pwc-archive/papers-with-abstracts huggingface dataset:
SAGA: Stochastic Whole-Body Grasping with Contact
Authors: Yan Wu, Jiahao Wang, Yan Zhang, Siwei Zhang, Otmar Hilliges, Fisher Yu, Siyu Tang
Date: 2021-12-19 00:00:00
Tasks: Object

The synthesis of human grasping has numerous applications including AR/VR, video games and robotics. While methods have been proposed to generate realistic hand-object interaction for object grasping and manipulation, these typically only consider interacting hand alone. Our goal is to synthesize whole-body grasping motions. Starting from an arbitrary initial pose, we aim to generate diverse and natural whole-body human motions to approach and grasp a target object in 3D space. This task is challenging as it requires modeling both whole-body dynamics and dexterous finger movements. To this end, we propose SAGA (StochAstic whole-body Grasping with contAct), a framework which consists of two key components: (a) Static whole-body grasping pose generation. Specifically, we propose a multi-task generative model, to jointly learn static whole-body grasping poses and human-object contacts. (b) Grasping motion infilling. Given an initial pose and the generated whole-body grasping pose as the start and end of the motion respectively, we design a novel contact-aware generative motion infilling module to generate a diverse set of grasp-oriented motions. We demonstrate the effectiveness of our method, which is a novel generative framework to synthesize realistic and expressive whole-body motions that approach and grasp randomly placed unseen objects. Code and models are available at https://jiahaoplus.github.io/SAGA/saga.html.
Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness
Authors: Daniel Neamati, Yashwanth Kumar Nakka, Soon-Jo Chung
Date: 2021-12-18 00:00:00
Tasks: Gaussian Processes

Accurate gravity field models are essential for safe proximity operations around small bodies. State-of-the-art techniques use spherical harmonics or high-fidelity polyhedron shape models. Unfortunately, these techniques can become inaccurate near the surface of the small body or have high computational costs, especially for binary or heterogeneous small bodies. New learning-based techniques do not encode a predefined structure and are more versatile. In exchange for versatility, learning-based techniques can be less robust outside the training data domain. In deployment, the spacecraft trajectory is the primary source of dynamics data. Therefore, the training data domain should include spacecraft trajectories to accurately evaluate the learned model's safety and robustness. We have developed a novel method for learning-based gravity models that directly uses the spacecraft's past trajectories. We further introduce a method to evaluate the safety and robustness of learning-based techniques via comparing accuracy within and outside of the training domain. We demonstrate this safety and robustness method for two learning-based frameworks: Gaussian processes and neural networks. Along with the detailed analysis provided, we empirically establish the need for robustness verification of learned gravity models when used for proximity operations.
Distortion Mitigation in Millimeter-Wave Interferometric Radar Angular Velocity Estimation Using Signal Response Decomposition
Authors: Eric Klinefelter, Jason M. Merlo, Jeffrey A. Nanzer
Date: 2021-12-17 00:00:00

A new method of distortion mitigation for multitarget interferometric angular velocity estimation in millimeter-wave radar is presented. In general, when multiple targets are present, the response of a correlation interferometer is corrupted by intermodulation distortion, making it difficult to estimate individual target angular velocities. We present a distortion mitigation method that works by decomposing the responses at each antenna element into the responses from the individual targets. Data association is performed to match individual target responses at each antenna such that cross-correlation is performed only between associated targets. Thus, the intermodulation distortion (cross-terms) from correlating unlike targets are eliminated, and the result is a frequency response whose individual frequencies are proportional to the angular velocities of the targets. We demonstrate the approach with a custom 40 GHz interferometric radar, a high-accuracy motion capture system which provides ground-truth position measurements, and two robotic platforms. The multitarget experiments consist of three scenarios, designed to represent easy, medium, and difficult cases for the distortion mitigation technique. We show that the reduction in distortion yields angular velocity estimation errors in the three cases of less than $0.008$ rad/s, $0.020$ rad/s, and $0.033$ rad/s for the easy, medium, and hard cases, respectively.
Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans
Authors: Lukáš Gajdošech, Viktor Kocur, Martin Stuchlík, Lukáš Hudec, Martin Madaras
Date: 2021-12-17 00:00:00
Tasks: 6D Pose Estimation, Pose Estimation

An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
Authors: Yuefan Shen, Yanchao Yang, Mi Yan, He Wang, Youyi Zheng, Leonidas Guibas
Date: 2021-12-17 00:00:00
Proceeding: CVPR 2022 1
Tasks: Autonomous Driving, Domain Adaptation, Unsupervised Domain Adaptation

As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet unsolved issue for learning on point cloud is that point clouds of the same object can have significant geometric variations if generated using different procedures or captured using different sensors. These inconsistencies induce domain gaps such that neural networks trained on one domain may fail to generalize on others. A typical technique to reduce the domain gap is to perform adversarial training so that point clouds in the feature space can align. However, adversarial training is easy to fall into degenerated local minima, resulting in negative adaptation gains. Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot. First, the geometric information in the point clouds is preserved through the implicit representations for downstream tasks. More importantly, the domain-specific variations can be effectively learned away in the implicit space. We also propose an adaptive strategy to compute unsigned distance fields for arbitrary point clouds due to the lack of shape models in practice. When combined with a task loss, the proposed outperforms state-of-the-art unsupervised domain adaptation methods that rely on adversarial domain alignment and more complicated self-supervised tasks. Our method is evaluated on both PointDA-10 and GraspNet datasets. The code and trained models will be publicly available.
All-photon Polarimetric Time-of-Flight Imaging
Authors: Seung-Hwan Baek, Felix Heide
Date: 2021-12-17 00:00:00
Proceeding: CVPR 2022 1
Tasks: All, Autonomous Driving

Time-of-flight (ToF) sensors provide an imaging modality fueling diverse applications, including LiDAR in autonomous driving, robotics, and augmented reality. Conventional ToF imaging methods estimate the depth by sending pulses of light into a scene and measuring the ToF of the first-arriving photons directly reflected from a scene surface without any temporal delay. As such, all photons following this first response are typically considered as unwanted noise. In this paper, we depart from the principle of using first-arriving photons and propose an all-photon ToF imaging method by incorporating the temporal-polarimetric analysis of first- and late-arriving photons, which possess rich scene information about its geometry and material. To this end, we propose a novel temporal-polarimetric reflectance model, an efficient capture method, and a reconstruction method that exploits the temporal-polarimetric changes of light reflected by the surface and sub-surface reflection. The proposed all-photon polarimetric ToF imaging method allows for acquiring depth, surface normals, and material parameters of a scene by utilizing all photons captured by the system, whereas conventional ToF imaging only obtains coarse depth from the first-arriving photons. We validate our method in simulation and experimentally with a prototype.
Effects of discordance between species and gene trees on phylogenetic diversity conservation
Authors: Kristina Wicke, Mareike Fischer, Laura Kubatko
Date: 2021-12-20 00:00:00
Tasks: Diversity

Phylogenetic diversity indices such as the Fair Proportion (FP) index are frequently discussed as prioritization criteria in biodiversity conservation. They rank species according to their contribution to overall diversity by taking into account the unique and shared evolutionary history of each species as indicated by its placement in an underlying phylogenetic tree. Traditionally, phylogenetic trees were inferred from single genes and the resulting gene trees were assumed to be a valid estimate for the species tree, i.e., the "true" evolutionary history of the species under consideration. However, nowadays it is common to sequence whole genomes of hundreds or thousands of genes, and it is often the case that conflicting genealogical histories exist in different genes throughout the genome, resulting in discordance between individual gene trees and the species tree. Here, we analyze the effects of gene and species tree discordance on prioritization decisions based on the FP index. In particular, we consider the ranking order of taxa induced by (i) the FP index on a species tree, and (ii) the expected FP index across all gene tree histories associated with the species tree. On one hand, we show that for particular tree shapes, the two rankings always coincide. On the other hand, we show that for all leaf numbers greater than or equal to five, there exist species trees for which the two rankings differ. Finally, we illustrate the variability in the rankings obtained from the FP index across different gene tree and species tree estimates for an empirical multilocus mammal data set.
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Jinfeng Wei, Yunxin Wang, Mengli Guo, Pei Lv, Xiaoshan Yang, Mingliang Xu
Date: 2021-12-20 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition

Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task. However, the skeleton graph cannot fully represent the motion information contained in skeleton data. In addition, the topology of the skeleton graph in the GCN-based methods is manually set according to natural connections, and it is fixed for all samples, which cannot well adapt to different situations. In this work, we propose a novel dynamic hypergraph convolutional networks (DHGCN) for skeleton-based action recognition. DHGCN uses hypergraph to represent the skeleton structure to effectively exploit the motion information contained in human joints. Each joint in the skeleton hypergraph is dynamically assigned the corresponding weight according to its moving, and the hypergraph topology in our model can be dynamically adjusted to different samples according to the relationship between the joints. Experimental results demonstrate that the performance of our model achieves competitive performance on three datasets: Kinetics-Skeleton 400, NTU RGB+D 60, and NTU RGB+D 120.
Improving Subgraph Recognition with Variational Graph Information Bottleneck
Authors: Junchi Yu, Jie Cao, Ran He
Date: 2021-12-18 00:00:00
Proceeding: CVPR 2022 1
Tasks: Graph Classification

Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information Bottleneck (GIB) with a mutual information estimator. However, GIB suffers from training instability and degenerated results due to its intrinsic optimization process. To tackle these issues, we reformulate the subgraph recognition problem into two steps: graph perturbation and subgraph selection, leading to a novel Variational Graph Information Bottleneck (VGIB) framework. VGIB first employs the noise injection to modulate the information flow from the input graph to the perturbed graph. Then, the perturbed graph is encouraged to be informative to the graph property. VGIB further obtains the desired subgraph by filtering out the noise in the perturbed graph. With the customized noise prior for each input, the VGIB objective is endowed with a tractable variational upper bound, leading to a superior empirical performance as well as theoretical properties. Extensive experiments on graph interpretation, explainability of Graph Neural Networks, and graph classification show that VGIB finds better subgraphs than existing methods. Code is avaliable at https://github.com/Samyu0304/VGIB
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
Authors: Junchi Yu, Tingyang Xu, Ran He
Date: 2021-12-18 00:00:00
Tasks: Prediction

As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions. Existing explainers discover an explanatory subgraph relevant to the prediction. However, such a subgraph is insufficient to reveal all the critical biological substructures for the prediction because the prediction will remain unchanged after removing that subgraph. Hence, an explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions for the explanation. Such explanation requires a measurement of information transferred from different input subgraphs to the predictive output, which we define as information flow. In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs. To evaluate the information flow within GNN's prediction, we first propose a novel notion of predictiveness, named $f$-information, which is directional and incorporates the realistic capacity of the GNN model. Based on it, IFEXPLAINER generates the explanatory subgraph with maximal information flow to the prediction. Meanwhile, it minimizes the information flow from the input to the predictive result after removing the explanation. Thus, the produced explanation is necessarily important to the prediction and sufficient to reveal the most crucial substructures. We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer subtyping. Experimental results on the BRACS dataset show the superior performance of the proposed method.
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Authors: Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas
Date: 2021-12-21 00:00:00
Tasks: Federated Learning

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probabilities. Based on the bound, we analytically establish the relationship between the total learning time and sampling probabilities, which results in a non-convex optimization problem for training time minimization. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes. Notably, our scheme in hardware prototype spends 73% less time than the uniform sampling baseline for reaching the same target loss.
Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
Authors: Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang
Date: 2021-12-21 00:00:00

Motivation: We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix $Q$ which depends on a parameter $\theta$. Computing the probability distribution over states at time $t$ requires the matrix exponential $\exp(tQ)$, and inferring $\theta$ from data requires its derivative $\partial\exp\!(tQ)/\partial\theta$. Both are challenging to compute when the state space and hence the size of $Q$ is huge. This can happen when the state space consists of all combinations of the values of several interacting discrete variables. Often it is even impossible to store $Q$. However, when $Q$ can be written as a sum of tensor products, computing $\exp(tQ)$ becomes feasible by the uniformization method, which does not require explicit storage of $Q$. Results: Here we provide an analogous algorithm for computing $\partial\exp\!(tQ)/\partial\theta$, the differentiated uniformization method. We demonstrate our algorithm for the stochastic SIR model of epidemic spread, for which we show that $Q$ can be written as a sum of tensor products. We estimate monthly infection and recovery rates during the first wave of the COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian analysis. Availability: Implementation and data are available at https://github.com/spang-lab/TenSIR.
Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems
Authors: Andrea Stocco, Brian Pulfer, Paolo Tonella
Date: 2021-12-21 00:00:00
Tasks: Autonomous Driving, Neural Rendering, Self-Driving Cars, Visual Odometry

Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing whether such techniques transfer to and are effective with a physical real-world vehicle. In this paper, we shed light on the problem of generalizing testing results obtained in a driving simulator to a physical platform and provide a characterization and quantification of the sim2real gap affecting SDC testing. In our empirical study, we compare SDC testing when deployed on a physical small-scale vehicle vs its digital twin. Due to the unavailability of driving quality indicators from the physical platform, we use neural rendering to estimate them through visual odometry, hence allowing full comparability with the digital twin. Then, we investigate the transferability of behavior and failure exposure between virtual and real-world environments, targeting both unintended abnormal test data and intended adversarial examples. Our study shows that, despite the usage of a faithful digital twin, there are still critical shortcomings that contribute to the reality gap between the virtual and physical world, threatening existing testing solutions that only consider virtual SDCs. On the positive side, our results present the test configurations for which physical testing can be avoided, either because their outcome does transfer between virtual and physical environments, or because the uncertainty profiles in the simulator can help predict their outcome in the real world.
A next-generation platform for Cyber Range-as-a-Service
Authors: Vittorio Orbinato
Date: 2021-12-21 00:00:00
Tasks: Cloud Computing, Management

In the last years, Cyber Ranges have become a widespread solution to train professionals for responding to cyber threats and attacks. Cloud computing plays a key role in this context since it enables the creation of virtual infrastructures on which Cyber Ranges are based. However, the setup and management of Cyber Ranges are expensive and time-consuming activities. In this paper, we highlight the novel features for the next-generation Cyber Range platforms. In particular, these features include the creation of a virtual clone for an actual corporate infrastructure, relieving the security managers from the setup of the training scenarios and sessions, the automatic monitoring of the participants' activities, and the emulation of their behavior.
Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients
Authors: Jiafei Lyu, Yu Yang, Jiangpeng Yan, Xiu Li
Date: 2021-12-21 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning

It is vital to accurately estimate the value function in Deep Reinforcement Learning (DRL) such that the agent could execute proper actions instead of suboptimal ones. However, existing actor-critic methods suffer more or less from underestimation bias or overestimation bias, which negatively affect their performance. In this paper, we reveal a simple but effective principle: proper value correction benefits bias alleviation, where we propose the generalized-activated weighting operator that uses any non-decreasing function, namely activation function, as weights for better value estimation. Particularly, we integrate the generalized-activated weighting operator into value estimation and introduce a novel algorithm, Generalized-activated Deep Double Deterministic Policy Gradients (GD3). We theoretically show that GD3 is capable of alleviating the potential estimation bias. We interestingly find that simple activation functions lead to satisfying performance with no additional tricks, and could contribute to faster convergence. Experimental results on numerous challenging continuous control tasks show that GD3 with task-specific activation outperforms the common baseline methods. We also uncover a fact that fine-tuning the polynomial activation function achieves superior results on most of the tasks.