Article
Mathematics
Sahiba Arora, Jochen Glueck
Summary: This article investigates the maximum principles and uniform anti-maximum principles in PDE theory, combining classical ideas from abstract operator theory with recent ideas from the theory of eventually positive operator semigroups. The necessary and sufficient conditions for (anti-)maximum principles are derived in a very general setting, allowing for the proof or disproof of (anti-)maximum principles for various concrete differential operators. Additionally, the theory provides a clear and concise explanation for the behavior of operators that already satisfy or do not satisfy anti-maximum principles.
JOURNAL OF DIFFERENTIAL EQUATIONS
(2022)
Article
Chemistry, Analytical
Cornelia Meckbach, Sabrina Elsholz, Caroline Siede, Imke Traulsen
Summary: Sensor technologies like GNSS produce vast amounts of animal tracking data with high temporal resolution, and social network construction faces challenges such as noise and appropriate null model determination. Bioinformaticians use methods like average product correction on sequencing data to estimate noise. In a proof of concept on GPS data of heifers, stable results were obtained with up to 30% missing data points, and predicted associations aligned with null model results, with animal activity strongly influencing network structure.
Article
Chemistry, Multidisciplinary
Yinan Chen, Chuanpeng Wang, Dong Li
Summary: Community structure is a prevalent characteristic in social, biological, and technological networks, where nodes can be naturally divided into densely connected groups. Understanding community structure helps in exploring the interactions and associations between elements in the network and uncovering their potential information. However, defining the quality of a community and finding the best partition are challenging due to the complexity of the network.
APPLIED SCIENCES-BASEL
(2022)
Article
Physics, Multidisciplinary
D. Bernal-Casas, J. M. Oller
Summary: This work introduces a mathematical framework based on information geometry to understand the relationship between physical matter and information theory. It explores how information can be represented and distributed over quantum harmonic oscillators, and demonstrates the quantization and lower bound of the estimator's variance. The study also connects quantum harmonic oscillators with Bayes' theorem, showing the relationship between the global probability density function and the sources of information.
Article
Psychology, Multidisciplinary
Gui Wang, Hui Wang, Li Wang
Summary: Based on 774 argumentative writings by Chinese EFL learners, this study examined the ability of Kolmogorov complexity to distinguish different proficiency levels. The results showed that Kolmogorov overall and syntactic complexity were effective in distinguishing proficiency levels, while other metrics were not. Furthermore, Kolmogorov syntactic complexity had weak or no correlation with fine-grained syntactic complexity metrics, suggesting they may address different linguistic features and can complement each other in predicting proficiency levels.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Psychology, Experimental
Sihan Chen, Richard Futrell, Kyle Mahowald
Summary: This study examines systems of spatial deictic words using typological data from over 200 languages. It argues that real languages tend to adopt spatial deictic systems that balance informativity and complexity under certain conditions. The findings are consistent with the cognitive science literature on spatial cognition.
Article
Computer Science, Theory & Methods
Wenjing Zhang, Bo Jiang, Ming Li, Xiaodong Lin
Summary: This paper proposes an optimal centralized privacy-preserving aggregate mobility data release mechanism that minimizes information leakage by releasing perturbed versions of the raw aggregate location. The user-level and aggregate-level privacy leakage is measured using mutual information, and leakage minimization problems are formulated under utility constraints. Reinforcement learning models and an efficient RL algorithm are employed to derive the optimal solutions.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Artificial Intelligence
Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee
Summary: Feature selection is crucial in multi-label classification for building predictive models. Existing methods often disregard cost information associated with considered features. We address the problem of cost-constrained multilabel feature selection, aiming to select a feature subset relevant to multiple labels while adhering to a user-defined budget. Our approach ensures high predictive power without exceeding the specified budget per prediction. We propose a novel criterion combining relevance and cost for feature selection, along with an effective method for determining the trade-off between relevancy and cost. Experimental results demonstrate the superiority of our method over conventional methods on multilabel datasets.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Jin-Young Kim, Sung-Bae Cho
Summary: Algorithmic bias refers to discrimination caused by algorithms, often involving protected features such as gender and race. There is currently no unanimous definition and evaluation metrics for fairness, but this paper introduces three evaluation metrics and seven methods to address algorithmic bias, with pre-processing being widely used but having limitations.
Article
Physics, Multidisciplinary
Andrew D. Back, Janet Wiles
Summary: This paper discusses the importance of identifying probabilistic symbols and introducing symbolization methods for entropy-based models and synthetic languages. New symbolization algorithms are proposed and demonstrated with real-world data.
Article
Telecommunications
Seyedeh Bahereh Hassanpour, Abolfazl Diyanat, Ahmad Khonsari, Seyed Pooya Shariatpanahi, Aresh Dadlani
Summary: In this letter, a mathematical model is proposed to preserve privacy in a network caching system involving a server and a cache-aided end user. An efficient content caching method is presented to maximize privacy preservation while maintaining the average delivery load at a given level. The Pareto optimal nature of the proposed epsilon-constraint optimization approach allows for achieving the maximum privacy degree possible under any given average delivery load.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
History & Philosophy Of Science
Christopher Ranalli, Rene van Woudenberg
Summary: We are limited in our understanding of things. We cannot access all information, sometimes cannot access any information at all, and are unsure if our cognitive abilities are processing all the information we receive.
Article
Computer Science, Hardware & Architecture
Marcel Boehme, Valentin J. M. Manes, Sang Kil Cha
Summary: This paper discusses fuzzing from a learning perspective, using Shannon's entropy to quantify and evaluate its efficiency. By introducing the concept of entropy, the efficiency of fuzzers can be measured in terms of information gained. Experimental results demonstrate that an entropy-based power schedule can significantly improve the efficiency of fuzzing.
COMMUNICATIONS OF THE ACM
(2023)
Article
Computer Science, Information Systems
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani, Hamed Hassani
Summary: This paper proposes an algorithmic framework to solve a decentralized consensus problem in a private manner. By designing the transmission of noisy messages, the proposed method achieves convergence and accuracy while minimizing the leakage of local values.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2023)
Article
Economics
Alje van Dam, Andres Gomez-Lievano, Frank Neffke, Koen Frenken
Summary: This article proposes a statistical framework for quantifying the location and colocation associations of economic activities using information-theoretic measures. The framework is related to existing measures and provides measures of uncertainty and statistical significance.
JOURNAL OF REGIONAL SCIENCE
(2023)
Article
Environmental Sciences
Felix Creutzig, Leila Niamir, Xuemei Bai, Max Callaghan, Jonathan Cullen, Julio Diaz-Jose, Maria Figueroa, Arnulf Grubler, William F. Lamb, Adrian Leip, Eric Masanet, Erika Mata, Linus Mattauch, Jan C. Minx, Sebastian Mirasgedis, Yacob Mulugetta, Sudarmanto Budi Nugroho, Minal Pathak, Patricia Perkins, Joyashree Roy, Stephane de la Rue du Can, Yamina Saheb, Shreya Some, Linda Steg, Julia Steinberger, Diana Urge-Vorsatz
Summary: The study highlights the significant potential of demand-side measures in mitigating greenhouse gas emissions and improving human well-being outcomes, with largely positive effects.
NATURE CLIMATE CHANGE
(2022)
Article
Biochemical Research Methods
Joram J. Keijser, Henning Sprekeler
Summary: The brain's cortical circuits process information through recurrent interactions between excitatory neurons and inhibitory interneurons. This study investigates the specificity of inhibitory feedback in stabilizing the circuit by enabling separate feedback control loops for different synaptic input streams. Using an optimization approach, the researchers found that the resulting circuit can be seen as a neural decoder that reverses the nonlinear biophysical computations within pyramidal cells. This study provides a proof of concept for understanding the structure-function relationships in cortical circuits.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biology
Laura B. Naumann, Joram Keijser, Henning Sprekeler
Summary: This study investigates the establishment of context-invariant representations through feedback processing. The results show that feedback-modulated feedforward neural networks can dynamically generate invariant sensory representations, rather than on the level of individual neurons. This invariance is achieved by dynamically reorienting the manifold of neural activity and maintaining an invariant neural subspace at the population level.
Article
Environmental Sciences
Lynn H. Kaack, Priya L. Donti, Emma Strubell, George Kamiya, Felix Creutzig, David Rolnick
Summary: This article presents a framework to assess the impact of artificial intelligence on greenhouse gas emissions and suggests approaches to mitigate its effects on climate change.
NATURE CLIMATE CHANGE
(2022)
Article
Green & Sustainable Science & Technology
Eva Ayaragarnchanakul, Felix Creutzig, Aneeque Javaid, Nattapong Puttanapong
Summary: Individual motorized vehicles in urban environments are oversupplied and inefficient, but shared mobility offers a more efficient use of vehicles. Commuters value time and fuel costs, but dislike walking, waiting, and searching for parking. Shared taxis have the potential to be used as a door-to-door mode of transportation.
Review
Energy & Fuels
Behnam Zakeri, Katsia Paulavets, Leonardo Barreto-Gomez, Luis Gomez Echeverri, Shonali Pachauri, Benigna Boza-Kiss, Caroline Zimm, Joeri Rogelj, Felix Creutzig, Diana Uerge-Vorsatz, David G. Victor, Morgan D. Bazilian, Steffen Fritz, Dolf Gielen, David L. McCollum, Leena Srivastava, Julian D. Hunt, Shaheen Pouya
Summary: The COVID-19 pandemic and Russia's war on Ukraine have had a negative impact on the global energy industry, causing fluctuations in energy demand, disruptions in energy supply chains, and hindering energy investments. However, these crises also presented opportunities for low-carbon energy transitions, which may have been missed due to short-term policy focus on supporting the fossil fuel industry.
Article
Environmental Sciences
Li-Jing Liu, Hong-Dian Jiang, Qiao-Mei Liang, Felix Creutzig, Hua Liao, Yun-Fei Yao, Xiang-Yan Qian, Zhong-Yuan Ren, Jing Qing, Qi-Ran Cai, Ottmar Edenhofer, Yi-Ming Wei
Summary: The Russia-Ukraine conflict exposes the EU's reliance on fossil fuel imports from Russia. Using a global computable general equilibrium model, this study examines the impact of embargoing Russian fossil fuels on CO2 emissions and GDP. The findings show that embargoes would result in a more than 10% reduction in CO2 emissions in the EU and slight increases in Russia, but both regions would experience GDP losses. Implementing demand-side response within the EU could increase CO2 emission savings and mitigate GDP losses.
NATURE CLIMATE CHANGE
(2023)
Article
Environmental Studies
Ivan Savin, Felix Creutzig, Tatiana Filatova, Joel Foramitti, Theo Konc, Leila Niamir, Karolina Safarzynska, Jeroen van den Bergh
Summary: Ambitious climate mitigation policies face resistance due to social and political factors, partly because they fail to incorporate diverse insights from the social sciences regarding potential policy outcomes. Agent-based models can serve as a powerful tool for integrating elements from different disciplines, enabling a more comprehensive assessment of climate policies.
WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE
(2023)
Article
Green & Sustainable Science & Technology
Charlotte Liotta, Vincent Viguie, Felix Creutzig
Summary: This study uses a spatially explicit monocentric urban economic model to analyze the impact of four representative policies on reducing transport greenhouse gas emissions in 120 cities worldwide. The results show that implementing these policies in all cities can reduce transportation greenhouse gas emissions by 31% in 15 years. However, the consequences of the same policies vary widely between cities, depending on factors such as the policy considered, income level, population growth rate, spatial organization, and existing public transport supply. Applying welfare-increasing policy portfolios can reduce emissions by 22% in 15 years.
NATURE SUSTAINABILITY
(2023)
Article
Environmental Studies
Erik Haites, Paolo Bertoldi, Michael Koenig, Christopher Bataille, Felix Creutzig, Dipak Dasgupta, Stephane de la Rue du Can, Smail Khennas, Yong-Gun Kim, Lars J. Nilsson, Joyashree Roy, Agus Sari
Summary: This paper highlights the challenge of reducing emissions in emissions-intensive, trade-exposed sectors and proposes policy packages to achieve emission reduction targets while minimizing the risk of leakage.
Article
Environmental Sciences
Felix Creutzig, Frank Goetzke, Anjali Ramakrishnan, Marina Andrijevic, Patricia Perkins
Summary: Climate change mitigation is often evaluated based on technologies and policy instruments, but the role of governance and social capital in complex social systems should not be overlooked. This study explores the importance of quality of governance, social capital, and equality as prerequisites for effective climate policies. By analyzing national-level indicators of social systems with Qualitative Comparative Analysis (QCA) and Structural Equation Models (SEM), we find that impartiality in governance is crucial for fostering social capital, interpersonal trust, equality, and effective climate policies such as carbon pricing. Socio-economic inequalities can undermine trust and political engagement, posing challenges to achieving the overarching goal of climate change mitigation. However, evidence suggests that fairly implemented climate policies can contribute to a virtuous cycle by improving the quality of governance and strengthening the capacity to implement strong climate policies. Our findings highlight the significance of impartial governance and social capital in driving effective climate policies.
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS
(2023)
Correction
Energy & Fuels
Kavya Madhu, Stefan Pauliuk, Sumukha Dhathri, Felix Creutzig
Article
Multidisciplinary Sciences
Helmut Haberl, Markus Loew, Alejandro Perez-Laborda, Sarah Matej, Barbara Plank, Dominik Wiedenhofer, Felix Creutzig, Karl-Heinz Erb, Juan Antonio Duro
Summary: The extent and spatial patterns of settlements and infrastructures have a significant impact on the resource demand of national economies worldwide, almost as much as GDP. While built structures at the urban level are known to influence energy demand and CO2 emissions, their role at the national level is often overlooked due to limited data availability. Instead, factors such as GDP are more commonly assessed. In this study, we present national-level indicators to characterize patterns of built structures and find that they are almost equally important as GDP for predicting energy demand and CO2 emissions.
NATURE COMMUNICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Felix Creutzig, Steffen Lohrey, Mercedes Vazquez Franza
Summary: COVID-19-induced confinements have significantly changed human behavior and social norms in street spaces worldwide. This study examines the shifting urban mobility during the confinement period and in response to COVID-19 physical distancing policies. The analysis shows that public transit decreased by 80% during confinement, while individual motorized transport decreased by 64%. Some cities experienced an increase in cycling modal share. COVID-19 and sustainability are closely linked, particularly in terms of health, gender equality, sustainable cities, and climate action. The study reveals both positive and negative outcomes of the confinement, including reduced congestion and improved air quality, but also unhealthy eating habits and domestic violence. Cities around the world have taken measures to provide more space for cyclists and pedestrians, such as pedestrianizing streets and implementing temporary bicycle infrastructure. The findings suggest that increased uptake of cycling has resulted in greenhouse gas emission reductions, with cities with pop-up bicycle lanes achieving greater savings.
ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY
(2022)
Review
Green & Sustainable Science & Technology
Jia-Wei Hu, Felix Creutzig
Summary: This study systematically reviews the current state of shared mobility in China, analyzing the factors shaping shared mobility patterns from the perspectives of consumers, service providers, the government, and the environment. It also discusses governance measures guiding shared mobility and investigates the impact of shared mobility on a potential low-carbon transportation system transition.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
