Special metrics in hypercomplex geometry

We provide a detailed treatment of special hyperhermitian metrics on hypercomplex manifolds. The quaternionic Gauduchon and quaternionic balanced conditions are investigated at length: we describe their properties and characterize their existence. We also consider strong HKT metrics proving an incompatibility result with balanced hyperhermitian metrics. Additionally, we introduce an Einstein-type condition and determine basic properties and obstructions. Finally, we collect several examples and counterexamples regarding the existence of these types of special metrics as well as two constructions that allow to build new examples.Comment: 50 pages. Comments are welcome, minor change

Peeking inside Sparse Neural Networks using Multi-Partite Graph Representations

Modern Deep Neural Networks (DNNs) have achieved very high performance at the expense of computational resources. To decrease the computational burden, several techniques have proposed to extract, from a given DNN, efficient subnetworks which are able to preserve performance while reducing the number of network parameters. The literature provides a broad set of techniques to discover such subnetworks, but few works have studied the peculiar topologies of such pruned architectures. In this paper, we propose a novel \emph{unrolled input-aware} bipartite Graph Encoding (GE) that is able to generate, for each layer in an either sparse or dense neural network, its corresponding graph representation based on its relation with the input data. We also extend it into a multipartite GE, to capture the relation between layers. Then, we leverage on topological properties to study the difference between the existing pruning algorithms and algorithm categories, as well as the relation between topologies and performance

Large-scale multi-objective influence maximisation with network downscaling

Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several methods have been proposed for tackling the influence maximisation (IM) problem, their runtime typically scales poorly when the network size increases. Here, we propose an original method, based on network downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to solve the IM problem on a reduced scale network, while preserving the relevant properties of the original network. The downscaled solution is then upscaled to the original network, using a mechanism based on centrality metrics such as PageRank. Our results on eight large networks (including two with $\sim$50k nodes) demonstrate the effectiveness of the proposed method with a more than 10-fold runtime gain compared to the time needed on the original network, and an up to $82\%$ time reduction compared to CELF

Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time

The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and it is usually studied by maximizing the influence (spread) and, optionally, optimizing a second objective, such as minimizing the seed set size or maximizing the influence fairness. However, in many practical scenarios multiple aspects of the IM problem must be optimized at the same time. In this work, we propose a first case study where several IM-specific objective functions, namely budget, fairness, communities, and time, are optimized on top of the maximization of influence and minimization of the seed set size. To this aim, we introduce MOEIM (Many-Objective Evolutionary Algorithm for Influence Maximization) a Multi-Objective Evolutionary Algorithm (MOEA) based on NSGA-II incorporating graph-aware operators and a smart initialization. We compare MOEIM in two experimental settings, including a total of nine graph datasets, two heuristic methods, a related MOEA, and a state-of-the-art Deep Learning approach. The experiments show that MOEIM overall outperforms the competitors in most of the tested many-objective settings. To conclude, we also investigate the correlation between the objectives, leading to novel insights into the topic. The codebase is available at https://github.com/eliacunegatti/MOEIM.Comment: To appear in Genetic and Evolutionary Computation Conference (GECCO 24 Companion), July 14 18, 2024, Melbourne, VIC, Australia. ACM, New York, NY, US

Rapid Implementation of Inpatient Telepalliative Medicine Consultations During COVID-19 Pandemic.

As coronavirus disease 2019 cases increase throughout the country and health care systems grapple with the need to decrease provider exposure and minimize personal protective equipment use while maintaining high-quality patient care, our specialty is called on to consider new methods of delivering inpatient palliative care (PC). Telepalliative medicine has been used to great effect in outpatient and home-based PC but has had fewer applications in the inpatient setting. As we plan for decreased provider availability because of quarantine and redeployment and seek to reach increasingly isolated hospitalized patients in the face of coronavirus disease 2019, the need for telepalliative medicine in the inpatient setting is now clear. We describe our rapid and ongoing implementation of telepalliative medicine consultation for our inpatient PC teams and discuss lessons learned and recommendations for programs considering similar care models

Learned Inertial Odometry for Autonomous Drone Racing

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the thrust measurements. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual-inertial odometry as well as the state-of-the-art learned-inertial odometry in estimating the pose of an autonomous racing drone. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight

Learned Inertial Odometry for Autonomous Drone Racing

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the control commands. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual- inertial odometry as well as the state-of-the-art learned-inertial odometry. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight. We will release the code upon acceptance

RESPONSE OF BEETLE COMMUNITIES FIVE YEARS AFTER WILDFIRE IN MEDITERRANEAN FOREST ECOSYSTEMS

Wildfires are one of the most important drivers of forest composition and biodiversity in the Mediterranean Basin. Many studies have demonstrated that fires can affect insect diversity by altering the functional traits of species groups. We examined the 5-year response of beetles to wildfires by assessing patterns of community composition across a gradient from forest interior to forest edge to burnt forest area in Southern Italy. Our objective was to characterize the relationship between distance from the forest edge and occurrence of beetle taxonomic assemblages. We analyzed the composition, similarity, and dominance of ground beetle communities in randomly selected plots located along the forest-to-burned-area gradient. We found a negative relationship between community similarity and distance from the forest edge; moreover, the composition of species assemblages (within each family) became increasingly similar with proximity to the forest edge. As the distance from the forest edge into the burned area became greater the dominance of few species increased, and species composition shifted toward habitat generalists. The results partially support the notion that the differences in beetle communities probably are driven by habitat changes caused by fires, especially for those taxa with many specialist species in feeding and oviposition habitats. Understanding the biological effects of wildfires is necessary prior to design management strategies and policies for counteracting the loss of biodiversity at the global, regional and national levels

Frantz Fanon. Per un approccio decoloniale del sapere

L'elaborato, suddiviso in due parti ha nei suoi intendimenti l'obiettivo di rintracciare le coordinate e la portata del pensiero critico del medico e psichiatra martinicano Frantz Fanon. In particolare, attraverso una preliminare ricostruzione delle vicende che hanno strettamente legato la figura di Fanon a Aimé Césaire e A Jean-Paul Sartre si è posta l'attenzione sulla necessità di ripartire criticamene da un'analisi del suo primo scritto “Pelle nera, maschere bianche”, in sintesi, un'autentica denuncia attraverso una narrazione in parte autobiografica, della condizione di assoggettamento vissuta dall'uomo di colore come diretta conseguenza della retorica e della visione pseudo scientifica dell'uomo bianco come emblema della perfezione e della presunta superiorità razziale. L'analisi si sposta necessariamente anche sulla pubblicazione più nota del medico martinicano, “I dannati della terra” opera oggetto di una lettura che soprattutto negli anni successivi alla morte del medico (Sessanta-Settanta) ha portato ad un distorsione del pensiero e della figura che Fanon ha rappresentato. Tale distorsione ha adombrato un pensiero che soprattutto negli anni Ottanta ha cominciato ad interessare tutta una serie di studiosi che abbracciando ed ispirandosi alla Critica Postcoloniale, ai Cultural Studies e ai Subaltern Studies, hanno (ri)scoperto il pensiero critico del medico martinicano privandolo sostanzialmente dell'alone di fascinazione per l'eversione rivoluzionaria e per il gesto violento di rivolta (frutto soprattutto del non celato sostegno alla causa dell'indipendenza algerina) restituendoci un pensiero funzionale ad una differente interpretazione del periodo coloniale e del nostro presente post-coloniale

A distributional approach to fractional Sobolev spaces and fractional variation: asymptotics II

We continue the study of the space $BV^\alpha(\mathbb R^n)$ of functions with bounded fractional variation in $\mathbb R^n$ and of the distributional fractional Sobolev space $S^{\alpha,p}(\mathbb R^n)$, with $p\in [1,+\infty]$ and $\alpha\in(0,1)$, considered in the previous works arXiv:1809.08575 and arXiv:1910.13419. We first define the space $BV^0(\mathbb R^n)$ and establish the identifications $BV^0(\mathbb R^n)=H^1(\mathbb R^n)$ and $S^{\alpha,p}(\mathbb R^n)=L^{\alpha,p}(\mathbb R^n)$, where $H^1(\mathbb R^n)$ and $L^{\alpha,p}(\mathbb R^n)$ are the (real) Hardy space and the Bessel potential space, respectively. We then prove that the fractional gradient $\nabla^\alpha$ strongly converges to the Riesz transform as $\alpha\to0^+$ for $H^1\cap W^{\alpha,1}$ and $S^{\alpha,p}$ functions. We also study the convergence of the $L^1$-norm of the $\alpha$-rescaled fractional gradient of $W^{\alpha,1}$ functions. To achieve the strong limiting behavior of $\nabla^\alpha$ as $\alpha\to0^+$, we prove some new fractional interpolation inequalities which are stable with respect to the interpolating parameter.Comment: 43 page