## UAV Localization Using Autoencoded Satellite Images

Previous work using satellite images has large storage and computation costs and is unable to run in realtime . In this work, we collect Google Earth (GE) images for a desired flightpath offline and an autoencoder is trained to compress these images to alow-dimensional vector representation while retaining the key features .…

## PLGRIM Hierarchical Value Learning for Large scale Exploration in Unknown Environments

PLGRIM (Probabilistic Local and Global Reasoning on InformationroadMaps) bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives . The framework is a step toward enabling belief space planners onphysical robots operating in unknown and complex environments .…

## Systematic Generalization for Predictive Control in Multivariate Time Series

In our context, systematic generalization implies that a good modelshould perform well on all new combinations of future actions after being trained on all of them, but only on a limited set of combinations . We conduct a rigorous study of useful inductive biases that learn topredict the trajectories up to large horizons well, and capture true dependencyrelations between the states and the controls through our synthetic setup, and simulated data from electric motors .…

## Belief Space Planning for Mobile Robots with Range Sensors using iLQG

In this work, we use iterative Linear Quadratic Gaussian to plan plans for a mobile robot with range sensors in belief space . We address twolimitations that prevent applications of iLQG to the considered robotic system . We propose to use a derivative-free filter to approximate thebelief dynamics, which does not require explicit differentiability of themeasurement model .…

## Manipulability optimization for multi arm teleoperation

Teleoperation provides a way for human operators to guide robots in situations where full autonomy is challenging or where direct human intervention is required . The increased availability ofcollaborative robot arms and Virtual Reality (VR) devices provides ampleopportunity for development of novel teleoperation methods .…

## Learning Interaction Aware Trajectory Predictions for Decentralized Multi Robot Motion Planning in Dynamic Environments

This paper presents a data-driven trajectory optimization approach for multi-robot motion planning in dynamic environments . Whennavigating in a shared space, each robot needs accurate motion predictions ofneighboring robots to achieve predictive collision avoidance . These motionpredictions can be obtained among robots by sharing their future plannedtrajectories with each other via communication .…

## Origami spring inspired shape morphing for flexible robotics

Flexible robotics are capable of achieving various functionalities by shapemorphing, benefiting from their compliant bodies and reconfigurable structures . The proposed concept of origami-aideddesign is expected to pave the way to facilitate diverse shape morphing offlexible robotics . To improve the mechanical performance such asthe damage resistance, we establish an origami rigidization method by adding additional creases to the spring system .…

## Policy Augmentation An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning Algorithms

Policy Augmentation is based on a newly developed inductive matrix completion method . The proposed algorithm augmentsthe values of unexplored state-action pairs, helping the agent take actionsthat will result in high-value returns while the agent is in the early episodes .…

## Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior

This paper is concerned with polarimetric dense map reconstruction based on apolarization camera with the help of relative depth information as a prior . In general, polarization imaging is able to reveal information about surfacenormal such as azimuth and zenith angles, which can support the development of solutions to the problem of dense reconstruction, especially in texture-poor regions .…

## Transfer Reinforcement Learning across Homotopy Classes

The ability for robots to transfer their learned knowledge to new tasks is a fundamental challenge for successful robotlearning . We propose a novel fine-tuning algorithm that consists of a relaxing stage and acurriculum learning stage to enable transfer learning across homotopy classes .…

## Toward Safe and Efficient Human Robot Interaction via Behavior Driven Danger Signaling

This paper introduces the notion of danger awareness in the context of the Human-Robot Interaction (HRI) It decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in enforcing the safety .…

## Voice Cloning a Multi Speaker Text to Speech Synthesis Approach based on Transfer Learning

Text-to-Speech (TTS) synthesizes artificial speech from text . The proposed approach has the goal to overcome theselimitations trying to obtain a system which is able to model a multi-speakeracoustic space . This allows the generation of speech audio similar to the voice of different target speakers, even if they were not observed during the training phase .…

## Dompteur Taming Audio Adversarial Examples

In our digital society, this poses a significant threat to machine learning systems . We accept the presence of adversarial examples against ASR systems, but we require them to be perceivable by human listeners . By applying the principles of psychoacoustics, we can remove semantically irrelevant information from the ASR input and train a model that resembles human perception more closely .…

## Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events

In conventional sound event detection (SED) models, two types of events are regarded as the same type of events . We propose a new objective function for SED, wherein the events are trained from easy- to difficult-to-train events . Experimental results show that the F-score of the proposed method is improved by 10.09 percentage points compared with that of the conventional binary cross entropy-based SED .…

## Exploring Automatic COVID 19 Diagnosis via voice and symptoms from Crowdsourced Data

The development of fast and accurate screening tools is key to the current pandemic of COVID-19 . We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease . We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants .…

## Enhancing Audio Augmentation Methods with Consistency Learning

Data augmentation is an inexpensive way to increase training data diversity . For tasks suchas classification, there is a good case for learning representations of the data that are invariant to such transformations . But this is not explicitly enforced by classification losses such as the cross-entropy loss .…

## No Echo in the Chambers of Political Interactions on Reddit

Echo chambers in online social networks, whereby users’ beliefs are reinforced by interactions with like-minded peers, have been decried as a cause of political polarization . Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump .…

## A Tale of Two Countries A Longitudinal Cross Country Study of Mobile Users Reactions to the COVID 19 Pandemic Through the Lens of App Popularity

The ongoing COVID-19 pandemic has profoundly impacted people’s life around the world, including how they interact with mobile technologies . In this paper, we seek to develop an understanding of how the dynamic trajectory of a pandemicshapes mobile phone users’ experiences .…

## A note on matricial and fast ways to compute Burt s structural holes

In this note I derive simple formulas based on the adjacency matrix of anetwork to compute measures associated with Burt’s structural holes . This not only can help to interpretthese measures, but also the computational advantage is enormous when analyzingreal-world networks .…

## GuiltyWalker Distance to illicit nodes in the Bitcoin network

Money laundering is a global phenomenon with wide-reaching social and economic consequences . Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity . We propose new features based on the structure of the graph and past labels to boost the performance of machine learning methodsto detect money laundering .…

## Sampling Subgraph Network with Application to Graph Classification

Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc. But existing research focuses on the basic statistics of certain motifs, largely ignoring the connection patterns among them . Recently, a subgraph network (SGN) model is proposed to study the potential structureamong motifs .…

## SCARLET Explainable Attention based Graph Neural Network for Fake News spreader prediction

Anefficient strategy to contain false information is to proactively identifyif nodes in the spread path are likely to endorse false information . SCARLET (truSt andCredibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likelyaction of nodes in spread path .…

## Effects of Renewable Energy Sources on Day Ahead Electricity Markets

The price of solar panels is decreasing rapidly and governments around the world are investing in solar power plants to increase the portion of solarenergy in the smart grid . In this paper we will calculate day-ahead spot prices using conventional generators .…

## A Hybrid Deep Learning Based State Forecasting Method for Smart Power Grids

Smart power grids are one of the most complex cyber-physical systems, delivering electricity from power generation stations to consumers . It is critically important to know exactly the current state of the system as well as its state variation tendency .…

## Instability Prediction in Smart Cyber physical Grids Using Feedforward Neural Networks

A novel stability condition conditionpredictor based on cascaded feedforward neural network is proposed . The method aims to identify anomaly due to cyber or physical disturbances as an early sign of instability . The proposed neural network utilizes cascadedconnections in order to increase accuracy of the prediction .…

## WAMS Based Model Free Wide Area Damping Control by Voltage Source Converters

A novel model-free wide-area damping control (WADC) method is proposed, which can achieve full decoupling of modes and damp multiple criticalinter-area oscillations simultaneously using grid-connected voltage sourceconverters . The proposed method is purely measurement based and requiresno knowledge of the network topology and the dynamic model parameters .…

## Safe visor Architecture for Sandboxing AI based Unverified Controllers in Stochastic Cyber Physical Systems

High performance but unverified controllers, e.g., artificialintelligence-based (a.k.a. AI-based) controllers, are widely employed incyber-physical systems (CPSs) to accomplish complex control missions . The proposed architecture contains a history-based supervisor, which checks inputs from theunverified controller, and a safety advisor that provides fallback when the unverified controller endangers the safety of the system .…

## Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply

Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivityand service delivery . Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda . In the current context, the large scale deployment of renewable-energy-supplied MECservers is perhaps the most promising solution for the incoming energy issue .…

## Error Convergence Analysis and Stability of a Cloud Control AGV

In this paper, we present a cloud based Automated Guided vehicle (AGV)control system . A controller in an Edge cloud sends the control inputs to anAGV to follow a predefined reference track over a wireless channel . The results show error convergenceat higher rate with optimal selection of feedback parameters.…

## Optimal adaptive inspection and maintenance planning for deteriorating structural systems

Luque and Straub (2019) proposed a heuristic approach in which I&Mplans for structural systems are defined through a set of simple decision rules . They formalize the optimization of these decision rules and extend the approach to enable adaptive planning .…

## Economic controls co design of hybrid microgrids with tidal PV generation and lithium ion flow battery storage

Islanded microgrids powered by renewable energy require costly energy storagesystems due to the uncontrollable generators . Energy storage needs areamplified when load and generation are misaligned on hourly, monthly, or seasonal timescales . Diversification of both loads and generation can smoothout such mismatches.…

## Multi Scale Neural Networks for to Fluid Flow in 3D Porous Media

The permeability of complex porous materials can be obtained via direct flowsimulation, which provides the most accurate results, but is very expensive . Data-driven machine learning approaches have shown great promise for building more general models . However, prior approaches building on Convolutional NeuralNetwork (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a RepresentativeElementary Volume .…

## On the Power and Limitations of Branch and Cut

The Stabbing Planes proof system is powerful enough to simulate Cutting Planes and refute the Tseitinformulas . Dadush and Tiwari showed that these short refutations of these formulas could be translated into quasi-polynomial size and depth Cutting Planes proofs, refuting a long-standing conjecture .…

## Attention Based Neural Networks for Chroma Intra Prediction in Video Coding

Anovel size-agnostic multi-model approach is proposed to reduce the complexity of the inference process . A reduction in the number of parameters of around 90% is achieved with respect to the original attention-based methodologies . The resulting simplified architecture is stillcapable of outperforming state-of-the-art methods .…

## Approximately counting independent sets of a given size in bounded degree graphs

We determine the computational complexity of approximately counting and sampling independent sets of a given size in bounded-degree graphs . The criticaldensity is the occupancy fraction of hard core model on the clique$K_{\Delta+1}$ at the uniqueness threshold on the infinite $\Delta$-regulartree .…

## Constant Approximating k Clique is W 1 hard

On input a graph $G$ a positive integer $k$ and a small constant $epsilon0$ is a simple algorithm . The algorithm can distinguish between the cases$\omega(G)$ and $f(k/c$ for any constant $c\ge 1$ and computablefunction $f$ unless $FPT= W[1]$. This implies that no$f(K))\cdot |G|^{O(1)$-time algorithm can .…

## Inapproximability of Minimizing a Pair of DNFs or Binary Decision Trees Defining a Partial Boolean Function

The desire to apply machine learning techniques in safety-critical environments has renewed interest in the learning of partial functions for distinguishing between positive, negative and unclear observations . Weshow: Minimizing the sum of the lengths or depths of these forms is the key to reducing the length or depth of these functions .…

## Structure vs Randomness for Bilinear Maps

We prove that the slice rank of a 3-tensor is equivalent up to anabsolute constant . We obtain strong trade-offs on the complexity of a biased bililnear map . Our result settles open questions of Haramaty and Shpilka [STOC 2010], and of Lovett [Discrete Anal.,2019]…

## OptSmart A Space Efficient Optimistic Concurrent Execution of Smart Contracts

Popular blockchains such as . Ethereum execute complextransactions in blocks through user-defined scripts known as smart contracts . We rigorously prove the correctness of concurrentexecution of AUs and achieve significant performance gain over the state-of-the-art . We propose aconcurrent validator that re-executes the same AUs concurrently anddeterministically using a concurrent bin followed by a BG given by the miner to .verify…