Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Control and Pose Optimization for Wheel-legged Robots Navigating Uneven Terrains

—In this paper, we propose a novel approach on controlling wheel-legged quadrupedal robots using pose optimization and force control via quadratic programming (QP). Our method allows the robot to leverage wheel torques to navigate the terrain while keeping the wheel traction and balancing the robot body. In details, we present a rigid body dynamics with wheels that can be used for real-time balancing control of wheel-legged robots. In addition, we introduce an effective pose optimization method for wheel-legged robot’s locomotion over uneven terrains with ramps and stairs. The pose optimization utilized a nonlinear programming (NLP) solver to solve for the optimal poses in terms of joint positions based on kinematic and contact constraints during a stair-climbing task with rolling wheels. In simulation, our approach has successfully validated for the problem of a wheel-legged robot climbing up a 0 . 34 m stair with a slope angle of 80 ◦ and shown its versatility in multiple-stair climbing with varied stair runs and rises with wheel traction. Experimental validation on the real robot demonstrated the capability of climbing up on a 0 . 25 m stair with a slope angle of 30 ◦ .
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver

A classification technique incorporating a novel feature derivation method is proposed for predicting failure of a system or device with multivariate time series sensor data. We treat the multivariate time series sensor data as images for both visualization and computation. Failure follows various patterns which are closely related to the root causes. Different predefined transformations are applied on the original sensors data to better characterize the failure patterns. In addition to feature derivation, ensemble method is used to further improve the performance. In addition, a general algorithm architecture of deep neural network is proposed to handle multiple types of data with less manual feature engineering. We apply the proposed method on the early predict failure of computer disk drive in order to improve storage systems availability and avoid data loss. The classification accuracy is largely improved with the enriched features, named smart features.
Metamorphic relation prioritization for effective regression testing

Metamorphic testing (MT) is widely used for testing programs that face the oracle problem. It uses a set of metamorphic relations (MRs), which are relations among multiple inputs and their corresponding outputs to determine whether the program under test is faulty. Typically, MRs vary in their ability to detect faults in the program under test, and some MRs tend to detect the same set of faults. In this paper, we propose approaches to prioritize MRs to improve the efficiency and effectiveness of MT for regression testing. We present two MR prioritization approaches: (i) fault‐based and (ii) coverage‐based. To evaluate these MR prioritization approaches, we conduct experiments on three complex open‐source software systems. Our results show that the MR prioritization approaches developed by us significantly outperform the current practice of executing the source and follow‐up test cases of the MRs in an ad‐hoc manner in terms of fault detection effectiveness. Further, fault‐based MR prioritization leads to reducing the number of source and follow‐up test cases that needs to be executed as well as reducing the average time taken to detect a fault, which would result in saving time and cost during the testing process.
Learning Kernel-Smoothed Machine Translation with Retrieved Examples

How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a challenge. Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples. However, non-parametric methods are prone to overfit the retrieved examples. In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online. Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods. The code and trained models are released at https://github.com/jiangqn/KSTER.
Stochastic MPC with Multi-modal Predictions for Traffic Intersections

We propose a Stochastic MPC (SMPC) formu-lation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles given by Gaussian Mixture Models (GMM) for collision avoid-ance constraints. Our main theoretical contribution is a SMPC formulation that optimizes over a novel feedback policy class designed to exploit additional structure in the GMM predictions, and that is amenable to convex programming. The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios. We evaluate our algorithm along axes of mobility, comfort, conservatism and computational efficiency at a simulated intersection in CARLA. Our simulations use a kinematic bicycle model and multimodal predictions trained on a subset of the Lyft Level 5 prediction dataset. To demonstrate the impact of optimizing over feedback policies, we compare our algorithm with two SMPC baselines that handle multi-modal collision avoidance chance constraints by optimizing over open-loop sequences.
Seriema: RDMA-based Remote Invocation with a Case-Study on Monte-Carlo Tree Search

We introduce Seriema, a middleware that integrates RDMA-based remote invocation, asynchronous data transfer, NUMA-aware automatic management of registered memory, and message aggregation in idiomatic C++1x, targeted for distributed data structure support for ML applications that benefit both from low-latency communication and message aggregation for high throughput. We evaluate the usability of Seriema by implementing a Monte-Carlo Tree Search (MCTS) application framework, which runs distributed simulations given only a sequential problem specification. Micro-benchmarks show that Seriema provides remote invocations with low overhead, and that our MCTS application framework scales well up to the number of non-hyperthreaded CPU cores while simulating plays of the board game Hex.
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics

The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fréchet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.
A semi-Lagrangian scheme for Hamilton-Jacobi-Bellman equations with oblique boundary conditions

We investigate in this work a fully-discrete semi-Lagrangian approximation of second order possibly degenerate Hamilton-Jacobi-Bellman (HJB) equations on a bounded domain with oblique boundary conditions. These equations appear naturally in the study of optimal control of diffusion processes with oblique reflection at the boundary of the domain. The proposed scheme is shown to satisfy a consistency type property, it is monotone and stable. Our main result is the convergence of the numerical solution towards the unique viscosity solution of the HJB equation. The convergence result holds under the same asymptotic relation between the time and space discretization steps as in the classical setting for semi-Lagrangian schemes. We present some numerical results that confirm the numerical convergence of the scheme.
Mitosis: Practically Scaling Permissioned Blockchains

Scalability remains one of the biggest challenges to the adoption of permissioned blockchain technologies for large-scale deployments. Namely, permissioned blockchains typically exhibit low latencies, compared to permissionless deployments—however at the cost of poor scalability. As a remedy, various solutions were proposed to capture “the best of both worlds”, targeting low latency and high scalability simultaneously. Among these, blockchain sharding emerges as the most prominent technique. Most existing sharding proposals exploit features of the permissionless model and are therefore restricted to cryptocurrency applications. A few permissioned sharding proposals exist, however, they either make strong trust assumptions on the number of faulty nodes or rely on trusted hardware or assume a static participation model where all nodes are expected to be available all the time. In practice, nodes may join and leave the system dynamically, which makes it challenging to establish how to shard and when. In this work, we address this problem and present Mitosis, a novel approach to practically improve scalability of permissioned blockchains. Our system allows the dynamic creation of blockchains, as more participants join the system, to meet practical scalability requirements. Crucially, it enables the division of an existing blockchain (and its participants) into two—reminiscent of mitosis, the biological process of cell division. Mitosis inherits the low latency of permissioned blockchains while preserving high throughput via parallel processing. Newly created chains in our system are fully autonomous, can choose their own consensus protocol, and yet they can interact with each other to share information and assets—meeting high levels of interoperability. We analyse the security of Mitosis and evaluate experimentally the performance of our solution when instantiated over Hyperledger Fabric. Our results show that Mitosis can be ported with little modifications and manageable overhead to existing permissioned blockchains, such as Hyperledger Fabric. As far as we are aware, Mitosis emerges as the first workable and practical solution to scale existing permissioned blockchains.
Sequential Joint Shape and Pose Estimation of Vehicles with Application to Automatic Amodal Segmentation Labeling

Shape and pose estimation is a critical perception problem for a self-driving car to fully understand its surrounding environment. One fundamental challenge in solving this problem is the incomplete sensor signal (e.g., LiDAR scans), especially for faraway or occluded objects. In this paper, we propose a novel algorithm to address this challenge, which explicitly leverages the sensor signal captured over consecutive time: the consecutive signals can provide more information about an object, including different viewpoints and its motion. By encoding the consecutive signals via a recurrent neural network, not only our algorithm improves the shape and pose estimates, but also produces a labeling tool that can benefit other tasks in autonomous driving research. Specifically, building upon our algorithm, we propose a novel pipeline to automatically annotate high-quality labels for amodal segmentation on images, which are hard and laborious to annotate manually. Our code and data will be made publicly available.
Multifield Cosmology with Artificial Intelligence

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $\Omega_{\rm m}$ with higher accuracy than the maps from the N-body simulations.
Double Schubert polynomials do have saturated Newton polytopes

Abstract We prove that double Schubert polynomials have the saturated Newton polytope property. This settles a conjecture by Monical, Tokcan and Yong. Our ideas are motivated by the theory of multidegrees. We introduce a notion of standardization of ideals that enables us to study nonstandard multigradings. This allows us to show that the support of the multidegree polynomial of each Cohen–Macaulay prime ideal in a nonstandard multigrading, and in particular, that of each Schubert determinantal ideal is a discrete polymatroid.
Off-line approximate dynamic programming for the vehicle routing problem with stochastic customers and demands via decentralized decision-making

: This paper studies a stochastic variant of the vehicle routing problem (VRP) where both customer locations and demands are uncertain. In particular, potential customers are not restricted to a predeﬁned customer set but are continuously spatially distributed in a given service area. The objective is to maximize the served demands while fulﬁlling vehicle capacities and time restrictions. We call this problem the VRP with stochastic customers and demands (VRPSCD). For this problem, we ﬁrst propose a Markov Decision Process (MDP) formulation representing the classical centralized decision-making perspective where one decision-maker establishes the routes of all vehicles. While the resulting formulation turns out to be intractable, it provides us with the ground to develop a new MDP formulation of the VRPSCD representing a decentralized decision-making framework, where vehicles autonomously establish their own routes. This new formulation allows us to develop several strategies to reduce the dimension of the state and action spaces, resulting in a considerably more tractable problem. We solve the decentralized problem via Reinforcement Learning, and in particular, we develop a Q-learning algorithm featuring state-of-the-art acceleration techniques such as Replay Memory and Double Q Network. Computational results show that our method considerably outperforms two commonly adopted benchmark policies (random and heuristic). Moreover, when comparing with existing literature, we show that our approach can compete with specialized methods developed for the particular case of the VRPSCD where customer locations and expected demands are known in advance. Finally, we show that the value functions and policies obtained by our algorithm can be easily embedded in Rollout algorithms, thus further improving their performances.
CorAl – Are the point clouds Correctly Aligned?

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.
Chemical-Reaction-Aware Molecule Representation Learning

Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take SMILES strings as input that have difficulty in encoding molecule structure information, or over-emphasize the importance of GNN architectures but neglect their generalization ability. Here we propose using chemical reactions to assist learning molecule representation. The key idea of our approach is to preserve the equivalence of molecules with respect to chemical reactions in the embedding space, i.e., forcing the sum of reactant embeddings and the sum of product embeddings to be equal for each chemical equation. This constraint is proven effective to 1) keep the embedding space well-organized and 2) improve the generalization ability of molecule embeddings. Moreover, our model can use any GNN as the molecule encoder and is thus agnostic to GNN architectures. Experimental results demonstrate that our method achieves state-of-the-art performance in a variety of downstream tasks, e.g., 17.4% absolute Hit@1 gain in chemical reaction prediction, 2.3% absolute AUC gain in molecule property prediction, and 18.5% relative RMSE gain in graph-edit-distance prediction, respectively, over the best baseline method. The code is available at https://github.com/hwwang55/MolR.
Graph Neural Networks for Graph Drawing

Graph drawing techniques have been developed in the last few years with the purpose of producing esthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has paved the road to the massive usage of gradient descent and related optimization algorithms. In this article, we propose a novel framework for the development of Graph Neural Drawers (GNDs), machines that rely on neural computation for constructing efficient and complex maps. GND is Graph Neural Networks (GNNs) whose learning process can be driven by any provided loss function, such as the ones commonly employed in Graph Drawing. Moreover, we prove that this mechanism can be guided by loss functions computed by means of feedforward neural networks, on the basis of supervision hints that express beauty properties, like the minimization of crossing edges. In this context, we show that GNNs can nicely be enriched by positional features to deal also with unlabeled vertexes. We provide a proof-of-concept by constructing a loss function for the edge crossing and provide quantitative and qualitative comparisons among different GNN models working under the proposed framework.
Introduction to Neural Network Verification

Deep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we need to provide formal guarantees on the safety, security, correctness, or robustness of neural networks. This book covers foundational ideas from formal verification and their adaptation to reasoning about neural networks and deep learning.
The Per-Tau Shell: A Giant Star-forming Spherical Shell Revealed by 3D Dust Observations

A major question in the field of star formation is how molecular clouds form out of the diffuse interstellar medium (ISM). Recent advances in 3D dust mapping are revolutionizing our view of the structure of the ISM. Using the highest-resolution 3D dust map to date, we explore the structure of a nearby star-forming region, which includes the well-known Perseus and Taurus molecular clouds. We reveal an extended near-spherical shell, 156 pc in diameter (hereafter called the “Per-Tau Shell”), in which the Perseus and Taurus clouds are embedded. We also find a large ring structure at the location of Taurus (hereafter called the “Tau Ring”). We discuss a formation scenario for the Per-Tau Shell, in which previous stellar and supernova feedback events formed a large expanding shell, where the swept-up ISM has condensed to form both the shell and the Perseus and Taurus molecular clouds within it. We present auxiliary observations of H i, Hα, 26Al, and X-rays that further support this scenario, and estimate the Per-Tau Shell’s age to be ≈6–22 Myrs. The Per-Tau shell offers the first 3D observational view of a phenomenon long-hypothesized theoretically, molecular cloud formation and star formation triggered by previous stellar and supernova feedback.
Manifesto for Putting 'Chartjunk' in the Trash 2021!

In this provocation we ask the visualization research community to join us in removing chartjunk from our research lexicon. We present an etymology of chartjunk, framing its provocative origins as misaligned, and harmful, to the ways the term is currently used by visualization researchers. We call on the community to dissolve chartjunk from the ways we talk about, write about, and think about the graphical devices we design and study. As a step towards this goal we contribute a performance of maintenance through a trio of acts: editing the Wikipedia page on chartjunk, cutting out chartjunk from IEEE papers, and scanning and posting a repository of the pages with chartjunk removed to invite the community to re-imagine how we describe visualizations. This contribution blurs the boundaries between research, activism, and maintenance art, and is intended to inspire the community to join us in taking out the trash.
Identifying biases in legal data: An algorithmic fairness perspective

The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the problem of identifying and measuring biases in large-scale legal case data from an algorithmic fairness perspective. Our approach utilizes two regression models: A baseline that represents the decisions of a"typical"judge as given by the data and a"fair"judge that applies one of three fairness concepts. Comparing the decisions of the"typical"judge and the"fair"judge allows for quantifying biases across demographic groups, as we demonstrate in four case studies on criminal data from Cook County (Illinois).