composition, and magnetic field of the Cassiopeia A neutron star and young central compact objects

We present analysis of multiple Chandra and XMM–Newton spectra, separated by 9–19 yr, of four of the youngest central compact objects (CCOs) with ages <2500 yr: CXOU J232327.9+584842 (Cassiopeia A), CXOU J160103.1−513353 (G330.2+1.0), 1WGA J1713.4−3949 (G347.3−0.5), and XMMU J172054.5−372652 (G350.1−0.3). By fitting these spectra with thermal models, we attempt to constrain each CCO’s long-term cooling rate, composition, and magnetic field. For the CCO in Cassiopeia A, 14 measurements over 19 yr indicate a decreasing temperature at a 10-yr rate of 2.2 ± 0.2 or 2.8 ± 0.3 per cent (1σ error) for a constant or changing X-ray absorption, respectively. We obtain cooling rate upper limits of 17 per cent for CXOU J160103.1−513353 and 6 per cent for XMMU J172054.5−372652. For the oldest CCO, 1WGA J1713.4−3949, its temperature seems to have increased by 4 ± 2 per cent over a 10-yr period. Assuming each CCO’s preferred distance and an emission area that is a large fraction of the total stellar surface, a non-magnetic carbon atmosphere spectrum is a good fit to spectra of all four CCOs. If distances are larger and emission areas are somewhat smaller, then equally good spectral fits are obtained using a hydrogen atmosphere with B ≤ 7 × 1010 G or B ≥ 1012 G for CXOU J160103.1−513353 and B ≤ 1010 G or B ≥ 1012 G for XMMU J172054.5−372652 and non-magnetic hydrogen atmosphere for 1WGA J1713.4−3949. In a unified picture of CCO evolution, our results suggest most CCOs, and hence a sizable fraction of young neutron stars, have a surface magnetic field that is low early in their life but builds up over several thousand years.
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source GPUMD package, which can attain a computational speed over $10^7$ atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods.
The appearance of non trivial torsion for some Ricci dependent theories in the Palatini formalism

As is known from studies of gravity models in the Palatini formalism, there exist two inequivalent definitions of the generalized Ricci tensor in terms of the generalized curvature namely, R~μν=Rμρνρ and Rνμ=gαβRανβμ . A deep formal investigation of theories with Lagrangians of the form L=L(R~(μν)) was initiated in Alfonso et al (2017 Class. Quantum Grav. 34 235003). In that work, the authors leave the connection free, and find out that the torsion only appears as a projective mode. This agrees with the widely employed condition of vanishing torsion in these theories as a simple gauge choice. In the present work the complementary scenario is studied namely, the one described by a Lagrangian that depends on the other possible Ricci tensor L = L(R (μν)). The torsion is completely characterized in terms of the metric and the connection, and a rather detailed description of the equations of motion is presented. It is shown that these theories are non trivial even for 1 + 1 space time dimensions, and admit non zero torsion even in this apparently simple case. It is suggested that to impose zero torsion by force may result into an incompatible system. In other words, the presence of torsion may be beneficial for insuring that the equations of motion are well posed. The results of the present paper do not contradict the ones of Alfonso et al (2017 Class. Quantum Grav. 34 235003), as the underlying theories are inequivalent.
A Proposed Hybrid Effect Size Plus p-Value Criterion: A Replication of Goodman et al. (2019)

In a recent simulation study, Goodman et al. (2019) compare several methods with regard to their performance of type I and type II error rates in case of a thick null hypothesis that includes all values that are practically equivalent to the point null hypothesis. They propose a hybrid decision criterion only declaring a result “significant” if both a small p-value and a sufficiently large effect size are obtained. We successfully replicate the results using our own software code in R and discuss an additional decision method that maintains a pre-defined false positive rate. We confirm that the hybrid decision criterion has comparably low error rates in settings one can check for but point out that the false discovery rate cannot be easily controlled by the researcher. Our analyses are readily accessible and customizable on https://github.com/drehero/goodman-replication. ∗peter.puetz@uni-bielefeld.de 1 ar X iv :2 10 7. 08 86 0v 1 [ st at . M E ] 1 9 Ju l 2 02 1
Probing the progenitors of Type Ia supernovae using circumstellar material interaction signatures

This work aims to study different probes of Type Ia supernova (SN Ia) progenitors that have been suggested to be linked to the presence of circumstellar material (CSM). In particular, we have investigated, for the first time, the link between narrow blueshifted Na i D absorption profiles and the presence and strength of the broad high-velocity Ca ii near-infrared triplet absorption features seen in SNe Ia around maximum light. With the probes exploring different distances from the SN; Na i D > 1017cm, high-velocity Ca ii features < 1015cm. For this, we have used a new intermediate-resolution X-shooter spectral sample of 15 SNe Ia. We do not identify a link between these two probes, implying either that, one (or both) is not physically related to the presence of CSM or that the occurrence of CSM at the distance explored by one probe is not linked to its presence at the distance probed by the other. However, the previously identified statistical excess in the presence of blueshifted (over redshifted) Na i D absorption is confirmed in this sample at high significance and is found to be stronger in SNe Ia hosted by late-type galaxies. This excess is difficult to explain as being from an interstellar-medium origin as has been suggested by some recent modelling, as such an origin is not expected to show a bias for blueshifted absorption. However, a circumstellar origin for these features also appears unsatisfactory based on our new results, given the lack of link between the two probes of CSM investigated.
A New Search for Neutrino Point Sources with IceCube

The IceCube Neutrino Observatory, deployed inside the deep glacial ice at the South Pole, is the largest neutrino telescope in the world. While eight years have passed since IceCube discovered a diffuse flux of high-energy astrophysical neutrinos, the sources of the vast majority of these neutrinos remain unknown. Here, we present a new search for neutrino point sources that improves the accuracy of the statistical analysis, especially in the low energy regime. We replaced the usual Gaussian approximations of IceCube’s point spread function with precise numerical representations, obtained from simulations, and combined them with new machine learningbased estimates of event energies and angular errors. Depending on the source properties, the new analysis provides improved source localization, flux characterization and thereby discovery potential (by up to 30%) over previous works. The analysis will be applied to IceCube data that has been recorded with the full 86-string detector configuration from 2011 to 2020 and includes improved detector calibration.
SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics

Soft robotics is a thriving branch of robotics which takes inspiration from nature and uses affordable flexible materials to design adaptable non-rigid robots. However, their flexible behavior makes these robots hard to model, which is essential for a precise actuation and for optimal control. For system modelling, learning-based approaches have demonstrated good results, yet they fail to consider the physical structure underlying the system as an inductive prior. In this work, we take inspiration from sensorimotor learning, and apply a Graph Neural Network to the problem of modelling a non-rigid kinematic chain (i.e. a robotic soft hand) taking advantage of two key properties: 1) the system is compositional, that is, it is composed of simple interacting parts connected by edges, 2) it is order invariant, i.e. only the structure of the system is relevant for predicting future trajectories. We denote our model as the "Sensorimotor Graph" since it learns the system connectivity from observation and uses it for dynamics prediction. We validate our model in different scenarios and show that it outperforms the non-structured baselines in dynamics prediction while being more robust to configurational variations, tracking errors or node failures.
Using our newest VLT-KMOS HII Galaxies and other cosmic tracers to test the ΛCDM tension

We place novel constraints on the cosmokinetic parameters by using a joint analysis of the newest VLT-KMOS HII galaxies (HIIG) with the Supernovae Type Ia (SNIa) Pantheon sample. We combine the latter data sets in order to reconstruct, in a model independent way, the Hubble diagram to as high redshifts as possible. Using a Gaussian process we derive the basic cosmokinetic parameters and compare them with those of ΛCDM. In the case of SNIa we find that the extracted values of the cosmokinetic parameters are in agreement with the predictions of ΛCDM model. Combining SNIa with high redshift tracers of the Hubble relation, namely HIIG data we obtain consistent results with those based on ΛCDM as far as the present values of the cosmokinetic parameters are concerned, but find significant deviations in the evolution of the cosmokinetic parameters with respect to the expectations of the concordance ΛCDM model.
Prediction of the final rank of Players in PUBG with the optimal number of features

PUBG is an online video game that has become very popular among the youths in recent years. Final rank, which indicates the performance of a player, is one of the most important feature for this game. This paper focuses on predicting the final rank of the players based on their skills and abilities. In this paper we have used different machine learning algorithms to predict the final rank of the players on a dataset obtained from kaggle which has 29 features. Using the correlation heatmap,we have varied the number of features used for the model. Out of these models GBR and LGBM have given the best result with the accuracy of 91.63% and 91.26% respectively for 14 features and the accuracy of 90.54% and 90.01% for 8 features. Although the accuracy of the models with 14 features is slightly better than 8 features, the empirical time taken by 8 features is 1.4x lesser than 14 features for LGBM and 1.5x lesser for GBR. Furthermore, reducing the number of features any more significantly hampers the performance of all the ML models. Therefore, we conclude that 8 is the optimal number of features that can be used to predict the final rank of a player in PUBG with high accuracy and low run-time.
A Benchmark for Gait Recognition under Occlusion Collected by Multi-Kinect SDAS

Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have not paid enough attention to it, thus this paper focuses on gait recognition under occlusion. We collect a new gait recognition database called OG RGB+D database, which breaks through the limitation of other gait databases and includes multimodal gait data of various occlusions (self-occlusion, active occlusion, and passive occlusion) by our multiple synchronous Azure Kinect DK sensors data acquisition system (multi-Kinect SDAS) that can be also applied in security situations. Because Azure Kinect DK can simultaneously collect multimodal data to support different types of gait recognition algorithms, especially enables us to effectively obtain camera-centric multi-person 3D poses, and multi-view is better to deal with occlusion than single-view. In particular, the OG RGB+D database provides accurate silhouettes and the optimized human 3D joints data (OJ) by fusing data collected by multi-Kinects which are more accurate in human pose representation under occlusion. We also use the OJ data to train an advanced 3D multi-person pose estimation model to improve its accuracy of pose estimation under occlusion for universality. Besides, as human pose is less sensitive to occlusion than human appearance, we propose a novel gait recognition method SkeletonGait based on human dual skeleton model using a framework of siamese spatio-temporal graph convolutional networks (siamese ST-GCN). The evaluation results demonstrate that SkeletonGait has competitive performance compared with state-of-art gait recognition methods on OG RGB+D database and popular CAISA-B database.