Tootfinder

The Legal Coup -- In late 2020, a group of conservative movement attorneys set out to build ... (Josh Kovensky/Talking Points Memo)
https://talkingpointsmemo.com/feature/intro-chesebro-docs
http://www.memeorandum.com/240212/p68#a240212p68

A trove of documents details many of the conversations among Trump campaign lawyers, and the theories offered by Kenneth Chesebro, an attorney who worked with the campaign in the months leading up to Jan. 6. 
Within weeks of Trump denouncing the election itself and claiming that he had won, Chesebro and Trump campaign attorneys around him began to explore exotic legal theories in which endless chaos in Congress would prove that the legislature could not certify a winner.
That sta…

New Docs Reveal How Trump Lawyers Sought ‘Chaos’ to Force SCOTUS to Anoint Trump
In late 2020, a group of conservative movement attorneys set out to build a legal pathway by which Donald Trump could stay in power, having lost the election. We’ve known about the outcome of their…

The Legal Coup -- In late 2020, a group of conservative movement attorneys set out to build ... (Josh Kovensky/Talking Points Memo)
https://talkingpointsmemo.com/feature/intro-chesebro-docs
http://www.memeorandum.com/240212/p68#a240212p68

Distributed Estimation by Two Agents with Different Feature Spaces
Aneesh Raghavan, Karl Henrik Johansson
https://arxiv.org/abs/2403.07749 https://<…

Distributed Estimation by Two Agents with Different Feature Spaces
We consider the problem of estimation of a function by two agents (and a fusion center) given local data. Data comprises of samples of an independent variable and the corresponding value of a dependent variable. The agents are given a set of features using which they construct suitable function spaces to formulate and solve the estimation problem. The estimated functions are to be uploaded to a fusion space where they are fused to obtain the system estimate of the mapping and then downloaded by…

Sources: Gemini's image generation tool had a technical fix to reduce bias in its outputs but Google didn't anticipate the overcorrection and wasn't transparent (Bloomberg)
https://www.bloomberg.com/news/articles/20

Google Left in ‘Terrible Bind’ By Pulling AI Feature After Right-Wing Backlash
Google tried using a technical fix to reduce bias in a feature that generates realistic-looking images of people. Instead, it set off a new diversity firestorm.

VAEMax: Open-Set Intrusion Detection based on OpenMax and Variational Autoencoder
Zhiyin Qiu, Ding Zhou, Yahui Zhai, Bo Liu, Lei He, Jiuxin Cao
https://arxiv.org/abs/2403.04193

VAEMax: Open-Set Intrusion Detection based on OpenMax and Variational Autoencoder
Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on system or equipment. This paper aims to develop an open-set intrusion detection model to classify known attacks as well as inferring unknown ones. To achieve this, we employ OpenMax and variational autoencoder to propose a dual detection model, VAEMax. First, we extract flow payload feature based on one-dimensional convolutional neural network. Then, the OpenMax is used to classify flows, dur…

RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Tanya Chowdhury, Yair Zick, James Allan
https://arxiv.org/abs/2405.01848 https://…

RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Several works propose various post-hoc, model-agnostic explanations for the task of ranking, i.e. the task of ordering a set of documents, via feature attribution methods. However, these attributions are seen to weakly correlate and sometimes contradict each other. In classification/regression, several works focus on \emph{axiomatic characterization} of feature attribution methods, showing that a certain method uniquely satisfies a set of desirable properties. However, no such efforts have been…

FMLFS: A federated multi-label feature selection based on information theory in IoT environment
Afsaneh Mahanipour, Hana Khamfroush
https://arxiv.org/abs/2405.00524

FMLFS: A federated multi-label feature selection based on information theory in IoT environment
In certain emerging applications such as health monitoring wearable and traffic monitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount of multi-label datasets. Within these datasets, each instance is linked to a set of labels. The presence of noisy, redundant, or irrelevant features in these datasets, along with the curse of dimensionality, poses challenges for multi-label classifiers. Feature selection (FS) proves to be an effective strategy in enhancing classif…

Distributed and Rate-Adaptive Feature Compression
Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma
https://arxiv.org/abs/2404.02179 https://

Distributed and Rate-Adaptive Feature Compression
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretrained linear regression model, trained on a dataset of the entire uncompressed data. At inference time, the sensors compress their observations and send them to the fusion center through communication-constrained channels, whose rates can change with time. Our goal is to design a fe…

This https://arxiv.org/abs/2404.03004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Jeg havde faktisk aldrig set videoen til denne ikoniske melodi fŸr gemalen lige viste mig den. Det er simpelthen så gakket!
#danskertrut
https://youtu.be/Yx0KBLFG8qc?feature=s

#CoolMastodonClientFeatureOfTheMoment
Client: Toot!
Platform: iOS
Feature: it has a “relax “ option!
It will let you select a various number of relaxation things summer which are interactive so you can just take a break.
I mean, yeah, you could just put your phone down but who’s gonna do that? That’s silly talk.

This https://arxiv.org/abs/2301.10909 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Optimizing Feature Set for Click-Through Rate Prediction
Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both feature and its interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the…

Ah, it was caused by mac-auto-operator-composition-mode (Emacs-Mac port): it seems that “]]” was replaced by a ligature, which then didn't match anymore. So, if one wants to use this feature with a font that supports such ligatures, one should probably set mac-auto-operator-composition-characters to a smaller set of characters to avoid surprises…
#Emacs

Ah, it was caused by mac-auto-operator-composition-mode (Emacs-Mac port): it seems that “]]” was replaced by a ligature, which then didn't match anymore. So, if one wants to use this feature with a font that supports such ligatures, one should probably set mac-auto-operator-composition-characters to a smaller set of characters to avoid surprises…
#Emacs

This https://arxiv.org/abs/2405.00712 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning
Human Activity Recognition (HAR) is a well-studied field with research dating back to the 1980s. Over time, HAR technologies have evolved significantly from manual feature extraction, rule-based algorithms, and simple machine learning models to powerful deep learning models, from one sensor type to a diverse array of sensing modalities. The scope has also expanded from recognising a limited set of activities to encompassing a larger variety of both simple and complex activities. However, there …

FMLFS: A federated multi-label feature selection based on information theory in IoT environment
Afsaneh Mahanipour, Hana Khamfroush
https://arxiv.org/abs/2405.00524

FMLFS: A federated multi-label feature selection based on information theory in IoT environment
In certain emerging applications such as health monitoring wearable and traffic monitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount of multi-label datasets. Within these datasets, each instance is linked to a set of labels. The presence of noisy, redundant, or irrelevant features in these datasets, along with the curse of dimensionality, poses challenges for multi-label classifiers. Feature selection (FS) proves to be an effective strategy in enhancing classif…

Negative-energy spin waves in antiferromagnets for spin-current amplification and analogue gravity
Valentina Errani, Steven H. Schoenmaker, Xiangrong R. Wang, Olena Gomonay, Rembert A. Duine
https://arxiv.org/abs/2405.04996

Negative-energy spin waves in antiferromagnets for spin-current amplification and analogue gravity
Magnonic black holes-analogue event horizons for the spin-wave collective excitations of ordered magnets-can be used for fundamental research, for example for investigating Hawking radiation, but also for technological applications of spin waves. Here we show how to engineer a horizon for spin waves in antiferromagnets, which have the attractive feature of fast magnetization dynamics and linear dispersion relation. We propose a set-up with spatially varying exchange interaction with spin transf…

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data
Daewoo Pak, Jianrui Zhang, Di Wu, Haolei Weng, Chenxi Li
https://arxiv.org/abs/2405.01275

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data
We develop a set of variable selection methods for the Cox model under interval censoring, in the ultra-high dimensional setting where the dimensionality can grow exponentially with the sample size. The methods select covariates via a penalized nonparametric maximum likelihood estimation with some popular penalty functions, including lasso, adaptive lasso, SCAD, and MCP. We prove that our penalized variable selection methods with folded concave penalties or adaptive lasso penalty enjoy the orac…

A single shooting method with approximate Fr\'{e}chet derivative for computing geodesics on the Stiefel manifold
Marco Sutti
https://arxiv.org/abs/2404.04089

A single shooting method with approximate Fréchet derivative for computing geodesics on the Stiefel manifold
This paper shows how to use the shooting method, a classical numerical algorithm for solving boundary value problems, to compute the Riemannian distance on the Stiefel manifold $ \mathrm{St}(n,p) $, the set of $ n \times p $ matrices with orthonormal columns. The proposed method is a shooting method in the sense of the classical shooting methods for solving boundary value problems; see, e.g., Stoer and Bulirsch, 1991. The main feature is that we provide an approximate formula for the Fréchet d…

This https://arxiv.org/abs/2310.18394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Astrophysical and Cosmological Relevance of the High-Frequency Features in the Stochastic Gravitational-Wave Background
The stochastic gravitational-wave background (SGWB) produced by merging neutron stars exhibits a peak in the kHz band. In this paper, we develop a theoretical framework to exploit this distinctive feature through a Markov Chain Monte Carlo analysis using a simulated dataset of SGWB measurements within this frequency range. The aim is to use the SGWB peak as an observable to constrain a set of astrophysical and cosmological parameters that accurately describe the sources of the SGWB. We examine …

To tell Firefox that you're ok with downloading content from http sites, set dom.block_download_insecure = True.
Some sort of "always trust this site" setting would have been nicer than the blanket disable of the feature imho.

This https://arxiv.org/abs/2210.14484 has been replaced.
link: https://scholar.google.com/scholar?q=a

Imputation of missing values in multi-view data
Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This leads to very large quantities of missing data which, especially when combined with high-dimensionality, makes the application of conditional imputation methods computationally infeasible. We introduce a new imputation method based on the existing stacked penal…

Strava has all my runs for the past ten years. My default route exploration tool, which they wrote, is set to show ten mile routes by default. They rolled out the minimum viable feature for the lowest common denominator. A well-intentioned idea completely ruined by poor execution.

Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
Vivek Pandey, Arash Amini, Guangyi Liu, Ufuk Topcu, Qiyu Sun, Kostas Daniilidis, Nader Motee
https://arxiv.org/abs/2403.12279

Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, we establish a crucial connection between graph Laplacian and the importance of features. We show th…

This https://arxiv.org/abs/2306.14572 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks to enhance their performance. In this work, we perform the first evaluation of FINs for biomedical image processing tasks. We begin by training a set of FINs to imitate six common radiomics features, and then compare the performance of larger networks (with and without embedding the FINs) for three experimental tasks: …

Speaker Characterization by means of Attention Pooling
Federico Costa, Miquel India, Javier Hernando
https://arxiv.org/abs/2405.04096 https://

Speaker Characterization by means of Attention Pooling
State-of-the-art Deep Learning systems for speaker verification are commonly based on speaker embedding extractors. These architectures are usually composed of a feature extractor front-end together with a pooling layer to encode variable-length utterances into fixed-length speaker vectors. The authors have recently proposed the use of a Double Multi-Head Self-Attention pooling for speaker recognition, placed between a CNN-based front-end and a set of fully connected layers. This has shown to b…

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games
Ninell Oldenburg, Tan Zhi-Xuan
https://arxiv.org/abs/2402.13399

Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games
A universal feature of human societies is the adoption of systems of rules and norms in the service of cooperative ends. How can we build learning agents that do the same, so that they may flexibly cooperate with the human institutions they are embedded in? We hypothesize that agents can achieve this by assuming there exists a shared set of norms that most others comply with while pursuing their individual desires, even if they do not know the exact content of those norms. By assuming shared no…

The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success
Andr\'e Calero Valdez, Moreen Heine, Thomas Franke, Nicole Jochems, Hans-Christian Jetter, Tim Schrills
https://arxiv.org/abs/2402.14728

The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success
The evolution of AI is set to profoundly reshape the future. The European Union, recognizing this impending prominence, has enacted the AI Act, regulating market access for AI-based systems. A salient feature of the Act is to guard democratic and humanistic values by focusing regulation on transparency, explainability, and the human ability to understand and control AI systems. Hereby, the EU AI Act does not merely specify technological requirements for AI systems. The EU issues a democratic ca…

This https://arxiv.org/abs/2310.18394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Astrophysical and Cosmological Relevance of the High-Frequency Features in the Stochastic Gravitational-Wave Background
The stochastic gravitational-wave background (SGWB) produced by merging neutron stars exhibits a peak in the kHz band. In this paper, we develop a theoretical framework to exploit this distinctive feature through a Markov Chain Monte Carlo analysis using a simulated dataset of SGWB measurements within this frequency range. The aim is to use the SGWB peak as an observable to constrain a set of astrophysical and cosmological parameters that accurately describe the sources of the SGWB. We examine …

Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
Jun Yu, Wangyuan Zhu, Jichao Zhu
https://arxiv.org/abs/2403.11757

Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
In this paper, we present the solution to the Emotional Mimicry Intensity (EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to evaluate the emotional intensity of seed videos by assessing them from a set of predefined emotion categories (i.e., "Admiration," "Amusement," "Determination," "Empathic Pain," "Excitement," and "Joy").

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Associative Memories in the Feature Space
Tommaso Salvatori, Beren Millidge, Yuhang Song, Rafal Bogacz, Thomas Lukasiewicz
https://arxiv.org/abs/2402.10814

Associative Memories in the Feature Space
An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. However, popular memory models fail to retrieve images even when the corruption is mild and easy to detect for a human evaluator. This is because similarities are evaluated in the raw pixel space, which does not contain any semantic information about the images. This problem can be easily solved by computing \emph{simi…

This https://arxiv.org/abs/2310.04286 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate…

This https://arxiv.org/abs/2306.02194 has been replaced.
link: https://scholar.google.com/scholar?q=a

PathFinder: A unified approach for handling paths in graph query languages
Path queries are a core feature of modern graph query languages such as Cypher, SQL/PGQ, and GQL. These languages provide a rich set of features for matching paths, such as restricting to certain path modes (shortest, simple, trail) and constraining the edge labels along the path by a regular expression. In this paper we present PathFinder, a unifying approach for dealing with path queries in all these query languages. PathFinder leverages a compact representation of the (potentially exponentia…

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction
Romina Wild, Emanuela Sozio, Riccardo G. Margiotta, Fabiana Dellai, Angela Acquasanta, Fabio Del Ben, Carlo Tascini, Francesco Curcio, Alessandro Laio
https://arxiv.org/abs/2404.14275

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction
Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features, and automatically select the set of features which is maximally informative for a given clinical purpose, espe…

CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
Aissam Djahnine, Alexandre Popoff, Emilien Jupin-Delevaux, Vincent Cottin, Olivier Nempont, Loic Boussel
https://arxiv.org/abs/2403.18514

CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-based feature extraction and one-class classifiers, or reconstruction-score-based methods such as AEs, GANs and Diffusion models. Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-ba…

Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
Jun Yu, Wangyuan Zhu, Jichao Zhu
https://arxiv.org/abs/2403.11757

Efficient Feature Extraction and Late Fusion Strategy for Audiovisual Emotional Mimicry Intensity Estimation
In this paper, we present the solution to the Emotional Mimicry Intensity (EMI) Estimation challenge, which is part of 6th Affective Behavior Analysis in-the-wild (ABAW) Competition.The EMI Estimation challenge task aims to evaluate the emotional intensity of seed videos by assessing them from a set of predefined emotion categories (i.e., "Admiration," "Amusement," "Determination," "Empathic Pain," "Excitement," and "Joy").

This https://arxiv.org/abs/2310.04286 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate…

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction
Romina Wild, Emanuela Sozio, Riccardo G. Margiotta, Fabiana Dellai, Angela Acquasanta, Fabio Del Ben, Carlo Tascini, Francesco Curcio, Alessandro Laio
https://arxiv.org/abs/2404.14275

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction
Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features, and automatically select the set of features which is maximally informative for a given clinical purpose, espe…

This https://arxiv.org/abs/2403.07749 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Distributed Estimation by Two Agents with Different Feature Spaces
We consider the problem of estimation of a function by two agents (and a fusion center) given local data. Data comprises of samples of an independent variable and the corresponding value of a dependent variable. The agents are given a set of features using which they construct suitable function spaces to formulate and solve the estimation problem. The estimated functions are to be uploaded to a fusion space where they are fused to obtain the system estimate of the mapping and then downloaded by…

Multitask Kernel-based Learning with Logic Constraints
Michelangelo Diligenti, Marco Gori, Marco Maggini, Leonardo Rigutini
https://arxiv.org/abs/2402.10617

Multitask Kernel-based Learning with Logic Constraints
This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervised examples. In particular, we consider a multi-task learning scheme, where multiple unary predicates on the feature space are to be learne…

This https://arxiv.org/abs/2305.11514 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A new family of fourth-order energy-preserving integrators
For Hamiltonian systems with non-canonical structure matrices, a new family of fourth-order energy-preserving integrators is presented. The integrators take a form of a combination of Runge--Kutta methods and continuous-stage Runge--Kutta methods and feature a set of free parameters that offer greater flexibility and efficiency. Specifically, we demonstrate that by carefully choosing these free parameters a simplified Newton iteration applied to the integrators of order four can be parallelizab…

Advancing COVID-19 Detection in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen
https://arxiv.org/abs/2403.11953

Advancing COVID-19 Detection in 3D CT Scans
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt50 as the strong feature extractor, initializing it with pretrained weights which have COVID-19-specific prior knowledge. Our model achieves a Macro F1 Score of 0.94 on the validation set of the 4th COV19D Competi…