Higher order phase averaging for highly oscillatory systems

We introduce a higher order phase averaging method for nonlinear oscillatorysystems . Phaseaveraging is useful for deriving reduced models that can be solved numerically . We illustrate the properties of this method on an ODE thatdescribes the dynamics of a swinging spring due to Lynch (2002) Although idealized, this model shows an interesting analogy to geophysical flows as it exhibits a slow dynamics that arises through the resonance between fastoscillations .…

Models we Can Trust Toward a Systematic Discipline of Agent Based Model Interpretation and Validation

We advocate the development of a discipline of interacting with and extracting information from models, both mathematical (e.g. game-theoreticones) and computational models . We outline some directions for the development . of a such a discipline: – development of logical frameworks for the systematic formalspecification of stylized facts and social mechanisms in (mathematical andcomputational) social science .…

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 .…

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 .…

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 .…

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 .…

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 .…

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.…

A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning

Hippocampal reverse replay is thought to contribute to learning, andparticularly reinforcement learning, in animals . We present a computational model of learning in the hippocampus that builds on a previous model of the hippocampus . We conclude that reverse replay may enhance reinforcement learning inthe mammalian hippocampal-striatal system rather than provide its coremechanism.…

Syntactic completeness of proper display calculi

A recent strand of research in structural proof theory aims at exploring thenotion of analytic calculi that support general and modularproof-strategies for cut elimination . In this context, the proof that the givencalculus is complete w.r.t. the original logic is usually carried outsyntactically, i.e.…

On Sexual Selection

Sexual selection is a fundamental aspect of evolution for all eukaryotic organisms with mating types . This paper suggests intersexual selection is bestviewed as a mechanism to compensate for the unavoidable dynamics of coevolution between sexes that emerge with isogamy .…

Comparative evaluation of CNN architectures for Image Caption Generation

Aided by recent advances in Deep Learning, Image Caption Generation has seentremendous progress over the last few years . Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the outputsentences .…

Text to Audio Grounding Building Correspondence Between Captions and Sound Events

Automated Audio Captioning is a cross-modal task, generating natural languagedescriptions to summarize the audio clips’ sound events . This paper contributes an AudioGrounding dataset, which provides correspondence between sound events and captions provided inAudiocaps . A baseline approach is provided, resulting in an event-F1 score of 28.3% and a Polyphonic Sound Detection Score (PSDS) score of 14.7% .…

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 .…

Practical Mutation Testing at Scale

Mutation testing builds on top of mutation analysis and is a testingtechnique that uses mutants as test goals to create or improve a test suite . Google has a codebase of two billion lines of code and more than 500,000,000 tests are executed on adaily basis .…

Toward Speeding up Mutation Analysis by Memoizing Expensive Methods

Mutation analysis has many applications, such as assessing the quality of test cases, fault localization, test input generation, security analysis, etc. Such applications involve running test suites against a large number of programmutants leading to poor scalability . This paper presents anovel approach, named MeMu, for reducing the execution time of the mutants, bymemoizing the most expensive methods in the system .…

The SmartSHARK Repository Mining Data

The SmartSHARK repository mining data is a collection of rich and detailed information about the evolution of software projects . The data set provides a rich source of data that enables us to explore research questions that require data from different sources and/or longitudinal data over time .…

Exact epidemic models from a tensor product formulation

A general framework for obtaining exact transition rate matrices forstochastic systems on networks is presented and applied to many well-known models of epidemiology . The state of the population is described as a vector in the tensor product space of $N$ individual probability vectorspaces, whose dimension equals the number of compartments of theepidemiological model $n_c$.…

Grounded Relational Inference Domain Knowledge Driven Explainable Autonomous Driving

Explainability is essential for autonomous vehicles and other roboticssystems interacting with humans and other objects during operation . Humans need to understand and anticipate the actions taken by the machines for trustful and safe cooperation . Grounded Relational Inference (GRI) aims to enable the explainability of an autonomous driving system at the design stage by incorporating expert domainknowledge into the model .…

Recurrent Model Predictive Control

Recurrent Model Predictive Control (RMPC) algorithm can converge to the optimal policy by minimizing the designed loss function . The number of prediction steps is equal to the number of recurrent cycles of the learned policy function . We further prove the convergence and optimality of the RMPC algorithm thoroughBellman optimality principle, and demonstrate its generality and efficiencyusing two numerical examples .…

DeepThermal Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning

Thermal power generation plays a dominant role in the world’s electricity supply . It consumes large amounts of coal worldwide, and causes serious airpollution . At its core, is a new model-based reinforcement learning (RL) framework, called MORE, which leverages historical operational data of a TPGU to solve a highly complex Markov decision process problem via purely offline training .…

Revisiting the Memristor Concept within Basic Circuit Theory

In this paper we revisit the memristor concept within circuit theory . We show that for general memristive systems the $\varphi-q$ curve is not single-valued or not even closed . An approach suitable to explain the inductive behavior of the giant squid axon had already been developed in the 1960s, with the introduction of “time-variant resistors” We also point out the ambiguities resulting from anon rigorous usage of the flux linkage concept .…

An Interaction aware Evaluation Method for Highly Automated Vehicles

It is important to build a rigorous verification and validation (V&V) processto evaluate the safety of highly automated vehicles before their widedeployment on public roads . In this paper, we propose an interaction-aware framework for HAV safety evaluation which is suitable for somehighly-interactive driving scenarios including highway merging, roundaboutentering, etc.…

The Curious Case of Integrator Reach Sets Part I Basic Theory

This is the first of a two part paper investigating the geometry of theintegrator reach sets . In this Part I, we establish that this compact convex set is semialgebraic, translated zonoid, andnot a spectrahedron . We also deduce the closedform formula for the volume and diameter of this set, and discuss their scaling with state dimension and time .…

Automating Test Case Identification in Open Source Projects on GitHub

Software testing is one of the very important Quality Assurance (QA) components . The analysis was performed on 38 GitHub open sourcerepositories thoroughly selected from the set of 4.3M GitHub projects . The results show that: (i) there exists weak correlation (r= 0.655) between the word test and test cases presence in a class; (ii) the proposed algorithm using static file analysis correctly detected 95\% of test cases; (iii) 15% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework.…

A discontinuous Galerkin method based on a hierarchical orthogonal basis for Lagrangian hydrodynamics on curvilinear grids

We present a new high-order accurate Lagrangian discontinuous Galerkin (DG)hydrodynamic method to simulate material dynamics (for e.g., gasses, fluids,and solids) with up to fourth-order accuracy on cubic meshes . The variables, such as specific volume, velocity, specific total energy, and deformation gradient fields within a cell, are represented with a polynomial constructed with a novel hierarchical orthogonal basis about the center of mass .…

Explore the Context Optimal Data Collection for Context Conditional Dynamics Models

In this paper, we learn dynamics models for parametrized families of systems with varying properties . The dynamics models are formulated as stochastic processes conditioned on a latent context variable . The probabilisticformulation allows us to compute an action sequence which, for a limited number of environment interactions, optimally explores the given system within the given family .…

The landscape of software for tensor computations

Tensors (also commonly seen as multi-linear operators) are ubiquitous in scientific computing and in data science . We have observed an explosion in libraries, compilers, packages, and toolboxes for tensor operations . These efforts are scattered among the different domains, and inevitably suffer from replication, suboptimalimplementations, and in many cases, limited visibility .…