911 Works

RTRMC - Low-rank matrix completion via preconditioned optimization on the Grassmann manifold

Nicolas Boumal & Pierre-Antoine Absil
RTRMC is an algorithm to solve the low-rank matrix completion problem. This is notably useful in recommender systems (for example, to recommend movies to people based on movies they have rated in the past and based on the ratings obtained from other users as well.) The algorithm runs Manopt (a toolbox for optimization on Riemannian manifolds) on a non-convex formulation of the low-rank matrix completion problem on the Grassmann manifold. RTRMC homepage: http://www.nicolasboumal.net/RTRMC/ Manopt homepage:...

Improving Energy Efficiency on Partially Reversible Pipelined QCA Circuits

Marco A. Ribeiro, Iago A. Carvalho, Jeferson F. Chaves & Omar P. Vilela Neto
Recent studies show that Field-Coupled Nanocomputing devices, such as Quantum-dot Cellular Automata (QCA), can reach ultra-low power consumption, notably when operating with reversible computing techniques. Partially Reversible Pipelined QCA circuits are one of such examples where improving energy efficiency can be balanced with throughput degrading. In this work, we propose an algorithm to divide those circuits into stages, reducing energy without meaningful changes in its throughput when compared to a näive division. Furthermore, we manipulate...

Enhancing Fundamental Energy Limits of Field-Coupled Nanocomputing Circuits

Jeferson F. Chaves, Marco A. Ribeiro, Frank S. Torres & Omar P. Vilela Neto
Energy dissipation of future integrated systems, consisting of a myriad of devices, is a challenge that cannot be solved solely by emerging technologies and process improvements. Even though approaches like Field-Coupled Nanocomputing allow computations near the fundamental energy limits, there is a demand for strategies that enable the recycling of bits' energy to avoid thermalization of information. In this direction, we propose a new kind of partially reversible systems by exploiting fan-outs in logic networks....

Non-Linear Phase Noise Mitigation over Systems using Constellation Shaping

Dario Pilori, Antonino Nespola, Fabrizio Forghieri & Gabriella Bosco
This code plots the results that are presented in the article "Non-Linear Phase Noise Mitigation over Systems using Constellation Shaping", published in the Journal of Lightwave Technology. These results were obtained from this dataset http://dx.doi.org/10.21227/nfkc-vf85, and processed using the DSP library https://github.com/dario-pilori/dsp-library.

PIETOOLS for Time-Delay Systems

Matthew Peet
PIETOOLS uses transforms a Time-delay System to the Partial-Integral representation and then uses the standard PIETOOLS executives for: stability analysis; a dual version of stability analysis; Hinf-gain analysis; A dual version of Hinf-gain analysis; Hinf-optimal controller synthesis; Hinf-optimal observer synthesis.

GiniClust3: a fast and memory-efficient tool for rare cell type identification

Rui Dong
GiniClust is a clustering method specifically designed for rare cell type detection. It uses the Gini index to identify genes that are associated with rare cell types without prior knowledge. This differs from traditional clustering methods using highly variable genes. Using a cluster-aware, weighted consensus clustering approach, we can combine the outcomes from Gini index and Fano factor-based clustering and identify both common and rare cell types. In this new version (GiniClust3), we have substantially...

Elastic Cuckoo Filter: Virtualizing, Shrinking, and Extending Cuckoo Filters

Jinyang Li, Jizhou Li, Tong Yang, Zhaodong Kang, Aoran Li, Dagang Li, Steve Uhlig & Ori Rottenstreich
Compared with Bloom filters, Cuckoo filters achieve a similar false positive rate for a given amount of memory. However, Cuckoo filters can support deletions while Bloom filters cannot. Therefore, some recent applications have replaced Bloom filters with Cuckoo filters. Unfortunately, the Cuckoo filter data structure is inflexible: 1) its size must be 2^n2 , where n is a positive integer; and 2) its size cannot be dynamically tuned. In this paper, we aim to make...

Robust Estimation and Filtering for Poorly Known Models

Marcos R. Fernandes, Joao Bosco R. Do Val & Rafael. F. Souto
This Matlab code implements the numerical example presented in the paper M. R. Fernandes, J. B. R. do Val and R. F. Souto, "Robust Estimation and Filtering for Poorly Known Models" in IEEE Control Systems Letters.

Block Design-based Key Agreement for Group Data Sharing in Cloud Computing: Efficiency comparison for different simulation times

Tianqi Zhou
A comparative simulation analysis for "Block Design-based Key Agreement for Group Data Sharing in Cloud Computing" with respect to the time cost for each participant in different phases in the scheme.

High-throughput variable-to-fixed entropy codec using selective, stochastic code forests

Miguel Hernández-Cabronero
This repository contains the benchmark implementation used to gather data for the homonymous scientific paper, as well as all tools used to analyze data. It is intended to be self-contained to allow reproducibility, therefore a copy of the dataset used for comparison is provided as well. Several existing codec implementations, as well as all non-synthetic data samples, are included for reproducibility, and no authorship is claimed. Original authors are cited in the published manuscript, and...

(Quasi)periodicity in Videos Using Sliding Windows And Topology

Christopher Tralie & Jose Perea
ARXIV LINK: https://arxiv.org/pdf/1704.08382.pdf ABSTRACT: This work introduces a novel framework for quantifying the presence and strength of recurrent dynamics in video data. Specifically, we provide continuous measures of periodicity (perfect repetition) and quasiperiodicity (superposition of periodic modes with non-commensurate periods), in a way which does not require segmentation, training, object tracking or 1-dimensional surrogate signals. Our methodology operates directly on video data. The approach combines ideas from nonlinear time series analysis (delay embeddings) and computational...

GBEA: Single- and multi-objective game-benchmark for evolutionary algorithms

Vanessa Volz, Boris Naujoks, Pascal Kerschke & Tea Tušar
Despite a large interest in real-world problems from the research field of evolutionary optimisation, established benchmarks in the field are mostly artificial. We propose to use game optimisation problems in order to form a benchmark and implement function suites designed to work with the established COCO benchmarking framework. Game optimisation problems are real-world problems that are safe, reasonably complex and at the same time practical, as they are relatively fast to compute. We have created...

C++ Code for "A Shifting Strategy for Efficient Block-based NMPC Solutions Using the RTI Scheme"

Oscar Julian Gonzalez Villarreal
The C++ codes in Test_NMPC and Test_NMPC_32bits folders were used to create the results presented in table III of the paper "A Shifting Strategy for Efficient Block-based NMPC Solutions Using the RTI Scheme", submitted for peer-review on IEEE Transactions on Control Systems Technology. The extra folders contain the MATLAB codes used to generate the C++ codes that were tested. Abstract—Nonlinear Model Predictive Control requires the use of efficient solutions and strategies that allow its implementation...

Java code for Sudoku Schemes Generator

Andrea Bianchini
This capsule is a Java application that generates initial and final sudoku's schemas through the use of simplex method. The library used to solve simplex is SSC.

Unmixing Signal and Noise for Photon-Efficient Active Imaging

Joshua Rapp
We introduce a new approach to depth and reflectivity estimation that emphasizes the unmixing of contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, data are combined from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content.

Cognition Open Data (COD) Reproducibility Report: LcquD

Tom Hardwicke
This is one of 35 analytic reproducibility reports arising from the Cognition Open Data (COD) project. LcquD is the ID code used to refer to this specific report. The COD project involved attempting to reproduce a subset of key target outcomes reported in 35 articles published in the journal Cognition by repeating the original analyses upon the original data. For more details, please visit the Open Science Framework project: https://osf.io/wn8fd/.

Soft Video Parsing by LDL with Local Adjustment (ASP)

Ling Miaogen
The codes show an example of our algorithm ASP on the fris-catch (the 9th) action of THUMOS2014 dataset of our paper 'Soft Video Parsing by Label Distribution Learning' IEEE TIP.

Algorithm based on Physical Surface Tension for the Prediction of Psychosocial-risk Level in Public School Teachers.

Rodolfo Mosquera, Omar Danilo Castrillon & Liliana Parra Osorio
The objective of this new intelligent algorithm to improve the prediction of psychosocial risk level in public school teachers in Colombia. This new model algorithm is composed of the physical theory of superficial tension in the liquids linked to an neural net. The results for the physical surface tension-neural net model for each type of psychosocial risk are used as inputs and the level of risk is used as an output in the algorithm. The...

Stopwords-filtering: A universal information-theoretic approach to the identification of stopwords

Martin Gerlach, Hanyu Shi & Luis A.N Amaral
A universal information-theoretic approach to the identification of stopwords. One of the most widely used approaches in natural language processing and information retrieval is the so-called bag-of-words model. A common component of such methods is the removal of un-informative words, commonly referred to as stopwords. Currently, most practitioners use manually curated stopword lists. This approach is problematic because it cannot be readily generalized across knowledge domains or languages. Because of the difficulty in rigorously defining...

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

Kuangen Zhang, Ming Hao, Jing Wang, Clarence W. De Silva & Chenglong Fu
We propose a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly. We remove the transformation network, link hierarchical features from dynamic graphs, freeze feature extractor, and retrain the classifier to increase the performance of LDGCNN.

CLICS3: Database of Cross-Linguistic Colexifications

Christoph Rzymski, Tiago Tresoldi, Simon J. Greenhill, Robert Forkel & Johann-Mattis List
This capsule illustrates the workflow behind the Database of Cross-Linguistic Colexifications.

Estimation of the Direction of Maximum Information (tDMI) in Images with Straight Line Segments

Leila Nourbala, Seyed Nami Niyakan & Seyed Mohammad Mahdi Alavi
This package estimates the Direction of Maximum Information (tDMI) in images with straight line segments by using Hough Transform, Holmholtz Principle Line Segment Detection (HT-LSD, and HP-LSD) methods, and Phase Congruency (PC). In images with straight line segments, the information of a particular angle $\theta$, $J(\theta)$, is formulated as follows: $ J(\theta)=\sum_i (L_i\times D_i),~0\leq \theta < \pi$ where, $L_i$ and $D_i$ denote the length and width of the $i-$th line segment at the direction $\theta$....

Approaches to Mutivalued Mathematical Morphology Based on Uncertain Reduced Orderings

Mateus Sangalli & Marcos Eduardo Valle
Mathematical morphology (MM) is a powerful non-linear theory that can be used for signal and image processing and analysis. Although MM can be very well defined on complete lattices, which are partially ordered sets with well defined extrema operations, there is no natural ordering for multivalued images such as hyper-spectral and color images. Thus, a great deal of effort has been devoted to ordering schemes for multivalued MM. In a reduced ordering, in particular, elements...

TwiRole: A Hybrid Model for Role-related User Classification on Twitter

Liuqing Li, Ziqian Song, Xuan Zhang & Edward A. Fox
TwiRole is a hybrid model for role-related user classification on Twitter. It makes use of multiple classifiers and various types of features to predict the roles of users (i.e., female, male, and brand). TwiRole is a graduate research of the Global Event and Trend Archive Research (GETAR) project supported by NSF (IIS-1619028 and 1619371) in Digital Library Research Laboratory (DLRL) at Virginia Tech.

Progressive Focusing Algorithm for Reliable Pose Estimation of Latent Fingerprints

Chonlatid Deerada, Krisada Phromsuthirak, Arucha Rungchokanun & Vutipong Areekul
This is a MATLAB software for estimate reference point of latent fingerprint image.

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