Miracle-dz/UniFAR-FTUs huggingface dataset:
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (v) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition. ar X iv :2 10 3. 11 47 0v 3 [ cs . R O ] 7 J ul 2 02 1
Towards Superior High Temperature Properties in Low Density AlCrFeNiTi Compositionally Complex Alloys

Three novel precipitation strengthened bcc alloys which exhibit a smooth microstructural gradient with composition have been fabricated in bulk form by induction casting. All three alloys are comprised of a mixture of disordered A2-(Fe, Cr) and L21-ordered (Ni, Fe)2AlTi type phases both as-cast and after longterm annealing at 900 ◦C. The ratio of disordered to ordered phase, primary dendrite fraction, and overall microstructural coarseness all decrease as Cr is replaced by Al and Ti. Differences in phase composition are quantified through domain averaged principal component analysis of energy dispersive spectroscopy data obtained during scanning transmission electron microscopy. Bulk tensile testing reveals retained strengths of nearly 250 MPa up to 900 ◦C for the alloys which contain a nanoscale maze-like arrangement of ordered and disordered phases. One alloy, containing a duplex microstructure with ductile dendritic regions and highly creep resistant interdendritic regions, shows a promising balance between high temperature ductility and strength. For this alloy, tension creep testing was carried out at 700, 750, and 800 ◦C for a broad range of loading conditions and revealed upper bound creep rates which surpass similar ferritic superalloys and rival those of several conventionally employed high temperature structural alloys, including Inconel 617 and 718, at much lower density and raw material cost.
Tensor quasi-random groups

<p>Gowers [Combin. Probab. Comput. 17 (2008), pp. 363–387] elegantly characterized the finite groups <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G"> <mml:semantics> <mml:mi>G</mml:mi> <mml:annotation encoding="application/x-tex">G</mml:annotation> </mml:semantics> </mml:math> </inline-formula> in which <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper A 1 upper A 2 upper A 3 equals upper G"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>3</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mi>G</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">A_1A_2A_3=G</mml:annotation> </mml:semantics> </mml:math> </inline-formula> for any positive density subsets <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper A 1 comma upper A 2 comma upper A 3"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>A</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">A_1,A_2,A_3</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. This property, <italic>quasi-randomness</italic>, holds if and only if <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G"> <mml:semantics> <mml:mi>G</mml:mi> <mml:annotation encoding="application/x-tex">G</mml:annotation> </mml:semantics> </mml:math> </inline-formula> does not admit a nontrivial irreducible representation of constant dimension. We present a dual characterization of <italic>tensor quasi-random</italic> groups in which multiplication of subsets is replaced by tensor product of representations. </p>
Boundary Attributions Provide Normal (Vector) Explanations

Recent work on explaining Deep Neural Networks (DNNs) focuses on attributing the model’s output scores to input features. However, when it comes to classiﬁcation problems, a more fundamental question is how much does each feature contribute to the model’s decision to classify an input instance into a speciﬁc class? Our ﬁrst contribution is Boundary Attribution (BA) , a new explanation method to address this question. BA leverages an understanding of the geometry of activation regions. Speciﬁcally, they involve computing (and aggregating) normal vectors of the local decision boundaries for the target input. Our second contribution is a set of analytical results connecting adversarial robustness of the network and the quality of gradient-based explanations. Speciﬁcally, we prove two theorems for ReLU networks: BA of randomized smoothed networks or robustly trained networks is much closer to non-boundary attribution methods than that in standard networks. These analytics encourage users to improve model robustness for high-quality explanations. Finally, we evaluate the proposed methods on ImageNet and show BAs produce more concentrated and sharper visualizations compared with non-boundary ones. We further demonstrate that our method also helps to reduce the sensitivity of attributions to the baseline input if one is required. An example of the implementation is available on Github 1
Improving Actor-Critic Reinforcement Learning via Hamiltonian Policy

Approximating optimal policies in reinforcement learning (RL) is often necessary in many real-world scenarios, which is termed as policy optimization. By viewing the reinforcement learning from the perspective of variational inference (VI), the policy network is trained to obtain the approximate posterior of actions given the optimality criteria. However, in practice, the policy optimization may lead to suboptimal policy estimates due to the amortization gap and insufﬁcient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate policy optimization with HMC. As such we choose evolving actions from the base policy according to HMC, which has two beneﬁts: i) HMC can improve the policy distribution to better approximate the posterior and hence reduces the amortization gap; ii) HMC can also guide the exploration more to the regions with higher action values, enhancing the exploration efﬁciency. Instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics. With comprehensive empirical experiments on continuous control baselines, including MuJoCo and PyBullet Roboschool, we show that the proposed approach is a data-efﬁcient, and an easy-to-implement improvement over previous policy optimization methods. Besides, the proposed approach can also outperform previous methods on DeepMind Control Suite which has image-based high-dimensional observation space.
Modeling and Control of Internal Oscillations in Energy-Synchronous Direct Antenna Modulation

Internal oscillations in switched antenna transmitters cause undesirable fluctuations of the stored energy in the system, which is critical to the concept of energy-synchronous direct antenna modulation. A detailed circuit model for a direct antenna modulation system is proposed that includes the high frequency parasitics that lead to these oscillations. Analytical and numerical studies show that the proposed model accurately predicts the oscillations of the system and the resulting signal degradation. To mitigate the parasitic oscillations, a modified direct antenna modulation system with an auxiliary DC source is introduced to stabilize the stored voltage on the antenna. Measured phase shift keyed waveforms transmitted by the modified system show significant increases in signal fidelity, including a 10-20 dB decrease in error vector magnitude compared to an LTI system. Comparison to an equivalent, scalable time-invariant antenna suggests that the switched transmitter behaves as though it has 2-3 times lower Q factor bandwidth and 20% higher radiation efficiency.
Machine Learning Emulation of 3D Cloud Radiative Effects

The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for Medium‐Range Weather Forecasts 1D radiation scheme ecRad for 3D cloud effects using computationally cheap neural networks. 3D cloud effects are learned as the difference between ecRad's fast 1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them but is about five times more computationally expensive. With typical errors between 20% and 30% of the 3D signal, neural networks improve Tripleclouds' accuracy for about 1% increase in runtime. Thus, rather than emulating the whole of SPARTACUS, we keep Tripleclouds unchanged for cloud‐free parts of the atmosphere and 3D‐correct it elsewhere. The focus on the comparably small 3D correction instead of the entire signal allows us to improve predictions significantly if we assume a similar signal‐to‐noise ratio for both.
Model-Based Learning Network for 3-D Localization in mmWave Communications

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.
TDIOT: Target-Driven Inference for Deep Video Object Tracking

Recent tracking-by-detection approaches use deep object detectors as target detection baseline, because of their high performance on still images. For effective video object tracking, object detection is integrated with a data association step performed by either a custom design inference architecture or an end-to-end joint training for tracking purpose. In this work, we adopt the former approach and use the pre-trained Mask R-CNN deep object detector as the baseline. We introduce a novel inference architecture placed on top of FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking, without requiring additional training for tracking purpose. The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association. To tackle tracking discontinuities, we incorporate a local search and matching module into the inference head layer that exploits SiamFC. Moreover, to improve robustness to scale changes, we introduce a scale adaptive region proposal network that enables to search for the target at an adaptively enlarged spatial neighborhood specified by the trace of the target. In order to meet long term tracking requirements, a low cost verification layer is incorporated into the inference architecture to monitor presence of the target based on its LBP histogram model. Performance evaluation on videos from VOT2016, VOT2018, and VOT-LT2018 datasets demonstrate that TDIOT achieves higher accuracy compared to the state-of-the-art short-term trackers while it provides comparable performance in long term tracking. We also compare our tracker on LaSOT dataset where we observe that TDIOT provides comparable performance with other methods that are trained on LaSOT. The source code and TDIOT output videos are accessible at https://github.com/msprITU/TDIOT.
Incorporating Convolution Designs into Visual Transformers

Motivated by the success of Transformers in natural language processing (NLP) tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to the vision domain. However, pure Transformer architectures often require a large amount of training data or extra supervision to obtain comparable performance with convolutional neural networks (CNNs). To overcome these limitations, we analyze the potential drawbacks when directly borrowing Transformer architectures from NLP. Then we propose a new Convolution-enhanced image Transformer (CeiT) which combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantages of Transformers in establishing long-range dependencies. Three modifications are made to the original Transformer: 1) instead of the straightforward tokenization from raw input images, we design an Image-to-Tokens (I2T) module that extracts patches from generated low-level features; 2) the feed-froward network in each encoder block is replaced with a Locally-enhanced Feed-Forward (LeFF) layer that promotes the correlation among neighboring tokens in the spatial dimension; 3) a Layer-wise Class token Attention (LCA) is attached at the top of the Transformer that utilizes the multi-level representations.Experimental results on ImageNet and seven down-stream tasks show the effectiveness and generalization ability of CeiT compared with previous Transformers and state- of-the-art CNNs, without requiring a large amount of training data and extra CNN teachers. Besides, CeiT models demonstrate better convergence with 3× fewer training iterations, which can reduce the training cost significantly 1.
Autoregressive Belief Propagation for Decoding Block Codes

We revisit recent methods that employ graph neural networks for decoding error correcting codes and employ messages that are computed in an autoregressive manner. The outgoing messages of the variable nodes are conditioned not only on the incoming messages, but also on an estimation of the SNR and on the inferred codeword and on two downstream computations: (i) an extended vector of parity check outcomes, (ii) the mismatch between the inferred codeword and the re-encoding of the information bits of this codeword. Unlike most learned methods in the field, our method violates the symmetry conditions that enable the other methods to train exclusively with the zero-word. Despite not having the luxury of training on a single word, and the inability to train on more than a small fraction of the relevant sample space, we demonstrate effective training. The new method obtains a bit error rate that outperforms the latest methods by a sizable margin.
Dominant energy condition and spinors on Lorentzian manifolds

Let (M, g) be a timeand space-oriented Lorentzian spin manifold, and let M be a compact spacelike hypersurface of M with induced Riemannian metric g and second fundamental form K. If (M, g) satisfies the dominant energy condition in a strict sense, then the Dirac–Witten operator of M ⊆M is an invertible, self-adjoint Fredholm operator. This allows us to use index theoretical methods in order to detect non-trivial homotopy groups in the space of initial on M satisfying the dominant energy condition in a strict sense. The central tool will be a Lorentzian analogue of Hitchin’s α-invariant. In case that the dominant energy condition only holds in a weak sense, the Dirac–Witten operator may be non-invertible, and we will study the kernel of this operator in this case. We will show that the kernel may only be non-trivial if π1(M) is virtually solvable of derived length at most 2. This allows to extend the index theoretical methods to spaces of initial data, satisfying the dominant energy condition in the weak sense. We will show further that a spinor φ is in the kernel of the Dirac–Witten operator on (M, g, K) if and only if (M, g, K, φ) admits an extension to a Lorentzian manifold (N, h) with parallel spinor φ̄ such that M is a Cauchy hypersurface of (N, h), such that g and K are the induced metric and second fundamental form of M , respectively, and φ is the restriction of φ̄ to M .
Language Models have a Moral Dimension

Artiﬁcial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, its variants, GPT-2/3, and others. Using them as pre-trained models and ﬁne-tuning them for speciﬁc tasks, researchers have extended state of the art for many NLP tasks and shown that they capture not only linguistic knowledge but also retain general knowledge implicitly present in the data. Unfortunately, LMs trained on unﬁltered text corpora suﬀer from degenerated and biased behaviour. While this is well established, we show that recent improvements of LMs also store ethical and moral norms of the society and actually bring a “moral direction” to surface. In this study, we show that these norms can be captured geometrically by a direction, which can be computed, e.g., by a PCA, in the embedding space, reﬂecting well the agreement of phrases to social norms implicitly expressed in the training texts. Furthermore, this provides a path for attenuating or even preventing toxic degeneration in LMs. Being able to rate the (non-)normativity of arbitrary phrases without explicitly training the LM for this task, we demonstrate the capabilities of the moral direction for guiding (even other) LMs towards producing normative text and showcase it on RealToxicityPrompts testbed, preventing the neural toxic degeneration in GPT-2.
An Empirical Study of Developer Discussions on Low-Code Software Development Challenges

Low-code software development (LCSD) is an emerging paradigm that combines minimal source code with interactive graphical interfaces to promote rapid application development. LCSD aims to democratize application development to software practitioners with diverse backgrounds. Given that LCSD is relatively a new paradigm, it is vital to learn about the challenges developers face during their adoption of LCSD platforms. The online developer forum, Stack Overflow (SO), is popular among software developers to ask for solutions to their technical problems. We observe a growing body of posts in SO with discussions of LCSD platforms. In this paper, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We apply topic modeling on the posts to determine the types of topics discussed. We find 13 topics related to LCSD in SO. The 13 topics are grouped into four categories: Customization, Platform Adoption, Database Management, and Third-Party Integration. More than 40% of the questions are about customization, i.e., developers frequently face challenges with customizing user interfaces or services offered by LCSD platforms. The topic "Dynamic Event Handling" under the "Customization" category is the most popular (in terms of average view counts per question of the topic) as well as the most difficult. It means that developers frequently search for customization solutions such as how to attach dynamic events to a form in low-code UI, yet most (75.9%) of their questions remain without an accepted answer. We manually label 900 questions from the posts to determine the prevalence of the topics’ challenges across LCSD phases. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing. Our study findings offer implications for low-code practitioners, platform providers, educators, and researchers.
A Subgradient Algorithm for Data-Rate Optimization in the Remote State Estimation Problem

In the remote state estimation problem, an observer tries to reconstruct the state of a dynamical system at a remote location, where no direct sensor measurements are available. The observer only has access to information sent through a digital communication channel with a finite capacity. The recently introduced notion of restoration entropy provides a way to determine the smallest channel capacity above which an observer can be designed that observes the system without a degradation of the initial observation quality. In this paper, we propose a subgradient algorithm to estimate the restoration entropy via the computation of an appropriate Riemannian metric on the state space, which allows to determine the approximate value of the entropy from the time-one map (in the discrete-time case) or the generating vector field (for ODE systems), respectively.
Compositions of Digraphs: A Survey

In this survey we overview known results and get several new results on digraph compositions which generalize several classes of digraphs, such as quasi-transitive digraphs. After an introductory section, the paper is divided into six sections: connectivity and linkages, kings and kernels, paths, cycles, acyclic spanning subdigraphs, strong spanning subdigraphs. This survey also contains some conjectures and open problems for further study.
MasakhaNER: Named Entity Recognition for African Languages

Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1