R2LIVE A Robust Real time LiDAR Inertial Visual tightly coupled state Estimator and mapping

In this letter, we propose a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses measurement from LiDAR, inertial sensor, and visual camera to achieve robust and accurate state estimation . The algorithm is robust enough to run inreal-time on an on-board computation platform, as shown by extensive experiments conducted in indoor, outdoor, and mixed environment of different scale .…

Learning to Shift Attention for Motion Generation

One challenge of motion generation using robot learning from demonstrationtechniques is that human demonstrations follow a distribution with multiplemodes for one task query . Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories .…

Explaining Safety Failures in NetKAT

This work introduces a concept of explanations with respect to the violation of safe behaviours within software defined networks . In our setting, a safe behaviour is characterised by aNetKAT policy, or program, which does not enable forwarding packets from aningress i to an undesirable egress e .…

Multichannel LSTM CNN for Telugu Technical Domain Identification

Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering,Information Extraction, and Sentiment Analysis . System got 69.9% of the F1score on the test dataset and 90.01% on the validation set . This architecture was used and evaluated in the context of the ICONshared task TechDOfication 2020 (task h) Thematic keywords give a compressedrepresentation of the text, and usually, Domain Identification is used in machine translation .…

Measuring HTTP 3 Adoption and Performance

The third version of the Hypertext Transfer Protocol (HTTP) is currently in its final standardization phase by the IETF . Besides better security and increased flexibility, it promises benefits in terms of performance . We run a large-scalemeasurement campaign toward thousands of websites adopting HTTP/3, aiming at understanding to what extent it achieves better performance than HTTP/2 .…

Sparse online variational Bayesian regression

This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context ofsparsity-promoting priors . For linear modelsthe method requires only the iterative solution of deterministic least squaresproblems . For large p an approximation is able toachieve promising results for a cost of O(p) in both computation and memory .…

A Survey on Consortium Blockchain Consensus Mechanisms

Consensus algorithm is an agreement to validate the correctness of blockchain transactions . Unlike a public blockchain, a consortium blockchain does not contend with the issues of creating a resource-savingglobal consensus protocol . This paper presents the mechanisms of these and other consensus protocols, and analyzes and compares their advantages and drawbacks .…

The non positive circuit weight problem in parametric graphs a fast solution based on dioid theory

In this paper, we design an algorithm thatsolves the Non-positive Circuit weight Problem (NCP) on this class ofparametric graphs . The proposed algorithm isbased on max-plus algebra and formal languages and runs faster than otherexisting approaches . It achieves strongly polynomial time complexity$\mathcal{O}(n^4)$ (where $n$ is the number of nodes in the graph) The proposed algorithms are based on max plus algebra, and run faster than existing approaches .…

Density Sketches for Sampling and Estimation

We introduce Density sketches (DS) as a succinct online summary of the datadistribution . DS can accurately estimate point wise probability density . DS also provides a capability to sample unseen novel data from the underlying data distribution . DS construction is an online algorithm.…

Hero On the Chaos When PATH Meets Modules

The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management issues, incurring reference inconsistencies and even build failures . We reported 280 issues, among which 181 (64.6\%) issues have been confirmed, and 160 of them (88.4\%) have been fixed or areunder fixing .…

The Logical Options Framework

Logical Options Framework (LOF) learns policies that are satisfying, optimal, and composable . LOF efficiently learns policies thatsatisfy tasks by representing the task as an automaton and integrating it into learning and planning . We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment .…

vrCAPTCHA Exploring CAPTCHA Designs in Virtual Reality

With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA designs for VR devices . In this paper, we present four traditional two-dimensional (2D) CAPTCHAs in VR . Then, based on the three-dimensional interactioncharacteristics of VR devices, we propose two vrCAPTCHA design prototypes .…

A Trident Quaternion Framework for Inertial based Navigation Part I Rigid Motion Representation and Computation

Strapdown inertial navigation research involves the parameterization andcomputation of the attitude, velocity and position of a rigid body in a chosen reference frame . This paper proposes a compact andelegant representation of the body’s attitude, position and velocity . Thekinematics of strapdown INS are cohesively unified in one concise differentialequation .…

Contingency Model Predictive Control for Linear Time Varying Systems

Contingency Model Predictive Control (CMPC) anticipates emergency and keeps the controlled system in asafe state that is selectively robust to the identified hazard . This article presents a linear formulation for CMPC, illustrates its keyfeatures on a toy problem, and then demonstrates its efficacy experimentally on a full-size automated road vehicle that encounters a realistic pop-outobstacle .…

Revisit Recommender System in the Permutation Prospective

Recommender systems (RS) work effective at alleviating information overloadand matching user interests in various web-scale applications . Most RS retrieve the user’s favorite candidates and then rank them by the rating scores in thegreedy manner . We propose a novel permutation-wise framework PRS in there-ranking stage of RS, which consists of Permutation-Matching (PMatch) and PRank) stages successively .…

No Regret Algorithms for Private Gaussian Process Bandit Optimization

The widespread proliferation of data-driven decision-making has ushered in interest in the design of privacy-preserving algorithms . We propose a solution for differentially private GP bandit optimization that combines a uniform kernelapproximator with random perturbations . For twospecific DP settings – joint and local differential privacy, we provide algorithms based on efficient quadrature Fourier feature approximators that are computationally efficient and provably no-regret for popular stationary kernel functions .…

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

This research proposes a new integrated framework for identifying safelanding locations and planning in-flight divert maneuvers . The proposed framework wasable to achieve 94.8% of successful landing in highly challenging landingsites where over 80$\%$ of the area around the initial target lading point ishazardous, by effectively updating the target landing site and feedback controlgain during descent .…

Memory based Deep Reinforcement Learning for POMDP

A promising characteristic of Deep Reinforcement Learning (DRL) is itsability to learn optimal policy in an end-to-end manner without relying on feature engineering . Most approaches assume a fully observable statespace, i.e. fully observable Markov Decision Process (MDP) In real-worldrobotics, this assumption is unpractical, because of sensor issues such assensors’ capacity limitation and sensor noise .…

Parameterized Temperature Scaling for Boosting the Expressive Power in Post Hoc Uncertainty Calibration

Standard deepneural networks typically yield uncalibrated predictions, which can be betransformed into confidence scores using post-hoc calibration methods . We address the problem of uncertainty calibration and introduce a novel method, Parametrized Temperature Scaling (PTS) We show that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of modelarchitectures, datasets and metrics .…

Adversarial Robustness with Non uniform Perturbations

Robustness of machine learning models is critical for security related applications . We propose using characteristics of the empirical datadistribution, both on correlations between the features and the importance of the features themselves . The key idea of our proposed approach is to enable non-uniformperturbations that can adequately represent these feature dependencies during training .…

Lossless Compression of Efficient Private Local Randomizers

Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting . LDP reports are known to have relatively little information about the user’s data due to randomization . Several schemes are known that exploit this fact to design low-communication versions of LDP algorithms but all of them do so at the expense of a significant loss in utility .…