## Minimal Virtual Machines on IoT Microcontrollers The Case of Berkeley Packet Filters with rBPF

Virtual machines (VM) are widely used to host and isolate software modules . However, extremely small memory and low-energy budgets have so far prevented wide use of VMs on typical microcontroller-based IoT devices . In this paper, we explore the potential of two minimal VM approaches on such low-power hardware .…

## Efficient risk estimation via nested multilevel quasi Monte Carlo simulation

We consider the problem of estimating the probability of a large loss from afinancial portfolio, where the future loss is expressed as a conditionalexpectation . Since the conditional expectation is intractable in most cases, one may resort to nested simulation .…

## Non Invertible Element Constacyclic Codes over Finite PIRs

In this paper we introduce the notion of $\lambda$-constacyclic codes overfinite rings $R$ for arbitary element $R$. We study thenon-invertible-element constacyClic codes (NIE-consticclic code) over finiteprincipal ideal rings (PIRs) We determine the algebraic structures of all NIE codes over finite chain rings, give the unique form of thesets of the defining polynomials and obtain their minimum Hamming distances .…

## Stability of Discontinuous Galerkin Spectral Element Schemes for Wave Propagation when the Coefficient Matrices have Jumps

We use the behavior of the $L_{2}$ norm of the solutions of linear hyperbolicequations with discontinuous coefficient matrices as a surrogate to inferstability of discontinuous Galerkin spectral element methods (DGSEM) We show that the DGSEM with an upwind numerical flux that satisfies the Rankine-Hugoniot (or conservation) condition has the same energy bound as the partial differential equation does in $L_2$ norm, plus an added addeddissipation that depends on how much the approximate solution fails to satisfy the conservation condition .…

## Modeling skier behavior for planning and management Dynaski an agent based in congested ski areas

A multi-agent simulation tool simulates the behavior of skiers in a ski-area . This is the first tool able to simulate and compare management and planning scenarios as well as their impact on skier waiting times . An addition of 1620 new skiers delays the skier average waiting time by 12 pourcents .…

## Fractional Matchings under Preferences Stability and Optimality

We study a generalized version of the classic Stable Marriage and Stable Roommates problems where agents may share partners . We consider twoprominent stability concepts: ordinal stability [Aharoni and Fleiner, Journalof Combinatorial Theory, 2003] and cardinal stability [Caragiannis et al., ACMEC 2019] and two optimality criteria: maximizing social welfare (i.e.…

## Language Generation via Combinatorial Constraint Satisfaction A Tree Search Enhanced Monte Carlo Approach

Wepropose TSMH, an efficient method to generate high likelihood sentences withrespect to a pre-trained language model while satisfying the constraints . Tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidatesthat satisfy more constraints .…

## Interpretable Models in ANNs

Artificial neural networks are often very complex and too deep for a human to understand . In this paper, we try to find a way to explain anetwork and extract a human-readable equation that describes the model . In some cases, laws of physics, for example, the pattern can be described by relatively simplemathematical expressions .…

## The Human Effect Requires Affect Addressing Social Psychological Factors of Climate Change with Machine Learning

Machine learning has the potential to aid in mitigating the human effects of climate change . Previous applications of machine learning to tackle the humaneffects in climate change include informing individuals of their carbon footprint and strategies to reduce it .…

## Algorithms and Experiments Comparing Two Hierarchical Drawing Frameworks

We present algorithms that extend the path-based hierarchical drawing framework and give experimental results . Our algorithms run in $O(km)$ time,where $k$ is the number of paths and $m$ is number of edges of the graph . We also provide somecomparison to a well known .…

## A Statistical Characterization of Localization Performance in Millimeter Wave Cellular Networks

Millimeter-wave communication is a promising solution for achieving high data rate and low latency in 5G wireless cellular networks . Sincedirectional beamforming and antenna arrays are exploited in the mmWavenetworks, accurate angle-of-arrival (AOA) information can be obtained andutilized for localization purposes .…

## Deep learning based discovery of partial differential equations in integral form from sparse and noisy data

Data-driven discovery of partial differential equations (PDEs) has attracted attention in recent years . For PDEs with high-orderderivatives, the performance of existing methods is unsatisfactory, especially when the data is sparse and noisy . New framework combining deep-learning and integral form is proposed to handle the above-mentionedproblems simultaneously, and improve the accuracy and stability of PDEdiscovery .…

## Efficient Sampling for Predictor Based Neural Architecture Search

Recently, predictor-based algorithms emerged as a promising approach for neural architecture search (NAS) For NAS, we typically have to calculate thevalidation accuracy of a large number of Deep Neural Networks (DNNs), what is computationally complex . Predictor-based NAS algorithms address this problem .…

## Wyner Ziv Estimators Efficient Distributed Mean Estimation with Side Information

distributed mean estimation is an important primitive that arises in many distributed learning and optimization scenarios such as federated learning . We present an alternative Wyner-Ziv estimator that uses correlated sampling . This latter setting offers . perhaps will be of interest in practice when the number of users is large and .…

## Lethean Attack An Online Data Poisoning Technique

Data poisoning is an adversarial scenario where an attacker feeds a speciallycrafted sequence of samples to an online model in order to subvert learning . We apply the attack in the contextof Test-Time Training, a modern online learning framework aimed forgeneralization under distribution shifts .…

## ADCME Learning Spatially varying Physical Fields using Deep Neural Networks

ADCME is a novel computational framework to solve inverse problems involving physical simulations and deep neural networks . This paper benchmarks itsability to learn spatially-varying physical fields using DNNs . We apply a physics constrained learning algorithm (PCL) to efficiently back-propagate gradients through iterativesolvers for nonlinear equations .…

Stochastic sparse adversarial attacks (SSAA) are simple, fast and purely noise-based targeted and untargeted attacks of NNC . SSAA offer new examples of sparse (or $L_0$) attacks for which only few methodshave been proposed previously . These attacks are devised by exploiting asmall-time expansion idea widely used for Markov processes .…

## CCIC WSN An Architecture for Single Channel Cluster based Information Centric Wireless Sensor Networks

Named Data Networking (NDN) has attracted extensive attention in the context of the Internet of Things (IoT) and Wireless SensorNetworks (WSNs) A comprehensive NDN/ICN-based architectural designfor WSNs has yet to be explored . In this paper, we present single-ChannelCluster-based Information-Centric WSN (CCIC-WSN) We demonstrate that CCIC-wSNachieves 71-90% lower energy consumption and 74-96% lower data retrieval delay .…

## C Learning Horizon Aware Cumulative Accessibility Estimation

Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization . Current algorithms suffer from high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks . We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity and path optimality .…

## Multiple Transmit Power Levels based NOMA for Massive Machine type Communications

This paper proposes a tractable solution for integrating non-orthogonalmultiple access (NOMA) into massive machine-type communications (mMTC) to increase the uplink connectivity . Multiple transmit power levels are provided at the user end to enable open-loop power control, which is absent from the traditional uplink NOMA with the fixed transmit power .…

## General Purpose Atomic Crosschain Transactions

The General Purpose Atomic Crosschain Transaction protocol allows composableprogramming across multiple Ethereum blockchains . It allows for inter-contractand inter-blockchain function calls that are both synchronous and atomic . If one part fails, the whole call graph of function calls is rolled back .…

## Hyper parameter estimation method with particle swarm optimization

Particle swarm optimization (PSO) method cannot be directly used in the problem of hyper-parameter estimation . Bayesian optimization (BO) framework is capable of converting the optimization of an acquisition function . The proposed method in this paper uses theparticle swarm method to optimize the acquisition function in the BO framework .…

## Zero Shot Visual Slot Filling as Question Answering

This paper presents a new approach to visual zero-shot slot filling . The approach extends previous approaches by reformulating the slot filling task asQuestion Answering . The multi-task approach facilitates the incorporation of a large number of refinements and transfer learning across similar tasks .…

## OrgMining 2 0 A Novel Framework for Organizational Model Mining from Event Logs

Process mining offers a promising way to help analyze resource grouping, authors say . Organizations need to acquire an accurate and timelyunderstanding of human resource grouping . Authors propose a novel framework built upon a richer definition of organizationalmodels coupling resource grouping with process execution knowledge .…

## Min Sum Clustering with Outliers

We give a constant factor polynomial time pseudo-approximation algorithm formin-sum clustering with or without outliers . The algorithm is allowed to exclude an arbitrarily small constant fraction of the points . Our results apply to instances of points in real space, as well as to points in a metricspace, where the number of clusters, and also the dimension if relevant, isarbitrary (part of the input, not an absolute constant) The approximation guarantee growswith $\frac{1-\eps) n’$ points .…

## Provably robust blind source separation of linear quadratic near separable mixtures

In this work, we consider the problem of blind source separation (BSS) by focusing on the linear-quadratic (LQ) model . We propose two provably robust and computationally tractable algorithms to tackle this problem . SNPALQ is shown to be able to recover the ground truth factors thatgenerated the data, even in the presence of noise .…

## Provably Robust Runtime Monitoring of Neuron Activation Patterns

For safety-critical autonomous driving tasks, it is desirable to monitor in operation time if the input for the DNN is similar to the data used in DNN training . The algorithm performs a soundworst-case estimate of neuron values with inputs (or features) subject toperturbation, before the abstraction function is applied to build the monitor .…

## The 4 Adic Complexity of A Class of Quaternary Cyclotomic Sequences with Period 2p

In cryptography, we hope a sequence over $m$ with period $N$ having larger $m$-adic complexity . We determine the complexity of thequaternary cyclotomic sequences with period 2$p$ defined in [6] The mainmethod we utilized is a quadratic Gauss sum $G_{p}$ valued in$\mathbb{Z}_{4^N-1}$ which can be seen as a version of classical quadraticallyGauss sum .…

## Envy Free Allocations Respecting Social Networks

Finding an envy-free allocation of indivisible resources to agents is acentral task in many multiagent systems . Classical envy-freeness requires that every agent likesthe resources allocated to it at least as much as those allocated to any other agent . In many situations this assumption can be relaxed since agentsoften do not even know each other .…

## Acceleration of Cooperative Least Mean Square via Chebyshev Periodical Successive Over Relaxation

A faster algorithm requires less communications overhead and it results in a narrower network bandwidth . The convergence speed of an algorithm is a critical factor . Accelerations of convergence speed are empiricallyconfirmed in a wide range of networks, such as known small graphs (e.g.,…

## Recurrent Multi view Alignment Network for Unsupervised Surface Registration

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data . In this paper, we propose a differentiable loss function that measures the 3D shapesimilarity on the projected multi-view 2D depth images so that our fullframework can be trained end to-end without ground truth supervision .…

## Model Elicitation through Direct Questioning

Teammates interact, and the robot’s interaction has to be about getting useful information about the human’s(teammate’s) model . There are many challenges before a robot can interact, suchas incorporating the structural differences in the human model, ensuringsimpler responses, etc. The evaluation shows that these questions can be generated offline, and can help refine the model through simple answers .…

## A reinforcement learning control approach for underwater manipulation under position and torque constraints

Under position and torque constraints the requirements for the control system are greatly increased . Reinforcement learning is a datadriven control technique that can learn complex control policies without the need of a model . The learning capabilities of these type of agents allow forgreat adaptability to changes in the operative conditions .…

## When Machine Learning Meets Privacy A Survey and Outlook

The newly emerged machine learning (e.g. deep learning) methods have become a driving force to revolutionize a wide range of industries, such as smarthealthcare, financial technology, and surveillance systems . Privacy has emerged as a big concern in this machine learning-based artificialintelligence era .…

## Solving Two Dimensional H curl elliptic Interface Systems with Optimal Convergence On Unfitted Meshes

In this article, we develop and analyze a finite element method with the first family N\’ed\’elec elements of the lowest degree for solving a Maxwellinterface problem . We establish a few important properties for the IFEfunctions including the unisolvence according to the edge degrees of freedom, the exact sequence relating to the $H^1$ IFE functions and the optimalapproximation capabilities .…

## Evaluating Feedback Strategies for Virtual Human Trainers

In this paper we address feedback strategies for an autonomous virtual trainer . The first provides correctness feedback by fully correcting user responses at each stage of the task . The second provides suggestive feedback by only notifying if and how a response can be corrected .…

## Exploring the landscapes of computing digital neuromorphic unconventional and beyond

The acceleration race of digital computing technologies seems to be steeringtoward impasses — technological, economical and environmental — a condition that has spurred research efforts in alternative, “neuromorphic” (brain-like)computing technologies . The idea of exploiting nonlinear physical phenomena “directly” for non-digital computing has been explored under names like “unconventional computing”, “natural computing”,”physical computing”, or “in-materio computing” I stake out how a general concept of “computing” can bedeveloped which comprises digital, neuromorphic, unconventional and possible future paradigms .…

## Reinforced optimal control

Least squares Monte Carlo methods are a popular numerical approximationmethod for solving stochastic control problems . The choice of basis functions is crucial for the accuracy of the method . We extend the reinforced regression method to a generalclass of stochastically control problems, while considerably improving the method’s efficiency .…

## Prediction Based Reachability for Collision Avoidance in Autonomous Driving

Hamilton-Jacobi: Reachability is a formal method that verifies safety in multi-agent interaction . Safety is an important topic in autonomous driving since any collision may cause serious damage to people and the environment . Instead of always assuming for the worst-case, we first cluster the car’sbehaviors into multiple driving modes, e.g.…

## Energy Efficient Resource Allocation in Multi UAV Assisted Two Stage Edge Computing for Beyond 5G Networks

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices . We formulate a joint task offloading, communication and computation resource allocationproblem to minimize the energy consumption of MDs and UAVs by considering the limited communication resources for the uplink transmission, the computation resources of UAV and the computation of the tasks .…

## A3D Adaptive 3D Networks for Video Action Recognition

This paper presents A3D, an adaptive 3D network that can infer at a widerange of computational constraints with one-time training . Instead of training multiple models in a grid-search manner, it generates good configurations by trading off between network width and spatio-temporal resolution .…

## Dynamics of epidemic spreading on connected graphs

We propose a new model that describes the dynamics of epidemic spreading on connected graphs . Our model consists in a PDE-ODE system where at each vertices we have a standard SIR model and connexions between vertices are given by heat equations on the edges .…

## Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Navigation policies are commonly learned on idealized cylinder agents insimulation . Such policies perform poorly when deployed on complex and dynamic robots . We learn hierarchical navigation policies that accountfor the low-level dynamics of legged robots . They achieve good performance at navigating cluttered indoor environments .…

## Applying the Quantum Alternating Operator Ansatz to the Graph Matching Problem

The Quantum Alternating Operator Ansatz (QAOA+) framework has recently gained attention due to its ability to solve discrete optimization problems on noisyintermediate-scale quantum (NISQ) devices . We design a technique in this framework totackle a few problems over maximal matchings in graphs .…

## Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation

Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and questionanswering . Most of the existing works train the models on either distantlysupervised data or human-annotated data . To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose thedual supervision framework .…

## A Priori Error Analysis for an Optimal Control Problem Governed by a Variational Inequality of the Second Kind

We consider an optimal control problem governed by an elliptic variationalinequality of the second kind . The problem is discretized by linear finiteelements for the state and a variational discrete approach for the control . We establish second order sufficient optimality conditionsthat ensure a quadratic growth condition .…

## Low Complexity Precoding and Detection in Multi user Massive MIMO OTFS Downlink

Orthogonal Time Frequency Space (OTFS) modulation has been shown tobe robust to channel induced Doppler spread . In OTFS based systems, informationsymbols are embedded in the delay-Doppler (DD) domain where they are jointlymodulated to generate the time-domain transmit signal . The complexity of the proposedprecoder increases only linearly with increasing number of BS antennas Q and thenumber of UTs .…

## On the Serverless Nature of Blockchains and Smart Contracts

Serverless architecture is more frequently associated with the architectural style for developing cloud-native applications . Blockchains are distributed systems designed to enable collaborative scenarios involving untrusted parties . The decentralizedpeer-to-peer nature of blockchains makes it interesting to consider them inserverless architectures, since resource allocation and management tasks are not required to be performed by users .…

## On the Adversarial Robustness of 3D Point Cloud Classification

3D point clouds play pivotal roles in various safety-critical fields, such as autonomous driving, which desires the corresponding deep neural networks to berobust to adversarial perturbations . We present the first in-depth study showing how AT behaves in point cloud classification .…

## The 2 Adic Complexity of Two Classes of Binary Sequences with Interleaved Structure

The autocorrelation values of two classes of binary sequences are shown to begood in [6]. We study the 2-adic complexity of these sequences . Our resultsshow that the complexity of such sequences is large enough to resist the attack of the rational approximation algorithm .…