Koopman NMPC Koopman based Learning and Nonlinear Model Predictive Control of Control affine Systems

Using the Koopman canonical transform, control-affine dynamics can be expressed by a lifted bilinear model . The model is used for nonlinear model predictive control (NMPC) design . The benefits are highlighted through an example of a simulated planarquadrotor . Prediction error is greatly reduced and closed loop performancesimilar to NMPC with full model knowledge is achieved .…

Studying the association of online brand importance with museum visitors An application of the semantic brand score

This paper explores the association between brand importance and growth in museum visitors . We analyzed 10 years of online forum discussions and applied the Semantic Brand Score (SBS) to assess the brand importance of five European museums . Results suggest that in order to attract more visitors, museum brand managers should focus on increasing the volume of online posting and the richness of information generated by users around the brand, rather than controlling for the posts’ overall positivity or negativity .…

Distributed Computation over MAC via Kolmogorov Arnold Representation

Kolmogorov’s representation theorem provides a framework for decomposing anyarbitrary real-valued, multivariate, and continuous function into a two-layernested superposition of a finite number of functions . This paper capitalizes on such modularity and universality infunctional representation to propose two frameworks for distributed computation .…

Adaptive Density Tracking by Quadrature for Stochastic Differential Equations

Density tracking by quadrature (DTQ) is a numerical procedure for computingsolutions to Fokker-Planck equations . We propose and describe the procedure for $N$-dimensions . We demonstrate that the resulting adaptive procedure issignificantly more efficient than a tensorized approach . Although we considertwo-dimensional examples, all our computational procedures are extendable to higher dimensional problems .…

Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD

A widely studied decentralized algorithm for this problem is D-SGD in which each node applies a stochastic gradient descent step, thenaverages its estimate with its neighbors . For smooth objective functions, the transient stage is on the order of $O(n/(1-\beta)^2)$ forstrongly convex and generally convex cost functions, respectively, where $1-beta \in (0,1)$ is a topology-dependent quantity that approaches $0$ for alarge and sparse networks .…

Hash MAC DSDV Mutual Authentication for Intelligent IoT Based Cyber Physical Systems

Cyber-Physical Systems (CPS) connected in the form of Internet of Things(IoT) are vulnerable to various security threats . Device-to-Device (D2D)authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area . A lightweight Hash-MAC-DSDV (HashMedia Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies .…

Disentangled Variational Information Bottleneck for Multiview Representation Learning

Multiview data contain information from multiple modalities and have potential to provide more comprehensive features for diverse machine learning tasks . We formulate feature disentanglement in the framework of information bottleneck and propose disentangled variational informationbottleneck (DVIB) DVIB explicitly defines properties of shared and privaterepresentations using constrains from mutual information .…

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre Ranking A Learnable Feature Selection based Approach

In real-world search, recommendation, and advertising systems, themulti-stage ranking architecture is commonly adopted . In this paper, anovel pre-ranking approach is proposed which supports complicated models withinteraction-focused architecture . It achieves a better tradeoff betweeneffectiveness and efficiency by utilizing the proposed learnable FeatureSelection method based on feature Complexity and variational Dropout (FSCD) E-commerce sponsored search system for a searchengine is significantly improved, compared to the systems with conventional pre- ranking models, an identical amount of computational resource is consumed .…

Thin Film Smoothed Particle Hydrodynamics Fluid

We propose a particle-based method to simulate thin-film fluid that jointlyfacilitates aggressive surface deformation and vigorous tangential flows . We can simulate complexvortical swirls, fingering effects due to Rayleigh-Taylor instability,capillary waves, Newton’s interference fringes, and the Marangoni effect onliberally deforming surfaces by presenting both realistic visual results andnumerical validations .…

On Decentralization of Bitcoin An Asset Perspective

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from bothacademia and industry . We present in this paper the first systematicinvestigation of the degree of decentralization for Bitcoin based on its entiretransaction history .…

Stochastic Control through Approximate Bayesian Input Inference

Optimal control under uncertainty is a prevailing challenge in control . By framing the control problem as one of inputestimation, advanced approximate inference techniques can be used to handle thestatistical approximations in a principled and practical manner . Analyzing theGaussian setting, we present a solver capable of several stochastic controlmethods, and was found to be superior to popular baselines on nonlinearsimulated tasks .…

p robust equilibrated flux reconstruction in boldsymbol H mathrm curl based on local minimizations Application to a posteriori analysis of the curl curl problem

We present a local construction of H(curl)-conforming piecewise polynomialssatisfying a prescribed curl constraint . The outcome is, up to a generic constant independent of the underlying polynomorphic degree, as accurate as the best-approximations over the entire local versions ofH(Curl) This allows to design guaranteed, fully computable, constant-free, andpolynomial-degree-robust a posteriori error estimates of Prager-Synge type forN\’ed\’elec finite element approximations of the curl-curl problem .…

Buying time in software development how estimates become commitments

Despite years of research for improving accuracy, software practitioners still face software estimation difficulties . Researchers’ focus on raising realism inestimates when using it seems not to be enough for the much-expectedimprovements . Instead of focusing on the estimation process’s technicalities, we investigated interaction of the establishment of commitments with customers and software estimation .…

Towards Demystifying Serverless Machine Learning Training

The appeal of serverless (FaaS) has triggered a growing interest on how touse it in data-intensive applications such as ETL, query processing, or machinelearning (ML) Several systems exist for training large-scale ML models on topof serverless infrastructures (e.g., AWS Lambda) but with inconclusive results .…

Controlling an Inverted Pendulum with Policy Gradient Methods A Tutorial

This paper provides the details of implementing two important policy gradient algorithms to solve the inverted pendulum problem . These are namely the DeepDeterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO) The problem is solved by using an actor-critic model where anactor-network is used to learn the policy function and a critic network is toevaluate the actor-network by learning to estimate the Q function .…

Distributionally Robust Chance Constrained Flexibility Planning for Integrated Energy System

Inflexible combined heat and power plants and uncertain wind power production result in excess power in distribution networks . Power-to-X facilities such aselectrolyser and electric boilers can offer extra flexibility to theintegrated energy system . A case study validates the effectiveness of introducing the electrolyserand electric boiler into the integrated energy system, with respect to the decreased system cost, expanded CHP plant flexibility and reduced inverse powerflow .…

MUSER MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task

The capability to automatically detect human stress can benefit artificialintelligent agents involved in affective computing and human-computer interaction . Stress and emotion are both human affective states, and stress has important implications on the regulation and expression of emotion . We propose MUSER — a transformer-based model architecture and a novelmulti-task learning algorithm .…

On exploration requirements for learning safety constraints

Enforcing safety for dynamical systems is challenging, since it requiresconstraint satisfaction along trajectory predictions . Equivalent controlconstraints can be computed in the form of sets that enforce positiveinvariance . However, these constraints are cumbersome to compute from models, and it is not yet well established how to infer constraints from data .…

A Scalable Concurrent Algorithm for Dynamic Connectivity

Dynamic Connectivity is a fundamental algorithmic graph problem, motivated by a wide range of applications to social and communication networks . In brief, we wish to maintain theconnected components of the graph under dynamic edge insertions and deletions . The most efficient variant improves the performance of a coarse-grained based implementation on realistic scenarios up to 6x on average and up to 30x when connectivity queries dominate .…

Choice Set Confounding in Discrete Choice

Standard methods in preference learning involve estimating parameters of choice models from data of selections (choices) made by individuals from a discrete set of alternatives . Ignoring these assignment mechanisms can mislead choice models into making biased estimates of preferences .…

StRETcH a Soft to Resistive Elastic Tactile Hand

Soft optical tactile sensors enable robots to manipulate deformable objects . StRETcH, a Soft to Resistive Elastic Tactile Hand, is easily manufactured and integrated with a robotic arm . An elasticmembrane is suspended between two robotic fingers, and a depth sensor captures deformations of the elastic membrane .…

Dual Stage Low Complexity Reconfigurable Speech Enhancement

This paper proposes a dual-stage, low complexity, and reconfigurabletechnique to enhance the speech contaminated by various types of noise sources . The proposed speechenhancement scheme can be easily adopted in both capture path and speech renderpath for speech communication and conferencing systems, and voice-triggerapplications .…

A Data Efficient Approach to Behind the Meter Solar Generation Disaggregation

With the emergence of battery storage and the decline in thesolar photovoltaic (PV) levelized cost of energy (LCOE), the number ofbehind-the-meter solar PV systems is expected to increase steadily . The ability to estimate solar generation from these latent systems is crucial for a range of applications, including distribution system planning and operation, demandresponse, and non-intrusive load monitoring .…

To be a fast adaptive learner using game history to defeat opponents

In real-world games, it is very hard for a single AI trader to make good deals with customers in a few turns . Webelieve that past game history plays a vital role in such a learning procedure . They propose a novel framework (named, F3) to fuse the past and current game history with an Opponent Action Estimator (OAE) module that uses past history to estimate the opponent’s future behaviors .…

EasyFL A Low code Federated Learning Platform For Dummies

EasyFL requires only three lines of code to build avanilla FL application, at least 10x lesser than other platforms . EasyFL expedites training by 1.5x. It also improves the efficiency of experiments and deployment. EasyFL will increase the productivity of data scientists and democratize FL to wideraudiences.…

The Confluence of Networks Games and Learning

Recent years have witnessed significant advances in technologies and services in modern network applications, including smart grid management, wirelesscommunication, cybersecurity as well as multi-agent autonomous systems . Emerging network applications call for game-theoretic models and learning-based approaches in order to create distributed network intelligence that responds to uncertainties and disruptions in a dynamic or an adversarial environment .…