Tootfinder

Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Xiuting Ge, Chunrong Fang, Quanjun Zhang, Daoyuan Wu, Bowen Yu, Qirui Zheng, An Guo, Shangwei Lin, Zhihong Zhao, Yang Liu, Zhenyu Chen
https://arxiv.org/abs/2403.02716

Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limited number of labeled warnings to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have been trained through billions …

This https://arxiv.org/abs/2404.10073 has been replaced.
link: https://scholar.google.com/scholar?q=a

Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification
Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time f…

The identification of #airbursts in the past - insights from the BIT-58 layer: #asteroid may have exploded over #Antarctica about 2.5 million years ago: https://www.sciencenews.org/article/asteroid-exploded-antarctica-millions-years-ago - bits of rock may point to the oldest known midair asteroid disintegration.

Solution for Authenticity Identification of Typical Target Remote Sensing Images
Yipeng Lin, Xinger Li, Yang Yang
https://arxiv.org/abs/2405.02362 https://…

Solution for Authenticity Identification of Typical Target Remote Sensing Images
In this paper, we propose a basic RGB single-mode model based on weakly supervised training under pseudo labels, which performs high-precision authenticity identification under multi-scene typical target remote sensing images. Due to the imprecision of Mask generation, we divide the task into two sub-tasks: generating pseudo-mask and fine-tuning model based on generated Masks. In generating pseudo masks, we use MM-Fusion as the base model to generate masks for large objects such as planes and s…

Linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization via L-BFGS-B
Alberto Bemporad
https://arxiv.org/abs/2403.03827 ht…

Linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization via L-BFGS-B
In this paper, we propose an approach for identifying linear and nonlinear discrete-time state-space models, possibly under $\ell_1$- and group-Lasso regularization, based on the L-BFGS-B algorithm. For the identification of linear models, we show that, compared to classical linear subspace methods, the approach often provides better results, is much more general in terms of the loss and regularization terms used, and is also more stable from a numerical point of view. The proposed method not o…

Data-Driven Model Identification Near a Supercritical Hopf Bifurcation Using Phase-Based Approaches
Dan Wilson
https://arxiv.org/abs/2405.03668 https://

Data-Driven Model Identification Near a Supercritical Hopf Bifurcation Using Phase-Based Approaches
A data-driven model identification strategy is developed for dynamical systems near a supercritical Hopf bifurcation with nonautonomous inputs. This strategy draws on phase-amplitude reduction techniques, leveraging an analytical representation for the phase and amplitude response curves of the Hopf normal form to infer system parameters. Fitting can be performed by recording the system output during the relaxation to the stable limit cycle after applying as few as two carefully timed pulse inp…

Mint Mobile has added scam call identification! Saves me money per year having to rely on a 3rd party app.

This https://arxiv.org/abs/2308.00071 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Interpretable Stereotype Identification through Reasoning
Given that language models are trained on vast datasets that may contain inherent biases, there is a potential danger of inadvertently perpetuating systemic discrimination. Consequently, it becomes essential to examine and address biases in language models, integrating fairness into their development to ensure these models are equitable and free from bias. In this work, we demonstrate the importance of reasoning in zero-shot stereotype identification based on Vicuna-13B-v1.3. While we do observ…

there are at least a couple of uploads of PROJECT MOON BASE on youtube, which is handy if you have youtube premium. one is stretched wide, like the archive.org copy, but another one is taped from a tv broadcast and has the right aspect ratio, but it has about a minute or so of station identification at the start. be sure to skip the intro if watching that copy! #monsterdon

SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
Sourabh Magare, Anupreeta More, Sunil Choudary
https://arxiv.org/abs/2403.02994

SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which comprises a parallel network based on deep learning. We analyse the Q-transform maps (QT maps) and the Sine-Gaussian maps (SGP-maps) generat…

This https://arxiv.org/abs/2308.05882 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the l…

This https://arxiv.org/abs/2404.01613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Identification of High-dimensional ARMA Models with Binary-Valued Observations
This paper studies system identification of high-dimensional ARMA models with binary-valued observations. Compared with existing quantized identification of ARMA models, this problem is more challenging since the accessible information is much less. Different from the identification of FIR models with binary-valued observations, the prediction of original system output and the parameter both need to be estimated in ARMA models. We propose an online identification algorithm consisting of paramet…

Will I be asked to show an ID when voting?
Some form of identification is requested upon voting in 36 states. In 14 states and Washington, D.C., no form of ID is requested to vote.
The stricter requirements ahead of Tuesday’s primary elections were years in the making.
North Carolina’s law, passed in 2018, was blocked three years later by the state Supreme Court, which ruled the law was “motivated at least in part by an unconstitutional intent to target African American vot…

Map: If you’re voting on Super Tuesday, you’ll need an ID in these states
Eight states have enacted voter ID laws since the 2020 election, lifting the total up to 36. See if your state has new ID requirements.

Deterministic Identification Codes for Fading Channels
Ilya Vorobyev, Christian Deppe, Holger Boche
https://arxiv.org/abs/2404.02723 https://

Deterministic Identification Codes for Fading Channels
Many communication applications incorporate event-triggered behavior, where the conventional Shannon capacity may not effectively gauge performance. Consequently, we advocate for the concept of identification capacity as a more suitable metric for assessing these systems. We consider deterministic identification codes for the Gaussian AWGN, the slow fading, and the fast fading channels with power constraints. We prove lower bounds on capacities for the slow and the fast fading channels with sid…

This https://arxiv.org/abs/2311.10065 has been replaced.
link: https://scholar.google.com/scholar?q=a

Visual Environment Assessment for Safe Autonomous Quadrotor Landing
Autonomous identification and evaluation of safe landing zones are of paramount importance for ensuring the safety and effectiveness of aerial robots in the event of system failures, low battery, or the successful completion of specific tasks. In this paper, we present a novel approach for detection and assessment of potential landing sites for safe quadrotor landing. Our solution efficiently integrates 2D and 3D environmental information, eliminating the need for external aids such as GPS and …

Map: 29 million Americans live under new voter ID laws put in place since 2020 (Catherine Allen/NBC News)
https://www.nbcnews.com/politics/2024-election/voter-identification-states-law-map-rcna137555
http://www.memeorandum.com/240303/p23#a240303p23

This https://arxiv.org/abs/2402.03015 has been replaced.
link: https://scholar.google.com/scholar?q=a

Open-separating dominating codes in graphs
Using dominating sets to separate vertices of graphs is a well-studied problem in the larger domain of identification problems. In such problems, the objective is to choose a suitable dominating set $C$ of a graph $G$ such that the neighbourhoods of all vertices of $G$ have distinct intersections with $C$. Such a dominating and separating set $C$ is often referred to as a \emph{code} in the literature. Depending on the types of dominating and separating sets used, various problems arise under v…

This https://arxiv.org/abs/2402.19280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Mobile Health Text Misinformation Identification Using Mobile Data Mining
More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries to find out whether the mobile health text information sent to peoples devices is correct as smartphones becoming the major information source for people. The proposed method uses various mobile information retrieval and data mining technologies inc…

This https://arxiv.org/abs/2311.00369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach
This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular pro…

HoLens: A Visual Analytics Design for Higher-order Movement Modeling and Visualization
Zezheng Feng, Fang Zhu, Hongjun Wang, Jianing Hao, ShuangHua Yang, Wei Zeng, Huamin Qu
https://arxiv.org/abs/2403.03822

HoLens: A Visual Analytics Design for Higher-order Movement Modeling and Visualization
Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analysis, which depicts only first-order geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements, then extract higher-order patterns from the DAG. However, DAG-based methods heavily rely on the identification of movement keypoints that are challenging for sparse movements and fail to consid…

SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
Sourabh Magare, Anupreeta More, Sunil Choudary
https://arxiv.org/abs/2403.02994

SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which comprises a parallel network based on deep learning. We analyse the Q-transform maps (QT maps) and the Sine-Gaussian maps (SGP-maps) generat…

This https://arxiv.org/abs/2403.14980 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Rheo-SINDy: Finding a Constitutive Model from Rheological Data for Complex Fluids Using Sparse Identification for Nonlinear Dynamics
Rheology plays a pivotal role in understanding the flow behavior of fluids by discovering governing equations that relate deformation and stress, known as constitutive equations. Despite the importance of these equations, current methods for deriving them lack a systematic methodology, often relying on expert knowledge and incurring substantial costs. To overcome this problem, we propose a novel method named {\it Rheo}-SINDy, which employs the sparse identification of nonlinear dynamics (SINDy)…

This https://arxiv.org/abs/2404.05696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data
BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic divers…

This https://arxiv.org/abs/2305.12844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and…

"""
Predictive processing also sheds considerable light on a wide range of typical and atypical forms of human experience. A good starting point is to notice that there are two very broad ways for such processing to go wrong. The first is for the brain to underweight predictions and expectations. This will make it hard to detect faint but predictable patterns in a noisy or ambiguous environment. But the second general way to go wrong is for the brain to overweight expectations. In extreme cases, overweighting results in hallucinations. You seem to see and hear things that aren't there, just because […] they are at some level strongly expected.
Autism spectrum condition was initially thought to reflect a specific imbalance of the first kind — a systematic underweighting of prior expectations. […] Underweighting prior knowledge would make weak or elusive patterns hard to detect, and hard to learn too. Such patterns would include things like facial expressions, intonation, or body language, things that delicately hint, in context, at other people's mental states and attitudes. An imbalance of that kind would also make it very hard to learn these patterns in the first place, and even harder to recognize them in situations that are complicated or ambiguous. Recent evidence casts subtle doubt, however, on this bald initial hypothesis. Rather than weakened predictions, intriguing evidence is emerging that suggests that the core issue involves (not underweighting knowledge-based predictions but) actively overweighting the incoming sensory evidence.
[…]
She doesn't just feel "hunger," instead the more fine-grained specifics of the bodily signals dominate. You are feeling a whole lot of something — but what is it? According to the overweighted sensory information theory, autism spectrum condition individuals constantly encounter an excess of highly detailed and apparently very salient sensory information of this kind, coming from both inside their own body and the outside world. This sensory excess impedes the moment-by-moment identification of the broader context or scenario (in this case, hunger). In other words, the emphasis on every aspect of sensory detail effectively makes it impossible to spot the larger forest for the trees.
"""
(Andy Clark, The Experience Machine: How Our Minds Predict and Shape Reality)
#ActuallyAutistic

Identification of SNPs in genomes using GRAMEP, an alignment-free method based on the Principle of Maximum Entropy
Matheus Henrique Pimenta-Zanon, Andr\'e Yoshiaki Kashiwabara, Andr\'e Lu\'is Laforga Vanzela, Fabricio Martins Lopes
https://arxiv.org/abs/2405.01715

Identification of SNPs in genomes using GRAMEP, an alignment-free method based on the Principle of Maximum Entropy
Advances in high throughput sequencing technologies provide a large number of genomes to be analyzed, so computational methodologies play a crucial role in analyzing and extracting knowledge from the data generated. Investigating genomic mutations is critical because of their impact on chromosomal evolution, genetic disorders, and diseases. It is common to adopt aligning sequences for analyzing genomic variations, however, this approach can be computationally expensive and potentially arbitrary…

Searching for globular clusters in the inner halo of the Circinus galaxy
C. O. Obasi, M. Gomez, D. Minniti, L. D. Baravalle, M. V. Alonso, B. I. Okere
https://arxiv.org/abs/2403.03177 https://arxiv.org/pdf/2403.03177
arXiv:2403.03177v1 Announce Type: new
Abstract: In this study, we search for Globular Clusters (GCs) in the inner halo of the Circinus galaxy using a combination of observational data. Our dataset includes observations from the VISTA Variables in the V\'ia L\'actea Extended Survey (VVVX), optical data from Gaia Release 3 (DR3), and observations from the Dark Energy Camera (DECam). These multiple data sources provide a comprehensive basis for our analysis. Our search was concentrated within a 50 kpc radius from the centre, leading to the identification of 93 sources that met our established criteria. To ensure the reliability of our findings, we conducted multiple examinations for sample contamination. These examinations incorporated tests based on Gaia Astrometric Excess Noise (AEN), the Blue Photometer (BP)/Red Photometer (RP) Excess Factor (BRexcess), as well as comparisons with stellar population models.
This analysis confidently classified 41 sources as genuine GCs, as they successfully passed both the 3$\sigma$ Gaia AEN and BRexcess tests. We used the ISHAPE program to determine the structural parameters (half-light radii) of the GC candidates, with a peak effective radius of 4$\pm$ 0.5 pc. The catalogue mainly consists of bright GCs. Relationships between colour, size, and distance were found in the GC candidates, alongside confirmation of bi-modality in colour distributions.

Which Identities Are Mobilized: Towards an automated detection of social group appeals in political texts
Felicia Riethm\"uller, Julian Dehne, Denise Al-Gaddooa
https://arxiv.org/abs/2405.01904

Which Identities Are Mobilized: Towards an automated detection of social group appeals in political texts
This paper proposes a computational text classification strategy to identify references to social groups in European party manifestos and beyond. Our methodology uses machine learning techniques, including BERT and large language models, to capture group-based appeals in texts. We propose to combine automated identification of social groups using the Mistral-7B-v0.1 Large Language Model with Embedding Space-based filtering to extend a sample of core social groups to all social groups mentioned …

Stable Reduced-Rank VAR Identification
Xinhui Rong, Victor Solo
https://arxiv.org/abs/2403.00237 https://arxiv.org/pdf/2403.00237

Stable Reduced-Rank VAR Identification
The vector autoregression (VAR) has been widely used in system identification, econometrics, natural science, and many other areas. However, when the state dimension becomes large the parameter dimension explodes. So rank reduced modelling is attractive and is well developed. But a fundamental requirement in almost all applications is stability of the fitted model. And this has not been addressed in the rank reduced case. Here, we develop, for the first time, a closed-form formula for an estima…

“That's because kangaroos are completely irrational animals, said David Pickett, Volvo Australia’s technical lead.”
“Carmakers give up on software that avoids kangaroos”
#cars #animals #kangaroos

Carmakers give up on software that avoids kangaroos
Australia is turning to virtual fences to cut down on car-kangaroo impacts.

Best Arm Identification with Resource Constraints
Zitian Li, Wang Chi Cheung
https://arxiv.org/abs/2402.19090 https://arxiv.org/pdf/2…

Best Arm Identification with Resource Constraints
Motivated by the cost heterogeneity in experimentation across different alternatives, we study the Best Arm Identification with Resource Constraints (BAIwRC) problem. The agent aims to identify the best arm under resource constraints, where resources are consumed for each arm pull. We make two novel contributions. We design and analyze the Successive Halving with Resource Rationing algorithm (SH-RR). The SH-RR achieves a near-optimal non-asymptotic rate of convergence in terms of the probabilit…

This https://arxiv.org/abs/2309.16666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Probing Parity Violation in the Stochastic Gravitational Wave Background with Astrometry
Astrometry holds the potential for testing fundamental physics through the effects of the Stochastic Gravitational Wave Background (SGWB) in the $\sim 1-100$ nHz frequency band on precision measurements of stellar positions. Such measurements are complementary to tests made possible by the detection of the SGWB using Pulsar Timing Arrays. Here, the feasibility of using astrometry for the identification of parity-violating signals within the SGWB is investigated. This is achieved by defining and…

This https://arxiv.org/abs/2402.18674 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

QRIS: A Quantitative Reflectance Imaging System for the Pristine Sample of Asteroid Bennu
The Quantitative Reflectance Imaging System (QRIS) is a laboratory-based spectral imaging system constructed to image the sample of asteroid Bennu delivered to Earth by the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft. The system was installed in the OSIRIS-REx cleanroom at NASA's Johnson Space Center to collect data during preliminary examination of the Bennu sample. QRIS uses a 12-bit machine vision camera to measure reflect…

Coarsening in Bent-core Liquid Crystals: Intermediate Splay Bend State en route to the Twist Bend Phase
Nishant Birdi, Nigel B. Wilding, Sanjay Puri, Varsha Banerjee
https://arxiv.org/abs/2405.03189

Coarsening in Bent-core Liquid Crystals: Intermediate Splay Bend State en route to the Twist Bend Phase
We use molecular dynamics simulations to study coarsening dynamics in achiral banana-shaped bent-core liquid crystals following a quench from the high concentration polar smectic (SmX) phase to lower concentrations that favor the exotic twist-bend (TB) phase. Our novel result is the identification of an intermediate splay-bend state emerging prior to the eventual TB phase. The latter coarsens via the annihilation of {\it beta lines} which are analogous to string defects in nematic liquid crysta…

This https://arxiv.org/abs/2311.00369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach
This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular pro…

Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms
Marta Wolinska, Aron Walsh, Antoine Cully
https://arxiv.org/abs/2403.03511

Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms
The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} algorithms to the field of crystal structure prediction. The objective of these algorithms is to identify a diverse set of high-performing solutions, which has been successful in a range of fields such as robotics, architecture and aeronautical engineering. As these methods rely on a high number of evaluations, we employ…

Whispy: Adapting STT Whisper Models to Real-Time Environments
Antonio Bevilacqua, Paolo Saviano, Alessandro Amirante, Simon Pietro Romano
https://arxiv.org/abs/2405.03484

Whispy: Adapting STT Whisper Models to Real-Time Environments
Large general-purpose transformer models have recently become the mainstay in the realm of speech analysis. In particular, Whisper achieves state-of-the-art results in relevant tasks such as speech recognition, translation, language identification, and voice activity detection. However, Whisper models are not designed to be used in real-time conditions, and this limitation makes them unsuitable for a vast plethora of practical applications. In this paper, we introduce Whispy, a system intended …

This https://arxiv.org/abs/2403.07236 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model
It is well known that the relationship between variables at the individual level can be different from the relationship between those same variables aggregated over individuals. In this paper, I develop a methodology to partially identify linear combinations of conditional mean outcomes for individual-level outcomes of interest without imposing parametric assumptions when the researcher only has access to aggregate data. I construct identified sets using an optimization program that allows for …

An Experimentally Benchmarked Geant4 SiPM-Based Scintillator Detector Simulation Platform for Gamma Ray Detection
Lysander Miller, Airlie Chapman, Katie Auchettl, Jeremy M. C. Brown
https://arxiv.org/abs/2403.02668

An Experimentally Benchmarked Geant4 SiPM-Based Scintillator Detector Simulation Platform for Gamma Ray Detection
Radiation detection is vital for naturally occurring radioactive material detection, port and border monitoring, and homeland security operations. SiPM-based single-volume scintillator detectors offer a cost-effective and robust solution that enables timely radioactive site surveys, while retaining accuracy and sensitivity sufficient for isotope identification. Enhanced site surveying can be achieved by optimising scintillator detector design. In this work, a detailed GAGG:Ce, CLLBC:Ce, BGO, Na…

"Respectfully, I’d suggest this may be a great time to dust off his identification of Trump’s six main co-conspirators and roll out indictments against each of them. None were president so, even if Trump did have “total immunity for life,” they are all vulnerable to immediate prosecution."
It's time to hold co-conspirator Ginni Thomas accountable - Alternet.org

It's time to hold co-conspirator Ginni Thomas accountable
Now that Republicans on the Supreme Court have forced a pause in Donald Trump’s federal trial for trying to overthrow the government of the United States and install himself as dictator for life, Jack Smith and his team may have a little time on their hands.Respectfully, I’d suggest this may be a gr...

We all know what possessed the IDF to expedite the process of mass murdering people, but they should really rein themselves in before this gets to the attention of an international tribunal:
“The IDF does not use an artificial intelligence system that identifies terrorist operatives or tries to predict whether a person is a terrorist. Information systems are merely tools for analysts in the target identification process...”
"... they were authorised to kill 'up to 20'…

Learning Stochastic Dynamics from Data
Ziheng Guo, Igor Cialenco, Ming Zhong
https://arxiv.org/abs/2403.02595 https://arxiv.org/pdf/2…

Learning Stochastic Dynamics from Data
We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.

Low-resource speech recognition and dialect identification of Irish in a multi-task framework
Liam Lonergan, Mengjie Qian, Neasa N\'i Chiar\'ain, Christer Gobl, Ailbhe N\'i Chasaide
https://arxiv.org/abs/2405.01293

Low-resource speech recognition and dialect identification of Irish in a multi-task framework
This paper explores the use of Hybrid CTC/Attention encoder-decoder models trained with Intermediate CTC (InterCTC) for Irish (Gaelic) low-resource speech recognition (ASR) and dialect identification (DID). Results are compared to the current best performing models trained for ASR (TDNN-HMM) and DID (ECAPA-TDNN). An optimal InterCTC setting is initially established using a Conformer encoder. This setting is then used to train a model with an E-branchformer encoder and the performance of both ar…

At what point will a meme show up where he's holding up clearly professional protest items that are actually just really common things?
"We have evidence that these outside agitators are providing vehicle identification manuals."

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/2402.19280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Mobile Health Text Misinformation Identification Using Mobile Data Mining
More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries to find out whether the mobile health text information sent to peoples devices is correct as smartphones becoming the major information source for people. The proposed method uses various mobile information retrieval and data mining technologies inc…

This https://arxiv.org/abs/2404.00201 has been replaced.
link: https://scholar.google.com/scholar?q=a

Angular analysis of $B \to K^* e^+ e^-$ in the low-$q^2$ region with new electron identification at Belle
We perform an angular analysis of the $B\to K^* e^+ e^-$ decay for the dielectron mass squared, $q^2$, range of $0.0008$ to $1.1200 ~\text{GeV}^2 /c^4$ using the full Belle data set in the $K^{*0} \to K^+ π^-$ and $K^{*+} \to K_S^0 π^+$ channels, incorporating new methods of electron identification to improve the statistical power of the data set. This analysis is sensitive to contributions from right-handed currents from physics beyond the Standard Model by constraining the Wilson coefficien…

Did you follow the story of John Demjanjuk, a Ukrainian-born USA citizen initially accused of being "Ivan the Terrible" and extradited to Israel to stand trial. He was convicted, but - to their credit - the Israelis became aware of evidence that called the identification into question and repatriated him to USA. The old Israel wasn't all about hatred and revenge - there was also room for integrity.

Data-Driven Superstabilizing Control under Quadratically-Bounded Errors-in-Variables Noise
Jared Miller, Tianyu Dai, Mario Sznaier
https://arxiv.org/abs/2403.03624

Data-Driven Superstabilizing Control under Quadratically-Bounded Errors-in-Variables Noise
The Error-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization problems. This paper performs full-state-feedback stabilizing control of all discrete-time linear systems that are consistent with observed data for which the input and measurement noise obey quadratic bounds. Instances of such quadratic bounds include elementwise norm bounds (at each time sample), energy bounds (acr…

Assholes at Stanford, you say?
https://www.vice.com/en/article/jge377/stanford-smart-toilet-uses-butthole-for-identification
Via @…

This Smart Toilet Identifies You by Your Butthole
A team of researchers at Stanford University developed a prototype smart toilet with four cameras that can identify users based on their "analprint."

Gait-Based Privacy Protection for Smart Wearable Devices
Yu Su, Yongjiao Li, Zhu Cao
https://arxiv.org/abs/2402.15797 https://arxiv.o…

Gait-Based Privacy Protection for Smart Wearable Devices
Smart wearable devices (SWDs) collect and store sensitive daily information of many people. Its primary method of identification is still the password unlocking method. However, several studies have shown serious security flaws in that method, which makes the privacy and security concerns of SWDs particularly urgent. Gait identification is well suited for SWDs because its built-in sensors can provide data support for identification. However, existing gait identification methods have low accurac…

Galaxies in the Zone of Avoidance: Misclassifications using machine learning tools
P. Marchant Cort\'es, J. L. Nilo Castell\'on, M. V. Alonso, L. Baravalle, C. Villal\'on, M. A. Sgr\'o, I. V. Daza-Perilla, M. Soto, F. Milla Castro, D. Minniti, N. Masetti, C. Valotto, M. Lares
https://arxiv.org/abs/2403.03098 https://arxiv.org/pdf/2403.03098
arXiv:2403.03098v1 Announce Type: new
Abstract: Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. In this study, we explore the identification and classification of galaxies in the Zone of Avoidance (ZoA). In particular, we compare our results in the near-infrared with X-ray data. We analize the appearance of the objects classified as galaxies using machine learning by Zhang et al. (2021) in the Galactic disk and make a comparison with the visually confirmed galaxies from the VVV NIRGC (Baravalle et al. (2021). Our analysis, which includes the visual inspection of all sources catalogued as galaxies throughout the Galactic disk using machine learning techniques reveals significant differences. Only 4 galaxies were found in both the near-Infrared and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. The results indicate the difficulty of using machine learning methods for galaxy classification in the ZoA mainly due to the scarce information on galaxies behind the Galactic plane in the training set. They also stress the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.

Learning Nonlinear Dynamics Using Kalman Smoothing
Jacob Stevens-Haas, Yash Bhangale, Aleksandr Aravkin, Nathan Kutz
https://arxiv.org/abs/2405.03154 https…

Learning Nonlinear Dynamics Using Kalman Smoothing
Identifying Ordinary Differential Equations (ODEs) from measurement data requires both fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data. The Sparse Identification of Nonlinear Dynamics (SINDy) method involves a derivative estimation step (and optionally, smoothing) and a sparse regression step on a library of candidate ODE terms. Kalman smoothing is a classical framework for assimilating the measurement data with known noise statistics. Previously, de…

Superconductivity of Bulk Abnormal Magic-stoichiometric Na3Cl Salt Crystals at Normal Pressure
Shuqiang He, Yi-Feng Zheng, Guosheng Shi, Yi-Jie Xiang, Meihui Xiao, Qituan Zhang, Yue-Yu Zhang, Haiping Fang
https://arxiv.org/abs/2405.01570

Superconductivity of Bulk Abnormal Magic-stoichiometric Na3Cl Salt Crystals at Normal Pressure
The identification of new materials with superconducting properties is the pursuit in the realm of superconductivity research. Here, excitedly, we show that the simplest salt daily used can be made a superconductor at normal pressure only by adjusting its stoichiometry of Na and Cl as Na3Cl at normal pressure based on first-principles calculations. This bulk stable abnormal Na-Cl stoichiometric crystal of 3:1, the first 'magic' ratio, includes metallic (Na) atoms in the core as well as hybridiz…

This https://arxiv.org/abs/2401.05235 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Survey on Optimization Studies of Group Centrality Metrics
Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of s…

Drug-target interaction prediction by integrating heterogeneous information with mutual attention network
Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhana, Aoyi Wang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li
https://arxiv.org/abs/2404.03516

Drug-target interaction prediction by integrating heterogeneous information with mutual attention network
Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction. Here, we pr…

This https://arxiv.org/abs/2212.06108 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tandem clustering with invariant coordinate selection
For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly d…

Incommensurate broken-helix and broken-fanlike states in axion insulator candidate EuIn$_{2}$As$_{2}$
Masaki Gen, Yukako Fujishiro, Kazuki Okigami, Satoru Hayami, Kiyohiro Adachi, Daisuke Hashizume, Takashi Kurumaji, Hajime Sagayama, Hironori Nakao, Yoshinori Tokura, Taka-hisa Arima
https://arxiv.org/abs/2403.03022

Incommensurate broken-helix and broken-fanlike states in axion insulator candidate EuIn$_{2}$As$_{2}$
Zintl phase EuIn$_{2}$As$_{2}$ has garnered growing attention as an axion insulator candidate, triggered by the identification of a commensurate double-${\mathbf Q}$ broken-helix state in previous studies, however, its periodicity and symmetry remain subjects of debate. Here, we perform resonant x-ray scattering experiments on EuIn$_{2}$As$_{2}$, revealing an incommensurate nature of the broken-helix state, where both the wave number and the amplitude of the helical modulation exhibit systemati…

Vibrational Entanglement through the Lens of Quantum Information Measures
Nina Glaser, Alberto Baiardi, Annina Z. Lieberherr, Markus Reiher
https://arxiv.org/abs/2405.02031

Vibrational Entanglement through the Lens of Quantum Information Measures
We introduce a quantum information analysis of vibrational wave functions to understand complex vibrational spectra of molecules with strong anharmonic couplings and vibrational resonances. For this purpose, we define one- and two-modal entropies to guide the identification of strongly coupled vibrational modes and to characterize correlations within modal basis sets. We evaluate these descriptors for multi-configurational vibrational wave functions which we calculate with the n-mode vibrationa…

This https://arxiv.org/abs/2306.05735 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Optically addressable spin defects coupled to bound states in the continuum metasurfaces
Van der Waals (vdW) materials, including hexagonal boron nitride (hBN), are layered crystalline solids with appealing properties for investigating light-matter interactions at the nanoscale. hBN has emerged as a versatile building block for nanophotonic structures, and the recent identification of native optically addressable spin defects has opened up exciting possibilities in quantum technologies. However, these defects exhibit relatively low quantum efficiencies and a broad emission spectrum…

Identification and Uses of Deep Learning Backbones via Pattern Mining
Michael Livanos, Ian Davidson
https://arxiv.org/abs/2403.18278 https://

Identification and Uses of Deep Learning Backbones via Pattern Mining
Deep learning is extensively used in many areas of data mining as a black-box method with impressive results. However, understanding the core mechanism of how deep learning makes predictions is a relatively understudied problem. Here we explore the notion of identifying a backbone of deep learning for a given group of instances. A group here can be instances of the same class or even misclassified instances of the same class. We view each instance for a given group as activating a subset of neu…

This https://arxiv.org/abs/2312.11843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification
The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decision-making game theory to augment the social decision-making prowess of AVs in mixed driving environments. This comprehensive framework is …

This https://arxiv.org/abs/2401.05235 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Survey on Optimization Studies of Group Centrality Metrics
Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of s…

Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
Dimitri Staufer, Frank Pallas, Bettina Berendt
https://arxiv.org/abs/2405.01097

Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
Whistleblowing is essential for ensuring transparency and accountability in both public and private sectors. However, (potential) whistleblowers often fear or face retaliation, even when reporting anonymously. The specific content of their disclosures and their distinct writing style may re-identify them as the source. Legal measures, such as the EU WBD, are limited in their scope and effectiveness. Therefore, computational methods to prevent re-identification are important complementary tools …

Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis
Tian Tian, Jiahao Deng
https://arxiv.org/abs/2405.01913

Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis
This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management. The study integrates machine learning techniques like Ridge Regression, Markov chain analysis, and radar charts to optimize North American Crawler Cranes market production processes. Ridge Regression enables growth pattern identification and performance assessment, facilitating comparisons and addressing industry challenges. Markov chain analy…

First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
Enzo Rucci, Gonzalo Tittarelli, Franco Ronchetti, Jorge F. Elgart, Laura Lanzarini, Juan Jos\'e Gagliardino
https://arxiv.org/abs/2403.18631

First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for medicine due to the absence of pathogenic symptoms and the lack of known associated risk factors. Even though some proposals for machine learning models enable the identification of people at risk, the nature of the condition makes it so that a model suitable for one population may not necessarily be suitable for another. In this article, the development and assessment of predictive models to identify people at risk fo…

This https://arxiv.org/abs/2402.18554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Extended Kalman filter -- Koopman operator for tractable stochastic optimal control
The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual control is generally computationally prohibitive. In recent years, however, the use of Koopman operator theory for control applications has been emerging. This paper presents a new reformulation of the stochastic optimal control problem that, employing the Koopman operator, yields a standard LQR problem with the dual …

This https://arxiv.org/abs/2312.11843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification
The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decision-making game theory to augment the social decision-making prowess of AVs in mixed driving environments. This comprehensive framework is …

This https://arxiv.org/abs/2402.17424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature Reduction
Our paper introduces a robust framework for the automated identification of diseases in plant leaf images. The framework incorporates several key stages to enhance disease recognition accuracy. In the pre-processing phase, a thumbnail resizing technique is employed to resize images, minimizing the loss of critical image details while ensuring computational efficiency. Normalization procedures are applied to standardize image data before feature extraction. Feature extraction is facilitated thro…

Superconductivity of Bulk Abnormal Magic-stoichiometric Na3Cl Salt Crystals at Normal Pressure
Shuqiang He, Yi-Feng Zheng, Guosheng Shi, Yi-Jie Xiang, Meihui Xiao, Qituan Zhang, Yue-Yu Zhang, Haiping Fang
https://arxiv.org/abs/2405.01570

Superconductivity of Bulk Abnormal Magic-stoichiometric Na3Cl Salt Crystals at Normal Pressure
The identification of new materials with superconducting properties is the pursuit in the realm of superconductivity research. Here, excitedly, we show that the simplest salt daily used can be made a superconductor at normal pressure only by adjusting its stoichiometry of Na and Cl as Na3Cl at normal pressure based on first-principles calculations. This bulk stable abnormal Na-Cl stoichiometric crystal of 3:1, the first 'magic' ratio, includes metallic (Na) atoms in the core as well as hybridiz…

This https://arxiv.org/abs/2401.05235 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Survey on Optimization Studies of Group Centrality Metrics
Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of s…

Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
Dimitri Staufer, Frank Pallas, Bettina Berendt
https://arxiv.org/abs/2405.01097

Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
Whistleblowing is essential for ensuring transparency and accountability in both public and private sectors. However, (potential) whistleblowers often fear or face retaliation, even when reporting anonymously. The specific content of their disclosures and their distinct writing style may re-identify them as the source. Legal measures, such as the EU WBD, are limited in their scope and effectiveness. Therefore, computational methods to prevent re-identification are important complementary tools …

Formalized Identification Of Key Factors In Safety-Relevant Failure Scenarios
Tim Maurice Julitz, Nadine Schl\"uter, Manuel L\"ower
https://arxiv.org/abs/2402.18194

Formalized Identification Of Key Factors In Safety-Relevant Failure Scenarios
This research article presents a methodical data-based approach to systematically identify key factors in safety-related failure scenarios, with a focus on complex product-environmental systems in the era of Industry 4.0. The study addresses the uncertainty arising from the growing complexity of modern products. The method uses scenario analysis and focuses on failure analysis within technical product development. The approach involves a derivation of influencing factors based on information fr…

Identification and characterization of three-dimensional crack propagation mechanism in the Aluminium alloy AA2024-T3 using high-resolution Digital Image Correlation
Vanessa Sch\"one, Florian Paysan, Eric Breitbarth
https://arxiv.org/abs/2404.01852

Identification and characterization of three-dimensional crack propagation mechanism in the Aluminium alloy AA2024-T3 using high-resolution Digital Image Correlation
Fatigue crack growth is usually a three-dimensional problem, but it is often simplified to two dimensions to reduce complexity. However, this study investigates the relationships between microscopic effects such as crack kinking, shear lips, and plasticity that are present in reality. Therefore, crack propagation tests were carried out on 2-mm-thick MT-160 specimens of AA2024-T3 sheet material in L-T and T-L orientation. Using high-resolution digital image correlation (DIC), the plastic zone wa…

CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Soufyan LakbirBioinformatics group Vrije Universiteit Amsterdam, Translational Gastrointestinal Oncology group Netherlands Cancer Institute, AI Technology for Life group Utrecht University, Caterina BuranelliBioinformatics group Vrije Universiteit Amsterdam, Translational Gastrointestinal Oncology group Netherlands Cancer Institute, Gerrit A. MeijerTranslational Gastrointestinal Oncology group Netherlands C…

CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Background: Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating geno…

This https://arxiv.org/abs/2403.00061 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The Multi-layer Nature of Molecular Gas toward the Cygnus Region
We study the physical properties and 3D distribution of molecular clouds (MCs) toward the Cygnus region using the MWISP CO survey and Gaia DR3 data. Based on Gaussian decomposition and clustering for $\rm ^{13}CO$ lines, over 70% of the fluxes are recovered. With the identification result of $\rm ^{13}CO$ structures, two models are designed to measure the distances of the molecular gas in velocity crowding regions. The distances of more than 200 large $\rm ^{13}CO$ structures are obtained towar…

This https://arxiv.org/abs/2307.14282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Causal Effects in Matching Mechanisms with Strategically Reported Preferences
A growing number of central authorities use assignment mechanisms to allocate students to schools in a way that reflects student preferences and school priorities. However, most real-world mechanisms incentivize students to strategically misreport their preferences. In this paper, we provide an approach for identifying the causal effects of school assignment on future outcomes that accounts for strategic misreporting. Misreporting may invalidate existing point-identification approaches, and we …

This https://arxiv.org/abs/2403.14259 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Minimal covariance realization and system identification algorithm for a class of stochastic linear switched systems with i.i.d. switching
In this paper, we consider stochastic realization theory of Linear Switched Systems (LSS) with i.i.d. switching. We characterize minimality of stochastic LSSs and show existence and uniqueness (up to isomorphism) of minimal LSSs in innovation form. We present a realization algorithm to compute a minimal LSS in innovation form from output and input covariances. Finally, based on this realization algorithm, by replacing true covariances with empirical ones, we propose a statistically consistent s…

Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Sonal Joshi, Thomas Thebaud, Jes\'us Villalba, Najim Dehak
https://arxiv.org/abs/2402.19355

Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Adversarial examples have proven to threaten speaker identification systems, and several countermeasures against them have been proposed. In this paper, we propose a method to detect the presence of adversarial examples, i.e., a binary classifier distinguishing between benign and adversarial examples. We build upon and extend previous work on attack type classification by exploring new architectures. Additionally, we introduce a method for identifying the victim model on which the adversarial a…

Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
Xing Qin, Xu Liu, Shuangge Ma, Mengyun Wu
https://arxiv.org/abs/2403.19994

Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervised and supervised analysis. Compared to unsupervised analysis, supervised approaches potentially hold greater clinical implications. Under the unsupervised analysis framework, several methods focusing on network-based subgroup identification have been developed, offering more comprehensive insights than those restricte…

A Distributed Model Identification Algorithm for Multi-Agent Systems
Vivek Khatana, Chin-Yao Chang, Wenbo Wang
https://arxiv.org/abs/2405.00637 https://

A Distributed Model Identification Algorithm for Multi-Agent Systems
In this study, we investigate agent-based approach for system model identification with an emphasis on power distribution system applications. Departing from conventional practices of relying on historical data for offline model identification, we adopt an online update approach utilizing real-time data by employing the latest data points for gradient computation. This methodology offers advantages including a large reduction in the communication network's bandwidth requirements by minimizing t…

Identification of important nodes in the information propagation network based on the artificial intelligence method
Bin Yuan, Tianbo Song, Jerry Yao
https://arxiv.org/abs/2403.00190

Identification of important nodes in the information propagation network based on the artificial intelligence method
This study presents an integrated approach for identifying key nodes in information propagation networks using advanced artificial intelligence methods. We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) method with the Global Structure Model (GSM), creating a synergistic model that effectively captures both local and global influences within a network. This method is applied across various complex networks, such as social, transportation,…

This https://arxiv.org/abs/2306.08616 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards Automated Identification of Violation Symptoms of Architecture Erosion
Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation drifts away from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One way to achieve this, is through the automated identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 …

CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Soufyan LakbirBioinformatics group Vrije Universiteit Amsterdam, Translational Gastrointestinal Oncology group Netherlands Cancer Institute, AI Technology for Life group Utrecht University, Caterina BuranelliBioinformatics group Vrije Universiteit Amsterdam, Translational Gastrointestinal Oncology group Netherlands Cancer Institute, Gerrit A. MeijerTranslational Gastrointestinal Oncology group Netherlands C…

CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Background: Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating geno…

Unsupervised identification of local atomic environment from atomistic potential descriptors
Luk\'a\v{s} K\'yvala, Pablo Montero De Hijes, Christoph Dellago
https://arxiv.org/abs/2405.01320

Unsupervised identification of local atomic environment from atomistic potential descriptors
Analyzing local structures effectively is key to unraveling the origin of many physical phenomena. Unsupervised algorithms offer an effective way of handling systems in which order parameters are unknown or computationally expensive. By combining novel unsupervised algorithm (Pairwise Controlled Manifold Approximation Projection) with atomistic potential descriptors, we distinguish between various chemical environments with minimal computational overhead. In particular, we apply this method to …

CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation
Vuong D. Nguyen, Shishir K. Shah
https://arxiv.org/abs/2402.14454 h…

CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation
Long-term Person Re-Identification (LRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing, pose, and viewpoint. In this work, we propose CCPA: Contrastive Clothing and Pose Augmentation framework for LRe-ID. Beyond appearance, CCPA captures body shape information which is cloth-invariant using a Relation Graph Attention Network. Training a robust LRe-ID model requires a wide range of clothing variations and expensive cloth labeling,…

The Nearest Graph Laplacian in Frobenius Norm
Kazuhiro Sato, Masato Suzuki
https://arxiv.org/abs/2404.03371 https://arxiv.org/pdf/240…

The Nearest Graph Laplacian in Frobenius Norm
We address the problem of finding the nearest graph Laplacian to a given matrix, with the distance measured using the Frobenius norm. Specifically, for the directed graph Laplacian, we propose two novel algorithms by reformulating the problem as convex quadratic optimization problems with a special structure: one based on the active set method and the other on direct computation of Karush-Kuhn-Tucker (KKT) points. The proposed algorithms can be applied to system identification and model reducti…

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, Mara Tanelli
https://arxiv.org/abs/2403.00578

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general,…

On Identification of Dynamic Treatment Regimes with Proxies of Hidden Confounders
Jeffrey Zhang, Eric Tchetgen Tchetgen
https://arxiv.org/abs/2402.14942 ht…

On Identification of Dynamic Treatment Regimes with Proxies of Hidden Confounders
We consider identification of optimal dynamic treatment regimes in a setting where time-varying treatments are confounded by hidden time-varying confounders, but proxy variables of the unmeasured confounders are available. We show that, with two independent proxy variables at each time point that are sufficiently relevant for the hidden confounders, identification of the joint distribution of counterfactuals is possible, thereby facilitating identification of an optimal treatment regime.

This https://arxiv.org/abs/2401.00264 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Identification of Nonlinear Dynamic Panels under Partial Stationarity
This paper studies identification for a wide range of nonlinear panel data models, including binary choice, ordered response, and other types of limited dependent variable models. Our approach accommodates dynamic models with any number of lagged dependent variables as well as other types of (potentially contemporary) endogeneity. Our identification strategy relies on a partial stationarity condition, which not only allows for an unknown distribution of errors but also for temporal dependencies…

This https://arxiv.org/abs/2401.13605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations
AI is increasingly being used in the public sector, including public security. In this context, the use of AI-powered remote biometric identification (RBI) systems is a much-discussed technology. RBI systems are used to identify criminal activity in public spaces, but are criticised for inheriting biases and violating fundamental human rights. It is therefore important to ensure that such systems are developed in the public interest, which means that any technology that is deployed for public u…

This https://arxiv.org/abs/2404.00354 has been replaced.
link: https://scholar.google.com/scholar?q=a

Follow me: an architecture for user identification and social navigation with a mobile robot
Over the past decade, a multitude of service robots have been developed to fulfill a wide range of practical purposes. Notably, roles such as reception and robotic guidance have garnered extensive popularity. In these positions, robots are progressively assuming the responsibilities traditionally held by human staff in assisting customers. Ensuring the safe and socially acceptable operation of robots in such environments poses a fundamental challenge within the context of Socially Responsible N…

This https://arxiv.org/abs/2307.06675 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions. In this paper, we introduce a novel identification method of such systems, which results in a dynamical model that is able to produce the time-varying output distribution accurately without taking restrictive assumptions on the data-generating pr…

Execution-free Program Repair
Li Huang, Bertrand Meyer, Manuel Oriol
https://arxiv.org/abs/2405.01309 https://arxiv.org/pdf/2405.0130…

Execution-free Program Repair
Automatic program repair usually relies heavily on test cases for both bug identification and fix validation. The issue is that writing test cases is tedious, running them takes much time, and validating a fix through tests does not guarantee its correctness. The novel idea in the Proof2Fix methodology and tool presented here is to rely instead on a program prover, without the need to run tests or to run the program at all. Results show that Proof2Fix finds and fixes significant historical bugs.

This https://arxiv.org/abs/2401.13605 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations
AI is increasingly being used in the public sector, including public security. In this context, the use of AI-powered remote biometric identification (RBI) systems is a much-discussed technology. RBI systems are used to identify criminal activity in public spaces, but are criticised for inheriting biases and violating fundamental human rights. It is therefore important to ensure that such systems are developed in the public interest, which means that any technology that is deployed for public u…

A neural network-based approach to hybrid systems identification for control
Filippo Fabiani, Bartolomeo Stellato, Daniele Masti, Paul J. Goulart
https://arxiv.org/abs/2404.01814 …

A neural network-based approach to hybrid systems identification for control
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We propose a specific neural network (NN) architecture that yields a hybrid system with piecewise-affine dynamics that is differentiable with respect to the network's parameters, thereby enabling the use of derivative-based training procedures. We show that a…

Learning-based model augmentation with LFRs
Jan H. Hoekstra, Chris Verhoek, Roland T\'oth, Maarten Schoukens
https://arxiv.org/abs/2404.01901 https://<…

Learning-based model augmentation with LFRs
Artificial neural networks (ANN) have proven to be effective in dealing with the identification nonlinear models for highly complex systems. To still make use of the prior information available from baseline models derived from, e.g., first-principles (FP), methods have been developed that integrate the prior knowledge into the identification algorithm for the ANN in a variety of methods. These methods have shown better estimation speeds and/or accuracy on unseen data. Among these methods are m…

Finite Sample Analysis for a Class of Subspace Identification Methods
Jiabao He, Ingvar Ziemann, Cristian R. Rojas, H\r{a}kan Hjalmarsson
https://arxiv.org/abs/2404.17331

Finite Sample Analysis for a Class of Subspace Identification Methods
While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are $\mathcal{O}(1/\sqrt{N})$, in line with classical asymptotic results. B…

Nonlinear identification algorithm for online and offline study of pulmonary mechanical ventilation
Diego A. Riva, Carolina A. Evangelista, Paul F. Puleston, Luis Corsiglia, Nahuel Dargains
https://arxiv.org/abs/2402.18709

Nonlinear identification algorithm for online and offline study of pulmonary mechanical ventilation
This work presents an algorithm for determining the parameters of a nonlinear dynamic model of the respiratory system in patients undergoing assisted ventilation. Using the pressure and flow signals measured at the mouth, the model's quadratic pressure-volume characteristic is fit to this data in each respiratory cycle by appropriate estimates of the model parameters. Parameter changes during ventilation can thus also be detected. The algorithm is first refined and assessed using data derived f…

Parameter identification algorithm for a LTV system with partially unknown state matrix
Olga Kozachek, Nikolay Nikolaev, Olga Slita, Alexey Bobtsov
https://arxiv.org/abs/2402.13772

Parameter identification algorithm for a LTV system with partially unknown state matrix
In this paper an adaptive state observer and parameter identification algorithm for a linear time-varying system are developed under condition that the state matrix of the system contains unknown time-varying parameters of a known form. The state vector is observed using only output and input measurements without identification of the unknown parameters. When the state vector estimate is obtained, the identification algorithm is applied to find unknown parameters of the system.