Isomorphic Data Type Transformations

This paper describes the APT (AutomatedProgram Transformations) tools to carry out isomorphic data type refinements . The tools are also useful to verify existing programs, by turning more concrete data representations into more abstract ones to ease verification . Propagate-iso handles cases where the type is a component of a more complex one such as a list of the type or a record that has a field is automatically lifted to an isomorphism on the more complex type .…

Ethereum s Recursive Length Prefix in ACL2

Recursive Length Prefix (RLP) is used to encode a wide variety of data inEthereum, including transactions . The work described in this paper provides a specification of RLP encoding and a verified implementation . This work has led toimprovements to the Ethereum documentation and additions to the testsuite .…

Computing and Proving Well founded Orderings through Finite Abstractions

A common technique for checking properties of complex state machines is tobuild a finite abstraction then check the property on the abstract system . We developed a process for computing a finiteabstract graph of the target state machine . We theniteratively divide the abstract graph by splitting into strongly connected components and then building a measure for every node in the abstractgraph .…

New Rewriter Features in FGL

FGL is a successor to GL, a proof procedure for ACL2 that allows complicatedfinitary conjectures to be translated into efficient Boolean function representations and proved using SAT solvers . The FGL rewriter is modeled on ACL2’s rewriter, but we have added several features in order to make rewrite rules more powerful .…

Iteration in ACL2

ACL2 algorithms are traditionally expressed in ACL2 using recursion . Common Lisp provides a construct, loop, which provides direct support for iteration . We describe anACL2 analogue loop$ of loop that supports efficient ACL2 programming .…

An Overview on Optimal Flocking

The study of robotic flocking has received considerable attention in the past twenty years . As we begin to deploy flocking control algorithms on physicalmulti-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance .…

Learning to Play against Any Mixture of Opponents

Q-Mixing is a transfer learning method that starts by learning Q-values against each pure-strategy opponent . Then a Q-value for any distribution of opponents is approximated by averaging the separately learned Q values . From these components, we construct policies against all mixtures without any further training .…

Performance of AV1 Real Time Mode

COVID-19 puts real-time (or low-latency) codecs into a new light . It is shown how the speed at which content is made available impacts both latency and throughput . The authors introduce a new test set up, integrating a paced reader, which allows to runcodec in the same condition as real time media capture.…

Sequential Reinforced 360 Degree Video Adaptive Streaming with Cross user Attentive Network

In the tile-based 360-degree video streaming, predicting user’s futureviewpoints and developing adaptive bitrate (ABR) algorithms are essential foroptimizing user’s quality of experience (QoE) Traditional single-user based prediction methods fail to achieve good performance in long-termprediction . We propose a cross-userattentive network (CUAN) boosting the performance of long-term viewpointprediction .…

Residual based a posteriori error estimates for mathbf hp discontinuous Galerkin discretisations of the biharmonic problem

We introduce a residual-based a posteriori error estimator for a novel$hp$-version interior penalty discontinuous Galerkin method for the biharmonicproblem in two and three dimensions . We prove that the error estimate provides an upper bound and a local lower bound on the error, and that the lower boundis robust to the local mesh size but not the local polynomial degree .…

A novel block non symmetric preconditioner for mixed hybrid finite element based flow simulations

In this work we propose a novel block preconditioner, labelled ExplicitDecoupling Factor Approximation (EDFA), to accelerate the convergence of Krylovsubspace solvers used to address the sequence of non-symmetric systems of linear equations . The proposed blockpreconditioner has been tested through an extensive experimentation on both synthetic and real-case applications, pointing out its robustness and efficiency .…

p Multilevel preconditioners for HHO discretizations of the Stokes equations with static condensation

We propose a $p$-multilevel preconditioner for Hybrid High-Orderdiscretizations (HHO) of the Stokes equation . We numerically assess its performance on two variants of the method, and compare with a classical Galerkin scheme . An efficient implementation is proposed wherecoarse level operators are inherited using $L^2$-orthogonal projections defined over mesh faces and the restriction of the fine grid operators is performed recursively and matrix-free .…

Numerical approximations of chromatographic models

A numerical scheme based on modified method of characteristics with adjustedadvection (MMOCAA) is proposed to approximate the solution of the system liquidchromatography with multi components case . For the case of one component, themethod preserves the mass . Various examples and computational tests numericallyverify the accuracy and efficiency of the approach .…

Trustworthy Convolutional Neural Networks A Gradient Penalized based Approach

Convolutional neural networks (CNNs) are commonly used for image classification . Saliency methods are examples of approaches that can be used tointerpret CNNs post hoc, identifying the most relevant pixels for a prediction following the gradients flow . We propose a novel approach for training trustworthy CNNs by penalizing parameter choicesthat result in inaccurate saliency maps generated during training .…

Deep Evolution for Facial Emotion Recognition

Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack ofinterpretability . We propose a novel method based on evolutionary algorithms, that deals with both challenges . We are robustly able to reduce thenumber of parameters on average by 95% (e.g.…

Online Trainable Wireless Link Quality Prediction System using Camera Imagery

Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve the reliability of wireless communications . The proposed system collectsdatasets, updates a model, and infers the received power in real-time . The experimental evaluation was conducted using 5 GHz Wi-Fi, where received signalstrength could be degraded by 10 dB when the LOS path was blocked by largeobstacles.…

Enforcing nonholonomic constraints in Aerobat a roosting flapping wing model

Flapping wing flight is a challenging dynamical problem and is also a veryfascinating subject to study in the field of biomimetic robotics . Aerobat captures multiple biologically meaningfuldegrees-of-freedom for flapping flight that is present in biological bats . The work attempts to manifest closed-loop aerial body reorientation andpreparation for landing through the manipulation of inertial dynamics andaerodynamics by enforcing nonholonomic constraints onto the system .…

Reality assisted evolution of soft robots through large scale physical experimentation a review

In silico, data-driven models build, adapt and improve representations of the targetsystem using real-world experimental data . In reality, large-scale physical experimentation facilitates the fabrication,testing and analysis of multiple candidate designs . Automated assembly andreconfigurable modular systems enable significantly higher numbers ofreal-world design evaluations than previously possible .…

Learning Skills to Patch Plans Based on Inaccurate Models

Planners using accurate models can be effective for accomplishingmanipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to be reliable . In this paper, we propose a method that improves efficiency of sub-optimalplanners with approximate but simple and fast models by switching to amodel-free policy when unexpected transitions are observed .…

Long term Productivity for Long term Impact

We present a new conceptual definition of ‘productivity’ for sustainablydeveloping research software . Existing definitions are flawed as they are short-term biased, thus devaluing long-term impact . We view the outputs of the development process as knowledge and usersatisfaction . User satisfaction is used as a proxy for effective quality .…

Automatically Tailoring Static Analysis to Custom Usage Scenarios

In recent years, there has been significant progress in the development and adoption of static analyzers . A major hurdle is tuning options to custom usagescenarios, such as a particular code base or certain resource constraints . We propose a technique that automatically tailors a staticanalyzer, specifically an abstract interpreter, to the code under analysis and any given resource constraints.…

Dynamic Slicing for Deep Neural Networks

NNSlicer is the first approach for slicing deep neural networks based on data flow analysis . The method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset .…

Geometric Matrix Completion A Functional View

We propose a totally functional view of geometric matrix completion problem . On synthetic tasks with strong underlying geometric structure, ourframework outperforms state of the art by a huge margin . On real datasets, weachieve state-of-the-art results at a fraction of the computational effort of previous methods .…

StratLearner Learning a Strategy for Misinformation Prevention in Social Networks

Given a combinatorial optimization problem, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study themisinformation prevention problem . We design a structured prediction framework, where the main idea is toparameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs .…