Tootfinder

This https://arxiv.org/abs/2309.11238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sample- and computationally efficient data-driven predictive control
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data. Persistence of excitation necessitates that the data is sufficiently long and, hence, the computational complexity of the corresponding finite-horizon optimal control problem increases. In this paper, we propose an efficient data-driven predictive control (eDDPC) scheme which is both m…

Robust covariance estimation and explainable outlier detection for matrix-valued data
Marcus Mayrhofer, Una Radoji\v{c}i\'c, Peter Filzmoser
https://arxiv.org/abs/2403.03975 <…

Robust covariance estimation and explainable outlier detection for matrix-valued data
The minimum covariance determinant (MCD) estimator is a popular method for robustly estimating the mean and covariance of multivariate data. We extend the MCD to the setting where the observations are matrices rather than vectors and introduce the matrix minimum covariance determinant (MMCD) estimators for robust parameter estimation. These estimators hold equivariance properties, achieve a high breakdown point, and are consistent under elliptical matrix-variate distributions. We have also deve…

Performance of H-Matrix-Vector Multiplication with Floating Point Compression
Ronald Kriemann
https://arxiv.org/abs/2405.03456 https://

Performance of H-Matrix-Vector Multiplication with Floating Point Compression
Matrix-vector multiplication forms the basis of many iterative solution algorithms and as such is an important algorithm also for hierarchical matrices. However, due to its low computational intensity, its performance is typically limited by the available memory bandwidth. By optimizing the storage representation of the data within such matrices, this limitation can be lifted and the performance increased. This applies not only to hierarchical matrices but for also for other low-rank approximat…

Data Collaboration Analysis Over Matrix Manifolds
Keiyu Nosaka, Akiko Yoshise
https://arxiv.org/abs/2403.02780 https://arxiv.org/pdf/…

Data Collaboration Analysis Over Matrix Manifolds
The effectiveness of machine learning (ML) algorithms is deeply intertwined with the quality and diversity of their training datasets. Improved datasets, marked by superior quality, enhance the predictive accuracy and broaden the applicability of models across varied scenarios. Researchers often integrate data from multiple sources to mitigate biases and limitations of single-source datasets. However, this extensive data amalgamation raises significant ethical concerns, particularly regarding u…

This https://arxiv.org/abs/2309.11238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sample- and computationally efficient data-driven predictive control
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data. Persistence of excitation necessitates that the data is sufficiently long and, hence, the computational complexity of the corresponding finite-horizon optimal control problem increases. In this paper, we propose an efficient data-driven predictive control (eDDPC) scheme which is both m…

Multifamily Expenses Increase Above-Trend Levels
Yardi Matrix - April 3, 2024
“Expense growth varied by expense type and metro. By type it was led by property insurance (up 27.7% year-over-year), marketing (12.3%), administrative (9.6%) and repairs and maintenance (8.8%).”
https://www.

Multifamily Expenses Increase Above-Trend Levels, Reports Yardi Matrix
Annual per-unit expenses rose 7.1 year-over-year, led by property insurance increases, Yardi Matrix data shows.

Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression
Xin Li, Dongya Wu
https://arxiv.org/abs/2403.02696

Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression
High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including multivariate regression, system identification and matrix compressed sensing. Current studies mainly consider the idealized case that the covariate matrix is obtained without noise, while the more realistic scenario that the covariates may always be corrupted with noise or missing data has received little attention. We c…

This https://arxiv.org/abs/2404.02656 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
Unlike typical visual scene recognition domains, in which massive datasets are accessible to deep neural networks, medical image interpretations are often obstructed by the paucity of data. In this paper, we investigate the effectiveness of data-based few-shot learning in medical imaging by exploring different data attribute representations in a low-dimensional space. We introduce different types of non-negative matrix factorization (NMF) in few-shot learning, addressing the data scarcity issue…

This https://arxiv.org/abs/2404.03007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and o…

This https://arxiv.org/abs/2302.09211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Covariance Estimation for Multi-group Matrix-variate Data
Multi-group covariance estimation for matrix-variate data with small within group sample sizes is a key part of many data analysis tasks in modern applications. To obtain accurate group-specific covariance estimates, shrinkage estimation methods which shrink an unstructured, group-specific covariance either across groups towards a pooled covariance or within each group towards a Kronecker structure have been developed. However, in many applications, it is unclear which approach will result in m…

This https://arxiv.org/abs/2312.07520 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Estimating Counterfactual Matrix Means with Short Panel Data
We develop a new, spectral approach for identifying and estimating average counterfactual outcomes under a low-rank factor model with short panel data and general outcome missingness patterns. Applications include event studies and studies of outcomes of "matches" between agents of two types, e.g. workers and firms, typically conducted under less-flexible Two-Way-Fixed-Effects (TWFE) models of outcomes. Given an infinite population of units and a finite number of outcomes, we show our approach …

Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption
Yang Gao, Gang Quan, Soamar Homsi, Wujie Wen, Liqiang Wang
https://arxiv.org/abs/2405.02238

Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption
Despite the cloud enormous technical and financial advantages, security and privacy have always been the primary concern for adopting cloud computing facility, especially for government agencies and commercial sectors with high-security requirements. Homomorphic Encryption (HE) has recently emerged as an effective tool in assuring privacy and security for sensitive applications by allowing computing on encrypted data. One major obstacle to employing HE-based computation, however, is its excessi…

Real-time solution of quadratic optimization problems with banded matrices and indicator variables
Andres Gomez, Shaoning Han, Leonardo Lozano
https://arxiv.org/abs/2405.03051

Real-time solution of quadratic optimization problems with banded matrices and indicator variables
We consider mixed-integer quadratic optimization problems with banded matrices and indicator variables. These problems arise pervasively in statistical inference problems with time-series data, where the banded matrix captures the temporal relationship of the underlying process. In particular, the problem studied arises in monitoring problems, where the decision-maker wants to detect changes or anomalies. We propose to solve these problems using decision diagrams. In particular we show how to e…

Radial fields on the manifolds of symmetric positive definite matrices
Ha-Young Shin
https://arxiv.org/abs/2405.03297 https://arxiv.o…

Radial fields on the manifolds of symmetric positive definite matrices
On Hadamard manifolds, the radial fields, which are the negative gradients of the Busemann functions, can be used to designate a canonical sense of direction. This has many potential interesting applications to Hadamard manifold-valued data, for example in defining notions of quantiles or treatment effects. Some of the most commonly encountered Hadamard manifolds in statistics are the spaces of symmetric positive definite matrices, which are used in, for example, covariance matrix analysis and …

I love Ben Jann's #Stata graphics add-ons.
But I could never figure out how to change the label on top of the colour bar/legend.
label(subtitle("It goes here"))
Example
sysuse auto, clear

pwcorr price mpg trunk weight length turn foreign, sig
matrix C = r(C)
matrix sig = r(sig)
heatplot C, values(label(sig) format(%9.3f)) color(hcl…

This https://arxiv.org/abs/2211.09638 has been replaced.
link: https://scholar.google.com/scholar?q=a

Revisiting leptonic nonunitarity
In the presence of extra neutrino states at high scales, the low-energy effective $3\times 3$ leptonic mixing matrix (LMM) is in general nonunitary. We revisit the question of what is our current knowledge of individual LMM matrix elements without assuming unitarity. We first demonstrate that a minimal set of experimental constraints suffices in bounding LMM nonunitarity parameters to the level of ${\cal O}(10^{-3})$, without the use of neutrino oscillation data. We then revisit oscillation res…

This https://arxiv.org/abs/2404.02656 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
Unlike typical visual scene recognition domains, in which massive datasets are accessible to deep neural networks, medical image interpretations are often obstructed by the paucity of data. In this paper, we investigate the effectiveness of data-based few-shot learning in medical imaging by exploring different data attribute representations in a low-dimensional space. We introduce different types of non-negative matrix factorization (NMF) in few-shot learning, addressing the data scarcity issue…

Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian
https://arxiv.org/abs/2403.03905

Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
The $k$-principal component analysis ($k$-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications. In statistical settings, the goal of $k$-PCA is to identify a top eigenspace of the covariance matrix of a distribution, which we only have implicit access to via samples. Motivated by these implicit settings, we analyze black-box deflation methods as a framework for designing $k$-PCA algorithms, where we model access to th…

An overlooked source of uncertainty in the mass of the Milky Way
Kyle A. Oman (Durham ICC), Alexander H. Riley (Durham ICC)
https://arxiv.org/abs/2404.03726

An overlooked source of uncertainty in the mass of the Milky Way
In the conventional approach to decomposing a rotation curve into a set of contributions from mass model components, the measurements of the rotation curve at different radii are taken to be independent. It is clear, however, that radial correlations are present in such data, for instance (but not only) because the orbital speed depends on the mass distribution at all (or, minimally, inner) radii. We adopt a very simple parametric form for a covariance matrix and constrain its parameters using …

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang, Piotr Luczynski, Saleh Ashkboos, Florian Scheidl, Armon Carigiet, Chio Ge, Nabil Abubaker, Maciej Besta, Tal Ben-Nun, Torsten Hoefler
https://arxiv.org/abs/2402.19364<…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

This https://arxiv.org/abs/2311.04855 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values
Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two…

This https://arxiv.org/abs/2305.07362 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

The use of trade data in the analysis of global phosphate flows
In this paper we present a new method to trace the flows of phosphate from the countries where it is mined to the countries where it is used in agricultural production. We achieve this by combining data on phosphate rock mining with data on fertilizer use and data on international trade of phosphate-related products. We show that by making adjustments to data on net exports and by estimating weighting factors we can derive the matrix of phosphate flows on the country level to a large degree and…

This https://arxiv.org/abs/2210.14100 has been replaced.
link: https://scholar.google.com/scholar?q=a

The capacity of a finite field matrix channel
The Additive-Multiplicative Matrix Channel (AMMC) was introduced by Silva, Kschischang and Kötter in 2010 to model data transmission using random linear network coding. The input and output of the channel are $n\times m$ matrices over a finite field $\mathbb{F}_q$. On input the matrix $X$, the channel outputs $Y=A(X+W)$ where $A$ is a uniformly chosen $n\times n$ invertible matrix over $\mathbb{F}_q$ and where $W$ is a uniformly chosen $n\times m$ matrix over $\mathbb{F}_q$ of rank $t$. Silv…

This https://arxiv.org/abs/2404.17496 has been replaced.
initial toot: https://mastoxiv.page/@a…

From STEM-EDXS data to phase separation and quantification using physics-guided NMF
We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-induci…

Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector
Sung Hoon Choi, Donggyu Kim
https://arxiv.org/abs/2403.02591 https://<…

Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector
In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instanta…

Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian
https://arxiv.org/abs/2403.03905

Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
The $k$-principal component analysis ($k$-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications. In statistical settings, the goal of $k$-PCA is to identify a top eigenspace of the covariance matrix of a distribution, which we only have implicit access to via samples. Motivated by these implicit settings, we analyze black-box deflation methods as a framework for designing $k$-PCA algorithms, where we model access to th…

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
M. Rashkovetskyi, D. Forero-S\'anchez, A. de Mattia, D. J. Eisenstein, N. Padmanabhan, H. Seo, A. J. Ross, J. Aguilar, S. Ahlen, O. Alves, U. Andrade, D. Brooks, E. Burtin, T. Claybaugh, S. Cole, A. de la Macorra, Z. Ding, P. Doel, K. Fanning, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, H. Gil-Mar\'in, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, G. Gutierrez, K. …

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and o…

Validating Prior-informed Fisher-matrix Analyses against GWTC Data
Ulyana Dupletsa, Jan Harms, Ken K. Y. Ng, Jacopo Tissino, Filippo Santoliquido, Andrea Cozzumbo
https://arxiv.org/abs/2404.16103

Validating Prior-informed Fisher-matrix Analyses against GWTC Data
Next-generation gravitational-wave detectors, such as the Einstein Telescope (ET), are expected to observe a few 100,000 signals each year. This will require efficient analysis tools and computational resources well beyond the needs of current detectors. Such resources are not presently available to the science community. Therefore, to investigate ET observational capabilities and science cases, Fisher-matrix methods are used to predict how precisely parameters like mass, spin, source distance …

Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
Mohamed Assem Ibrahim, Mahzabeen Islam, Shaizeen Aga
https://arxiv.org/abs/2403.20297

Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving considerable attention. A challenge with GEMVs is the high memory bandwidth this primitive demands. Multiple memory vendors have proposed commercially viable processing-in-memory (PIM) prototypes that attain bandwidth boost over processor via augmenting memory banks with compute capabilities and broadcasting same command t…

Privacy-Preserving Distributed Nonnegative Matrix Factorization
Ehsan Lari, Reza Arablouei, Stefan Werner
https://arxiv.org/abs/2403.18326 https://<…

Privacy-Preserving Distributed Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns due to the conventional approach of sharing raw data among network agents. To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while sa…

This https://arxiv.org/abs/2403.03905 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications
The $k$-principal component analysis ($k$-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications. In statistical settings, the goal of $k$-PCA is to identify a top eigenspace of the covariance matrix of a distribution, which we only have implicit access to via samples. Motivated by these implicit settings, we analyze black-box deflation methods as a framework for designing $k$-PCA algorithms, where we model access to th…

Time-Varying Matrix Factor Models
Bin Chen, Elynn Y. Chen, Stevenson Bolivar, Rong Chen
https://arxiv.org/abs/2404.01546 https://arxi…

Time-Varying Matrix Factor Models
Matrix-variate data of high dimensions are frequently observed in finance and economics, spanning extended time periods, such as the long-term data on international trade flows among numerous countries. To address potential structural shifts and explore the matrix structure's informational context, we propose a time-varying matrix factor model. This model accommodates changing factor loadings over time, revealing the underlying dynamic structure through nonparametric principal component analysi…

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
M. Rashkovetskyi, D. Forero-S\'anchez, A. de Mattia, D. J. Eisenstein, N. Padmanabhan, H. Seo, A. J. Ross, J. Aguilar, S. Ahlen, O. Alves, U. Andrade, D. Brooks, E. Burtin, T. Claybaugh, S. Cole, A. de la Macorra, Z. Ding, P. Doel, K. Fanning, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, H. Gil-Mar\'in, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, G. Gutierrez, K. …

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and o…

This https://arxiv.org/abs/2303.02566 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality. Unlike existing methods that mainly rely on simple linear models to combine auxiliary information with the main data matrix, we propose to integ…

Masked Matrix Multiplication for Emergent Sparsity
Brian Wheatman, Meghana Madhyastha, Randal Burns
https://arxiv.org/abs/2402.14118 https://

Masked Matrix Multiplication for Emergent Sparsity
Artificial intelligence workloads, especially transformer models, exhibit emergent sparsity in which computations perform selective sparse access to dense data. The workloads are inefficient on hardware designed for dense computations and do not map well onto sparse data representations. We build a vectorized and parallel matrix-multiplication system A X B = C that eliminates unnecessary computations and avoids branches based on a runtime evaluation of sparsity. We use a combination of dynamic …

This https://arxiv.org/abs/2403.02324 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Communication of Measurement Anomalies in the Smart Grid
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals consumption and system matrix data, while simultaneously enabling an untrusted third party to test hypotheses of anomalies, such as the presence of bad data, by releasing a randomized sufficient statistic for hypothesis-testing. We consider a measurement model corrupted by Gaussian noise and a sparse noise vector repre…

Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging
Han Wang, Yiming Zhou, Eduardo Perez, Florian Roemer
https://arxiv.org/abs/2402.19023

Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging
Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices simultaneously for transmitters, receivers, and Fourier coefficients. This task-based algorithm is realized by introducing a specialized measurement model and integrating a customized Complex Learned FI…

This https://arxiv.org/abs/2307.12368 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

S-Matrix Path Integral Approach to Symmetries and Soft Theorems
We explore a formulation of the S-matrix in terms of the path integral with specified asymptotic data, as originally proposed by Arefeva, Faddeev, and Slavnov. In the tree approximation the S-matrix is equal to the exponential of the classical action evaluated on-shell. This formulation is well-suited to questions involving asymptotic symmetries, as it avoids reference to non-gauge/diffeomorphism invariant bulk correlators or sources at intermediate stages. We show that the soft photon theorem,…

MARec: Metadata Alignment for cold-start Recommendation
Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder, Anton van den Hengel
https://arxiv.org/abs/2404.13298

MARec: Metadata Alignment for cold-start Recommendation
For many recommender systems the primary data source is a historical record of user clicks. The associated click matrix which is often very sparse, however, as the number of users x products can be far larger than the number of clicks, and such sparsity is accentuated in cold-start settings. The sparsity of the click matrix is the reason matrix factorization and autoencoders techniques remain highly competitive across collaborative filtering datasets. In this work, we propose a simple approach …

Active Sensing for Reciprocal MIMO Channels
Tao Jiang, Wei Yu
https://arxiv.org/abs/2403.00134 https://arxiv.org/pdf/2403.00134

Active Sensing for Reciprocal MIMO Channels
This paper addresses the design of transmit precoder and receive combiner matrices to support $N_{\rm s}$ independent data streams over a time-division duplex (TDD) point-to-point massive multiple-input multiple-output (MIMO) channel with either a fully digital or a hybrid structure. The optimal precoder and combiner design amounts to finding the top-$N_{\rm s}$ singular vectors of the channel matrix, but the explicit estimation of the entire high-dimensional channel would require significant p…

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu, Rui Zhou, Yida Yin, Huidong Gao, Masayoshi Tomizuka, Jiachen Li
https://arxiv.org/abs/2403.06041

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the other hand, algorithmic data generation methods are usually limited by their model capacities, making them unable to offer realistic and diverse data needed by various application users. In this work,…

Time-efficient filtering of polarimetric data by checking physical realizability of experimental Mueller matrices
Tatiana Novikova, Alexey Ovchinnikov, Gleb Pogudin, Jessica C. Ramella-Roman
https://arxiv.org/abs/2402.18555

Time-efficient filtering of polarimetric data by checking physical realizability of experimental Mueller matrices
Imaging Mueller polarimetry has already proved its potential for metrology, remote sensing and biomedicine. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, i.e. to test the positive semi-definiteness of the 4x4 Hermitian coherency matrix calculated from the elements of the corresponding Mue…

This https://arxiv.org/abs/2307.11589 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Data-based system representations from irregularly measured data
Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are missing, obtaining such representations becomes a difficult task. By exploiting the kernel structure of Hankel matrices of irregularly measured data generated by a linear time-invariant system, we provide computational methods for which any complete finite-length behavior of the system can be obtained. For the special cas…

Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
Mohamed Assem Ibrahim, Mahzabeen Islam, Shaizeen Aga
https://arxiv.org/abs/2403.20297

Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving considerable attention. A challenge with GEMVs is the high memory bandwidth this primitive demands. Multiple memory vendors have proposed commercially viable processing-in-memory (PIM) prototypes that attain bandwidth boost over processor via augmenting memory banks with compute capabilities and broadcasting same command t…

Linear shrinkage for optimization in high dimensions
Naqi Huang, Nestor Parolya, Thereisa van Essen
https://arxiv.org/abs/2402.18666 https://

Linear shrinkage for optimization in high dimensions
In large-scale, data-driven applications, parameters are often only known approximately due to noise and limited data samples. In this paper, we focus on high-dimensional optimization problems with linear constraints under uncertain conditions. To find high quality solutions for which the violation of the true constraints is limited, we develop a linear shrinkage method that blends random matrix theory and robust optimization principles. It aims to minimize the Frobenius distance between the es…

This https://arxiv.org/abs/1803.09034 has been replaced.
link: https://scholar.google.com/scholar?q=a

Derivation of the Invariant Free-Energy Landscape Based on Langevin Dynamics
Conventionally defined free-energy landscape (FEL) exhibits unphysical dependence on the choice of reaction coordinates and hence lacks universal predictive ability. We here show that three physically plausible requirements uniquely determine the FEL formula for a given reaction coordinate. Our FEL is expressed solely in terms of quantities obtained through time-series data analysis, namely, the probability distribution and the diffusion matrix. It is free from any unphysical coordinate depende…

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

This https://arxiv.org/abs/2306.03116 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
Learning from crowds describes that the annotations of training data are obtained with crowd-sourcing services. Multiple annotators each complete their own small part of the annotations, where labeling mistakes that depend on annotators occur frequently. Modeling the label-noise generation process by the noise transition matrix is a power tool to tackle the label noise. In real-world crowd-sourcing scenarios, noise transition matrices are both annotator- and instance-dependent. However, due to …

This https://arxiv.org/abs/2403.01879 has been replaced.
link: https://scholar.google.com/scholar?q=a

High curvature means low-rank: On the sectional curvature of Grassmann and Stiefel manifolds and the underlying matrix trace inequalities
Methods and algorithms that work with data on nonlinear manifolds are collectively summarised under the term `Riemannian computing'. In practice, curvature can be a key limiting factor for the performance of Riemannian computing methods. Yet, curvature can also be a powerful tool in the theoretical analysis of Riemannian algorithms. In this work, we investigate the sectional curvature of the Stiefel and Grassmann manifold from the quotient space view point. On the Grassmannian, tight curvature …

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu, Rui Zhou, Yida Yin, Huidong Gao, Masayoshi Tomizuka, Jiachen Li
https://arxiv.org/abs/2403.06041

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the other hand, algorithmic data generation methods are usually limited by their model capacities, making them unable to offer realistic and diverse data needed by various application users. In this work,…

Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
Sangil Han, Kyoowon Kim, Sungkyu Jung
https://arxiv.org/abs/2402.09754 ht…

Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
The singular value decomposition (SVD) is a crucial tool in machine learning and statistical data analysis. However, it is highly susceptible to outliers in the data matrix. Existing robust SVD algorithms often sacrifice speed for robustness or fail in the presence of only a few outliers. This study introduces an efficient algorithm, called Spherically Normalized SVD, for robust SVD approximation that is highly insensitive to outliers, computationally scalable, and provides accurate approximati…

Better than best low-rank approximation with the singular value decomposition
David F. Gleich
https://arxiv.org/abs/2402.18427 https://

Better than best low-rank approximation with the singular value decomposition
The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on the organization of the matrix data. In particular, we will show examples where a rank 2 approximation of the matrix data in a different representation more accurately represents the entire matrix than a rank 5 approximation of the original matrix dat…

A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix
Soumya Sahu, Hakan Demirtas
https://arxiv.org/abs/2404.14149

A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix
Cohen's kappa is a useful measure for agreement between the judges, inter-rater reliability, and also goodness of fit in classification problems. For binary nominal and ordinal data, kappa and correlation are equally applicable. We have found a linear relationship between correlation and kappa for binary data. Exact bounds of kappa are much more important as kappa can be only .5 even if there is very strong agreement. The exact upper bound was developed by Cohen (1960) but the exact lower bound…

This https://arxiv.org/abs/2403.20200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile
High-dimensional linear regression has been thoroughly studied in the context of independent and identically distributed data. We propose to investigate high-dimensional regression models for independent but non-identically distributed data. To this end, we suppose that the set of observed predictors (or features) is a random matrix with a variance profile and with dimensions growing at a proportional rate. Assuming a random effect model, we study the predictive risk of the ridge estimator for …

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
Farzan Kaviani, Ivan Markovsky, Hamid R. Ossareh
https://arxiv.org/abs/2404.15098

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
We revisit the problem of predicting the output of an LTI system directly using offline input-output data (and without the use of a parametric model) in the behavioral setting. Existing works calculate the output predictions by projecting the recent samples of the input and output signals onto the column span of a Hankel matrix consisting of the offline input-output data. However, if the offline data is corrupted by noise, the output prediction is no longer exact. While some prior works propose…

Neutrinoless double beta decay in the minimal type-I seesaw model: mass-dependent nuclear matrix element, current limits and future sensitivities
Dong-Liang Fang, Yu-Feng Li, Yi-Yu Zhang, Jing-Yu Zhu
https://arxiv.org/abs/2404.12316

Neutrinoless double beta decay in the minimal type-I seesaw model: mass-dependent nuclear matrix element, current limits and future sensitivities
In this work we discuss the neutrino mass dependent nuclear matrix element (NME) of the neutrinoless double beta decay process and derive the limit on the parameter space of the minimal Type-I seesaw model from the current available experimental data as well as the future sensitivities from the next-generation experiments. Both the explicit many-body calculations and naive extrapolations of the mass dependent NME are employed in the current work. The uncertainties of the theoretical nuclear str…

A comparison of shrinkage estimators of the cosmological precision matrix
Marnix J. LooijmansShengbo, MikeShengbo, Wang, Florian Beutler
https://arxiv.org/abs/2402.13783

A comparison of shrinkage estimators of the cosmological precision matrix
The determination of the covariance matrix and its inverse, the precision matrix, is critical in the statistical analysis of cosmological measurements. The covariance matrix is typically estimated with a limited number of simulations at great computational cost before inversion into the precision matrix; therefore, it can be ill-conditioned and overly noisy when the sample size $n$ used for estimation is not much larger than the data vector dimension. In this work, we consider a class of method…

Better than best low-rank approximation with the singular value decomposition
David F. Gleich
https://arxiv.org/abs/2402.18427 https://

Better than best low-rank approximation with the singular value decomposition
The Eckhart-Young theorem states that the best low-rank approximation of a matrix can be constructed from the leading singular values and vectors of the matrix. Here, we illustrate that the practical implications of this result crucially depend on the organization of the matrix data. In particular, we will show examples where a rank 2 approximation of the matrix data in a different representation more accurately represents the entire matrix than a rank 5 approximation of the original matrix dat…

This https://arxiv.org/abs/2303.17992 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A fast Multiplicative Updates algorithm for Non-negative Matrix Factorization
Nonnegative Matrix Factorization is an important tool in unsupervised machine learning to decompose a data matrix into a product of parts that are often interpretable. Many algorithms have been proposed during the last three decades. A well-known method is the Multiplicative Updates algorithm proposed by Lee and Seung in 2002. Multiplicative updates have many interesting features: they are simple to implement and can be adapted to popular variants such as sparse Nonnegative Matrix Factorization…

A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix
Soumya Sahu, Hakan Demirtas
https://arxiv.org/abs/2404.14149

A Linear Relationship between Correlation and Cohen's Kappa for Binary Data and Simulating Multivariate Nominal and Ordinal Data with Specified Kappa Matrix
Cohen's kappa is a useful measure for agreement between the judges, inter-rater reliability, and also goodness of fit in classification problems. For binary nominal and ordinal data, kappa and correlation are equally applicable. We have found a linear relationship between correlation and kappa for binary data. Exact bounds of kappa are much more important as kappa can be only .5 even if there is very strong agreement. The exact upper bound was developed by Cohen (1960) but the exact lower bound…

OzMAC: An Energy-Efficient Sparsity-Exploiting Multiply-Accumulate-Unit Design for DL Inference
Harideep Nair, Prabhu Vellaisamy, Tsung-Han Lin, Perry Wang, Shawn Blanton, John Paul Shen
https://arxiv.org/abs/2402.19376

OzMAC: An Energy-Efficient Sparsity-Exploiting Multiply-Accumulate-Unit Design for DL Inference
General Matrix Multiply (GEMM) hardware, employing large arrays of multiply-accumulate (MAC) units, perform bulk of the computation in deep learning (DL). Recent trends have established 8-bit integer (INT8) as the most widely used precision for DL inference. This paper proposes a novel MAC design capable of dynamically exploiting bit sparsity (i.e., number of `0' bits within a binary value) in input data to achieve significant improvements on area, power and energy. The proposed architecture, c…

This https://arxiv.org/abs/2211.02920 has been replaced.
link: https://scholar.google.com/scholar?q=a

GmGM: a Fast Multi-Axis Gaussian Graphical Model
This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the…

A comparison of shrinkage estimators of the cosmological precision matrix
Marnix J. LooijmansShengbo, MikeShengbo, Wang, Florian Beutler
https://arxiv.org/abs/2402.13783

A comparison of shrinkage estimators of the cosmological precision matrix
The determination of the covariance matrix and its inverse, the precision matrix, is critical in the statistical analysis of cosmological measurements. The covariance matrix is typically estimated with a limited number of simulations at great computational cost before inversion into the precision matrix; therefore, it can be ill-conditioned and overly noisy when the sample size $n$ used for estimation is not much larger than the data vector dimension. In this work, we consider a class of method…

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Yining Wu, Shengyu Duan, Gaole Sai, Chenhong Cao, Guobing Zou
https://arxiv.org/abs/2404.04265

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either putting in additional computational resources or utilizing parallel systems, intro…

Efficient quaternion CUR method for low-rank approximation to quaternion matrix
Peng-Ling Wu, Kit Ian Kou, Hongmin Cai, Zhaoyuan Yu
https://arxiv.org/abs/2402.19147

Efficient quaternion CUR method for low-rank approximation to quaternion matrix
The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the quaternion singular value decomposition (QSVD), which limits their application to real large-scale data. To address this deficiency, an efficient quaternion matrix CUR (QMCUR) method for low-rank …

This https://arxiv.org/abs/2403.20200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile
High-dimensional linear regression has been thoroughly studied in the context of independent and identically distributed data. We propose to investigate high-dimensional regression models for independent but non-identically distributed data. To this end, we suppose that the set of observed predictors (or features) is a random matrix with a variance profile and with dimensions growing at a proportional rate. Assuming a random effect model, we study the predictive risk of the ridge estimator for …

A multiscale cavity method for sublinear-rank symmetric matrix factorization
Jean Barbier, Justin Ko, Anas A. Rahman
https://arxiv.org/abs/2403.07189 https…

A multiscale cavity method for sublinear-rank symmetric matrix factorization
We consider a statistical model for symmetric matrix factorization with additive Gaussian noise in the high-dimensional regime where the rank $M$ of the signal matrix to infer scales with its size $N$ as $M = o(N^{1/10})$. Allowing for a $N$-dependent rank offers new challenges and requires new methods. Working in the Bayesian-optimal setting, we show that whenever the signal has i.i.d. entries the limiting mutual information between signal and data is given by a variational formula involving a…

This https://arxiv.org/abs/2211.07132 has been replaced.
link: https://scholar.google.com/scholar?q=a

The $\ell_p$-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines
In the $\ell_p$-subspace sketch problem, we are given an $n\times d$ matrix $A$ with $n>d$, and asked to build a small memory data structure $Q(A,ε)$ so that, for any query vector $x\in\mathbb{R}^d$, we can output a number in $(1\pmε)\|Ax\|_p^p$ given only $Q(A,ε)$. This problem is known to require $\tildeΩ(dε^{-2})$ bits of memory for $d=Ω(\log(1/ε))$. However, for $d=o(\log(1/ε))$, no data structure lower bounds were known. We resolve the memory required to solve the $\ell…

Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization
Maryam Abdolali, Giovanni Barbarino, Nicolas Gillis
https://arxiv.org/abs/2403.20197

Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization
Simplex-structured matrix factorization (SSMF) is a generalization of nonnegative matrix factorization, a fundamental interpretable data analysis model, and has applications in hyperspectral unmixing and topic modeling. To obtain identifiable solutions, a standard approach is to find minimum-volume solutions. By taking advantage of the duality/polarity concept for polytopes, we convert minimum-volume SSMF in the primal space to a maximum-volume problem in the dual space. We first prove the iden…

This https://arxiv.org/abs/2211.02920 has been replaced.
link: https://scholar.google.com/scholar?q=a

GmGM: a Fast Multi-Axis Gaussian Graphical Model
This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the…

Testing for common structures in high-dimensional factor models
Marie-Christine D\"uker, Vladas Pipiras
https://arxiv.org/abs/2403.19818 https://

Testing for common structures in high-dimensional factor models
This work proposes a novel procedure to test for common structures across two high-dimensional factor models. The introduced test allows to uncover whether two factor models are driven by the same loading matrix up to some linear transformation. The test can be used to discover inter individual relationships between two data sets. In addition, it can be applied to test for structural changes over time in the loading matrix of an individual factor model. The test aims to reduce the set of possib…

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
Qi Zhang, Bing Li, Lingzhou Xue
https://arxiv.org/abs/2404.06735 https://<…

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors. Under the Gaussian assumption, the conditional independence between vectors is characterized by blockwise zeros in the precision matrix. To relax the restrictive Gaussian assumption, in this paper, we introduce a novel semiparametric multi-attribute graphical model based on a new copula named Cyclically Monotone Copula. This…

Eigenvector overlaps in large sample covariance matrices and nonlinear shrinkage estimators
Zeqin Lin, Guangming Pan
https://arxiv.org/abs/2404.18173 https…

Eigenvector overlaps in large sample covariance matrices and nonlinear shrinkage estimators
Consider a data matrix $Y = [\mathbf{y}_1, \cdots, \mathbf{y}_N]$ of size $M \times N$, where the columns are independent observations from a random vector $\mathbf{y}$ with zero mean and population covariance $Σ$. Let $\mathbf{u}_i$ and $\mathbf{v}_j$ denote the left and right singular vectors of $Y$, respectively. This study investigates the eigenvector/singular vector overlaps $\langle {\mathbf{u}_i, D_1 \mathbf{u}_j} \rangle$, $\langle {\mathbf{v}_i, D_2 \mathbf{v}_j} \rangle$ and $\langle…

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
Cristian Ram\'irez, Adri\'an Castell\'o, H\'ector Mart\'inez, Enrique S. Quintana-Ort\'i
https://arxiv.org/abs/2403.07731

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
The devices designed for the Internet-of-Things encompass a large variety of distinct processor architectures, forming a highly heterogeneous zoo. In order to tackle this, we employ a simulator to estimate the performance of the matrix-matrix multiplication (GEMM) kernel on processors designed to operate at the edge. Our simulator adheres to the modern implementations of GEMM, advocated by GotoBLAS2, BLIS, OpenBLAS, etc., to carefully account for the amount of data transfers across the memory h…

This https://arxiv.org/abs/2401.15076 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Comparative Analysis of Practical Identifiability Methods for an SEIR Model
Identifiability of a mathematical model plays a crucial role in parameterization of the model. In this study, we establish the structural identifiability of a Susceptible-Exposed-Infected-Recovered (SEIR) model given different combinations of input data and investigate practical identifiability with respect to different observable data, data frequency, and noise distributions. The practical identifiability is explored by both Monte Carlo simulations and a Correlation Matrix approach. Our result…

Seer: Predictive Runtime Kernel Selection for Irregular Problems
Ryan Swann, Muhammad Osama, Karthik Sangaiah, Jalal Mahmud
https://arxiv.org/abs/2403.17017

Seer: Predictive Runtime Kernel Selection for Irregular Problems
Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the best kernel to map fine-grained irregular parallelism to a GPU. We instead propose Seer, an abstraction for producing a simple, reproduceable, and understandable decision tree selector model which performs runtime kernel selection for irregular workloads. To showcase our framework, we conduct a case study in Sparse Matrix Vector Multiplicati…

This https://arxiv.org/abs/2304.01878 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sampled-Data Controller Synthesis using Dissipative Linear Periodic Jump-Flow Systems with Design Applications
In this paper, we will propose linear-matrix-inequality-based techniques for the design of sampled-data controllers that render the closed-loop system dissipative with respect to \textcolor{blue}{quadratic supply functions}, which includes passivity and an upper-bound on the system's $\mathcal{H}_\infty$-norm as a special case. To arrive at these results, we model the sampled-data control system as a linear periodic jump-flow system, study dissipativity in terms of differential linear matrix in…

This https://arxiv.org/abs/2212.09833 has been replaced.
link: https://scholar.google.com/scholar?q=a

Direct covariance matrix estimation with compositional data
Compositional data arise in many areas of research in the natural and biomedical sciences. One prominent example is in the study of the human gut microbiome, where one can measure the relative abundance of many distinct microorganisms in a subject's gut. Often, practitioners are interested in learning how the dependencies between microbes vary across distinct populations or experimental conditions. In statistical terms, the goal is to estimate a covariance matrix for the (latent) log-abundances…

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
Cristian Ram\'irez, Adri\'an Castell\'o, H\'ector Mart\'inez, Enrique S. Quintana-Ort\'i
https://arxiv.org/abs/2403.07731

Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
The devices designed for the Internet-of-Things encompass a large variety of distinct processor architectures, forming a highly heterogeneous zoo. In order to tackle this, we employ a simulator to estimate the performance of the matrix-matrix multiplication (GEMM) kernel on processors designed to operate at the edge. Our simulator adheres to the modern implementations of GEMM, advocated by GotoBLAS2, BLIS, OpenBLAS, etc., to carefully account for the amount of data transfers across the memory h…

Extracting $H_{0}$ and $r_{d}$ in Pacif Parametrization Models through Late-Time Data with Covariance Matrix Simulation
Shibesh Kumar Jas Pacif, Himanshu Chaudhary, G. Mustafa, Amine Bouali
https://arxiv.org/abs/2402.10499

Extracting $H_{0}$ and $r_{d}$ in Pacif Parametrization Models through Late-Time Data with Covariance Matrix Simulation
The study analyzes five models derived from the Pacif parametrization scheme, a generalized form of the Hubble parameter ($H$), yielding various forms of the deceleration parameter (DP) that vary linearly, quadratically, cubically, quartically, and quintically. We aim to explore the impact of these DP variations on late-time evolution and their potential to alleviate cosmological tensions. Additionally, we enhance model constraints by introducing non-diagonal elements into the covariance matrix…

This https://arxiv.org/abs/2309.06985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

CARE: Large Precision Matrix Estimation for Compositional Data
High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we prop…

This https://arxiv.org/abs/2209.13485 has been replaced.
link: https://scholar.google.com/scholar?q=a

Improved covariance estimation: optimal robustness and sub-Gaussian guarantees under heavy tails
We present an estimator of the covariance matrix $Σ$ of random $d$-dimensional vector from an i.i.d. sample of size $n$. Our sole assumption is that this vector satisfies a bounded $L^p-L^2$ moment assumption over its one-dimensional marginals, for some $p\geq 4$. Given this, we show that $Σ$ can be estimated from the sample with the same high-probability error rates that the sample covariance matrix achieves in the case of Gaussian data. This holds even though we allow for very general distr…

This https://arxiv.org/abs/2304.01878 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Sampled-Data Controller Synthesis using Dissipative Linear Periodic Jump-Flow Systems with Design Applications
In this paper, we will propose linear-matrix-inequality-based techniques for the design of sampled-data controllers that render the closed-loop system dissipative with respect to \textcolor{blue}{quadratic supply functions}, which includes passivity and an upper-bound on the system's $\mathcal{H}_\infty$-norm as a special case. To arrive at these results, we model the sampled-data control system as a linear periodic jump-flow system, study dissipativity in terms of differential linear matrix in…

Applying Non-negative Matrix Factorization with Covariates to the Longitudinal Data as Growth Curve Model
Kenichi Satoh
https://arxiv.org/abs/2403.05359 ht…

Applying Non-negative Matrix Factorization with Covariates to the Longitudinal Data as Growth Curve Model
Using Non-negative Matrix Factorization (NMF), the observed matrix can be approximated by the product of the basis and coefficient matrices. Moreover, if the coefficient vectors are explained by the covariates for each individual, the coefficient matrix can be written as the product of the parameter matrix and the covariate matrix, and additionally described in the framework of Non-negative Matrix tri-Factorization (tri-NMF) with covariates. Consequently, this is equal to the mean structure of …

EN-TensorCore: Advancing TensorCores Performance through Encoder-Based Methodology
Qizhe Wu, Yuchen Gui, Zhichen Zeng, Xiaotian Wang, Huawen Liang, Xi Jin
https://arxiv.org/abs/2404.11887

EN-TensorCore: Advancing TensorCores Performance through Encoder-Based Methodology
Tensor computations, with matrix multiplication being the primary operation, serve as the fundamental basis for data analysis, physics, machine learning, and deep learning. As the scale and complexity of data continue to grow rapidly, the demand for tensor computations has also increased significantly. To meet this demand, several research institutions have started developing dedicated hardware for tensor computations. To further improve the computational performance of tensor process units, we…

This https://arxiv.org/abs/2311.18499 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Weighing neutrinos with Lyman-$α$ observations
The presence of massive neutrinos has still not been revealed by the cosmological data. We consider a novel method based on the two-point line-of-sight correlation function of high-resolution Lyman-$α$ data to achieve this end in the paper. We adopt semi-analytic models of Lyman-$α$ clouds for the study. We employ Fisher matrix technique to show that it is possible to achieve a scenario in which the covariance of the two-point function nearly vanishes for both the spectroscopic noise and the …

A Gray-Box Stability Analysis Mechanism for Power Electronic Converters
Rui Kong, Subham Sahoo, Yubo Song, Frede Blaabjerg
https://arxiv.org/abs/2404.09801

A Gray-Box Stability Analysis Mechanism for Power Electronic Converters
This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc)…

This https://arxiv.org/abs/2306.12690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Uniform error bound for PCA matrix denoising
Principal component analysis (PCA) is a simple and popular tool for processing high-dimensional data. We investigate its effectiveness for matrix denoising. We consider the clean data are generated from a low-dimensional subspace, but masked by independent high-dimensional sub-Gaussian noises with standard deviation $σ$. Under the low-rank assumption on the clean data with a mild spectral gap assumption, we prove that the distance between each pair of PCA-denoised data point and the clean da…

This https://arxiv.org/abs/2306.09427 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

A new open source framework for multiscale modeling of fibrous materials on heterogeneous supercomputers
This article presents MuMFiM, an open source application for multiscale modeling of fibrous materials on massively parallel computers. MuMFiM uses two scales to represent fibrous materials such as biological network materials (extracellular matrix, connective tissue, etc.). It is designed to make use of multiple levels of parallelism, including distributed parallelism of the macro and microscales as well as GPU accelerated data-parallelism of the microscale. Scaling results of the GPU accelerat…

Fast randomized algorithms for low-rank matrix approximations with applications in global comparative analysis of a class of data sets
Weiwei Xu, Weijie Shen, Wen Li, Weiguo Gao, Yingzhou Li
https://arxiv.org/abs/2404.09459

Fast randomized algorithms for low-rank matrix approximations with applications in global comparative analysis of a class of data sets
Generalized singular values (GSVs) play an essential role in the comparative analysis. In the real world data for comparative analysis, both data matrices are usually numerically low-rank. This paper proposes a randomized algorithm to first approximately extract bases and then calculate GSVs efficiently. The accuracy of both basis extration and comparative analysis quantities, angular distances, generalized fractions of the eigenexpression, and generalized normalized Shannon entropy, are rigurs…

This https://arxiv.org/abs/2403.05359 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Applying Non-negative Matrix Factorization with Covariates to the Longitudinal Data as Growth Curve Model
Using Non-negative Matrix Factorization (NMF), the observed matrix can be approximated by the product of the basis and coefficient matrices. Moreover, if the coefficient vectors are explained by the covariates for each individual, the coefficient matrix can be written as the product of the parameter matrix and the covariate matrix, and additionally described in the framework of Non-negative Matrix tri-Factorization (tri-NMF) with covariates. Consequently, this is equal to the mean structure of …

Tensor Time Series Imputation through Tensor Factor Modelling
Zetai Cen, Clifford Lam
https://arxiv.org/abs/2403.13153 https://arxiv.…

Tensor Time Series Imputation through Tensor Factor Modelling
We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for enough time points. The method is based on a tensor time series factor model with Tucker decomposition of the common component. One distinguished feature of the tensor time series factor model used is that there can be weak factors in the factor loadings matrix for each mode. This reflects reality better when real data …

A Linear Inverse Model for Colored-Gaussian Noise
Justin Lien, Yan-Ning Kuo, Hiroyasu Ando
https://arxiv.org/abs/2402.15184 https://a…

A Linear Inverse Model for Colored-Gaussian Noise
We propose a novel data-driven linear inverse model, called Colored-LIM, to extract the linear dynamics and diffusion matrix that define a linear stochastic process driven by an Ornstein-Uhlenbeck colored-noise. The Colored-LIM is a new variant of the classical linear inverse model (LIM) which relies on the white noise assumption. Similar to LIM, the Colored-LIM approximates the linear dynamics from a finite realization of a stochastic process and then solves the diffusion matrix based on, for …

Tensor Time Series Imputation through Tensor Factor Modelling
Zetai Cen, Clifford Lam
https://arxiv.org/abs/2403.13153 https://arxiv.…

Tensor Time Series Imputation through Tensor Factor Modelling
We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for enough time points. The method is based on a tensor time series factor model with Tucker decomposition of the common component. One distinguished feature of the tensor time series factor model used is that there can be weak factors in the factor loadings matrix for each mode. This reflects reality better when real data …

This https://arxiv.org/abs/2305.17744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Heterogeneous Matrix Factorization: When Features Differ by Datasets
In myriad statistical applications, data are collected from related but heterogeneous sources. These sources share some commonalities while containing idiosyncratic characteristics. One of the most fundamental challenges in such scenarios is to recover the shared and source-specific factors. Despite the existence of a few heuristic approaches, a generic algorithm with theoretical guarantees has yet to be established. In this paper, we tackle the problem by proposing a method called Heterogeneou…

This https://arxiv.org/abs/2305.17744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Heterogeneous Matrix Factorization: When Features Differ by Datasets
In myriad statistical applications, data are collected from related but heterogeneous sources. These sources share some commonalities while containing idiosyncratic characteristics. One of the most fundamental challenges in such scenarios is to recover the shared and source-specific factors. Despite the existence of a few heuristic approaches, a generic algorithm with theoretical guarantees has yet to be established. In this paper, we tackle the problem by proposing a method called Heterogeneou…

Computational Approaches for Exponential-Family Factor Analysis
Liang Wang, Luis Carvalho
https://arxiv.org/abs/2403.14925 https://ar…

Computational Approaches for Exponential-Family Factor Analysis
We study a general factor analysis framework where the $n$-by-$p$ data matrix is assumed to follow a general exponential family distribution entry-wise. While this model framework has been proposed before, we here further relax its distributional assumption by using a quasi-likelihood setup. By parameterizing the mean-variance relationship on data entries, we additionally introduce a dispersion parameter and entry-wise weights to model large variations and missing values. The resulting model is…

This https://arxiv.org/abs/2310.00636 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A DEIM-CUR factorization with iterative SVDs
A CUR factorization is often utilized as a substitute for the singular value decomposition (SVD), especially when a concrete interpretation of the singular vectors is challenging. Moreover, if the original data matrix possesses properties like nonnegativity and sparsity, a CUR decomposition can better preserve them compared to the SVD. An essential aspect of this approach is the methodology used for selecting a subset of columns and rows from the original matrix. This study investigates the eff…

This https://arxiv.org/abs/2404.07459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Safe subspace screening for the adaptive nuclear norm regularized trace regression
Matrix form data sets arise in many areas, so there are lots of works about the matrix regression models. One special model of these models is the adaptive nuclear norm regularized trace regression, which has been proven have good statistical performances. In order to accelerate the computation of this model, we consider the technique called screening rule. According to matrix decomposition and optimal condition of the model, we develop a safe subspace screening rule that can be used to identif…