Tootfinder

Someone asked today what a 'sense of self' is.
I want to tie this into ideas of relationality. I am a distinct individual, let's start with that. I'm not disavowing that entirely.
But I am also ‘an unschooler’: but this is not an attribute of my education for comparison. It is a community, a network of relationships with people working in similar ways. It's connections to anabaptist traditions and to a mode of interacting with parents (both in concept and my actual parents)
I'm from Colorado. But it's not just a place on the map: it's my relationship to my sense of smell, and my tolerance for cold weather and bright lights. It's a love of dark skies and clear views of the milky way. It's a relationship to knowing I am very very small in a vast universe. This is not a universal explanation of from-Colorado-ness, but it's mine.
I'm transgender, but that's not just I-myself-was-born-in-a-wrong-body, but I have a community and network and sense of belonging outside of myself. I've got embodied knowledge of being a dozen ways that others don't get to experience. It affects and informs my relationship to my communities and my work.
I’m argumentative and sometimes a little arrogant, but this is also because of how I'm connected: there are things I know deep in my body that I cannot explain how I know, but I do. I forget that others see me as a ‘you' and not a 'we' sometimes, and so misunderstandings happen. But to change that would change me in ways I'm not quite willing to grow into, so there is going to be a small callus in how I socialize. And it's fine. It works. I am who I am, but I also don't think about _me_ that much. To think about me is to think of all the connections instead. They're inseparable.

"... the attention pattern of a single layer can be ``nearly randomized'', while preserving the functionality of the network. We also show via extensive experiments that these constructions are not merely a theoretical artifact: even after severely constraining the architecture of the model, vastly different solutions can be reached via standard training."

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

This https://arxiv.org/abs/2403.11893 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Coordination Rates in Multi-Partite Networks
The optimal coordination rates are determined in three primary settings of multi-partite quantum networks, thus characterizing the minimal resources required in order to simulate a joint quantum state among multiple parties. We study the following models: (1) a cascade network with limited entanglement, (2) a broadcast network, which consists of a single sender and two receivers, (3) a multiple-access network with two senders and a single receiver. We establish the necessary and sufficient cond…

Approximation Algorithms for Hop Constrained and Buy-at-Bulk Network Design via Hop Constrained Oblivious Routing
Chandra Chekuri, Rhea Jain
https://arxiv.org/abs/2404.16725

Approximation Algorithms for Hop Constrained and Buy-at-Bulk Network Design via Hop Constrained Oblivious Routing
We consider two-cost network design models in which edges of the input graph have an associated cost and length. We build upon recent advances in hop-constrained oblivious routing to obtain two sets of results. We address multicommodity buy-at-bulk network design in the nonuniform setting. Existing poly-logarithmic approximations are based on the junction tree approach [CHKS09,KN11]. We obtain a new polylogarithmic approximation via a natural LP relaxation. This establishes an upper bound on …

This https://arxiv.org/abs/2306.14142 has been replaced.
link: https://scholar.google.com/scholar?q=a

Estimating Policy Effects in a Social Network with Independent Set Sampling
Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose a modeling strategy that combines existing work on stochastic actor-oriented models (SAOM) with a novel network sampling method based on the identification of independent sets. By assigning respondents f…

Dallas Cowboys, QB Dak Prescott not expecting deal anytime soon https://www.yardbarker.com/nfl/articles/dallas_cowboys_qb_dak_prescott_not_expecting_deal_anytime_soon/s1_17236_40158765

Dallas Cowboys, QB Dak Prescott not expecting deal anytime soon
The Dallas Cowboys and quarterback Dak Prescott are not expected to strike a new deal in the near future, Ian Rapoport of NFL Network reported Tuesday. Both the Cowboys and Prescott have a “mutual understanding of his contract situation,” Rapoport noted. Dallas has not made an offer to Prescott, even as he enters a contract year. “We are where we are, locked and loaded for this year,” team owner Jerry Jones said. Prescott, who signed a four-year, $160 million deal with the Cowboys in 20…

A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
Chunwei Tian, Xuanyu Zhang, Jia Ren, Wangmeng Zuo, Yanning Zhang, Chia-Wen Lin
https://arxiv.org/abs/2402.15704

A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, robustness of obtained models may have challenges in varying scenes. Bigger differences of a network architecture are beneficial to extract more complementary structural information to enhance robustness of an obtained super-resolution model. In this paper, we present a heterogeneous dynamic convolutional network in image super-resolution (HDSRNet). To capture more inf…

SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
Shuangyi Chen, Ashish Khisti
https://arxiv.org/abs/2404.16232 https://

SECO: Secure Inference With Model Splitting Across Multi-Server Hierarchy
In the context of prediction-as-a-service, concerns about the privacy of the data and the model have been brought up and tackled via secure inference protocols. These protocols are built up by using single or multiple cryptographic tools designed under a variety of different security assumptions. In this paper, we introduce SECO, a secure inference protocol that enables a user holding an input data vector and multiple server nodes deployed with a split neural network model to collaboratively …

Union: An Automatic Workload Manager for Accelerating Network Simulation
Xin Wang, Misbah Mubarak, Yao Kang, Robert B. Ross, Zhiling Lan
https://arxiv.org/abs/2403.17036

Union: An Automatic Workload Manager for Accelerating Network Simulation
With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hy…

Reciprocity Calibration of Dual-Antenna Repeaters
Erik G. Larsson, Joao Vieira, P{\aa}l Frenger
https://arxiv.org/abs/2403.17908 https://

Reciprocity Calibration of Dual-Antenna Repeaters
We present a reciprocity calibration method for dual-antenna repeaters in wireless networks. The method uses bi-directional measurements between two network nodes, A and B, where for each bi-directional measurement, the repeaters are configured in different states. The nodes A and B could be two access points in a distributed MIMO system, or they could be a base station and a mobile user terminal, for example. From the calibration measurements, the differences between the repeaters' forward and…

This https://arxiv.org/abs/2203.13847 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cluster Algebras: Network Science and Machine Learning
Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning. Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are represented without identifying by permutation equivalence between clusters an elegant symmetry emerges in the…

This https://arxiv.org/abs/2402.13699 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Explainable Classification Techniques for Quantum Dot Device Measurements
In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information science, which we consider here. While traditional image features are widely utilized in such cases, their use is rapidly being supplanted by Neural Network-based techniques that often sacrifice explainability in exchange for high accuracy. To am…

This https://arxiv.org/abs/2107.11246 has been replaced.
link: https://scholar.google.com/scholar?q=a

Chance Constrained Economic Dispatch Considering the Capability of Network Flexibility Against Renewable Uncertainties
This paper incorporates a continuous-type network flexibility into chance constrained economic dispatch (CCED). In the proposed model, both power generations and line susceptances are continuous variables to minimize the expected generation cost and guarantee a low probability of constraint violation in terms of generations and line flows under renewable uncertainties. From the analytical form of CCED, we figure out the mechanism of network flexibility against uncertainties -- while renewable u…

On the Stability of Learning in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli, Georgios Piliouras
https://arxiv.org/abs/2403.15848

On the Stability of Learning in Network Games with Many Players
Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are less likely to occur as the total number of agents increases. This seemingly prohibits convergence to stable strategies, such as Nash Equilibria, in games with many players. To make progress towards addressing this challenge we study the Q-Learning Dynamics, a classical model for exploration and exploitation in multi-…

This https://arxiv.org/abs/2208.02925 has been replaced.
link: https://scholar.google.com/scholar?q=a

Factor Network Autoregressions
We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (``multilayer network"), which are summarized into a smaller number of network matrices (``network factors") through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors and the coefficients of the FNAR. Our …

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

Limits on Dark Matter Compact Objects implied by Supermagnified Stars in Lensing Clusters
Claudi Vall M\"uller, Jordi Miralda-Escud\'e
https://arxiv.org/abs/2403.16989

Limits on Dark Matter Compact Objects implied by Supermagnified Stars in Lensing Clusters
Supermagnified stars are gravitationally lensed individual stars that are located close to a caustic of a lensing galaxy cluster, and have their flux magnified by a large enough factor (typically ~ 1000) to make them detectable with present telescopes. The maximum magnification is limited by microlensing caused by intracluster stars or other compact objects, which create a network of corrugated critical lines with an angular width proportional to the surface density of microlenses. We consider …

This https://arxiv.org/abs/2209.06751 has been replaced.
link: https://scholar.google.com/scholar?q=a

Urban transportation networks and optimal transport-based infrastructures: similarity and economy of scale
Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure, but rather extracts this directly from a continuous space using only a fe…

Penalized Generative Variable Selection
Tong Wang, Jian Huang, Shuangge Ma
https://arxiv.org/abs/2402.16661 https://arxiv.org/pdf/240…

Penalized Generative Variable Selection
Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors. In such analysis, variable selection can be needed along with estimation/model building. Many of the existing deep network studies that incorporate variable selection have been limited to methodological and numerical developments. In this study, we consider modeling/estimation using the conditional Wasserstein Generative Adversarial networks. Group Lasso penalization is applied for…

This https://arxiv.org/abs/2402.13597 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO Systems
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is…

The Active Communities Plan isn't dead but is now the Biking and Rolling plan and has been delayed "to be responsive to community needs." It will seek to find ways "goals can be reworked to focus on vibrancy, economic vitality and thriving communities." #BikeSF

Distributed Simulation of Large Multi-body Systems
Manas Kale, Paul G. Kry
https://arxiv.org/abs/2403.17261 https://arxiv.org/pdf/240…

Distributed Simulation of Large Multi-body Systems
We present a technique designed for parallelizing large rigid body simulations, capable of exploiting multiple CPU cores within a computer and across a network. Our approach can be applied to simulate both unilateral and bilateral constraints, requiring straightforward modifications to the underlying physics engine. Starting from an approximate partitioning, we identify interface bodies and add them to overlapping sets such that they are simulated by multiple workers. At each timestep, we blend…

This https://arxiv.org/abs/2305.10810 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithms
The problem of designing distributed optimization algorithms that are resilient to Byzantine adversaries has received significant attention. For the Byzantine-resilient distributed optimization problem, the goal is to (approximately) minimize the average of the local cost functions held by the regular (non adversarial) agents in the network. In this paper, we provide a general algorithmic framework for Byzantine-resilient distributed optimization which includes some state-of-the-art algorithms …

Approximation Algorithms for Hop Constrained and Buy-at-Bulk Network Design via Hop Constrained Oblivious Routing
Chandra Chekuri, Rhea Jain
https://arxiv.org/abs/2404.16725

Approximation Algorithms for Hop Constrained and Buy-at-Bulk Network Design via Hop Constrained Oblivious Routing
We consider two-cost network design models in which edges of the input graph have an associated cost and length. We build upon recent advances in hop-constrained oblivious routing to obtain two sets of results. We address multicommodity buy-at-bulk network design in the nonuniform setting. Existing poly-logarithmic approximations are based on the junction tree approach [CHKS09,KN11]. We obtain a new polylogarithmic approximation via a natural LP relaxation. This establishes an upper bound on …

A note on the adjoint method for neural ordinary differential equation network
Pipi Hu
https://arxiv.org/abs/2402.15141 https://arxiv…

A note on the adjoint method for neural ordinary differential equation network
Perturbation and operator adjoint method are used to give the right adjoint form rigourously. From the derivation, we can have following results: 1) The loss gradient is not an ODE, it is an integral and we shows the reason; 2) The traditional adjoint form is not equivalent with the back propagation results. 3) The adjoint operator analysis shows that if and only if the discrete adjoint has the same scheme with the discrete neural ODE, the adjoint form would give the same results as BP does.

This https://arxiv.org/abs/2310.01249 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Probing nontensorial gravitational waves with a next-generation ground-based detector network
In General Relativity, there are only two polarizations for gravitational waves. However, up to six polarizations are possible in a generic metric theory of gravity. Therefore, measuring the polarization content of gravitational waves provides an efficient way to test theories of gravity. We analyze the sensitivity of a next-generation ground-based detector network to nontensorial polarizations. We present our method to localize GW signals in the time-frequency domain and construct the model-in…

This https://arxiv.org/abs/2402.16731 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framew…

This https://arxiv.org/abs/2307.02216 has been replaced.
initial toot: https://mastoxiv.page/@…

Chimera states in neural networks and power systems
Partial, frustrated synchronization and chimera-like states are expected to occur in Kuramoto-like models if the spectral dimension of the underlying graph is low: $d_s < 4$. We provide numerical evidence that this really happens in case of the high-voltage power grid of Europe ($d_s < 2$), a large human connectome (KKI113) and in case of the largest, exactly known brain network corresponding to the fruit-fly (FF) connectome ($d_s < 4$), even though their graph dimensions are much higher, i.e.:…

Mixing cutoff for simple random walks on the Chung-Lu digraph
Alessandra Bianchi, Giacomo Passuello
https://arxiv.org/abs/2402.15356 https://

Mixing cutoff for simple random walks on the Chung-Lu digraph
In this paper, we are interested in the mixing behaviour of simple random walks on inhomogeneous directed graphs. We focus our study on the Chung-Lu digraph, which is an inhomogeneous network that generalizes the Erdős-Rényi digraph. In particular, under the Chung-Lu model, edges are included in the graph independently and according to given Bernoulli laws, so that the average degrees are fixed. To guarantee the a.s. existence of a unique reversible measure, which is implied by the strong con…

This https://arxiv.org/abs/2401.02251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Nonreciprocal Unconventional Photon Blockade with Kerr Magnons
Nonreciprocal devices, allowing to manipulate one-way signals, are crucial to quantum information processing and quantum network. Here we propose a nonlinear cavity-magnon system, consisting of a microwave cavity coupled to one or two yttrium-iron-garnet (YIG) spheres supporting magnons with Kerr nonlinearity, to investigate nonreciprocal unconventional photon blockade. The nonreciprocity originates from the direction-dependent Kerr effect, distinctly different from previous proposals with spin…

This https://arxiv.org/abs/2204.01368 has been replaced.
link: https://scholar.google.com/scholar?q=a

Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
We consider the problem of finding weights and biases for a two-layer fully connected neural network to fit a given set of data points as well as possible, also known as EmpiricalRiskMinimization. Our main result is that the associated decision problem is $\exists\mathbb{R}$-complete, that is, polynomial-time equivalent to determining whether a multivariate polynomial with integer coefficients has any real roots. Furthermore, we prove that algebraic numbers of arbitrarily large degree are requi…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 332 nodes and 18613039 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Enhanced extracellular matrix remodeling due to embedded spheroid fluidization
Tao Zhang, Shabeeb Ameen, Sounok Ghosh, Kyungeun Kim, Minh Thanh, Alison E. Patteson, Mingming Wu, J. M. Schwarz
https://arxiv.org/abs/2403.16784

Enhanced extracellular matrix remodeling due to embedded spheroid fluidization
Tumor spheroids are in vitro three-dimensional, cellular collectives consisting of cancerous cells. Embedding these spheroids in an in vitro fibrous environment, such as a collagen network, to mimic the extracellular matrix (ECM) provides an essential platform to quantitatively investigate the biophysical mechanisms leading to tumor invasion of the ECM. To understand the mechanical interplay between tumor spheroids and the ECM, we computationally construct and study a three-dimensional vertex m…

This https://arxiv.org/abs/2305.01932 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Fully Automatic Neural Network Reduction for Formal Verification
Formal verification of neural networks is essential before their deployment in safety-critical applications. However, existing methods for formally verifying neural networks are not yet scalable enough to handle practical problems involving a large number of neurons. We address this challenge by introducing a fully automatic and sound reduction of neural networks using reachability analysis. The soundness ensures that the verification of the reduced network entails the verification of the origi…

Sandwich GAN: Image Reconstruction from Phase Mask based Anti-dazzle Imaging
Xiaopeng Peng, Erin F. Fleet, Abbie T. Watnik, Grover A. Swartzlander
https://arxiv.org/abs/2402.15919

Sandwich GAN: Image Reconstruction from Phase Mask based Anti-dazzle Imaging
Conventional camera systems are susceptible to the adverse effects of laser dazzle, which may over-saturate an image or cause permanent damage to pixels. To address this problem, we developed an approach combining point spread function engineering whereby a wavefront-coded mask in the pupil plane blurs both the laser and scene, together with a deep neural sandwich network. In addition to protecting the sensor, our approach jointly removes the laser from the scene and reconstructs a satisfactory…

This https://arxiv.org/abs/2307.10583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Deep fused flow and topology features for botnet detection basing on pretrained GCN
Nowadays, botnets have become one of the major threats to cyber security. The characteristics of botnets are mainly reflected in bots network behavior and their intercommunication relationships. Existing botnet detection methods use flow features or topology features individually, which overlook the other type of feature. This affects model performance. In this paper, we propose a botnet detection model which uses graph convolutional network (GCN) to deeply fuse flow features and topology featu…

This https://arxiv.org/abs/2309.11438 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Brain-inspired computing with fluidic iontronic nanochannels
The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy to fabricate tapered microchannels that embed a conducting network of fl…

This https://arxiv.org/abs/2309.11639 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

The latent cognitive structures of social networks
When people are asked to recall their social networks, theoretical and empirical work tells us that they rely on shortcuts, or heuristics. Cognitive Social Structures (CSS) are multilayer social networks where each layer corresponds to an individual's perception of the network. With multiple perceptions of the same network, CSSs contain rich information about how these heuristics manifest, motivating the question, Can we identify people who share the same heuristics? In this work, we propose a …

Cache-Enabled Millimetre-Wave Fluid Antenna Systems: Modeling and Performance
Farshad Rostami Ghadi, Kai-Kit Wong, Kin-Fai Tong, Yangyang Zhang
https://arxiv.org/abs/2403.17265

Cache-Enabled Millimetre-Wave Fluid Antenna Systems: Modeling and Performance
This letter investigates the performance of content caching in a heterogeneous cellular network (HetNet) consisting of fluid antenna system (FAS)-equipped mobile users (MUs) and millimeter-wave (mm-wave) single-antenna small base stations (SBSs), distributed according to the independent homogeneous Poisson point processes (HPPP). In particular, it is assumed that the most popular contents are cached in the SBSs to serve the FAS-equipped MUs requests. To assess the system performance, we derive …

Approximation Algorithms for Network Design in Non-Uniform Fault Models
Chandra Chekuri, Rhea Jain
https://arxiv.org/abs/2403.15547 https://

Approximation Algorithms for Network Design in Non-Uniform Fault Models
The Survivable Network Design problem (SNDP) is a well-studied problem, motivated by the design of networks that are robust to faults under the assumption that any subset of edges up to a specific number can fail. We consider non-uniform fault models where the subset of edges that fail can be specified in different ways. Our primary interest is in the flexible graph connectivity model, in which the edge set is partitioned into safe and unsafe edges. The goal is to design a network that has desi…

This https://arxiv.org/abs/2305.10810 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithms
The problem of designing distributed optimization algorithms that are resilient to Byzantine adversaries has received significant attention. For the Byzantine-resilient distributed optimization problem, the goal is to (approximately) minimize the average of the local cost functions held by the regular (non adversarial) agents in the network. In this paper, we provide a general algorithmic framework for Byzantine-resilient distributed optimization which includes some state-of-the-art algorithms …

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry
https://arxiv.org/abs/2403.15700

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of ``clustering by fast search and find of density peaks'' (CFSFDP) and kernel density estimation (KDE) to improve the selection o…

An image-computable model of speeded decision-making
Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett, Russell A. Poldrack
https://arxiv.org/abs/2403.16382

An image-computable model of speeded decision-making
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAM…

Neural Slicer for Multi-Axis 3D Printing
Tao Liu, Tianyu Zhang, Yongxue Chen, Yuming Huang, Charlie C. L. Wang
https://arxiv.org/abs/2404.15061 https://

Neural Slicer for Multi-Axis 3D Printing
We introduce a novel neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. This advanced slicer can work on models with diverse representations and intricate topology. The approach involves employing neural networks to establish a deformation mapping, defining a scalar field in the space surrounding an input model. Isosurfaces are subsequently extracted from this field to generate curved layers for 3D printing. Creating a differentiable pipe…

Multi-Objective Learning for Deformable Image Registration
Monika Grewal, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten
https://arxiv.org/abs/2402.16658

Multi-Objective Learning for Deformable Image Registration
Deformable image registration (DIR) involves optimization of multiple conflicting objectives, however, not many existing DIR algorithms are multi-objective (MO). Further, while there has been progress in the design of deep learning algorithms for DIR, there is no work in the direction of MO DIR using deep learning. In this paper, we fill this gap by combining a recently proposed approach for MO training of neural networks with a well-known deep neural network for DIR and create a deep learning …

Strategic Network Creation for Enabling Greedy Routing
Julian Berger, Tobias Friedrich, Pascal Lenzner, Paraskevi Machaira, Janosch Ruff
https://arxiv.org/abs/2403.15307

Strategic Network Creation for Enabling Greedy Routing
In this paper, we present the first game-theoretic network creation model that incorporates greedy routing, i.e., the agents in our model are embedded in some metric space and strive for creating a network where all-pairs greedy routing is enabled. In contrast to graph-theoretic shortest paths, our agents route their traffic along greedy paths, which are sequences of nodes where the distance in the metric space to the respective target node gets strictly smaller by each hop. Besides enabling gr…

$(\omega_1, \omega_2)$-Temporal random hyperbolic graphs
Sofoclis Zambirinis, Fragkiskos Papadopoulos
https://arxiv.org/abs/2403.17440 https://

$(ω_1, ω_2)$-Temporal random hyperbolic graphs
We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities, $ω_1$ and $ω_2$. This extension, while conceptually simple, poses analytical challenges involving the Appell $F_1$ series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The in…

Blockchain in a box: A portable blockchain network implementation on Raspberry Pi's
Matija Pi\v{s}korec, Anton Ivaskevich, Said Haji Abukar, Lundrim Azemi, Md Rezuanul Haque, Mostafa Chegenizadeh, Claudio J. Tessone
https://arxiv.org/abs/2404.14282

Blockchain in a box: A portable blockchain network implementation on Raspberry Pi's
In this paper we describe a prototype of a blockchain-in-a-box system which allows users to easily bootstrap the whole Ethereum Proof-of-Work (PoW) network running on multiple Raspberry Pi nodes - an inexpensive modular computers. Users are able to orchestrate the whole blockchain network using a single web based interface, for example they are able to set the topology of the peer-to-peer (P2P) connections and control the initialization parameters. Each Raspberry Pi has a screen attached which …

Sparse Autoregressive Neural Networks for Classical Spin Systems
Indaco Biazzo, Dian Wu, Giuseppe Carleo
https://arxiv.org/abs/2402.16579 https://

Sparse Autoregressive Neural Networks for Classical Spin Systems
Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advancements in generative neural networks have significantly impacted this domain. However, those neural networks are often treated as black boxes, with architectures primarily influenced by data-driven problems in computational science. Addressing this gap, we introduce a novel autoregressive neural network arc…

xAI open sources the base model weights and network architecture of Grok-1, a 314B parameter Mixture-of-Experts model, under the Apache 2.0 license (x.ai)
https://x.ai/blog/grok-os

To the women in technology, if you have been inspired, made a career connection, leveled up your technical skills, or expanded your network through this movement,
we encourage you to keep paying that forward.
The world needs women and diverse perspectives at the helm of tech as a critical force that shapes our world every day. Please, keep going. 
As we embark on the process of winding down operations,
we are committed to ensuring a smooth transition and fulfilling a…

Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
David Gamba, Yulin Yu, Yuan Yuan, Grant Schoenebeck, Daniel M. Romero
https://arxiv.org/abs/2402.15683

Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of ``breakdown" in communication among the remaini…

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Christina Giannoula, Peiming Yang, Ivan Fernandez Vega, Jiacheng Yang, Yu Xin Li, Juan Gomez Luna, Mohammad Sadrosadati, Onur Mutlu, Gennady Pekhimenko
https://arxiv.org/abs/2402.16731

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framew…

On 8-9 April 2024 the Special Interest Group on Next Generation #Networks (SIG-NGN) workshop will take place at INFN in Catania, Sicily. In collaboration with #LHCONE, topics will include network #architecture

14th SIG-NGN Meeting - INFN Catania IT (Hybrid meeting)
SIG-NGN will be holding its 14th meeting as a in person workshop on Monday, 8-9 April 2024.  The meeting will be hosted jointly with the LHCONE meeting in Catania, Sicilly (https://indico.cern.ch/event/1349135/).Mark your calendar for the 14th SIG-NGN meeting - Catania, 8-9 April 2024Following a fruitful co-location of meetings in Prague in April 2023, SIG-NGN and the LHC networking group will once again co-locate face-to-face meetings this coming spring. This time we are adding networking...

Graph neural network coarse-grain force field for the molecular crystal RDX
Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan
https://arxiv.org/abs/2403.15266 …

Graph neural network coarse-grain force field for the molecular crystal RDX
Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable appro…

This https://arxiv.org/abs/2403.05434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's subword vocabulary. We show t…

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry
https://arxiv.org/abs/2403.15700

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of ``clustering by fast search and find of density peaks'' (CFSFDP) and kernel density estimation (KDE) to improve the selection o…

Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks
Mingcheng He (Sherman), Huaqing Wu (Sherman), Conghao Zhou (Sherman), Xuemin (Sherman), Shen
https://arxiv.org/abs/2404.13158

Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks
Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we propose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimi…

Breaking consensus in kinetic opinion formation models on graphons
Bertram D\"uring, Jonathan Franceschi, Marie-Therese Wolfram, Mattia Zanella
https://arxiv.org/abs/2403.14431

Breaking consensus in kinetic opinion formation models on graphons
In this work we propose and investigate a strategy to prevent consensus in kinetic models for opinion formation. We consider a large interacting agent system, and assume that agent interactions are driven by compromise as well as self-thinking dynamics and also modulated by an underlying static social network. This network structure is included using so-called graphons, which modulate the interaction frequency in the corresponding kinetic formulation. We then derive the corresponding limiting F…

Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
Chen Yiwei, Tang Chao, Aghabiglou Amir, Chu Chung San, Wiaux Yves
https://arxiv.org/abs/2403.17905

Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, are not affected by this limitation and have also proven effective, but their highly iterative nature also affects scalability. To…

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
David J. Schodt, Ryan Brown, Michael Merritt, Samuel Park, Delsin Menolascino, Mark A. Peot
https://arxiv.org/abs/2402.14532

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as outputs of the BNN in addition to the predictive means, however doing so may necessitate adding more learnable parameters to the network. In this work, we demonstrate that both the heteroscedastic aleatoric and epistemic variance can be embedded into the variances of learned BNN parameters, improving predictive perfo…

This https://arxiv.org/abs/2307.09470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
We study a new class of Markov games, \emph(multi-player) zero-sum Markov Games} with \emph{Networked separable interactions} (zero-sum NMGs), to model the local interaction structure in non-cooperative multi-agent sequential decision-making. We define a zero-sum NMG as a model where {the payoffs of the auxiliary games associated with each state are zero-sum and} have some separable (i.e., polymatrix) structure across the neighbors over some interaction network. We first identify the necessary …

Distance-Based Hierarchical Cutting of Complex Networks with Non-Preferential and Preferential Choice of Seeds
Alexandre Benatti, Luciano da F. Costa
https://arxiv.org/abs/2403.17713

Distance-Based Hierarchical Cutting of Complex Networks with Non-Preferential and Preferential Choice of Seeds
Graphs and complex networks can be successively separated into connected components associated to respective seed nodes, therefore establishing a respective hierarchical organization. In the present work, we study the properties of the hierarchical structure implied by distance-based cutting of Erdős-Rényi, Barabási-Albert, and a specific geometric network. Two main situations are considered regarding the choice of the seeds: non-preferential and preferential to the respective node degree. A…

It’s absolutely wild that we’ve allowed a CCP-controlled fully-algorithmic social network on virtually all phones while our apps are illegal there. That’s just as absurd as having allowed Soviet propaganda channels on cable TV in the Cold War. It’s even wilder that they found enough useful idiots to defend them using Soviet-style whataboutisms. Since Uyghur concentration camps aren’t enough, guess we have to wait for them to invade Taiwan for ppl to realize they’ve been played. (1/2)

Neural network representation of quantum systems
Koji Hashimoto, Yuji Hirono, Jun Maeda, Jojiro Totsuka-Yoshinaka
https://arxiv.org/abs/2403.11420 https://…

Neural network representation of quantum systems
It has been proposed that random wide neural networks near Gaussian process are quantum field theories around Gaussian fixed points. In this paper, we provide a novel map with which a wide class of quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters. Our simple idea is to use the universal approximation theorem of neural networks to generate arbitrary paths in the Feynman's path integral. The map can be applied to interac…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 232 nodes and 1283194 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

An image-computable model of speeded decision-making
Paul I. Jaffe, Gustavo X. Santiago-Reyes, Robert J. Schafer, Patrick G. Bissett, Russell A. Poldrack
https://arxiv.org/abs/2403.16382

An image-computable model of speeded decision-making
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAM…

This https://arxiv.org/abs/2401.16321 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers. Typically, for large companies, another important part of the bills are the costs related …

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Tianfu Wang, Qilin Fan, Chao Wang, Long Yang, Leilei Ding, Nicholas Jing Yuan, Hui Xiong
https://arxiv.org/abs/2404.12633

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

Traditional Transformation Theory Guided Model for Learned Image Compression
Zhiyuan Li, Chenyang Ge, Shun Li
https://arxiv.org/abs/2402.15744 https://

Traditional Transformation Theory Guided Model for Learned Image Compression
Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance and speed at medium and high bitrates, while research on ultra low bitrates is limited. In this work, we propose a ultra low bitrates enhanced invertible encoding network guided by traditional transformation theory, experiments show that our codec outperforms existing methods in both compression and reconstruction pe…

Validity of annealed approximation in a high-dimensional system
Jaegon Um, Hyunsuk Hong, Hyunggyu Park
https://arxiv.org/abs/2403.14984 https://

Validity of annealed approximation in a high-dimensional system
This study investigates the suitability of the annealed approximation in high-dimensional systems characterized by dense networks with quenched link disorder, employing models of coupled oscillators. We demonstrate that dynamic equations governing dense-network systems converge to those of the complete-graph version in the thermodynamic limit, where link disorder fluctuations vanish entirely. Consequently, the annealed-network systems, where fluctuations are attenuated, also exhibit the same dy…

This https://arxiv.org/abs/2302.07193 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Quantizing graphs, one way or two?
Quantum graphs were introduced to model free electrons in organic molecules using a self-adjoint Hamiltonian on a network of intervals. A second graph quantization describes wave propagation on a graph by specifying scattering matrices at the vertices. A question that is frequently raised is the extent to which these models are the same or complementary. In particular, are all energy independent unitary vertex scattering matrices associated with a self-adjoint Hamiltonian? Here we review result…

A Constrained Deep Reinforcement Learning Optimization for Reliable Network Slicing in a Blockchain-Secured Low-Latency Wireless Network
Xin Hao, Phee Lep Yeoh, Changyang She, Yao Yu, Branka Vucetic, Yonghui Li
https://arxiv.org/abs/2403.09693

A Constrained Deep Reinforcement Learning Optimization for Reliable Network Slicing in a Blockchain-Secured Low-Latency Wireless Network
Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain man…

This https://arxiv.org/abs/2403.14372 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilitate the application of distributed control techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem in the presence of renewable energy sources (RESs), and energy storage systems (ESSs). RESs are known to cause instability in power networks due to their inertia-less and intermittent characterist…

A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
G\"orkem Berkay Ko\c{c}, Berk \c{C}ilo\u{g}lu, Metin Ozturk, Halim Yanikomeroglu
https://arxiv.org/abs/2402.13096

A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in such a way that we attempt to meet the latency requirements of users with a best-effort strategy. To this end, a novel, simple, and lightwei…

Saving proof-of-work by hierarchical block structure
Valdemar Melicher
https://arxiv.org/abs/2404.14958 https://arxiv.org/pdf/2404.14…

Saving proof-of-work by hierarchical block structure
We argue that the current POW based consensus algorithm of the Bitcoin network suffers from a fundamental economic discrepancy between the real world transaction (txn) costs incurred by miners and the wealth that is being transacted. Put simply, whether one transacts 1 satoshi or 1 bitcoin, the same amount of electricity is needed when including this txn into a block. The notorious Bitcoin blockchain problems such as its high energy usage per txn or its scalability issues are, either partially …

This https://arxiv.org/abs/2402.13001 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

A unifying primary framework for quantum graph neural networks from quantum graph states
Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.

Fast Broadcast in Highly Connected Networks
Shashwat Chandra, Yi-Jun Chang, Michal Dory, Mohsen Ghaffari, Dean Leitersdorf
https://arxiv.org/abs/2404.12930

Fast Broadcast in Highly Connected Networks
We revisit the classic broadcast problem, wherein we have $k$ messages, each composed of $O(\log{n})$ bits, distributed arbitrarily across a network. The objective is to broadcast these messages to all nodes in the network. In the distributed CONGEST model, a textbook algorithm solves this problem in $O(D+k)$ rounds, where $D$ is the diameter of the graph. While the $O(D)$ term in the round complexity is unavoidable$\unicode{x2014}$given that $Ω(D)$ rounds are necessary to solve broadcast in a…

This https://arxiv.org/abs/2305.10540 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Generalization Bounds for Neural Belief Propagation Decoders
Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to improve upon classical decoding algorithms. In this paper, we investigate the generalization capabilities of NBP decoders. Specifically, the …

This https://arxiv.org/abs/2110.05282 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal Gradient Tracking for Decentralized Optimization
In this paper, we focus on solving the decentralized optimization problem of minimizing the sum of $n$ objective functions over a multi-agent network. The agents are embedded in an undirected graph where they can only send/receive information directly to/from their immediate neighbors. Assuming smooth and strongly convex objective functions, we propose an Optimal Gradient Tracking (OGT) method that achieves the optimal gradient computation complexity $O\left(\sqrtκ\log\frac{1}ε\right)$ and th…

A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
G\"orkem Berkay Ko\c{c}, Berk \c{C}ilo\u{g}lu, Metin Ozturk, Halim Yanikomeroglu
https://arxiv.org/abs/2402.13096

A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks
This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in such a way that we attempt to meet the latency requirements of users with a best-effort strategy. To this end, a novel, simple, and lightwei…

Graph Neural Network for Crawling Target Nodes in Social Networks
Kirill Lukyanov, Mikhail Drobyshevskiy, Danil Shaikhelislamov, Denis Turdakov
https://arxiv.org/abs/2403.13865

Graph Neural Network for Crawling Target Nodes in Social Networks
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this purpose and show they are competitive to traditional classifiers and are better for individual cases. Additionally we suggest a training sa…

This https://arxiv.org/abs/2210.05988 has been replaced.
link: https://scholar.google.com/scholar?q=a

CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We h…

Quantum Coordination Rates in Multi-Partite Networks
Hosen Nator, Uzi Pereg
https://arxiv.org/abs/2403.11893 https://arxiv.org/pdf/24…

Quantum Coordination Rates in Multi-Partite Networks
The optimal coordination rates are determined in three primary settings of multi-partite quantum networks, thus characterizing the minimal resources required in order to simulate a joint quantum state among multiple parties. We study the following models: (1) a cascade network with limited entanglement, (2) a broadcast network, which consists of a single sender and two receivers, (3) a multiple-access network with two senders and a single receiver. We establish the necessary and sufficient cond…

This https://arxiv.org/abs/2205.11043 has been replaced.
link: https://scholar.google.com/scholar?q=a

Coordinated Vehicle Platooning with Fixed Routes: Efficient Time Discretization, Strengthened Formulation and Approximation Algorithms
We consider the coordinated vehicle platooning problem over a road network with time constraints while the routes of vehicles are given. The problem is to coordinate the departure time of each vehicle to enable platoon formation hence maximizing the total fuel saving. All problem instances can be divided into two categories: the tree instances and the cycle-containing instances based on the graph formed by the routes of all vehicles in the system using a preprocessing algorithm. For tree instan…

This https://arxiv.org/abs/2404.12902 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Urban topology and dynamics can assess green areas importance
Green areas are a crucial element in evolution of a city, contributing to improve citizens' life, to reduce effects of climate change, and to make possible the survival of other species in urban areas. Unfortunately, the above effects are difficult to assess quantitatively for regulators, stakeholders and experts, making troublesome the planning of city development. Here we present a method to estimate the impact of these areas in the city life based on the network topology of the city itself a…

Access-Point to Access-Point Connectivity for PON-based OWC Spine and Leaf Data Centre Architecture
Abrar S. Alhazmi, Sanaa H. Mohamed, Ahmad Qidan, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani
https://arxiv.org/abs/2404.14143

Access-Point to Access-Point Connectivity for PON-based OWC Spine and Leaf Data Centre Architecture
In this paper, we propose incorporating Optical Wireless Communication (OWC) and Passive Optical Network (PON) technologies into next generation spine-and-leaf Data Centre Networks (DCNs). In this work, OWC systems are used to connect the Data Centre (DC) racks through Wavelength Division Multiplexing (WDM) Infrared (IR) transceivers. The transceivers are placed on top of the racks and at distributed Access Points (APs) in the ceiling. Each transceiver on a rack is connected to a leaf switch th…

What makes a small-world network? Leveraging machine learning for the robust prediction and classification of networks
Raima Carol Appaw, Nicholas Fountain-Jones, Michael A. Charleston
https://arxiv.org/abs/2403.13215

What makes a small-world network? Leveraging machine learning for the robust prediction and classification of networks
The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erdös-Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulate…

This https://arxiv.org/abs/2311.16188 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Bell pair extraction using graph foliage techniques
Future quantum networks can facilitate communication of quantum information between various nodes. We are particularly interested in whether multiple pairs can communicate simultaneously across a network. Quantum networks can be represented with graph states, and producing communication links amounts to performing certain quantum operations on graph states. This problem can be formulated in a graph-theoretic sense with the (Bell) vertex-minor problem. We discuss the recently introduced foliage …

This https://arxiv.org/abs/2402.11454 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Addressing Internally-Disconnected Communities in Leiden and Louvain Community Detection Algorithms
Community detection is the problem of identifying densely connected clusters of nodes within a network. The Louvain algorithm is a widely used method for this task, but it can produce communities that are internally disconnected. To address this, the Leiden algorithm was introduced. However, our analysis and empirical observations indicate that the Leiden algorithm still identifies disconnected communities, albeit to a lesser extent. To mitigate this issue, we propose two new parallel algorithm…

Stability of P2P Networks Under Greedy Peering (Full Version)
Lucianna Kiffer, Rajmohan Rajaraman
https://arxiv.org/abs/2402.14666 https://

Stability of P2P Networks Under Greedy Peering (Full Version)
Major cryptocurrency networks have relied on random peering choice rules for making connections in their peer-to-peer networks. Generally, these choices have good properties, particularly for open, permissionless networks. Random peering choices however do not take into account that some actors may choose to optimize who they connect to such that they are quicker to hear about information being propagated in the network. In this paper, we explore the dynamics of such greedy strategies. We study…

A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
A. Riccardi, L. Laurenti, B. De Schutter
https://arxiv.org/abs/2403.14372

A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilitate the application of distributed control techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem in the presence of renewable energy sources (RESs), and energy storage systems (ESSs). RESs are known to cause instability in power networks due to their inertia-less and intermittent characterist…

This https://arxiv.org/abs/2309.01604 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimal Real Time Drone Path Planning for Harvesting Information from a Wireless Sensor Network
We consider a remote sensing system in which fixed sensors are placed in a region, and a drone flies over the region to collect information from cluster heads. We assume that the drone has a fixed maximum range, and that the energy consumption for information transmission from the cluster heads increases with distance according to a power law. Given these assumptions, we derive local optimum conditions for a drone path that either minimizes the total energy or the maximum energy required by the…

An SEIR network epidemic model with manual and digital contact tracing allowing delays
Dongni Zhang, Tom Britton
https://arxiv.org/abs/2402.13392 https://<…

An SEIR network epidemic model with manual and digital contact tracing allowing delays
We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately not…

Experimental Quantum Byzantine Agreement on a Three-User Quantum Network with Integrated Photonics
Xu Jing, Cheng Qian, Chen-Xun Weng, Bing-Hong Li, Zhe Chen, Chen-Quan Wang, Jie Tang, Xiao-Wen Gu, Yue-Chan Kong, Tang-Sheng Chen, Hua-Lei Yin, Dong Jiang, Bin Niu, Liang-Liang Lu
https://arxiv.org/abs/2403.11441

Experimental Quantum Byzantine Agreement on a Three-User Quantum Network with Integrated Photonics
Quantum communication networks are crucial for both secure communication and cryptographic networked tasks. Building quantum communication networks in a scalable and cost-effective way is essential for their widespread adoption, among which a stable and miniaturized high-quality quantum light source is a key component. Here, we establish a complete polarization entanglement-based fully connected network, which features an ultrabright integrated Bragg reflection waveguide quantum source, managed…

This https://arxiv.org/abs/2402.19212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Deep Reinforcement Learning: A Convex Optimization Approach
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show …

Macroscopic pricing schemes for the utilization of pool ride-hailing vehicles in bus lanes
Lynn Fayed, Gustav Nilsson, Nikolas Geroliminis
https://arxiv.org/abs/2403.13651

Macroscopic pricing schemes for the utilization of pool ride-hailing vehicles in bus lanes
With the increasing popularity of ride-hailing services, new modes of transportation are having a significant impact on the overall performance of transportation networks. As a result, there is a need to ensure that both the various transportation alternatives and the spatial network resources are used efficiently. In this work, we analyze a network configuration where part of the urban transportation network is devoted to dedicated bus lanes. Apart from buses, we let pool ride-hailing trips us…

A Fisher Information based Receding Horizon Control Method for Signal Strength Model Estimation
Yancheng Zhu, Sean B. Andersson
https://arxiv.org/abs/2402.11483

A Fisher Information based Receding Horizon Control Method for Signal Strength Model Estimation
This paper considers the problem of localizing a set of nodes in a wireless sensor network when both their positions and the parameters of the communication model are unknown. We assume that a single agent moves through the environment, taking measurements of the Received Signal Strength (RSS), and seek a controller that optimizes a performance metric based on the Fisher Information Matrix (FIM). We develop a receding horizon (RH) approach that alternates between estimating the parameter values…

Learning Stable and Passive Neural Differential Equations
Jing Cheng, Ruigang Wang, Ian R. Manchester
https://arxiv.org/abs/2404.12554 https://

Learning Stable and Passive Neural Differential Equations
In this paper, we introduce a novel class of neural differential equation, which are intrinsically Lyapunov stable, exponentially stable or passive. We take a recently proposed Polyak Lojasiewicz network (PLNet) as an Lyapunov function and then parameterize the vector field as the descent directions of the Lyapunov function. The resulting models have a same structure as the general Hamiltonian dynamics, where the Hamiltonian is lower- and upper-bounded by quadratic functions. Moreover, it is al…