Dynamic Spectrum Access using Stochastic Multi User Bandits

The proposed algorithm consists of an estimation phase and an allocation phase . It is shown that if every user adopts the algorithm, the system wide regret isorder-optimal of order $O(\log T)$ over a time-horizon of duration $T$ The algorithm is extended to the dynamic case where the number of users in the system evolves over time, and isshown to lead to sub-linear regret .…

Benchmarking Simulation Based Inference

Recent advances in probabilistic modelling have led to a large number of ‘likelihood-free’ algorithms which do not require numerical evaluation of likelihoods . We provide a benchmark with inference tasks and performance metrics . We found that the choice of performance metric is critical, that even state-of-the-art algorithms have substantial room for improvement .…

Developing an OpenAI Gym compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem

A custom ambulance dispatch simulation environment was developed using OpenAI Gym and SimPy . Bagging Noisy Duelling DeepQ networks gave the most consistence performance . All methods had a tendency to lose performance if trained for too long, and so agents were saved at theiroptimal performance (and tested on independent simulation runs) All methods reduced time to respond to emergency calls compared with random allocation to dispatch points .…

Reliable Fleet Analytics for Edge IoT Solutions

Edge computing is emerging to enable AIoT applications. Edge computing enables generating insights and making decisions at or near the data source, reducing the amount of data sent to thecloud or a central repository . We propose a framework forfacilitating machine learning at the edge for AIOT applications, to enablecontinuous delivery, deployment, and monitoring of machine learning models atthe edge (Edge MLOps) The contribution is an architecture that includesservices, tools, and methods for delivering fleet analytics at scale.…

Interpretable discovery of new semiconductors with machine learning

Machine learning models of materials$^{1-5$ accelerate discovery compared toab initio methods . Deep Adaptive Regressive Regressive Weighted Intelligent Network (DARWIN) predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materialsfamily, based on its electronegativity difference . We synthesized and found these materials to be stable, direct bandgap UV emitters.…

Data driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning

In the field of mathematical physics, there exist many physically interestingnonlinear dispersive equations with peakon solutions . Peakon solutions are solitary waves with discontinuous first-order derivative at the wave peak . In this paper, we apply the multi-layer physics-informed neural networks (PINNs) deep learning to study the data-driven peakon and periodic peakon solution of somewell-known nonlinear dispersion equations with initial-boundary valueconditions such as the Camassa-Holm (CH) equation, Degasperis-Procesi equation, modified CH equation with cubic nonlinearity, mCH-Novikov equation, andetc.…

Activation Density based Mixed Precision Quantization for Energy Efficient Neural Networks

Quantization is one of the go-to methodsyielding state-of-the-art model compression . As neural networks gain widespread adoption in embedded devices, there is aneed for model compression techniques to facilitate deployment in resource-constrained environments . Our method calculates bit-width for each layer during training yielding a mixedprecision model with competitive accuracy .…

Seed Stocking Via Multi Task Learning

Sellers of crop seeds need to plan for variety and quantity of seeds to stock at least a year in advance . Farmers need to make decisions that balance high yield and low risk . Sellers need to be able to anticipate the needsof farmers and have them ready .…

Neural Network based Virtual Microphone Estimator

A neural network-based virtual microphoneestimator (NN-VME) estimates virtual microphone signals directly inthe time domain, by utilizing the precise estimation capability of the recent time-domain neural networks . We adopt a fully supervised learning frameworkthat uses actual observations at the locations of the virtual microphones attraining time .…

Trace Ratio Optimization with an Application to Multi view Learning

A trace ratio optimization problem over the Stiefel manifold is investigated from theory and numerical computations . A new frameworkand its instantiated concrete models are proposed and demonstrated on realworld data sets . Numerical results show that the efficiency of the proposed methods and effectiveness of the new multi-view subspace learningmodels are demonstrated .…

HighAir A Hierarchical Graph Neural Network Based Air Quality Forecasting Method

Existing air quality forecasting methods cannot effectively model thediffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region . In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecast method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g.,…

VIDA A simulation model of domestic VIolence in times of social DistAncing

The COVID-19 pandemic led Brazil to recommend and, at times, imposes social distancing, with the partial closure of economic activities, schools,and restrictions on events and public services . Preliminary evidence shows thatintense coexistence increases domestic violence, while social distancingmeasures may have prevented access to public services and networks,information, and help .…

Counting and localizing defective nodes by Boolean network tomography

Identifying defective items in larger sets is a main problem with manyapplications in real life situations . We investigate old and new identifiability conditions contributing this problem both from atheoretical and applied perspective . What is the precise tradeoff between number of nodes and number of paths such that at most $k$ nodes can beidentified unambiguously ?…

Two beams are better than one Enabling reliable and high throughput mmWave links

Millimeter-wave communication with high throughput and high reliability is poised to be a gamechanger for V2X and VR applications . mmReliable achieves close to 100% reliabilityproviding 1.5 times better throughput than traditional single-beam systems . It creates custom beam patterns with multiple lobes andoptimizes their angle, phase, and amplitude to maximize the signal strength at the receiver .…

Ergodic Exploration using Tensor Train Applications in Insertion Tasks

A large class of ergodic control algorithms relies on spectral analysis, which suffers from the curse of dimensionality . The proposed solution is efficient both computationally and storage-wise, hencemaking it suitable for its online implementation in robotic systems . The approach is applied to a peg-in-hole insertion task using a 7-axis Franka EmikaPanda robot, where the task can be achieved without the use of force/torque sensors.…

Airfoil GAN Encoding and Synthesizing Airfoils forAerodynamic aware Shape Optimization

The current design of aerodynamic shapes, like airfoils, involvescomputationally intensive simulations to explore the possible design space . In this work, we propose adata-driven shape encoding and generating method, which automatically learnsrepresentations . The representations are then used in the optimization ofsynthesized airfoil shapes based on their aerodynamic performance .…

GSM GPRS Based Smart Street Light

Street lighting system has always been the traditional manual system ofilluminating the streets in Bangladesh . A dedicated person is posted only to control the street lights of a zone, who roams around the zonal area to switch on and switch off the lights two times a day .…

Hybrid matheuristics to solve the integrated lot sizing and scheduling problem on parallel machines with sequence dependent and non triangular setup

This paper approaches the integrated lot sizing and scheduling problem(ILSSP) In which non-identical machines work in parallel with non-triangularsequence-dependent setup costs and times, setup carry-over and capacity limit . The proposed matheuristicssignificantly outperformed CPLEX solver with a fixed CPU time limit in most of the tested instances .…

Expanding Explainability Towards Social Transparency in AI systems

Explanations in human-human interactions are socially-situated . Explainable AI(XAI) approaches have been predominantly algorithm-centered . We introduce and explore Social Transparency (ST), a sociotechnically informed perspective thatorporates the socio-organizational context into explaining AI-mediateddecision-making . We suggestedconstitutive design elements of ST and developed a conceptual framework tounpack ST’s effect and implications at the technical, decision-making, and organizational level .…

Real or Virtual Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios

Augmented Reality is the fusion of virtual components and our realsurroundings . The simultaneous visibility of generated and natural objectsoften requires users to direct their selective attention to a specific target . In this study, we investigated whether thistarget is real or virtual by using machine learning techniques to classifyelectroencephalographic (EEG) data collected in Augmented reality scenarios .…

Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition

An important problem in decision theory concerns the aggregation of individual rankings/ratings into a collective evaluation . We illustrate a new aggregation method in the context of the 2007 MSOM’s student paper competition . This approach is potentially useful in managerialdecision making problems by a committee selecting projects from a large set or capital budgeting involving multiple priorities .…