Miracle-dz/UniFAR-FTUs huggingface dataset:
Eigen-Factors an Alternating Optimization for Back-end Plane SLAM of 3D Point Clouds

Modern depth sensors can generate a huge number of 3D points in few seconds to be latter processed by Localization and Mapping algorithms. Ideally, these algorithms should handle efficiently large sizes of Point Clouds under the assumption that using more points implies more information available. The Eigen Factors (EF) is a new algorithm that solves SLAM by using planes as the main geometric primitive. To do so, EF exhaustively calculates the error of all points at complexity $O(1)$, thanks to the {\em Summation matrix} $S$ of homogeneous points. The solution of EF is highly efficient: i) the state variables are only the sensor poses -- trajectory, while the plane parameters are estimated previously in closed from and ii) EF alternating optimization uses a Newton-Raphson method by a direct analytical calculation of the gradient and the Hessian, which turns out to be a block diagonal matrix. Since we require to differentiate over eigenvalues and matrix elements, we have developed an intuitive methodology to calculate partial derivatives in the manifold of rigid body transformations $SE(3)$, which could be applied to unrelated problems that require analytical derivatives of certain complexity. We evaluate EF and other state-of-the-art plane SLAM back-end algorithms in a synthetic environment. The evaluation is extended to ICL dataset (RGBD) and LiDAR KITTI dataset. Code is publicly available at https://github.com/prime-slam/EF-plane-SLAM.
Online Reinforcement Learning in Markov Decision Process Using Linear Programming

We consider online reinforcement learning in episodic Markov decision process (MDP) with unknown transition function and stochastic rewards drawn from some fixed but unknown distribution. The learner aims to learn the optimal policy and minimize their regret over a finite time horizon through interacting with the environment. We devise a simple and efficient model-based algorithm that achieves $\tilde{O}(LX\sqrt{TA})$ regret with high probability, where $L$ is the episode length, $T$ is the number of episodes, and $X$ and $A$ are the cardinalities of the state space and the action space, respectively. The proposed algorithm, which is based on the concept of “optimism in the face of uncertainty”, maintains confidence sets of transition and reward functions and uses occupancy measures to connect the online MDP with linear programming. It achieves a tighter regret bound compared to the existing works that use a similar confidence set framework and improves computational effort compared to those that use a different framework but with a slightly tighter regret bound.
Parallel evolutionary learning of fuzzy rule bases using the island injection genetic algorithm

In this paper the island injection genetic algorithm (iiGA) is applied to the evolutionary design of fuzzy rule bases. The iiGA is a parallel GA in which a hierarchy of subpopulations of candidate problem solutions employ representations at different resolutions. Emigration from one subpopulation to another occurs strictly down the hierarchy of increasing resolution, except at the lowest (i.e. highest resolution) level where subpopulations can exchange migrants. This arrangement allows subpopulations with more abstract representations to evolve approximate solutions, which are then "injected" into subpopulations at lower levels in the hierarchy for further refinement. We investigate the application of the technique to artificial evolution of fuzzy controllers. In applying the technique to this problem, different subpopulations use varying levels of abstraction in their representation of fuzzy set membership functions and number of fuzzy rules.
AirLoc: Object-based Indoor Relocalization

Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous approaches adopt geometrical information such as key-point features or local textures to carry out indoor relocalization, but they either easily fail in an environment with visually similar scenes or require many database images. Inspired by the fact that humans often remember places by recognizing unique landmarks, we resort to objects, which are more informative than geometry elements. In this work, we propose a simple yet effective object-based indoor relocalization approach, dubbed AirLoc. To overcome the critical challenges of object reidentification and remembering object relationships, we extract object-wise appearance embedding and inter-object geometric relationships. The geometry and appearance features are integrated to generate cumulative scene features. This results in a robust, accurate, and portable indoor relocalization system, which outperforms the state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7% of accuracy. In addition to exhaustive evaluation, we also carry out real-world tests, where AirLoc shows robustness in challenges like severe occlusion, perceptual aliasing, viewpoint shift, and deformation.
The Effects of Incentives on Choices and Beliefs in Games: An Experiment

How and why do incentive levels affect strategic behavior? This paper examines an experiment designed to identify the causal effect of scaling up incentives on choices and beliefs in strategic settings by holding fixed opponents' actions. In dominance-solvable games, higher incentives increase action sophistication and best-response rates and decrease mistake propensity. Beliefs tend to become more accurate with higher own incentives in simple games. However, opponents with higher incentive levels are harder to predict: while beliefs track opponents' behavior when they have higher incentive levels, beliefs about opponents also become more biased. We provide evidence that incentives affect cognitive effort and that greater effort increases performance and predicts choice and belief sophistication. Overall, the data lends support to combining both payoff-dependent mistakes and costly reasoning.
Application of FDKL method for order reduction of RFA unsteady aeroelastic models

An efficient time domain reduced-order aeroelastic model is presented based on the Roger's Rational Function Approximation model and the Frequency-Domain Karhunen-Loeve Method. Using frequency responses of the Roger's model, the method generates a real, orthogonal set of aeroelastic modes. These KL modes are optimal in the solution space with a minimum possible number of modes. When invoked with Galerkin's error minimization, they generate a reduced-order aeroelastic model. The proposed method is demonstrated using an aeroelastic model of a representative commercial airplane with 638 state variables. It is shown that the aeroelastic results of the reduced-order model match extremely well with the results obtained from the full-order model in both open-loop and closed-loop analyses. The new aeroelastic model can be efficiently used in repeated analysis of dynamic loads, optimization, and closed-loop designs.
Traffic flow clustering framework using drone video trajectories to identify surrogate safety measures

The utilization of traffic conflict indicators is crucial for assessing traffic safety and examining trajectory data, especially in cases where crash data is unavailable. Previous studies have demonstrated the effectiveness of traffic conflict indicators, including time-to-collision (TTC), proportion of stopping distance (PSD), and deceleration rate to avoid a crash (DRAC), in identifying and predicting conflicts. However, most of these studies limit their analysis to statistical heuristics based on traffic flow characteristics during different traffic states. To overcome this constraint, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds for identifying traffic conflicts across various traffic states. This automatic process is facilitated by high-resolution trajectory data obtained from the CitySim dataset. Given the observations, different traffic states and corresponding transitions are identified with the conventional three-phase theory. Meanwhile, the traffic conflicts, indicated with SSM including TTC, DRAC, and PSD, are mapped to the corresponding traffic states from the perspectives of time, space, and deceleration. After data processing, an unsupervised model is leveraged to identify traffic conflict and non-conflict clusters with derived traffic conflicts indicators. Furthermore, three models, including K-means, GMM, and Mclust, are investigated and compared to optimize the identification. It is observed that Mclust outperforms K-mans and GMM on three evaluation metrics, including Silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin index. Moreover, we apply our proposed framework to analyze traffic states and conflicts with vehicle trajectories. According to the results, there is a variation in the distribution of traffic conflict among different traffic states, wide moving jam(phase J) has the highest conflict risk, followed by synchronous flow(phase S), and free flow(phase F). Meanwhile, the thresholds of traffic conflicts cannot be fully represented by the same value through different traffic states. It reveals that the heterogeneity of thresholds is exhibited across traffic state transitions, which justifies the necessity of dynamic thresholds for traffic conflict analysis.
A Long-Baseline Atom Interferometer at CERN: Conceptual Feasibility Study

We present results from exploratory studies, supported by the Physics Beyond Colliders (PBC) Study Group, of the suitability of a CERN site and its infrastructure for hosting a vertical atom interferometer (AI) with a baseline of about 100 m. We first review the scientific motivations for such an experiment to search for ultralight dark matter and measure gravitational waves, and then outline the general technical requirements for such an atom interferometer, using the AION-100 project as an example. We present a possible CERN site in the PX46 access shaft to the Large Hadron Collider (LHC), including the motivations for this choice and a description of its infrastructure. We then assess its compliance with the technical requirements of such an experiment and what upgrades may be needed. We analyse issues related to the proximity of the LHC machine and its ancillary hardware and present a preliminary safety analysis and the required mitigation measures and infrastructure modifications. In conclusion, we identify primary cost drivers and describe constraints on the experimental installation and operation schedules arising from LHC operation. We find no technical obstacles: the CERN site is a very promising location for an AI experiment with a vertical baseline of about 100 m.
Finite element formulations for Maxwell's eigenvalue problem using continuous Lagrangian interpolations

We consider nodal-based Lagrangian interpolations for the finite element approximation of the Maxwell eigenvalue problem. The first approach introduced is a standard Galerkin method on Powell-Sabin meshes, which has recently been shown to yield convergent approximations in two dimensions, whereas the other two are stabilized formulations that can be motivated by a variational multiscale approach. For the latter, a mixed formulation equivalent to the original problem is used, in which the operator has a saddle point structure. The Lagrange multiplier introduced to enforce the divergence constraint vanishes in an appropriate functional setting. The first stabilized method we consider consists of an augmented formulation with the introduction of a mesh dependent term that can be regarded as the Laplacian of the multiplier of the divergence constraint. The second formulation is based on orthogonal projections, which can be recast as a residual based stabilization technique. We rely on the classical spectral theory to analyze the approximating methods for the eigenproblem. The stability and convergence aspects are inherited from the associated source problems. We investigate the numerical performance of the proposed formulations and provide some convergence results validating the theoretical ones for several benchmark tests, including ones with smooth and singular solutions.
Disentangled Pre-training for Image Matting

Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by proposing a self-supervised pre-training approach that can leverage infinite numbers of data to boost the matting performance. The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective. The pre-trained model is then used as an initialisation of the downstream matting task for fine-tuning. Extensive experimental evaluations show that the proposed approach outperforms both the state-of-the-art matting methods and other alternative self-supervised initialisation approaches by a large margin. We also show the robustness of the proposed approach over different backbone architectures. Our project page is available at https://crystraldo.github. io/dpt_mat/.
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next K items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models1 and Github2.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning

Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities. Recently, a new task, i.e., TKG few-shot out-of-graph (OOG) link prediction, is proposed, where TKGC models are required to achieve great link prediction performance concerning newly-emerged entities that only have few-shot observed examples. In this work, we propose a TKGC method FITCARL that combines few-shot learning with reinforcement learning to solve this task. In FITCARL, an agent traverses through the whole TKG to search for the prediction answer. A policy network is designed to guide the search process based on the traversed path. To better address the data scarcity problem in the few-shot setting, we introduce a module that computes the confidence of each candidate action and integrate it into the policy for action selection. We also exploit the entity concept information with a novel concept regularizer to boost model performance. Experimental results show that FITCARL achieves stat-of-the-art performance on TKG few-shot OOG link prediction.
Topological densities in Einstein-scalar-Gauss-Bonnet gravity

The present work is devoted to studying the background dynamical evolution of a scalar field in Einstein-Gauss-Bonnet gravity in maximally symmetric space-time. This study is useful for giving meaning to the presence of two Gauss-Bonnet vacua, instead of using the spherically symmetric bubbles of the"true"vacuum expand in the"false"vacuum. The theory admits two possible effective cosmological constants, which lead to two maximally symmetric vacuum solutions. The first solution corresponds to the dynamics of dark energy. When there is matter, the second solution describes dark matter. In Einstein-Gauss-Bonnet gravity, we establish the expression of the topological mass spectrum which depends on the golden ratio and its inverse. In the Schwarzschild limit, the topological density corresponds to the standard model radiation energy density. We find the mass loss rate which gives the evolution of mass over time.
Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times

While the inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance when there is lack of overlap in the propensity score distributions. By smoothly down-weighting the units with extreme propensity scores, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with right-censored survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST)-a clinically meaningful summary measure free of the proportional hazards assumption. In this article, we combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and propose computationally-efficient variance estimators. We conduct simulations to compare the performance of IPTW, trimming, and OW in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap. Regardless of overlap, we demonstrate the advantage of OW over IPTW and trimming methods in bias, variance, and coverage when the estimand is defined based on RMST.
Power-generation enhancements and upstream flow properties of turbines in unsteady inflow conditions

Abstract Energy-harvesting systems in complex flow environments, such as floating offshore wind turbines, tidal turbines and ground-fixed turbines in axial gusts, encounter unsteady streamwise flow conditions that affect their power generation and structural loads. In some cases, enhancements in time-averaged power generation above the steady-flow operating point are observed. To characterize these dynamics, a nonlinear dynamical model for the rotation rate and power extraction of a periodically surging turbine is derived and connected to two potential-flow representations of the induction zone upstream of the turbine. The model predictions for the time-averaged power extraction of the turbine and the upstream flow velocity and pressure are compared against data from experiments conducted with a surging-turbine apparatus in an open-circuit wind tunnel at a diameter-based Reynolds number $Re_D = 6.3\times 10^5$ and surge-velocity amplitudes up to 24 % of the wind speed. The combined modelling approach captures trends in both the time-averaged power extraction and the fluctuations in upstream flow quantities, while relying only on data from steady-flow measurements. The sensitivity of the observed increases in time-averaged power to steady-flow turbine characteristics is established, thus clarifying the conditions under which these enhancements are possible. Finally, the influence of unsteady fluid mechanics on time-averaged power extraction is explored analytically. The theoretical framework and experimental validation provide a cohesive modelling approach that can drive the design, control and optimization of turbines in unsteady flow conditions, as well as inform the development of novel energy-harvesting systems that can leverage unsteady flows for large increases in power-generation capacities.
Testing and Identifying Substitution and Complementarity Patterns

This paper studies semiparametric identification of substitution and complementarity patterns between two goods using a panel multinomial choice model with bundles. The model allows the two goods to be either substitutes or complements and admits heterogeneous complementarity through observed characteristics. I first provide testable implications for the complementarity relationship between goods. I then characterize the sharp identified set for the model parameters and provide sufficient conditions for point identification. The identification analysis accommodates endogenous covariates through flexible dependence structures between observed characteristics and fixed effects while placing no distributional assumptions on unobserved preference shocks. My method is shown to perform more robustly than the parametric method through Monte Carlo simulations. As an extension, I allow for unobserved heterogeneity in the complementarity, investigate scenarios involving more than two goods, and study a class of nonseparable utility functions.
Signal Extraction,Maximum Likelihood Estimation and the Start-Up Problem

In this paper, we portray the essential features of the finite-sample signal extraction problem in both the stationary and the nonstationary cases. The computational procedures can be simplified in the light of our analysis. An important outcome of the analysis is a demonstration that the start-up problem can be handled far more easily that one might expect from a passing acquaintance with the usual practices.
Optimal Mass Transport over the Euler Equation

We consider the finite horizon optimal steering of the joint state probability distribution subject to the angular velocity dynamics governed by the Euler equation. The problem and its solution amounts to controlling the spin of a rigid body via feedback, and is of practical importance, for example, in angular stabilization of a spacecraft with stochastic initial and terminal states. We clarify how this problem is an instance of the optimal mass transport (OMT) problem with bilinear prior drift. We deduce both static and dynamic versions of the Eulerian OMT, and provide analytical and numerical results for the synthesis of the optimal controller.
Modelling customer churn for the retail industry in a deep learning based sequential framework

As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.