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 .…

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 .…

Operator preconditioning the simplest case

Using the framework of operator or Calder\’on preconditioning, uniformpreconditioners are constructed for elliptic operators discretized withcontinuous finite (or boundary) elements . They are constructed as the composition of an opposite order operator, discretizing on the sameansatz space, and two diagonal scaling operators .…

State of the Art in Human Scanpath Prediction

The last years have seen a surge in models predicting the scanpaths offixations made by humans when viewing images . However, the field is lacking aprincipled comparison of those models with respect to their predictive power . We evaluate many existing models of scanpath prediction on the datasetsMIT1003, MIT300, CAT2000 train and CAT200 test, for the first time giving adetailed picture of the current state of the art of human scanpath predictions .…

Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing

We propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave cellular networks . We describe important challenges in using traditional CS in this setting and propose enhanced CSprotocols to address these challenges . We evaluate the downlink coverage probability of our shared mmWavenetwork using simulations as well as numerical examples based on our analysis .…

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 .…

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 .…

Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical tools emerging from modern computing and data science . Koopman spectral theory has emerged as a dominant perspective over the past decade . This linear representation of nonlinear dynamics has tremendous potential to enable the prediction,estimation, and control of non linear systems with standard textbook methods developed for linear systems .…

Object Detection in Aerial Images A Large Scale Benchmark and Challenges

The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-boxannotations collected from 11,268 aerial images . Previous challenges run on DOTA have attracted more than 1300 teams worldwide . We believe that the expanded large-scale DOTA datasets, the extensive baselines, the code library and the challenges can facilitate thedesigns of robust algorithms and reproducible research on the problem of object detection in aerial images.…

Localization Distillation for Object Detection

Knowledge distillation (KD) has witnessed its powerful ability in learningcompact models in deep learning field, but it is still limited in distillinglocalization information for object detection . Existing KD methods for objectdetection mainly focus on mimicking deep features between teacher model and student model .…

A CP Net based Qualitative Composition Approach for an IaaS Provider

We propose a novel CP-Net based composition approach to qualitatively select an optimal set of consumers for an IaaS provider . The provider’s and consumers’ qualitative preferences are captured using CP-Nets . A greedy-based and a heuristic-based consumer selection approaches are proposed that effectively reduce the search space of candidates in the composition .…

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.…

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 .…

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms

Aims to provide absolute lower bounds for the best possible runningtimes that can be achieved by $(1+\lambda)$-type search heuristics on common benchmark problems . We apply our hybrid Monte Carlo dynamic programming approach to aconcatenated jump function and demonstrate how the obtained bounds can be used to gain a deeper understanding into parameter control schemes .…

State Augmented Constrained Reinforcement Learning Overcoming the Limitations of Learning with Rewards

Constrained reinforcement learning involves multiple rewards that must accumulate to given thresholds . In this class of problems, we show a simple example in which the desired optimal policy cannot be induced by any combination of rewards . This work addresses this shortcoming by augmenting the state with Lagrange multipliers and reinterpreting primal-dualmethods as the portion of the dynamics that drives the multipliers evolution .…

Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos and NSGA II

In a first-of-its-kind study, this paper proposes the formulation ofconstructing prediction intervals (PIs) in a time series as a bi-objectiveoptimization problem and solves it with the help of Nondominated SortingGenetic Algorithm (NSGA-II) The proposed models when applied to the macroeconomic time series, yielded better results interms of both prediction interval coverage probability (PICP) and predictioninterval average width (PIAW) Compared to the state-of the art Lower UpperBound Estimation Method (LUBE) with Gradient Descent (GD) The 3-stage modelyielded better PICP compared to the 2-stage model but showed similarperformance in PIAW with added computation cost of running NSGA-I second time .…

Neuroscience Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

If machine failures can be detected preemptively, maintenance and repairs can be performed more efficiently, reducing production costs . Hierarchical Temporal Memory (HTM) outperforms state-of-the-art algorithms at preemptively detecting real-world cases of bearing failures and simulated 3D printer failures . Using the Numenta AnomalyBenchmark, our approach achieves anaverage score of 64.71 .…

A Generalized Eulerian Lagrangian Discontinuous Galerkin Method for Transport Problems

We propose a generalized Eulerian-Lagrangian (GEL) discontinuous Galerkin(DG) method . The GEL DG method is motivated for solving linear hyperbolicsystems with variable coefficients . The velocity field for adjointproblems of the test functions is frozen to constant . Numerical results on 1D and 2D linear transport problems are presented to demonstrate great properties of the method .…

A preconditioner based on sine transform for two dimensional Riesz space factional diffusion equations in convex domains

An implicit finite difference method isemployed to discretize the Riesz space fractional diffusion equations with apenalty term in a rectangular region by the volume-penalization approach . The spectrum of the preconditioned matrix is also investigated . Numerical experiments are carried out todemonstrate the efficiency of the proposed method, and the stability and convergence of the method are studied .…

EscapeWildFire Assisting People to Escape Wildfires in Real Time

Number of wildfires and area of landburned around the world has been steadily increasing, partly due to climaticchanges and global warming . There is a high probability that more people will be exposed to and endangered by forest fires . EscapeWildFire is an amobile application connected to a backend system which models and predict geographical progression, assisting citizens to escape wildfires inreal-time .…

Experimental Study on Probabilistic ToA and AoA Joint Localization in Real Indoor Environments

Joint localization algorithm significantly outperforms baselines using only ToA or AoA measurements . It achieves 2-D sub-meter accuracy at the90%-ile. We also numerically demonstrate that the algorithmis more robust to synchronization errors than the baseline using ToAmeasurements only. We evaluate the algorithm performance using aproprietary prototype deployed in an indoor factory environment.…

Artificial Intelligence as an Anti Corruption Tool AI ACT Potentials and Pitfalls for Top down and Bottom up Approaches

New hope is placed in Artificial Intelligence (AI) to serve as anunbiased anti-corruption agent . Ever more available (open) government datapaired with unprecedented performance of such algorithms render AI the nextfrontier in anti-corruption . Summarizing existing efforts to use AI-basedanti-corruption tools (AI-ACT), we introduce a conceptual framework to advanceresearch and policy .…

End to End Dereverberation Beamforming and Speech Recognition with Improved Numerical Stability and Advanced Frontend

Recently, the end-to-end approach has been successfully applied tomulti-speaker speech separation and recognition in both single-channel and multichannel conditions . However, severe performance degradation is stillobserved in the reverberant and noisy scenarios, and there is still a large performance gap between anechoic and reverberant conditions .…