Nonlinear state space identification using deep encoder networks

Nonlinear state-space identification for dynamical systems is most often performed by minimizing the simulation error to reduce the effect of modelerrors . This optimization problem becomes computationally expensive for largedatasets . This paper introduces a method that approximates the simulation loss by splitting the data set into multipleindependent sections similar to the multiple shooting method .…

A Multilevel Block Preconditioner for the HDG Trace System Applied to Incompressible Resistive MHD

We present a scalable block preconditioning strategy for the trace system coming from the high-order hybridized discontinuous Galerkin (HDG)discretization of incompressible resistive magnetohydrodynamics (MHD) We use a least squares commutator (BFBT)approximation for the inverse of the Schur complement . The remaining velocity, magnetic field,and Lagrange multiplier unknowns form a coupled nodal unknown block (the upper block), for which a system algebraic multigrid (AMG) is used for the approximate inverse .…

Learning to Schedule Halide Pipelines for the GPU

We present a new algorithm to automatically generate high-performance GPUimplementations of complex imaging and machine learning pipelines . It is fully automatic, requiring noschedule templates or hand-optimized kernels, and it targets a diverse range ofcomputations which is significantly broader than existing autoschedulers .…

Relaxed Peephole Optimization A Novel Compiler Optimization for Quantum Circuits

IBM’s Qiskit transpiler offers novel quantum compiler optimization for quantum computers . RPO leverages the single-qubit state information that can be determined statically by thecompiler . The circuits optimized with our optimization obtain up to 18.0% (11.7% on average) fewer CNOT gates and up to 8.2% (7.1%) lower transpilation time than that of the mostaggressive optimization level in the most aggressive optimization level .…

The Lottery Tickets Hypothesis for Supervised and Self supervised Pre training in Computer Vision Models

Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation . Latest studies suggest that thepre-training benefits from gigantic model capacity . We examine the supervised and self-supervised pre-trained models through the lens of lottery ticket hypothesis (LTH) LTH identifies highly sparse matching subnetworks that can be trained in isolation from scratch, to reach the full models’ performance .…

A Perturbation Resilient Framework for Unsupervised Learning

Designing learning algorithms that are resistant to perturbations of the underlying data distribution is a problem of wide practical and theoreticalimportance . We present a general approach to this problem focusing onunsupervised learning . The key assumption is that the perturbing distribution is characterized by larger losses relative to a given class of admissiblemodels .…

Non linear State space Model Identification from Video Data using Deep Encoders

Identifying systems with high-dimensional inputs and outputs is a challenging problem with numerous applications in robotics, autonomous vehicles and medical imaging . We propose a novel non-linear state-space identification method starting fromhigh-dimensional input and output data . Anencoder function, represented by a neural network, is introduced to learn areconstructability map to estimate the model states from past inputs and outputs.…

Prediction of High Performance Computing Input Output Variability and Its Application to Optimization for System Configurations

Performance variability is an important measure for a reliable highperformance computing (HPC) system . The prediction of HPC variability is a challenging problem in HPC systems and there is little statistical work on this problem to date . We evaluate performance of themethods by measuring prediction accuracy at previously unseen system configurations .…

Enacting Coordination Processes

A coordination process model specifies coordination constraints betweenthe interdependent processes in terms of semantic relationships . At run-time, these coordination constraints must be enforced by a coordination process instance . Processes must be allowed to run asynchronously andconcurrently, taking their complex relations into account .…

Reversing the Curse of Densification in mmWave Networks Through Spatial Multiplexing

The gold standard of a wireless network is that the throughput increaseslinearly with the density of access points (APs) In this paper, we propose to overcome the densification plateau of a mmWave network by employing spatial multiplexing at APs . We then demonstrate the necessity for deploying the multi-rate coding scheme in mmWave networks, especially when thespatialMultiplexing gain at mmWave APs is large, we say .…

Improved StyleGAN Embedding Where are the Good Latents

StyleGAN is able to produce photorealistic images almost indistinguishable from real ones . Embedding images into the StyleGAN latent space is not atrivial task due to the reconstruction quality and editing quality trade-off . In this paper, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes .…

An efficient Quasi Newton method for nonlinear inverse problems via learned singular values

Solving complex optimization problems in engineering and the physical sciences requires repetitive computation of multi-dimensional functionderivatives . In nonlinear inverse problems Gauss-Newton methods are used that require iterative updates to becomputed from the Jacobian . Computationally more efficient alternatives are Quasi-Newtons methods, where the repeated computation of theJacobian is replaced by an approximate update .…

Sparse Multi Family Deep Scattering Network

The Sparse Multi-Family Deep Scattering Network(SMF-DSN) is a novel architecture exploiting the interpretability of the DeepScattering Network (DSN). The DSN extractssalient and interpretable features in signals by cascading wavelet transforms, complex modulus and extract the representation of the data via atranslation-invariant operator .…

DeepSweep An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation

Public resources and services have been widely adopted to ease the development of DeepLearning-based applications . However, if the third-party providers areuntrusted, they can inject poisoned samples into the datasets or embedbackdoors in those models . We propose a systematic approach to discover the optimal policies for defending against differentbackdoor attacks by comprehensively evaluating 71 state-of-the-art dataaugmentation functions .…

Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks

Organizations collect vast quantities of sensitive information in ERP systems . Leakage of such information poses a severe threat for companies as the number of incidents and the reputational damage to those experiencing them continue to increase . Understanding the nature of such attacks becomes increasinglyimportant for the (internal) audit and fraud examination practice .…

Meticulous Object Segmentation

Meticulous ObjectSegmentation (MOS) is focused on segmenting well-defined foreground objects with elaborate shapes in high resolution images (e.g. 2k – 4k) Compared with common image segmentation tasks targeted at low-resolution images, higher resolution detailed image segmentations receives much less attention .…

A Dual Store Structure for Knowledge Graphs

Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge . Native graph stores are efficient in processing complex knowledge graph queries due to its index-freeadjacent property . But they are inapplicable to manage a large-sized knowledgegraph due to limited storage budgets or inflexible updating process .…

Fine Grained Lineage for Safer Notebook Interactions

Computational notebooks have emerged as the platform of choice for datascience and analytical workflows, enabling rapid iteration and exploration . NBSafety detects and prevents errors that users make during unaided notebook interactions, all while preserving the flexibility of existing notebook semantics .…

Semantic Networks for Engineering Design A Survey

There have been growing uses of semantic networks in the past decade, such asleveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research . The survey reveals that engineering design researchers have primarily relied on WordNet,ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources .…

A GPU Accelerated Fast Summation Method Based on Barycentric Lagrange Interpolation and Dual Tree Traversal

We present the barycentric Lagrange Lagrange dual tree traversal (BLDTT) fastsummation method for particle interactions . The scheme replaces well-separatedparticle-particle interactions by adaptively chosen particle-cluster, cluster-clusters approximations . The performance of the BLDTT is demonstrated for calculations with different problem sizes, particledistributions, geometric domains, and interaction kernels, as well as forunequal target and source particles .…

On Approximate Envy Freeness for Indivisible Chores and Mixed Resources

We study fair allocation of undesirable indivisible items (or chores) We show that determining the existence of anenvy-free allocation is NP-complete even when agents have binary additivevaluations . We provide a polynomial-time algorithm for computing anallocation that satisfies envy-freeness up to one chore (EF1) under monotonevaluations, correcting a existing proof of the same claim in the literature .…

Joint Hardware Design and Capacity Analysis for Intelligent Reflecting Surface Enabled Terahertz MIMO Communications

Terahertz (THz) communications have been envisioned as a promising enabler toprovide ultra-high data transmission for sixth generation (6G) wirelessnetworks . An intelligent reflectingsurface (IRS) is put forward to smartly control the incident THz waves by adjusting the phase shifts . An adaptive gradient descent (A-GD) algorithm is developed by dynamically updating the step size during theiterative process, which is determined by the second-order Taylor expansionformulation .…

Uniform Scattering of Robots on Alternate Nodes of a Grid

Homogeneous, autonomous mobile robots place themselves equidistant apart apart on the grid . The robots operate by executing cycles of the states”look-compute-move” The robots aresemi-synchronous, anonymous and have unlimited visibility . Eventually, the robots uniformly distribute themselves on alternate nodes of a grid, leaving adjacent nodes of the grid vacant.…

Towards Neurohaptic Brain Computer Interfaces for Decoding Intuitive Sense of Touch

Noninvasive brain-computer interface (BCI) decodes brain signals tounderstand user intention . decoding of sensation imagery based on brain signals could provide various industries such as developing advanced displays and more immersive virtual reality and augmented reality . Thispaper introduces a preliminary study to develop a neurohaptic-based BCI system using actual touch and touch imagery paradigms.…

Pre Training Graph Neural Networks for Cold Start Users and Items Representation

Cold-start problem is a fundamental challenge for recommendation tasks . Pre-training GNN simulates the cold-startscenarios from the users/items with sufficient interactions . Self-attention-based meta aggregator to enhance the aggregationability of each graph convolution step, and an adaptive neighbor sampler to select the effective neighbors according to the feedbacks from the pre-trainingGNN model .…

Neural network approaches to point lattice decoding

We characterize the lattice decoding problem from a neuralnetwork perspective . The notion of Voronoi-reduced basis is introduced to restrict the space of solutions to a binary set . This problem is shown to be equivalent to computing a continuous piecewise linear (CPWL)function restricted to the fundamental parallelotope .…

Adaptive and Oblivious Randomized Subspace Methods for High Dimensional Optimization Sharp Analysis and Lower Bounds

We propose novel randomized optimization methods for high-dimensional convexproblems . We consideroblivious and data-adaptive subspaces and study their approximation propertiesvia convex duality and Fenchel conjugates . A suitable adaptive subspace can begenerated by sampling a correlated random matrix whose second order statistics mirror the input data .…

Pseudo Shots Few Shot Learning with Auxiliary Data

In many practical few-shot learning problems, there are abundant auxiliary data sets that potentially contain useful information . We propose amasking module that adjusts the features of auxiliary data to be more similarto those of the target classes . We show that this masking module can improve accuracy by up to 18 accuracy points, particularly when the auxiliary data issemantically distant from the target task .…

Spontaneous Emotion Recognition from Facial Thermal Images

A large number of tasks for facial image processing in thermal infrared images that are currently solved using specialized rule-based methods or notsolved at all can be addressed with modern learning-based approaches . We have used USTC-NVIE database for training of a number of machine learning algorithms for facial landmark localization .…

MSAF Multimodal Split Attention Fusion

Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world . In this work, we propose a novel multimodal fusion module thatlearns to emphasize more contributive features across all modalities . The MSAF module is designed to be compatible withfeatures of various spatial dimensions and sequence lengths, suitable for bothCNNs and RNNs .…

Statistical CSI based Design for Reconfigurable Intelligent Surface aided Massive MIMO Systems with Direct Links

This paper investigates the performance of reconfigurable intelligent surface(RIS)-aided massive multiple multiple multiple-input multiple- input multiple-output (MIMO) systems with directlinks . We first derive the closed-form expression of the uplink ergodic data rate . Then, based on the derived expression, we use thegenetic algorithm (GA) to solve the sum data rate maximization problem .…

Comparing Generic and Community Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders

Targeting the right group of workers for crowdsourcing often achieves better quality results . We discuss the benefits andtrade-offs of recruiting paid vs. unpaid community-situated workers . We consider the context of Alcoholics Anonymous (AA), the largest peer support group for recovering alcoholics, and the task of identifying and validating AA meeting information .…