Designing for Interpersonal Museum Experiences

What does the age of participation look like from the perspective of a museum visitor? Arguably, the concept of participative experiences is already sodeeply ingrained in our culture that we may not even think about it as participation . How can museums tap into these activities – and make themselves relevant to visitors?…

Data aided Sensing for Gaussian Process Regression in IoT Systems

Gaussian process regression is used to learn data sets collected from sensors in Internet-of-Things systems . Data-aided sensing is generalized for distributedselective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one .…

Model Predictive Control for Micro Aerial Vehicles A Survey

This paper presents a review of the design and application of modelpredictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors . The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possiblefault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physicalinteraction or load transportation .…

An off the grid approach to multi compartment magnetic resonance fingerprinting

We propose a novel numerical approach to separate multiple tissuecompartments in image voxels . We estimate quantitatively their nuclearmagnetic resonance (NMR) properties and mixture fractions, given magneticresonance fingerprinting (MRF) measurements . The number of tissues, their types or quantitative properties are not a-priori known, but the image is assumed to be composed of sparse compartments with linearly mixed Bloch magnetisationresponses .…

Sparse linear regression CLuP achieves the ideal emph exact ML

In this paper we revisit one of the classical statistical problems, theso-called sparse maximum-likelihood (ML) linear regression . As a way ofattacking this type of regression, we present a novel CLuP mechanism that relies on the Random Duality Theory (RDT) basedalgorithmic machinery that we recently introduced in  Stojnicclupint19, StojnicClupcmpl19,StojNicclupplt19, and Stojnicluprephased20 .…

On inverse problems for semiconductor equations

This paper is devoted to the investigation of inverse problems related to drift-diffusion equations modeling semiconductor devices . In thiscontext we analyze several identification problems corresponding to different types of measurements, where the parameter to be reconstructed is aninhomogeneity in the PDE model (doping profile) For a particular type ofmeasurement (related to the voltage-current map) we consider special cases of Drift-Diffusion equations .…

Imagination enabled Robot Perception

Many of today’s robot perception systems aim at accomplishing perception tasks that are too simplistic and too hard . We propose a perception system that maintains its beliefs about its environment as a scene graph with physics simulation and visual rendering .…

Automatic differentiation of Sylvester Lyapunov and algebraic Riccati equations

Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists . They are used to compute infinite-horizon Gramians, solve optimal control problems in continuous or discrete time, and design observers . Here, we derive the forward and reverse-mode derivatives of the solutions to all three types of equations, and showcase their application on an inverse control problem .…

Speeding up decimal multiplication

Decimal multiplication is the task of multiplying two numbers in base $10^N.$ Using only portable techniques, we achieve a 3x—5x speedup over the mpdecimal library . We also present a simple cache-efficient algorithm for in-place $2n \times n$ or $n\times 2n$ matrix transposition .…

RISE SLAM A Resource aware Inverse Schmidt Estimator for SLAM

In order to achieve real-time operation, existing approaches often assume previously-estimated states to be perfectly known . The Schmidt-Kalman filter has processing cost linear in the size of the state vector but quadratic memory requirements . Inparticular, this method, the resource-aware inverse Schmidt estimator (RISE)allows estimation accuracy for computational efficiency .…

MAC for Machine Type Communications in Industrial IoT Part II Scheduling and Numerical Results

The proposed medium access control (MAC) design is designed for machine-type communications in the industrial internet of things . The proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <0.5ms average delay and <1% average collision probability among 50high priority devices . The two-stepapproach ensures the accuracy and granularity necessary for satisfying the QoSrequirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design fromPart I and the centralized scheduling from this part, the proposed MAC protocolachieves high performance, demonstrated through extensive simulations. Forexample, the results show that the proposedMAC can support1000 devices underan aggregated Traffic load of 3,000 packets perSecond with a one channel andachieve < 0.5 ms average delay, respectively . The proposal is demonstrated through simulations. The proposedMAC protocolachieve high performance. The proposal was demonstrated through . …

Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles

SARL-SGAN-KCE combines a deep socially aware attentive valuenetwork with a human multimodal trajectory prediction model . Thekinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories . We evaluate our method against the state-art methods forcrowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour .…

Data driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning based Depth Perception

Laparoscopic Field of View (FOV) control is one of the most fundamental andimportant components in Minimally Invasive Surgery (MIS) We present adata-driven framework to realize an automated laparoscopic optimal FOV control . We offline learn a motion strategy of laparoscoperelative to the surgeon’s hand-held surgical tool from our in-house surgicalvideos .…

DeepClimGAN A High Resolution Climate Data Generator

Earth system models (ESMs) are often used to generate future projections of climate change scenarios . Emulators are substantially less expensive but may not have all of the complexity of anESM . Here we demonstrate the use of a conditional generative adversarialnetwork (GAN) to act as an ESM emulator .…

Interpersonalizing Intimate Museum Experiences

We show howinterpersonalization can deliver engaging social visits in which visitors maketheir interpretations . We contrast the approach to previous research incustomization and algorithmic personalization . We propose that interpersonalization requires museums to step back to make space for interpretation, but that this then raises the challenge of how to reintroduce the museum’s own perspective .…

Efficient Broadcast for Timely Updates in Mobile Networks

This letter considers a wireless network where an access point (AP)broadcasts timely updates to several mobile users . The timeliness ofinformation owned by a user is characterized by the recently proposed age ofinformation . While frequently broadcasting the timely updates and always using the maximum power can minimize the age of information for all users, that can waste valuable communication resources .…

Mechanical Search on Shelves using Lateral Access X RAY

LAX-RAY (Lateral Access maXimal Reduction of OccupancY support Area) is a system to automate the mechanical search for occluded objects on shelves . LAx-RAY couples a perception pipelinepredicting a target object occupancy support distribution with a mechanicalsearch policy that sequentially selects occluding objects to push to the sideto reveal the target as efficiently as possible .…

Evolutionary Planning in Latent Space

Planning is a powerful approach to reinforcement learning with several desirable properties . However, it requires a model of the world, which is not available in many real-life problems . In this paper, we propose tolearn a world model that enables Evolutionary Planning in Latent Space (EPLS) We use a Variational Auto Encoder (VAE) to learn a compressed latent latentrepresentation of individual observations and extend a Mixture DensityRecurrent Neural Network (MDRNN) The planning agents are better than standard model-freereinforcement learning approaches demonstrating the viability of our approach .…

Data Driven Stabilization of Nonlinear Systems with Rational Dynamics

In this paper, we present a data-driven controller design method forcontinuous-time nonlinear systems with rational system dynamics . We use no model knowledge but only measured data affected by noise . We apply robust control techniques to this parametrization . We obtain sum-of-squares based criteria for designing controllers with closed-loop stability guarantees for all continuous-time systems withrational system dynamics which are consistent with the measured data and the assumed noise bound .…

V3H Incomplete Multi view Clustering via View Variation and View Heredity

Real data often appear in the form of multiple incomplete views . Previous clustering methods only learn the consistent information between different views and ignore the unique information of each view . We propose a novel View Variation and View Heredity approach (V 3H) Inspired by the variation and the heredity in genetics, V 3H first decomposes each subspace into a variation matrix for the corresponding view and a redity matrix for all the views .…

Causality Graph of Vehicular Traffic Flow

In an intelligent transportation system, the effects and relations of trafficflow at different points in a network are valuable features which can beexploited for control system design and traffic forecasting . In this work, directed information is used to determine the underlying graph structure of a network, denoted directedinformation graph, which expresses the causal relations among nodes in thenetwork .…

Approximation of a Multivariate Function of Bounded Variation from its Scattered Data

Radial basis function(RBF) interpolationmethods are known to approximate only functions in their native spaces . To date, there has been no known proof that they can approximate functions outsidethe native space associated with the particular RBF being used . In this paper, we describe a scattered data interpolation method which can approximate anyfunction of bounded variation from its scattered data as the data points growdense .…

Algorithmic random duality theory large scale CLuP

Based on our Random Duality Theory (RDT), we developed a powerful algorithmic mechanism (called CLuP) that can be utilized to solve NP hard optimization problems in polynomial time . Here we move things further and utilize another ofremarkable RDT features that we established in a long line of work in the past .…

A Game Theoretic Analysis for Cooperative Smart Farming

The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms . The growth in the number of smart farming has given rise to the CooperativeSmart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit .…

Statistical and computational thresholds for the planted k densest sub hypergraph problem

Recovery a planted signal perturbed by noise is a fundamental problem in machine learning . This fundamental problem appears in different contexts, e.g., community detection,average case complexity, and neuroscience applications . We provide tight information-theoretic upper and lowerbounds for the recovery problem, as well as the first non-trivial algorithmicbounds based on approximate message passing algorithms .…

Estimating network memberships by mixed regularized spectral clustering

Mixed regularized spectral clustering (Mixed-RSC) is an extension of the RSC method (Qin and Rohe, 2013) to deal with the mixed membership community detection problem . We show that the algorithm is asymptotically consistent under mild conditions . The approach is successfully applied to a small scale ofsimulations and substantial empirical networks with encouraging results compared to a number of benchmark methods .…

MEG Multi Evidence GNN for Multimodal Semantic Forensics

Fake news often involves semantic manipulations across modalities such asimage, text, location etc and requires the development of multimodal semanticforensics for its detection . The proposed model outperforms existing state-of-the-art algorithms with an error reduction of up to 25% . Existing methods arelimited to using a single evidence (retrieved package) which ignores potential improvement from the use of multiple evidences .…