## Recalibration of Neural Networks for Point Cloud Analysis

Spatial and channel re-calibration have become powerful concepts in computervision . Their ability to capture long-range dependencies is especially useful for those networks that extract local features, such as CNNs . We propose a set ofre-Calibration blocks that extend Squeeze and Excitation blocks and that can beadded to any network for 3D point cloud analysis that builds a globaldescriptor by hierarchically combining features from multiple localneighborhoods .…

## torchdistill A Modular Configuration Driven Framework for Knowledge Distillation

The framework is designed to enable users to design experiments by adeclarative PyYAML configuration file . It helps researchers complete therecently proposed ML Code Completeness Checklist . All the source code,configurations, log files and the trained model weights are publicly available at https://://://github.com/yoshitomo-matsubara/torchdistill…

## Open World Learning Without Labels

Open-world learning is a problem where an autonomous agent detects thingssthat it does not know and learns them over time from a non-stationary andnever-ending stream of data . In an open-world environment, the training data and objective criteria are never available at once .…

## Implicit bias of deep linear networks in the large learning rate phase

Different learning rates may lead to differences in optimization and generalization . We find that the large learning rate phase is closely related to the separability of data . We demonstrate that this interpretation can be applied to real settings on MNIST and CIFAR10 datasets with the fact that theoptimal performance is often found in this largelearning rate phase .…

## Online Detection of Low Quality Synchrophasor Data Considering Frequency Similarity

This letter proposes a new approach for online detection of low-qualitysynchrophasor data under both normal and event conditions . The proposed approach does not require any offline studyand it is more effective to detect low quality data with apparentlyindistinguishable profiles .…

## Global random walk solvers for fully coupled flow and transport in saturated unsaturated porous media extended version

In this article, we present new random walk methods to solve flow and transport problems in unsaturated/saturated porous media . The numerical schemes are based on global random walkalgorithms (GRW) which approximate the solution by moving large numbers ofcomputational particles on regular lattices according to specific random walkrules .…

## Transforming Data Flow Diagrams for Privacy Compliance Long Version

Privacy,like security, is a non-functional property, yet most software design tools are focused on functional aspects . Recent regulations, such as the European General Data Protection Regulation, put stringent constraints on the handling of personal data . In this paper, we provide an explicit algorithm and a proof-of-concept implementation to transform DFDs into PA-DFDs .…

## Learning sparse structures for physics inspired compressed sensing

In underwater acoustics, shallow water environments act as modal dispersivewaveguides when considering low-frequency sources . In this context, propagatingsignals can be described as a sum of few modal components, each of them propagating according to its own wavenumber . Estimating these wavenumbers is of interest to understand the propagating environment as well as the emittingssource .…

## PowerNet Multi agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids . The decentralized learning scheme and highsample efficiency also make it viable to large-scale power grids . The developed PowerNetoutperforms a conventional model-based control, as well as several state-of-the-art MARL algorithms, the paper says .…

## Genome assembly a universal theoretical framework unifying and generalizing the safe and complete algorithms

Genome assembly is a fundamental problem in Bioinformatics, requiring toreconstructing a source genome from an assembly graph built from a set of reads . The goal is to find what is definitely present in all solutions, orwhat is safe . The long-standing open problem offinding all the safe parts of the solutions was recently solved by a majortheoretical result [RECOMB’16].…

## COVID 19 and Social Distancing Disparities in Mobility Adaptation by Income

Current literature from the United Statesinfers that only workers from limited socioeconomic groups have the ability to topractice remote work . However, there has been little research on mobilitydisparity across income groups in US cities during the pandemic . A longitudinal study was performed onmobility as measured by the total travel distance, the radius of gyration, and the number of visited locations in April 2020 compared to the data in January 2020 .…

## Optimizing Resource Efficiency for Federated Edge Intelligence in IoT Networks

This paper studies an edge intelligence-based IoT network in which a set ofedge servers learn a shared model using federated learning (FL) based on thedatasets uploaded from a multi-technology-supported IoT network . The datauploading performance of IoT network and the computational capacity of edgeservers are entangled with each other in influencing the FL model training process .…

## Deep Physics aware Inference of Cloth Deformation for Monocular Human Performance Capture

existing methods either do not estimate clothing at all or model clothdeformations with simple geometric priors . This leads to noticeable artifacts in their constructions, such as baked-in wrinkles and implausible deformations that defy gravity . We propose a person-specific, learning-based method thatintegrates a finite element-based simulation layer into the training process .…

## Machine Learning ML In a 5G Standalone SA Self Organizing Network SON

Machine learning (ML) is included in Self-organizing Networks (SONs) that arekey drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities . Standalone (SA) system is one of the 5Gcommunication tracks that transforms 4G networking to next-generation technology that is based on mobile applications .…

## Energy Forecasting in Smart Grid Systems A Review of the State of the art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems . Traditional statistical and machine learning-based forecasting methods are extensively investigated in terms of their applicability to energyforecasting . Hybrid methods such as CNN-LSTM are also highly effective to deal with long sequences in energy data .…

## Towards Playing Full MOBA Games with Deep Reinforcement Learning

MOBA games pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. OpenAI’s Dota AI limits theplay to a pool of only 17 heroes. As a result, full MOBA . games without . restrictions are far from being mastered by any existing AI system.…

## Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning

Homomorphic encryption is a very useful gradient protection technique used inprivacy preserving federated learning . But existing encrypted federatedlearning systems need a trusted third party to generate and distribute keypairs to connected participants . The proposed method avoidssencryption and decryption of the entire model .…

## Emotional Semantics Preserved and Feature Aligned CycleGAN for Visual Emotion Adaptation

Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain . CycleEmotionGAN++ is a novel end-to-end cycle-consistent adversarial model . Cygany proposes a dynamic emotional semantic consistency loss to preserve the emotionlabels of the source images .…

## Redundancy Resolution and Disturbance Rejection via Torque Optimization in Hybrid Cable Driven Robots

This paper presents redundancy resolution and disturbance rejection optimization in Hybrid Cable-Driven Robots (HCDRs) Compared to existing approaches, this paper provides the first solution (TOAUJ-basedmethod) for HCDRs that can solve the redundancy resolution problem as well as the disturbance rejection problem .…

## Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale

Cytoarchitecture is a basic principle of the microstructural organization of the brain . The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts . It is based on a DeepConvolutional Neural Network (CNN) which is trained on a pair of sectionimages with annotations, with a large number of un-annotated sections inbetween .…

## Iterations for the Unitary Sign Decomposition and the Unitary Eigendecomposition

We construct fast, structure-preserving iterations for computing the signdecomposition of a unitary matrix $A$ with no eigenvalues equal to $\pm i$ We use our iterations to construct a stable spectraldivide-and-conquer algorithm . Our iterationsrely on a recently discovered formula for the best (in the minimax sense)unimodular rational approximant of the scalar function $S =\operatorname{sign}(A) = A(A(A^2)^{-1/2) times a matrix$N = (A#2) + (A/N) The iterations converge significantly faster than Pad\’e iterations.…

## Concurrent consideration of cortical and cancellous bone within continuum bone remodelling

Continuum bone remodelling is an important tool for predicting the effects of mechanical stimuli on bone density evolution . While the modelling of onlycancellous bone is considered in many studies based on continuum boneremodelling, this work presents an approach of modelling also cortical bone and the interaction of both bone types .…

## Nonlinearity Compensation Based on Identified NARX Polynomials Models

This paper deals with the compensation of nonlinearities in dynamical systems . The compensation approach is formulated for static and dynamical contexts . The core idea is to rewrite the model as analgebraic polynomial whose roots are potential compensation inputs .…

## A DC Autotransformer based Multilevel Inverter for Automotive Applications

This paper proposes a novel multilevel inverter for automotive applications . The topology consists of a modular DCDC converter and a tap selector . The DC-DC converter is capable of self-balancing its modules and thus does not require large capacitors which yields a high power density .…

## On stability of nonzero set point for non linear impulsive control systems

Interest in non-linear impulsive systems (NIS) has been growing due to the impact in application problems such as disease treatments (diabetes, HIV,influenza, among many others) This paper studies the asymptotic stability of controlequilibrium orbits for NSI, based on the underlying discrete time system .…

## Fiedler vector analysis for particular cases of connected graphs

Two families of block graphs withcliques of fixed size, the block-path and block-starlike graphs, are introduced . Cases A and B of classification for both families were considered, as well as the behavior of the algebraic connectivity for particular cases ofblock-path graphs .…

## Contract Scheduling With Predictions

Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarilyinterruptible . Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler .…

## 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 .…

## 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 .…

## Locate the Source of Resonance Involved Forced Oscillation in Power Systems Based on Mode Shape Analysis

Paper proposed a new method to locate the source of forced oscillation that involves resonance with natural oscillation modes . The new method is based on comparing the oscillation mode shape of the forced oscillations with that of the natural oscillations .…

## 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 .…

## Towards the sampling Lovász Local Lemma

We show that for $k, q = O(1)$ there is a deterministic, polynomial time algorithm to approximately count thenumber of satisfying assignments . For the special case of properly $q$-coloring $k$-uniform hypergraphs, the term $Delta^{7$ improves the previously best known $\Delta^{60}$ for deterministic algorithms [Moitra,J.ACM,…

## A Generalizable Model for Fault Detection in Offshore Wind Turbines Based on Deep Learning

This paper presents a new deep learning-based model for fault detection in offshore wind turbines . The proposed model uses the nonlinear relationships among multiple sensor variables and thetemporal dependency of each sensor on others that considerably increases the performance of the model .…

## Stabilizing Queuing Networks with Model Data Independent Control

This work studies the stability of multi-class queuing networks under a class of centralized or decentralized model data-independent (MDI) control policies . Controlactions include routing, sequencing, and holding . For stabilizable single-class networks, we show that a centralized, stabilizing MDI control policy exists .…

## 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 .…

## 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 .…

## Localism as Secrecy Efficiency Secrecy Tradeoffs in the Recruitment of ISIS Foreign Fighters

This paper compares networks of foreign fighters who joined the Islamic Stateof Iraq and Syria (ISIS) from Europe and the Arabian Peninsula . In places where recruitment needs to be hidden from legal scrutiny, recruitment networks are more secretive . In areas where recruitment could occur more freely, recruitment is more hierarchical .…

## 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 .…

## 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 .…

## Hindsight Network Credit Assignment

Hindsight Network Credit Assignment (HNCA) works by assigning credit to eachneuron’s stochastic output based on how it influences the output of its immediate children in the network . We prove that HNCA provides unbiased gradient estimates while reducing variance compared to the REINFORCE estimator .…

## A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control

This paper investigates the combination of two model predictive control concepts . The effectiveness of the proposed method is validatedvia numerical simulations at different scenarios on a three-level neutral pointclamped permanent magnet synchronous generator wind turbine system and comparedto otherlong-horizon model predictive controls methods .…

## 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 .…

## A Fixed Time Stable Adaptation Law for Safety Critical Control under Parametric Uncertainty

We present a novel technique for solving the problem of safe control for ageneral class of nonlinear, control-affine systems subject to parametric modeluncertainty . Invoking Lyapunov analysis and the notion of fixed-time stability(FxTS) We introduce a parameter adaptation law which guarantees convergence of the estimates of unknown parameters in the system dynamics to their true values within a fixed time independent of the initial parameter estimation error .…

## Dendritic trafficking synaptic scaling and structural plasticity

Neuronal circuits internally regulate electrical signaling via a host of homeostatic mechanisms . Two prominent mechanisms, synaptic scaling andstructural plasticity, are believed to maintain average activity within an operating range . However, both mechanisms operate on relatively slow timescales and thus face fundamental limits due to delays .…

## A Model Free Loop Shaping Method based on Iterative Learning Control

This paper aims to develop a model-free loop-shaping method in controldesign . The core idea is to convert the model matching problem to atrajectory tracking problem . The proposed method is validated through numerical simulation on athird order plant . This method does not require the model of the controlled plant; hence it provides better performance of loop-Shaping controllesign.…

## Health Focused Optimal Power Flow

Health-Focused Optimal Power Flow (HF-OPF) proposed to take into account equipment health in operational and physical constraints . The paper addresses theneed for understanding the relationship between health condition index and theoperational constraints in OPF problems . The results show that health conditioninflicts high cost of generation and can lead to infeasibility even with lesscritical faults .…

## A Data Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

The closed-loop performance of model predictive controllers (MPCs) issensitive to the choice of prediction models, controller formulation, and tuning parameters . MPC tuning is typically donemanually to satisfy (probabilistic) constraints . In this work, we demonstrate ageneral approach for automating the tuning of MPC under uncertainty .…

## 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 .…

## Safely Learning Dynamical Systems from Short Trajectories

A fundamental challenge in learning to control an unknown dynamical system is to reduce model uncertainty by making measurements while maintaining safety . In our framework, the state of the system is required to stay within a given safety region under the (possibly repeated) action of all systems that are consistent with the information gathered so far .…

## Policy Optimization for Markovian Jump Linear Quadratic Control Gradient Based Methods and Global Convergence

In this paper, we investigate the global convergence of gradient-based policyoptimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS) Despite the non-convexity of the resultant problem, we are still able to identify several useful properties such ascoercivity, gradient dominance, and almost smoothness .…