Application of twin delayed deep deterministic policy gradient learning for the control of transesterification process

Control of the batch transesterification process is difficult due to its complex and non-lineardynamics . This study explores the feasibility of reinforcement learning (RL) based control of the process . The results showcase that TD3 based controller is able to control batchtransesterification process and can be a promising direction towards the goal of artificial intelligence-based control in process industries .…

NSan A Floating Point Numerical Sanitizer

nsan uses compile-time instrumentation to augment each floating-pointcomputation in the program with a higher-precision shadow which is checked for consistency during program execution . This makes nsan between 1 and 4 orders ofmagnitude faster than existing approaches, which allows running it routinely as part of unit tests, or detecting issues in large production applications .…

A flapping feathered wing powered aerial vehicle

An aerial vehicle powered by flapping feathered wings was designed, developed and fabricated . Different from legacy flapping-wing aerial vehicles withmembrane wings, the new design uses authentic bird feathers to fabricate wings . In field tests, a radio-controlled electric-powered aerial vehicle successfully took off, flew up to 63.88 s and landedsafely .…

Control of Scanning Quantum Dot Microscopy

Scanning quantum dot microscopy is a recently developed high-resolution microscopy technique that is based on atomic force microscopy and is capable of imaging the electrostatic potential of nanostructures like molecules or singleatoms . The proposed control algorithms speed upscanning quantumdot microscopy by more than a magnitude and enable to scanlarge sample areas .…

Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases

Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and insome cases without increasing the complexity of reasoning tasks . The only known solver development is based onguess-and-verify, as formulated by Motik and Rosati in their original work . We formalize a notion of unfoundedsets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a solver .…

Motion Planning for a Pair of Tethered Robots

The problem involves planning motions for a pair of planar robots connected to oneanother via a cable of limited length . The paper shows how the reduced visibilitygraph provides a natural discretization and captures the essential topologicalconsiderations very effectively for the two robot case as well .…

Maximum Likelihood Constraint Inference from Stochastic Demonstrations

Stochastic models capture uncertainty and risk tolerance that are often present in real systems of interest . This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods . We propose an efficient algorithm that computes constraint likelihood and risktolerance in a unified Bellman backup, allowing us to generalize to Stochasticsystems without increasing computational complexity .…

Preview Reference Governors

This paper presents a constraint management strategy based on ScalarReference Governors (SRG) to enforce output, state, and control constraints . The strategy, referred to as the Preview Reference Governor (PRG), can outperformSRG while maintaining the computational benefits of SRG . However, performance of PRG may suffer if large preview horizons are used .…

Meta Learning for improving rare word recognition in end to end ASR

We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches and an approach of combining their outcomes into an end-to-end automatic speech recognition system . We verify the functionality of each of our three contributions in two experiments exploring their performance for differentamounts of classes (N-way) and examples per class (k-shot) in a few-shotsetting .…

Data Driven Characterization and Detection of COVID 19 Themed Malicious Websites

COVID-19 has hit hard on the global community, and organizations are working to cope with the new norm of “work from home” The volume of remote work is unprecedented and creates opportunities for cyber attackersto penetrate home computers . Attackers are agile and are deceptively crafty in designing geolocationtargeted websites, often leveraging popular domain registrars and top-leveldomains .…

Detection of Alzheimer s Disease Using Graph Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images

This paper presents an Alzheimer’s disease detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing this similarity as a graph . Methods: We construct thesimilarity graph using embedded features of the input image (i.e., Non-Demented(ND), Very Mild Demented (VMD), Mild Dementsed (MD) and Moderated (MDTD) We use the similarity graph as aregularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours .…

Multi Domain Learning by Meta Learning Taking Optimal Steps in Multi Domain Loss Landscapes by Inner Loop Learning

We consider a model-agnostic solution to the problem of Multi-Domain Learning(MDL) for multi-modal applications . We aim to enable MDL purely algorithmically so that neural networks can trivially achieve MDL in a model independent manner . We demonstrate our solution to a fitting problem in medical imaging, specifically in the automatic segmentation of white matter hyperintensity(WMH) We look at two neuroimaging modalities (T1-MR and FLAIR) withcomplementary information fitting for our problem.…

Images Emotions and Credibility Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Emotional images from sources of misinformation can greatly influence ourjudgements . Users are more likely to find sources as less credible and their content as biased . When sources portray specific politicians as angry, users find them less credible . These results highlight how implicit visual propositions manifested by emotions infacial expressions might have a substantial effect on our trust of news content and sources.…

Case Level Counterfactual Reasoning in Process Mining

Process mining is widely used to diagnose processes and uncover performance problems . We use results from causal inference and adapts these to be able to reason over event logs and process interventions . Our ProM plug-in produces recommendations that indicate how specific cases could have been handled differently to avoid a performance or compliance problem .…

Leveraged Trading on Blockchain Technology

We document an ongoing research process towards the implementation andintegration of a digital artefact, executing the lifecycle of a leveraged tradewith permissionless blockchain technology . By employing core functions of the’Dai Stablecoin system’ deployed on the . Ethereum blockchain, we produce theequivalent exposure of a .…

DeepSZ Identification of Sunyaev Zel dovich Galaxy Clusters using Deep Learning

Galaxy clusters identified from the Sunyaev Zel’dovich (SZ) effect are a keyingredient in multi-wavelength cluster-based cosmology . We present a comparisonbetween two methods of cluster identification: the standard Matched Filter (MF)method in SZ cluster finding and a method using Convolutional Neural Networks(CNN) The CNN method requires very littlepre-processing of images, while the MF method requires little pre-processing .…

Robust Pollen Imagery Classification with Generative Modeling and Mixup Training

Deep learning approaches have shown great success in image classification tasks and can aid greatly towards the fast and reliable classification of pollen grain aerial imagery . The proposed approachearned a fourth-place in the final rankings in the ICPR-2020 Pollen Grain Classification Challenge; with a 0.972578 weighted F1 score, 0.950828 macroaverage F1 scores, and .972877 recognition accuracy .…

Exact and heuristic approaches for multi objective garbage accumulation points location in real scenarios

Municipal solid waste management is a major challenge for nowadays urbansocieties, because it accounts for a large proportion of public budget and,when mishandled, it can lead to environmental and social problems . This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of servicemeasure), the investment cost, and the required frequency of waste removal from the bins .…

Graph Community Detection from Coarse Measurements Recovery Conditions for the Coarsened Weighted Stochastic Block Model

We study the problem of community recovery from coarse measurements of agraph . We build on thestochastic block model by mathematically formalizing the coarsening process . We characterize an error bound for communityrecovery . The error bound yields simple and closed-form asymptotic conditionsto achieve the perfect recovery of the coarse graph communities, the authors say .…

VPIC 2 0 Next Generation Particle in Cell Simulations

VPIC is a general purpose Particle-in-Cell simulation code for modeling plasma phenomena such as magnetic reconnection, fusion, solar weather, and laser-plasma interaction in three dimensions . VPIC’s capacity in both fidelity and scale makes it particularly well-suited for plasma research on pre-exascale and exascale platforms .…

Contrast independent partially explicit time discretizations for multiscale wave problems

In this work, we design and investigate contrast-independent partiallyexplicit time discretizations for wave equations in heterogeneous high-contrast media . We consider multiscale problems, where the spatial heterogeneities are not resolved . The splitting requires a careful design. We prove that the proposed splitting isunconditionally stable under some suitable conditions formulated for the secondspace (slow) We present numerical results and show that proposed methods provide results similar to implicit methods with the time step that is independent of the contrast.…

MEDAL An AI driven Data Fabric Concept for Elastic Cloud to Edge Intelligence

Current Cloud solutions for Edge Computing are inefficient for data-centric applications, as they focus on the IaaS/PaaS level and miss the datamodeling and operations perspective . MEDAL is an intelligent Cloud-to-Edge Data Fabric to support Data Operations(DataOps)across the continuum and to automate management and orchestrationoperations over a combined view of the data and the resource layer .…