Publications

Plant invasion in Mediterranean Europe: current hotspots and future scenarios

Luigi Cao Pinna

Laure Gallien

Laura J. Pollock

Irena Axmanová

Milan Chytrý

Marco Malavasi

Alicia T. R. Acosta

Juan Antonio Campos

Marta Carboni

The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry an… (see more)d densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders. We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches, and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change. Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far.

2024-03-05

Ecography (published)

doi.org

A Generative Model of Symmetry Transformations

James Urquhart Allingham

Bruno Mlodozeniec

Shreyas Padhy

Javier Antoran

David Scott Krueger

Richard E. Turner

Eric Nalisnick

José Miguel Hernández-Lobato

Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though method… (see more)s incorporating symmetries often require prior knowledge. While recent advancements have been made in learning those symmetries directly from the dataset, most of this work has focused on the discriminative setting. In this paper, we take inspiration from group theoretic ideas to construct a generative model that explicitly aims to capture the data's approximate symmetries. This results in a model that, given a prespecified but broad set of possible symmetries, learns to what extent, if at all, those symmetries are actually present. Our model can be seen as a generative process for data augmentation. We provide a simple algorithm for learning our generative model and empirically demonstrate its ability to capture symmetries under affine and color transformations, in an interpretable way. Combining our symmetry model with standard generative models results in higher marginal test-log-likelihoods and improved data efficiency.

2024-03-04

ArXiv (preprint)

doi.org

arxiv.org

A Generative Model of Symmetry Transformations

James Urquhart Allingham

Bruno Mlodozeniec

Shreyas Padhy

Javier Antoran

David Scott Krueger

Richard E. Turner

Eric Nalisnick

José Miguel Hernández-Lobato

Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though method… (see more)s incorporating symmetries often require prior knowledge. While recent advancements have been made in learning those symmetries directly from the dataset, most of this work has focused on the discriminative setting. In this paper, we take inspiration from group theoretic ideas to construct a generative model that explicitly aims to capture the data's approximate symmetries. This results in a model that, given a prespecified but broad set of possible symmetries, learns to what extent, if at all, those symmetries are actually present. Our model can be seen as a generative process for data augmentation. We provide a simple algorithm for learning our generative model and empirically demonstrate its ability to capture symmetries under affine and color transformations, in an interpretable way. Combining our symmetry model with standard generative models results in higher marginal test-log-likelihoods and improved data efficiency.

2024-03-04

ArXiv (preprint)

doi.org

arxiv.org

MagicClay: Sculpting Meshes With Generative Neural Fields

Amir Barda

Vladimir Kim

Noam Aigerman

Amit H. Bermano

Thibault Groueix

The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial proper… (see more)ties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.

2024-03-04

ArXiv (preprint)

doi.org

arxiv.org

Communicating Study Design Trade-offs in Software Engineering

Martin P. Robillard

Deeksha M. Arya

Neil Ernst

Jin Guo

Maxime Lamothe

Mathieu Nassif

Nicole Novielli

Alexander Serebrenik

Igor Steinmacher

Klaas-Jan Stol

Reflecting on the limitations of a study is a crucial part of the research process. In software engineering studies, this reflection is typi… (see more)cally conveyed through discussions of study limitations or threats to validity. In current practice, such discussions seldom provide sufficient insight to understand the rationale for decisions taken before and during the study, and their implications. We revisit the practice of discussing study limitations and threats to validity and identify its weaknesses. We propose to refocus this practice of self-reflection to a discussion centered on the notion of trade-offs. We argue that documenting trade-offs allows researchers to clarify how the benefits of their study design decisions outweigh the costs of possible alternatives. We present guidelines for reporting trade-offs in a way that promotes a fair and dispassionate assessment of researchers’ work.

2024-03-02

ACM Transactions on Software Engineering and Methodology (published)

doi.org

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

Sina Kiani

Keyhan Sheshyekani

Hanane Dagdougui

This article presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed… (see more) hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

2024-03-01

IEEE Transactions on Transportation Electrification (published)

doi.org

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

Sina Kiani

Keyhan Sheshyekani

Hanane Dagdougui

This article presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed… (see more) hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

2024-03-01

IEEE Transactions on Transportation Electrification (published)

doi.org

FedSwarm: An Adaptive Federated Learning Framework for Scalable AIoT

Haizhou Du

Chengdong Ni

Chaoqian Cheng

Qiao Xiang

Xi Chen

Xue (Steve) Liu

Federated learning (FL) is a key solution for datadriven the Artificial Intelligence of Things (AIoT). Although much progress has been made,… (see more) scalability remains a core challenge for real-world FL deployments. Existing solutions either suffer from accuracy loss or do not fully address the connectivity dynamicity of FL systems. In this article, we tackle the scalability issue with a novel, adaptive FL framework called FedSwarm, which improves system scalability for AIoT by deploying multiple collaborative edge servers. FedSwarm has two novel features: 1) adaptiveness on the number of local updates and 2) dynamicity of the synchronization between edge devices and edge servers. We formulate FedSwarm as a local update adaptation and perdevice dynamic server selection problem and prove FedSwarm‘s convergence bound. We further design a control mechanism consisting of a learning-based algorithm for collaboratively providing local update adaptation on the servers’ side and a bonus-based strategy for spurring dynamic per-device server selection on the devices’ side. Our extensive evaluation shows that FedSwarm significantly outperforms other studies with better scalability, lower energy consumption, and higher model accuracy.

2024-03-01

IEEE Internet of Things Journal (published)

doi.org

Implications of conscious AI in primary healthcare

Dorsai Ranjbari

Samira Abbasgholizadeh-Rahimi

The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of A… (see more)I identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients’, healthcare workers’ and policy-makers’ attitudes towards consciousness of AI systems in primary healthcare settings.

2024-03-01

Family Medicine and Community Health (published)

doi.org

Implications of conscious AI in primary healthcare

Dorsai Ranjbari

Samira Abbasgholizadeh-Rahimi

The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of A… (see more)I identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients’, healthcare workers’ and policy-makers’ attitudes towards consciousness of AI systems in primary healthcare settings.

2024-03-01

Family Medicine and Community Health (published)

doi.org

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

AJung Moon

Samira Abbasgholizadeh-Rahimi

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

doi.org