Miracle-dz/UniFAR-FTUs huggingface dataset:
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots

Recent years have seen a surge in commercially-available and affordable quadrupedal robots, with many of these platforms being actively used in research and industry. As the availability of legged robots grows, so does the need for controllers that enable these robots to perform useful skills. However, most learning-based frameworks for controller development focus on training robot-specific controllers, a process that needs to be repeated for every new robot. In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots with similar morphologies. We present a simple but effective morphology randomization method that procedurally generates a diverse set of simulated robots for training. We show that by training a controller on this large set of simulated robots, our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots with diverse morphologies, which were not observed during training.
A Complex Network based Graph Embedding Method for Link Prediction

Graph embedding methods aim at finding useful graph representations by mapping nodes to a low-dimensional vector space. It is a task with important downstream applications, such as link prediction, graph reconstruction, data visualization, node classification, and language modeling. In recent years, the field of graph embedding has witnessed a shift from linear algebraic approaches towards local, gradient-based optimization methods combined with random walks and deep neural networks to tackle the problem of embedding large graphs. However, despite this improvement in the optimization tools, graph embedding methods are still generically designed in a way that is oblivious to the particularities of real-life networks. Indeed, there has been significant progress in understanding and modeling complex real-life networks in recent years. However, the obtained results have had a minor influence on the development of graph embedding algorithms. This paper aims to remedy this by designing a graph embedding method that takes advantage of recent valuable insights from the field of network science. More precisely, we present a novel graph embedding approach based on the popularity-similarity and local attraction paradigms. We evaluate the performance of the proposed approach on the link prediction task on a large number of real-life networks. We show, using extensive experimental analysis, that the proposed method outperforms state-of-the-art graph embedding algorithms. We also demonstrate its robustness to data scarcity and the choice of embedding dimensionality.
Local estimates for vectorial Rudin–Osher–Fatemi type problems in one dimension

We consider the Rudin–Osher–Fatemi variational denoising model with general regularizing term and L2 fidelity in one-dimensional, vector-valued setting. We obtain local estimates on the singular part of the variation measure of the minimizer in terms of the singular part of the variation measure of the datum. In the case of homogeneous regularizer, we prove local estimates on the whole variation measure of the minimizer and deduce an analogous result for the gradient flow of the regularizer. We also discuss the question of extending our results to other fidelities.
Software Resurrection: Discovering Programming Pearls by Showing Modernity to Historical Software

Reading computer program code and documentation written by others is, we are told, one of the best ways to learn the art of writing readable, intelligible and maintainable code and documentation. The software resurrection exercise, introduced in this paper, requires a motivated learner to compile and test a historical release (e.g. 20 years old) version of a well maintained and widely adopted open source software on a modern hardware and software platform. The learner develops fixes for the issues encountered during compilation and testing of the software on a platform that could not have been foreseen at the time of its release. The exercise concludes by writing a critique which provides an opportunity to critically reflect on the experience of maintaining the historical software. An illustrative example of the software resurrection exercise pursued on a version of the SQLite database engine released 20 years ago shows that software engineering principles (or, programming pearls) emerge during the reflective learning cycle of the software resurrection exercise. The concept of software resurrection has the potential to lay foundations for a lifelong willingness to explore and learn from existing code and documentation.
Geometric data structures approximations for network optimisation problems

A frequent task in transportation, routing, robotics, and communications applications is to find the shortest path between two positions. In robot motion planning, the robot should pass around the obstacles touching none of them, i.e. the goal is to find a collision-free path from a starting to a target position. Research of path planning has yielded many fundamentally different approaches to its solution, mainly based on various decomposition and roadmap methods. In this paper, we show a possible use of geometric data structures in point-to-point motion planning in the Euclidean plane and present an approach using generalised Voronoi diagrams that decreases the probability of collisions with obstacles and generate smooth trajectories. The second application area, investigated here, is focused on problems of finding minimal networks connecting a set of given points in the Euclidean plane and their approximations using the Delaunay triangulation.
The Complexity and Expressive Power of Second-Order Extended Logic

We study the expressive powers of SO-HORN ∗ , SO-HORN r and SO-HORN ∗ r on all ﬁnite structures. We show that SO-HORN r , SO-HORN ∗ r , FO(LFP) coincide with each other and SO-HORN ∗ is proper sublogic of SO-HORN r . To prove this result, we introduce the notions of DATALOG ∗ program, DATALOG r program and their stratiﬁed versions, S-DATALOG ∗ program and S-DATALOG r program. It is shown that, on all structures, DATALOG r and S-DATALOG r are equivalent and DATALOG ∗ is a proper sublogic of DATALOG r . SO-HORN ∗ and SO-HORN r can be treated as the negations of DATALOG ∗ and DATALOG r , respectively. We also show that SO-EHORN r logic which is an extended version of SO-HORN captures co-NP on all ﬁnite structures.
On the Energy Consumption of Different Dataframe Processing Libraries - An Exploratory Study

Background : The energy consumption of machine learning and its impact on the environment has made energy efficient ML an emerging area of research. However, most of the attention stays focused on the model creation and the training and inferencing phase. Data oriented stages like preprocessing, cleaning and exploratory analysis form a critical part of the machine learning workflow. However, the energy efficiency of these stages have gained little attention from the researchers. Aim : Our study aims to explore the energy consumption of different dataframe processing libraries as a first step towards studying the energy efficiency of the data oriented stages of the machine learning pipeline. Method : We measure the energy consumption of 3 popular libraries used to work with dataframes, namely Pandas , Vaex and Dask for 21 different operations grouped under 4 categories on 2 datasets. Results : The results of our analysis show that for a given dataframe processing operation, the choice of library can indeed influence the energy consumption with some libraries consuming 202 times lesser energy over others. Conclusion : The results of our study indicates that there is a potential for optimizing the energy consumption of the data oriented stages of the machine learning pipeline and further research is needed in the direction.
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning

Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods. However, among existing methods, derivative-free ones suffer from poor scalability or low efficiency, while gradient-based ones are often unavailable due to possibly non-differentiable controller structure. To resolve the issues, we tackle the controller tuning problem using a novel derivative-free reinforcement learning (RL) framework, which performs timestep-wise perturbation in parameter space during experience collection and integrates derivative-free policy updates into the advanced actor-critic RL architecture to achieve high versatility and efficiency. To demonstrate the framework’s efficacy, we conduct numerical experiments on two concrete examples from autonomous driving, namely, adaptive cruise control with PID controller and trajectory tracking with MPC controller. Experimental results show that the proposed method outperforms popular baselines and highlight its strong potential for controller tuning.
Motion planning in task space with Gromov-Hausdorff approximations

Applications of industrial robotic manipulators such as cobots can require efficient online motion planning in environments that have a combination of static and non-static obstacles. Existing general purpose planning methods often produce poor quality solutions when available computation time is restricted, or fail to produce a solution entirely. We propose a new motion planning framework designed to operate in a user-defined task space, as opposed to the robot's workspace, that intentionally trades off workspace generality for planning and execution time efficiency. Our framework automatically constructs trajectory libraries that are queried online, similar to previous methods that exploit offline computation. Importantly, our method also offers bounded suboptimality guarantees on trajectory length. The key idea is to establish approximate isometries known as $\epsilon$-Gromov-Hausdorff approximations such that points that are close by in task space are also close in configuration space. These bounding relations further imply that trajectories can be smoothly concatenated, which enables our framework to address batch-query scenarios where the objective is to find a minimum length sequence of trajectories that visit an unordered set of goals. We evaluate our framework in simulation with several kinematic configurations, including a manipulator mounted to a mobile base. Results demonstrate that our method achieves feasible real-time performance for practical applications and suggest interesting opportunities for extending its capabilities.
Predicting the Next Action by Modeling the Abstract Goal

The problem of anticipating human actions is an inherently uncertain one. However, we can reduce this uncertainty if we have a sense of the goal that the actor is trying to achieve. Here, we present an action anticipation model that leverages goal information for the purpose of reducing the uncertainty in future predictions. Since we do not possess goal information or the observed actions during inference, we resort to visual representation to encapsulate information about both actions and goals. Through this, we derive a novel concept called abstract goal which is conditioned on observed sequences of visual features for action anticipation. We design the abstract goal as a distribution whose parameters are estimated using a variational recurrent network. We sample multiple candidates for the next action and introduce a goal consistency measure to determine the best candidate that follows from the abstract goal. Our method obtains impressive results on the very challenging Epic-Kitchens55 (EK55), EK100, and EGTEA Gaze+ datasets. We obtain absolute improvements of +13.69, +11.24, and +5.19 for Top-1 verb, Top-1 noun, and Top-1 action anticipation accuracy respectively over prior state-of-the-art methods for seen kitchens (S1) of EK55. Similarly, we also obtain significant improvements in the unseen kitchens (S2) set for Top-1 verb (+10.75), noun (+5.84) and action (+2.87) anticipation. Similar trend is observed for EGTEA Gaze+ dataset, where absolute improvement of +9.9, +13.1 and +6.8 is obtained for noun, verb, and action anticipation. It is through the submission of this paper that our method is currently the new state-of-the-art for action anticipation in EK55 and EGTEA Gaze+ https://competitions.codalab. org/competitions/20071#results Code available at https://github.com/debadityaroy/Abstract_Goal
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language

With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.
Systems of arcs on a torus with two punctures

For a compact surface $ S $ with a finite set of marked points $ P $, we define a 1-system to be a collection of arcs which are pairwise non-homotopic and intersect pairwise at most once. We prove that, up to equivalence, there are exactly 23 maximal 1-systems on $ (S, P) $ when $ S $ is a torus and $ |P| = 2 $. Along the way, we generalize some of the results of a previous paper to the context of surfaces with boundary. In particular, we prove that the maximal cardinality of a 1-system on $ (S, P) $ is $ 2 |\chi| (|\chi| + 1) - \frac{v}{2} $, where $ \chi $ is the Euler characteristic of $ (S, P) $ and $ v $ is the number of marked points of $ P $ in the boundary of $ S $.
Low rank prior and l0 norm to remove impulse noise in images

Patch-based low rank is an important prior assumption for image processing. Moreover, according to our calculation, the optimization of l 0 norm corresponds to the maximum likelihood estimation under random-valued impulse noise. In this article, we thus combine exact rank and l 0 norm for removing the noise. It is solved formally using the alternating direction method of multipliers (ADMM), with our previous patch-based weighted ﬁlter (PWMF) producing initial images. Since this model is not convex, we consider it as a Plug-and-Play ADMM, and do not discuss theoretical convergence properties. Experiments show that this method has very good performance, especially for weak or medium contrast images.
Rotation Measure Variations and Reversals of Repeating FRBs in Massive Binary Systems

Recent observations discovered that some repeating fast radio bursts (FRBs) show complicated variations and reversals of Faraday rotation measures (RMs), indicating that the sources of these FRBs are embedded in a dynamically magnetized environment. One possible scenario is that repeating FRBs are generated by pulsars in binary systems, especially containing a high-mass companion with strong stellar outflows. Here we study the RM variations caused by stellar winds and a possible stellar disk. If the magnetic field is radial in the stellar wind, RMs will not reverse except if the magnetic axis inclination angle is close to 90°. For the toroidal magnetic field in the wind, RMs will reverse at the superconjunction. For the case of the toroidal field in the disk, the RM variations may have a multimodal and multiple reversal profile because the radio signals travel through different components of the disk during periastron passage. We also apply this model to FRB 20180916B. By assuming that its 16.35 day period is from a slowly rotating or freely precessing magnetar, we find that the secular RM variation can be explained by the periastron passage of a magnetar in a massive binary system. In addition, the clumps in the stellar wind and disk can cause short timescale (<1 day) variations or reversals of RM. Therefore, long-term monitoring of RM variations can reveal the environments of repeating FRBs.
Optimising collective accuracy among rational individuals in sequential decision-making with competition

Theoretical results underpinning the wisdom of the crowd, such as the Condorcet Jury Theorem, point to substantial accuracy gains through aggregation of decisions or opinions, but the foundations of this theorem are routinely undermined in circumstances where individuals are able to adapt their own choices based after observing what other agents have chosen. In sequential decision-making, rational agents use the choices of others as a source of information about the correct decision, creating powerful correlations between different agents’ choices that violate the assumptions of independence on which the Condorcet Jury Theorem depends. In this paper, I show how such correlations emerge when agents are rewarded solely based on their individual accuracy, and the impact of this on collective accuracy. I then demonstrate how a simple competitive reward scheme, where agents’ rewards are greater if they correctly choose options that few have already chosen, can induce rational agents to make independent choices, returning the group to optimal levels of collective accuracy. I further show that this reward scheme is robust, offering improvements to collective accuracy across wide range of competition strengths, suggesting that such schemes could be effectively implemented in real-world contexts to improve collective wisdom.
Superconductivity in the Re-B system

Superconductivity was found for a rhenium boride Re3B at Tc=4.7 K and for ReB2 with Tc in the range from 4.5 to 6.3 K depending on the boron concentrations. Both compounds have the structure different from that of the simple layered diborides, in particular from MgB2.
SELTO: Sample-Efficient Learned Topology Optimization

Recent developments in Deep Learning (DL) suggest a vast potential for Topology Optimization (TO). However, while there are some promising attempts, the subfield still lacks a firm footing regarding basic methods and datasets. We aim to address both points. First, we explore physics-based preprocessing and equivariant networks to create sample-efficient components for TO DL pipelines. We evaluate them in a large-scale ablation study using end-to-end supervised training. The results demonstrate a drastic improvement in sample efficiency and the predictions' physical correctness. Second, to improve comparability and future progress, we publish the two first TO datasets containing problems and corresponding ground truth solutions.
On Faithfulness and Coherence of Language Explanations for Recommendation Systems

Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance. Specifically, previous work show that jointly learning to perform review generation improves rating prediction performance. Meanwhile, these model-produced reviews serve as recommendation explanations, providing the user with insights on predicted ratings. However, while existing models could generate fluent, human-like reviews, it is unclear to what degree the reviews fully uncover the rationale behind the jointly predicted rating. In this work, we perform a series of evaluations that probes state-of-the-art models and their review generation component. We show that the generated explanations are brittle and need further evaluation before being taken as literal rationales for the estimated ratings.
General Place Recognition Survey: Towards the Real-world Autonomy Age

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.
Using auxiliary marginal distributions in imputations for nonresponse while accounting for survey weights, with application to estimating voter turnout

The Current Population Survey is the gold-standard data source for studying who turns out to vote in elections. However, it suffers from poten-tially nonignorable unit and item nonresponse. Fortunately, after elections, the total number of voters is known from administrative sources and can be used to adjust for potential nonresponse bias. We present a model-based approach to utilize this known voter turnout rate, as well as other population marginal distributions of demographic variables, in multiple imputation for unit and item nonresponse. In doing so, we ensure that the imputations pro-duce design-based estimates that are plausible given the known margins. We introduce and utilize a hybrid missingness model comprising a pattern mixture model for unit nonresponse and selection models for item nonresponse. Using simulation studies, we illustrate repeated sampling performance of the model under different assumptions about the missingness mechanisms. We apply the model to examine voter turnout by subgroups using the 2018 Current Population Survey for North Carolina. As a sensitivity analysis, we examine how results change when we allow for over-reporting, i.e., individuals self-reporting that they voted when in fact they did not.