Extended Navigability of Small World Networks: Exact Results and New Insights

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The exploration of electronically excited states and the study of diverse photochemical and photophysical processes are the main goals of molecular electronic spectroscopy. Exact quantum-mechanical simulation of such experiments is, however, beyond current ...

Hydrogen clathrate hydrates are ice-like crystalline substances in which hydrogen molecules are trapped inside polyhedral cages formed by the water molecules. Small cages can host only a single H-2 molecule, while each large cage can be occupied by up to f ...

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

Last-mile delivery in the logistics chain contributes to congestion in urban networks due to frequent stops. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery but relies heavily on the availability of occasional couriers. In ...

We show that including pairing and repulsion into the description of one-dimensional spinless fermions, as in the domain wall theory of commensurate melting or the interacting Kitaev chain, leads, for strong enough repulsion, to a line of critical points i ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

The thesis develops a planning framework for ADNs to achieve their dispatchability by means of ESS allocation while ensuring a reliable and secure operation of ADNs. Second, the framework is extended to include grid reinforcements and ESSs planning. Finall ...

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

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 ...

Empowered by ever-increasing computational power and algorithmic developments, electronic-structure simulations continue to drive research and innovation in materials science. In this context, ab-initio calculations offer an unbiased platform for the under ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

Many physical and chemical reactions are driven by nonadiabatic processes, which imply the breakdown of the celebrated Born-Oppenheimer approximation. To understand these processes, experimentalists employ spectroscopic techniques. However, the obtained re ...

Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel t ...

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

Understanding epidemic propagation in large networks is an important but challenging task, especially since we usually lack information, and the information that we have is often counter-intuitive. An illustrative example is the dependence of the final siz ...

This paper presents a combined framework for power distribution network expansion planning (DNEP) and energy storage systems (ESSs) allocation in active distribution networks (ADNs) hosting large amount of photovoltaic (PV) generations and loads. The propo ...

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

Extended Navigability of Small World Networks: Exact Results and New Insights

Chattez avec Graph Search

Applications of the thawed Gaussian approximation to electronic spectroscopy

Large-cage occupation and quantum dynamics of hydrogen molecules in sII clathrate hydrates

Results on Sparse Integer Programming and Geometric Independent Sets

A continuum approximation approach to the depot location problem in a crowd-shipping system

Eight-vertex criticality in the interacting Kitaev chain

The inductive bias of deep learning: Connecting weights and functions

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Dispatch-aware Optimal Planning of Active Distribution Networks including Energy Storage Systems

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Global, Passive Detection of Connection Tampering

Spectral and thermodynamic properties of interacting electrons with dynamical functionals

Maximum Independent Set: Self-Training through Dynamic Programming

Bayesian reconstruction of memories stored in neural networks from their connectivity

Efficient geometric integrators for the linear and nonlinear time-dependent Schrödinger equation

Unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

Quantization for Distributed Processing and Learning of Structured Data

The Role of Adaptivity in Source Identification with Time Queries

Expansion planning of active distribution networks achieving their dispatchability via energy storage systems

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster