Metabolic principles of river basin organization

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Despite their high ecological value, non-perennial streams have received less attention than their perennial counterparts. This doctoral thesis addresses this disparity by advancing knowledge on the dynamics of the drainage density and hydrologic processes ...

The probability of detecting technosignatures (i.e., evidence of technological activity beyond Earth) increases with their longevity, or the time interval over which they manifest. Therefore, the assumed distribution of longevities has some bearing on the ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges

(u,v)

that are not the shortest path between

u

and

v

. In networks with well-separated communities, the metric backbone tends to pre ...

Due to stringent energy and performance constraints, edge AI computing often employs heterogeneous systems that utilize both general-purpose CPUs and accelerators. Analog in-memory computing (AIMC) is a well-known AI inference solution that overcomes compu ...

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling i ...

Electric vehicle charging facilities offer their capacity constrained electric charge and parking to users for a fee. As electric vehicle adoption grows, so too does the potential for excessive resource utilization. In this paper, we study how prices set b ...

We study socio-political systems in representative democracies. Motivated by problems that affect the proper functioning of the system, we build computational methods to answer research questions regarding the phenomena occurring in them. For each phenomen ...

Given two jointly distributed random variables (X,Y), a functional representation of X is a random variable Z independent of Y, and a deterministic function g(⋅,⋅) such that X=g(Y,Z). The problem of finding a minimum entropy functional representation is kn ...

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In ...

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

Metabolic principles of river basin organization

Chattez avec Graph Search

Spatiotemporal Dynamics of Flow and Hydrochemistry in Drainage Networks

Technosignatures Longevity and Lindy's Law

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

On distributional autoregression and iterated transportation

Why the Metric Backbone Preserves Community Structure

Which Coupled is Best Coupled? An Exploration of AIMC Tile Interfaces and Load Balancing for CNNs

Short-term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Resource aware pricing for electric vehicle charging

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Global, Passive Detection of Connection Tampering

Equilibria in Network Constrained Energy Markets

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Caching and Neutrality

Minimax rate for optimal transport regression between distributions

fGOT: Graph Distances Based on Filters and Optimal Transport