Tootfinder

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

This https://arxiv.org/abs/2302.09743 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Adaptive control of dynamic networks
Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. External control signals can be applied to a designated set of nodes within a network, known as the Minimum Driver Set (MDS), to steer the network from any state to a desired one. However, the efficacy of the incumbent MDS may diminish as the network topologies evolve. Previous research has often overlooked this challenge, assuming foreknowledge of future changes in network topolo…

Deep Backward and Galerkin Methods for the Finite State Master Equation
Asaf Cohen, Mathieu Lauri\`ere, Ethan Zell
https://arxiv.org/abs/2403.04975 https:/…

Deep Backward and Galerkin Methods for the Finite State Master Equation
This paper proposes and analyzes two neural network methods to solve the master equation for finite-state mean field games (MFGs). Solving MFGs provides approximate Nash equilibria for stochastic, differential games with finite but large populations of agents. The master equation is a partial differential equation (PDE) whose solution characterizes MFG equilibria for any possible initial distribution. The first method we propose relies on backward induction in a time component while the second …

Online Contention Resolution Schemes for Network Revenue Management and Combinatorial Auctions
Will Ma, Calum MacRury, Jingwei Zhang
https://arxiv.org/abs/2403.05378

Online Contention Resolution Schemes for Network Revenue Management and Combinatorial Auctions
In the Network Revenue Management (NRM) problem, products composed of up to L resources are sold to stochastically arriving customers. We take a randomized rounding approach to NRM, motivated by developments in Online Contention Resolution Schemes (OCRS). The goal is to take a fractional solution to NRM that satisfies the resource constraints in expectation, and implement it in an online policy that satisfies the resource constraints in any state, while (approximately) preserving all of the sal…

A look at States Newsroom, a nonprofit network in all 50 US states focused on state politics and policy, with 220 full-time staff and a $22M annual budget (Cameron Joseph/Columbia Journalism Review)
https://www.cjr.org/the_media_today/states-ne…

This nonprofit has newsrooms in all 50 state capitals. Is it the future of state journalism?
In late January, after wrapping up his annual State of the State speech, North Dakota governor Doug Burgum (R) headed out to meet the press. Only two reporters awaited him: the North Dakota Monitor’s Amy Dalrymple and Michael Achterling. “Michael and I were the only ones that were there to ask him questions,” Dalrymple, a […]

U.S., Japan to announce military cooperation, joint NASA lunar mission
The leaders of the United States and Japan this week will commit to modernizing their military alliance, with the aim of eventually creating a truly operational hub for the most consequential defense partnership in the Pacific.

They will also outline a vision for an integrated air defense network that links Japanese, Australian and U.S. sensors, so each country can have a full picture of airborne threats in th…

U.S., Japan to announce military cooperation, joint NASA lunar mission
President Biden is hosting Prime Minister Fumio Kishida for a state visit this week, deepening ties between close allies.

Subregions of the fusiform gyrus are differentially involved in the attentional mechanism supporting visual mental imagery in depression https://link.springer.com/article/10.1007/s11682-024-00885-8 "Impaired visual mental imagery is an important symptom of d…

Subregions of the fusiform gyrus are differentially involved in the attentional mechanism supporting visual mental imagery in depression - Brain Imaging and Behavior
Background Impaired visual mental imagery is an important symptom of depression and has gradually become an intervention target for cognitive behavioral therapy. Methods Our study involved a total of 25 healthy controls (HC) and 23 individuals with moderate depressive symptoms (MD). This study explored the attentional mechanism supporting visual mental imagery impairments in depression using the Vividness of Visual Imagery Questionnaire (VVIQ), attentional network test (ANT), and resting-state …

This https://arxiv.org/abs/2310.17187 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Explainable Gated Bayesian Recurrent Neural Network for Non-Markov State Estimation
The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely an explainable gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space …

This https://arxiv.org/abs/2201.00087 has been replaced.
link: https://scholar.google.com/scholar?q=a

Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the…

This https://arxiv.org/abs/2210.12788 has been replaced.
link: https://scholar.google.com/scholar?q=a

Expander graphs are globally synchronizing
The Kuramoto model is fundamental to the study of synchronization. It consists of a collection of oscillators with interactions given by a network, which we identify respectively with vertices and edges of a graph. In this paper, we show that a graph with sufficient expansion must be globally synchronizing, meaning that a homogeneous Kuramoto model of identical oscillators on such a graph will converge to the fully synchronized state with all the oscillators having the same phase, for every ini…

Very nice data visualisation by L'Echo on the state of the public #EV charging network in Brussels. A lot has changed in a few years - I remember visiting Brussels in 2015, when there were only a handful of public EV charging points in the city. Now half the population lives within 150 metres of one.
This compares favourably with Antwerp (Flanders' top EV city).

PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt
https://arxiv.org/abs/2404.06267

PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperforms state-of-the-art deep learning approaches across a diverse range of 20 publicly available real-world event logs. Notably, our approach is most promising for highly complex processes, where existing deep learning approaches encounter difficulties …

This https://arxiv.org/abs/2312.11834 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics
In recent years, simulations of pedestrians using the multi-agent reinforcement learning (MARL) have been studied. This study considered the roads on a grid-world environment, and implemented pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. Under this environment, the ability of these agents to learn to move forward by avoiding other agents was investigated. Specifically, we considered two types of tasks: the choice between a narrow direct ro…

This https://arxiv.org/abs/2404.04496 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Towards Better Graph Neural Neural Network-based Fault Localization Through Enhanced Code Representation
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization, a widely used technique, employs statistics on coverage spectra to rank code based on suspiciousness scores. However, the rigidity of statistical approaches calls for learning-based techniques. Amongst all, Grace, a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preser…

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

This https://arxiv.org/abs/2301.06774 has been replaced.
link: https://scholar.google.com/scholar?q=a

Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence
Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to id…

Complex network approach to the turbulent velocity gradient dynamics: High- and low-probability Lagrangian paths
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2404.05453

Complex network approach to the turbulent velocity gradient dynamics: High- and low-probability Lagrangian paths
Understanding the dynamics of the turbulent velocity gradient tensor (VGT) is essential to gain insights into the Navier-Stokes equations and improve small-scale turbulence modeling. However, characterizing the VGT dynamics conditional on all its relevant invariants in a continuous fashion is extremely difficult. In this paper, we represent the VGT Lagrangian dynamics using a network where each node represents a unique flow state. This approach enables us to discern how the VGT transitions from…

A look at States Newsroom, a nonprofit network in all 50 US states focused on state politics and policy, with 220 full-time staff and a $22M annual budget (Cameron Joseph/Columbia Journalism Review)
https://www.cjr.org/the_media_today/states-ne…

This nonprofit has newsrooms in all 50 state capitals. Is it the future of state journalism?
In late January, after wrapping up his annual State of the State speech, North Dakota governor Doug Burgum (R) headed out to meet the press. Only two reporters awaited him: the North Dakota Monitor’s Amy Dalrymple and Michael Achterling. “Michael and I were the only ones that were there to ask him questions,” Dalrymple, a […]

Ohio State WR Marvin Harrison Jr. visiting Bears on Monday https://www.nfl.com/news/ohio-state-wr-marvin-harrison-jr-visiting-bears-on-monday

Ohio State WR Marvin Harrison Jr. visiting Bears on Monday
Ohio State product Marvin Harrison Jr. is visiting the Chicago Bears on Monday, NFL Network Insider Tom Pelissero reported.

This https://arxiv.org/abs/2405.01828 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
Facial Expression Recognition (FER) plays a pivotal role in understanding human emotional cues. However, traditional FER methods based on visual information have some limitations, such as preprocessing, feature extraction, and multi-stage classification procedures. These not only increase computational complexity but also require a significant amount of computing resources. Considering Convolutional Neural Network (CNN)-based FER schemes frequently prove inadequate in identifying the deep, long…

The steady-states of splitter networks
Basile Cou\"etoux, Bastien Gastaldi, Guyslain Naves
https://arxiv.org/abs/2404.05472 https://

The steady-states of splitter networks
We introduce splitter networks, which abstract the behavior of conveyor belts found in the video game Factorio. Based on this definition, we show how to compute the steady-state of a splitter network. Then, leveraging insights from the players community, we provide multiple designs of splitter networks capable of load-balancing among several conveyor belts, and prove that any load-balancing network on $n$ belts must have $Ω(n \log n)$ nodes. Incidentally, we establish connections between split…

Very nice data visualisation by L'Echo on the state of the public #EV charging network in Brussels. A lot has changed in a few years - I remember visiting Brussels in 2015, when there were only a handful of public EV charging points in the city. Now half the population lives within 150 metres of one.
This compares favourably with Antwerp (Flanders' top EV city).

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

Network analysis for the steady-state thermodynamic uncertainty relation
Yasuhiro Utsumi
https://arxiv.org/abs/2405.03611 https://arx…

Network analysis for the steady-state thermodynamic uncertainty relation
We perform network analysis of a system described by the master equation to estimate the lower bound of the steady-state current noise, starting from the level 2.5 large deviation function and using the graph theory approach. When the transition rates are uniform, and the system is driven to a non-equilibrium steady state by unidirectional transitions, we derive a noise lower bound, which accounts for fluctuations of sojourn times at all states and is expressed using mesh currents. This bound i…

This https://arxiv.org/abs/2308.11959 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Scalable δ-Level Coherent State Synchronization of Multi-Agent Systems in the Presence of Bounded Disturbances
In this paper, we study scalable δ-Level coherent state synchronization for multi-agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale-free framework designed solely based on the knowledge of agent models and agnostic to the communication graphs and size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. The objective is to restrict the level of coherency…

This https://arxiv.org/abs/2405.02078 has been replaced.
initial toot: https://mastoxiv.page/@a…

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28x speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the…

Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectives
Yiran Guo, Wei Chen, Feifei Sun, Jiaming Cheng, Michail Matthaiou, Bo Ai
https://arxiv.org/abs/2405.05522

Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectives
The use of deep learning (DL) for channel state information (CSI) feedback has garnered widespread attention across academia and industry. The mainstream DL architectures, e.g., CsiNet, deploy DL models on the base station (BS) side and the user equipment (UE) side, which are highly coupled and need to be trained jointly. However, two-sided DL models require collaborations between different network vendors and UE vendors, which entails considerable challenges in order to achieve consensus, e.g.…

Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
Benjamin Doerr, Martin S. Krejca, Nguyen Vu
https://arxiv.org/abs/2404.04018

Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
The target set selection problem (TSS) asks for a set of vertices such that an influence spreading process started in these vertices reaches the whole graph. The current state of the art for this NP-hard problem are three recently proposed randomized search heuristics, namely a biased random-key genetic algorithm (BRKGA) obtained from extensive parameter tuning, a max-min ant system (MMAS), and a MMAS using Q-learning with a graph convolutional network. We show that the BRKGA with two simple …

This https://arxiv.org/abs/2405.01775 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
The development of model compression is continuously motivated by the evolution of various neural network accelerators with ASIC or FPGA. On the algorithm side, the ultimate goal of quantization or pruning is accelerating the expensive DNN computations on low-power hardware. However, such a "design-and-deploy" workflow faces under-explored challenges in the current hardware-algorithm co-design community. First, although the state-of-the-art quantization algorithm can achieve low precision with …

This https://arxiv.org/abs/2402.01558 has been replaced.
link: https://scholar.google.com/scholar?q=a

Confinement in the Transverse Field Ising model on the Heavy Hex lattice
We study the emergence of confinement in the transverse field Ising model on a decorated hexagonal lattice. Using an infinite tensor network state optimised with belief propagation we show how a quench from a broken symmetry state leads to striking nonthermal behaviour underpinned by persistent oscillations and saturation of the entanglement entropy. We explain this phenomenon by constructing a minimal model based on the confinement of elementary excitations, which take the form of various flav…

This https://arxiv.org/abs/2308.02494 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU …

Ohio State WR Marvin Harrison Jr. visiting Bears on Monday https://www.nfl.com/news/ohio-state-wr-marvin-harrison-jr-visiting-bears-on-monday

Ohio State WR Marvin Harrison Jr. visiting Bears on Monday
Ohio State product Marvin Harrison Jr. is visiting the Chicago Bears on Monday, NFL Network Insider Tom Pelissero reported.

The Discovery Channel is accused of “whitewashing genocide” against the Uyghurs and other ethnic minorities in far-western Xinjiang.
https://chinamediaproject.org/2024/03/05/new-frontiers-in-foreign-propaganda/

New Frontiers in Foreign Propaganda - China Media Project
The latest collaboration between the Discovery Channel and state-run media in China has been accused of whitewashing genocide in Xinjiang. Why is this American network so keen to “tell China’s story well?”

Propensity of water self-ions at air(oil)-water interfaces revealed by deep potential molecular dynamics with enhanced sampling
Pengchao Zhang, Axel Tosello Gardini, Xuefei Xu
https://arxiv.org/abs/2404.07027

Propensity of water self-ions at air(oil)-water interfaces revealed by deep potential molecular dynamics with enhanced sampling
The preference of water self-ions (hydronium and hydroxide) near air/oil-water interfaces is one of the hottest topics in water research due to its importance for understanding properties, phenomena, and reactions of interfaces. In this work, we performed enhanced-sampling molecular dynamics based on state-of-the-art neural network potentials with M06-2X accuracy to investigate the propensity of hydronium and hydroxide ions at air/oil-water interfaces, which can simultaneously describe well the…

“Tech isn’t political”
The Nazis in tech are, very vocally. That’s why if you have any sort of following in tech on social media you should vocally call them out and use your voice to denounce these absolute garbage people.
Because if you don’t, you’re quietly let them take away what you love.
ht…

The Tech Baron Seeking to “Ethnically Cleanse” San Francisco
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not okay.

Balaji Srinivasan is the spearhead of the new fascist movement establishing itself in Silicon Valley.
It's easy to dismiss him as a clown given how bad, inconsistent and dumb his ideas are but he's a clown with a lot of followers in tech who listen to his visions of "ethnic cleansing".
https://

The Tech Baron Seeking to “Ethnically Cleanse” San Francisco
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not okay.

This https://arxiv.org/abs/2401.12616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study
The language control network is vital among language-related networks responsible for solving the problem of multiple language switching. Researchers have expressed concerns about the instability of the language control network when exposed to external influences (e.g., Long-term second language learning). However, some studies have suggested that the language control network is stable. Therefore, whether the language control network is stable or not remains unclear. In the present study, we di…

Utility-driven Optimization of TTL Cache Hierarchies under Network Delays
Karim S. Elsayed, Fabien Geyer, Amr Rizk
https://arxiv.org/abs/2405.04402 https:/…

Utility-driven Optimization of TTL Cache Hierarchies under Network Delays
We optimize hierarchies of Time-to-Live (TTL) caches under random network delays. A TTL cache assigns individual eviction timers to cached objects that are usually refreshed upon a hit where upon a miss the object requires a random time to be fetched from a parent cache. Due to their object decoupling property, TTL caches are of particular interest since the optimization of a per-object utility enables service differentiation. However, state-of-the-art exact TTL cache optimization does not exte…

The Tech Baron Seeking to “Ethnically Cleanse” San Francisco | The New Republic
https://newrepublic.com/article/180487/balaji-srinivasan-network-state-plutocrat

The Tech Baron Seeking to “Ethnically Cleanse” San Francisco
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not okay.

This https://arxiv.org/abs/2204.03827 has been replaced.
link: https://scholar.google.com/scholar?q=a

IA-GCN: Interactive Graph Convolutional Network for Recommendation
Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on the representations. Despite effectiveness, existing algorithms neglect precious interactive features between user-item pairs in the embedding p…

This https://arxiv.org/abs/2308.11925 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
In this work, we investigate a neural network based solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems. It utilizes a coupled system derived from the first-order optimality system of the optimal control problem, and employs deep neural networks to represent the solutions to the reduced system. We present an error analysis of the scheme, and provide $L^2(Ω)$ error bounds on the state, control and adjoint in terms of neura…

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2405.03589

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Complex network analysis methods have been widely applied to nonlinear systems, but applications within fluid mechanics are relatively few. In this paper, we use a network for the Lagrangian dynamics of the velocity gradient tensor (VGT), where each node is a flow state, and the probability of transitioning between states follows from a direct numerical simulation of statistically steady and isotropic turbulence. The network representation of the VGT dynamics is much more compact than the conti…

This https://arxiv.org/abs/2310.02571 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

The role of local bounds on neighborhoods in the network for scale-free state synchronization of multi-agent systems
This paper provides necessary and sufficient conditions for the existence of solutions to the state synchronization problem of homogeneous multi-agent systems (MAS) via scale-free linear dynamic non-collaborative protocol for both continuous- and discrete-time. These conditions guarantee for which class of MAS, one can achieve scale-free state synchronization. We investigate protocol design with and without utilizing local bounds on neighborhood. The results show that the availability of local …

Inferring State Machine from the Protocol Implementation via Large Langeuage Model
Haiyang Wei, Zhengjie Du, Haohui Huang, Yue Liu, Guang Cheng, Linzhang Wang, Bing Mao
https://arxiv.org/abs/2405.00393

Inferring State Machine from the Protocol Implementation via Large Langeuage Model
State machines play a pivotal role in augmenting the efficacy of protocol analyzing to unveil more vulnerabilities. However, the task of inferring state machines from network protocol implementations presents significant challenges. Traditional methods based on dynamic analysis often overlook crucial state transitions due to limited coverage, while static analysis faces difficulties with complex code structures and behaviors. To address these limitations, we propose an innovative state machine …

Just straight up Fascism from Balaji. Masks off, no sugarcoating, just police-state ethnic cleansing.
https://mastodon.online/@parismarx/112350817463406119

Paris Marx (@parismarx@mastodon.online)
Attached: 1 image Balaji Srinivasan is calling for a “tech Zionism” that would see “Grays” loyal to the tech industry take over San Francisco by allying with the police to give themselves privileges as they oppress the “Blues” (liberals). Essential read by @gilduran@journa.host: https://newrepublic.com/article/180487/balaji-srinivasan-network-state-plutocrat #tech #politics #democracy

Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization
Hanzhi Yu, Mingzhe Chen, Yuchen Liu
https://arxiv.org/abs/2403.00665

Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization
In this article, the use of channel state information (CSI) for indoor positioning is studied. In the considered model, a server equipped with several antennas sends pilot signals to users, while each user uses the received pilot signals to estimate channel states for user positioning. To this end, we formulate the positioning problem as an optimization problem aiming to minimize the gap between the estimated positions and the ground truth positions of users. To solve this problem, we design a …

Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
Benjamin Doerr, Martin S. Krejca, Nguyen Vu
https://arxiv.org/abs/2404.04018

Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
The target set selection problem (TSS) asks for a set of vertices such that an influence spreading process started in these vertices reaches the whole graph. The current state of the art for this NP-hard problem are three recently proposed randomized search heuristics, namely a biased random-key genetic algorithm (BRKGA) obtained from extensive parameter tuning, a max-min ant system (MMAS), and a MMAS using Q-learning with a graph convolutional network. We show that the BRKGA with two simple …

Neural Network Quantum States for the Interacting Hofstadter Model with Higher Local Occupations and Long-Range Interactions
Fabian D\"oschl, Felix A. Palm, Hannah Lange, Fabian Grusdt, Annabelle Bohrdt
https://arxiv.org/abs/2405.04472

Neural Network Quantum States for the Interacting Hofstadter Model with Higher Local Occupations and Long-Range Interactions
Due to their immense representative power, neural network quantum states (NQS) have gained significant interest in current research. In recent advances in the field of NQS, it has been demonstrated that this approach can compete with state-of-the-art numerical techniques, making NQS a compelling alternative, in particular for the simulation of large, two-dimensional quantum systems. In this study, we show that recurrent neural network (RNN) wave functions can be employed to study systems releva…

Bringing memory to Boolean networks: a unifying framework
Maximilien Gadouleau, Lo\"ic Paulev\'e, Sara Riva
https://arxiv.org/abs/2404.03553 https…

Bringing memory to Boolean networks: a unifying framework
Boolean networks are extensively applied as models of complex dynamical systems, aiming at capturing essential features related to causality and synchronicity of the state changes of components along time. Dynamics of Boolean networks result from the application of their Boolean map according to a so-called update mode, specifying the possible transitions between network configurations. In this paper, we explore update modes that possess a memory on past configurations, and provide a generic fr…

Let's say it out loud. There is an anti-worker, anti-social, downright fascist and pseudo-nationalist vein running through Silicon Valley. Whether it's Musk, Google, Ellison in the backroom, or this CoinBro, it runs deep and wide.
I remember when Steve Jobs and Larry Ellision were, famously, best friends. I wonder what Steve would say today. On BitCoin and AI and all this neo-fascist BS…
At risk of sounding like I am calling upon a saviour... I wish he was here.
<…

Quantum 2D Liouville Path-Integral Is a Sum over Geometries in AdS$_3$ Einstein Gravity
Lin Chen, Ling-Yan Hung, Yikun Jiang, Bing-Xin Lao
https://arxiv.org/abs/2403.03179

Quantum 2D Liouville Path-Integral Is a Sum over Geometries in AdS$_3$ Einstein Gravity
There is a renowned solution of the modular bootstrap that defines the UV complete quantum Liouville theory. We triangulate the path-integral of this Liouville CFT on any 2D surface $\mathcal{M}$, by proposing a shrinkable boundary condition for this special CFT that allows small holes to close, analogous to the proposal in rational CFTs [1-3]. This is essentially a tensor network that admits an interpretation of a state-sum of a 3D topological theory constructed with quantum 6j symbols of $\ma…

Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Andr\'es Bell-Navas, Nourelhouda Groun, Mar\'ia Villalba-Orero, Enrique Lara-Pezzi, Jes\'us Garicano-Mena, Soledad Le Clainche
https://arxiv.org/abs/2404.19579 https://arxiv.org/pdf/2404.19579
arXiv:2404.19579v1 Announce Type: new
Abstract: Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine-learning-compatible collection of annotated images which can be used in the training stage of any kind of machine learning-based framework, and more specifically with deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors' knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the superiority of the proposed system and the efficacy of the HODMD algorithm, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

This https://arxiv.org/abs/2308.11925 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
In this work, we investigate a neural network based solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems. It utilizes a coupled system derived from the first-order optimality system of the optimal control problem, and employs deep neural networks to represent the solutions to the reduced system. We present an error analysis of the scheme, and provide $L^2(Ω)$ error bounds on the state, control and adjoint in terms of neura…

Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
Jian Meng, Yuan Liao, Anupreetham Anupreetham, Ahmed Hasssan, Shixing Yu, Han-sok Suh, Xiaofeng Hu, Jae-sun Seo
https://arxiv.org/abs/2405.01775

Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
The development of model compression is continuously motivated by the evolution of various neural network accelerators with ASIC or FPGA. On the algorithm side, the ultimate goal of quantization or pruning is accelerating the expensive DNN computations on low-power hardware. However, such a "design-and-deploy" workflow faces under-explored challenges in the current hardware-algorithm co-design community. First, although the state-of-the-art quantization algorithm can achieve low precision with …

Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive Anomaly Diagnosis of Industrial Cyber-physical Systems
Haili Sun, Yan Huang, Lansheng Han, Cai Fu, Chunjie Zhou
https://arxiv.org/abs/2403.02616

Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive Anomaly Diagnosis of Industrial Cyber-physical Systems
Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the wor…

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2405.03589

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Complex network analysis methods have been widely applied to nonlinear systems, but applications within fluid mechanics are relatively few. In this paper, we use a network for the Lagrangian dynamics of the velocity gradient tensor (VGT), where each node is a flow state, and the probability of transitioning between states follows from a direct numerical simulation of statistically steady and isotropic turbulence. The network representation of the VGT dynamics is much more compact than the conti…

Joint Active and Passive Beamforming for IRS-Aided Wireless Energy Transfer Network Exploiting One-Bit Feedback
Taotao Ji, Meng Hua, Chunguo Li, Yongming Huang, Luxi Yang
https://arxiv.org/abs/2404.05418

Joint Active and Passive Beamforming for IRS-Aided Wireless Energy Transfer Network Exploiting One-Bit Feedback
To reap the active and passive beamforming gain in an intelligent reflecting surface (IRS)-aided wireless network, a typical way is to first acquire the channel state information (CSI) relying on the pilot signal, and then perform the joint beamforming design. However, it is a great challenge when the receiver can neither send pilot signals nor have complex signal processing capabilities due to its hardware limitation. To tackle this problem, we study in this paper an IRS-aided wireless energy …

“Tech isn’t political”
The Nazis in tech are, very vocally. That’s why if you have any sort of following in tech on social media you should vocally call them out and use your voice to denounce these absolute garbage people.
Because if you don’t, you’re quietly let them take away what you love.
ht…

The Tech Baron Seeking to “Ethnically Cleanse” San Francisco
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not okay.

This https://arxiv.org/abs/2401.12616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study
The language control network is vital among language-related networks responsible for solving the problem of multiple language switching. Researchers have expressed concerns about the instability of the language control network when exposed to external influences (e.g., Long-term second language learning). However, some studies have suggested that the language control network is stable. Therefore, whether the language control network is stable or not remains unclear. In the present study, we di…

Utility-driven Optimization of TTL Cache Hierarchies under Network Delays
Karim S. Elsayed, Fabien Geyer, Amr Rizk
https://arxiv.org/abs/2405.04402 https:/…

Utility-driven Optimization of TTL Cache Hierarchies under Network Delays
We optimize hierarchies of Time-to-Live (TTL) caches under random network delays. A TTL cache assigns individual eviction timers to cached objects that are usually refreshed upon a hit where upon a miss the object requires a random time to be fetched from a parent cache. Due to their object decoupling property, TTL caches are of particular interest since the optimization of a per-object utility enables service differentiation. However, state-of-the-art exact TTL cache optimization does not exte…

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

Slicing for Dense Smart Factory Network: Current State, Scenarios, Challenges and Expectations
Regina Ochonu, Josep Vidal
https://arxiv.org/abs/2405.03230 …

Slicing for Dense Smart Factory Network: Current State, Scenarios, Challenges and Expectations
In the era of Industry 4.0, smart factories have emerged as a paradigm shift, redefining manufacturing with the integration of advanced digital technologies. Central to this transformation is the deployment of 5G networks, offering unprecedented levels of connectivity, speed, reliability, and ultra-low latency. Among the revolutionary features of 5G is network slicing, a technology that offers enhanced capabilities through the customization of network resources by allowing multiple logical netw…

ProPublica creates a dedicated newsroom team to sustain future investigative projects of current and prior Local Reporting Network partners (ProPublica)
https://www.propublica.org/atpropublica/propublica-announc…

ProPublica Announces 50 State Initiative, Boosting Local Journalism Commitment
ProPublica is an independent, non-profit newsroom that produces investigative journalism in the public interest.

This https://arxiv.org/abs/2404.03493 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve …

This https://arxiv.org/abs/2312.02184 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs. However, with the decoupling of uplink base stations and downlink base stations in FD-RAN, the traditional transmission mechanism, which relies on real-time channel feedback, is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transm…

This https://arxiv.org/abs/2306.11227 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Introduction to the Compute Express Link (CXL) Interconnect
The Compute Express Link (CXL) is an open industry-standard interconnect between processors and devices such as accelerators, memory buffers, smart network interfaces, persistent memory, and solid-state drives. CXL offers coherency and memory semantics with bandwidth that scales with PCIe bandwidth while achieving significantly lower latency than PCIe. All major CPU vendors, device vendors, and datacenter operators have adopted CXL as a common standard. This enables an inter-operable ecosystem …

Secure Link State Routing for Mobile Ad Hoc Networks
Panagiotis Papadimitratos, Zygmunt J. Haas
https://arxiv.org/abs/2403.19859 https://

Secure Link State Routing for Mobile Ad Hoc Networks
The secure operation of the routing protocol is one of the major challenges to be met for the proliferation of the Mobile Ad hoc Networking (MANET) paradigm. Nevertheless, security enhancements have been proposed mostly for reactive MANET protocols. The proposed here Secure Link State Routing Protocol (SLSP) provides secure proactive topology discovery, which can be multiply beneficial to the network operation. SLSP can be employed as a stand-alone protocol, or fit naturally into a hybrid routi…

My previous post introduced #Balaji #Srinivasan (let's call him B.S. for short),
the main brain behind the #Network #State cult of tech b…

Cool Gray City of Tech Authoritarians: Balaji's dark vision for San Francisco
Maybe you can literally have like the sovereign city of San Francisco and secede effectively ... if you have total control of the city ... maybe it's possible. – B.S. The Point: Like some mustache-twirling cartoon villain, the main tech figure behind the Network State cult lays out a "roadmap" for an

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick
https://arxiv.org/abs/2405.02078

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28x speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the…

This https://arxiv.org/abs/2302.01892 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics
Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents' variables (e.g., the team barycentre). In this paper, we address a distributed feedback optimization framework in which agents implement a local (distributed) policy to reach a steady-state minimizing an agg…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

This https://arxiv.org/abs/2209.14089 has been replaced.
link: https://scholar.google.com/scholar?q=a

Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations
We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks. We consider the RL actor-critic method, a model-free approach for solving RL problems, and introduce TNs as the approximators for its policy and value functions. Our "actor-critic with tensor networks" (ACTeN) method is especially well suited to problems with large and factorisable state and action spaces. As an illustration of the applicability of ACTeN we …

Physics-informed Data-driven Cavitation Model for a Specific MG EOS
Minsheng Huang, Chengbao Yao, Pan Wang, Lidong Cheng, Wenjun Ying
https://arxiv.org/abs/2405.02313

Physics-informed Data-driven Cavitation Model for a Specific MG EOS
We present a novel one-fluid cavitation model of a specific Mie-Grüneisen equation of state(EOS), named polynomial EOS, based on an artificial neural network. Not only the physics-informed equation but also the experimental data are embedded into the proposed model by an optimization problem. The physics-informed data-driven model provides the concerned pressure within the cavitation region, where the density tends to zero when the pressure falls below the saturated pressure. The present model…

ProPublica creates a dedicated newsroom team to sustain future investigative projects of current and prior Local Reporting Network partners (ProPublica)
https://www.propublica.org/atpropublica/propublica-announc…

ProPublica Announces 50 State Initiative, Boosting Local Journalism Commitment
ProPublica is an independent, non-profit newsroom that produces investigative journalism in the public interest.

This https://arxiv.org/abs/2404.03493 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve …

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction
K. Aditya Mohan, Massimiliano Ferrucci, Chuck Divin, Garrett A. Stevenson, Hyojin Kim
https://arxiv.org/abs/2404.19075 https://arxiv.org/pdf/2404.19075
arXiv:2404.19075v1 Announce Type: new
Abstract: 4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an extremely ill-posed inverse problem. Existing approaches assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. We use a continuous time and space forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued object coordinates. Unlike existing state-of-the-art neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions even for extremely large CT data sizes. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

Physics-informed Data-driven Cavitation Model for a Specific MG EOS
Minsheng Huang, Chengbao Yao, Pan Wang, Lidong Cheng, Wenjun Ying
https://arxiv.org/abs/2405.02313

Physics-informed Data-driven Cavitation Model for a Specific MG EOS
We present a novel one-fluid cavitation model of a specific Mie-Grüneisen equation of state(EOS), named polynomial EOS, based on an artificial neural network. Not only the physics-informed equation but also the experimental data are embedded into the proposed model by an optimization problem. The physics-informed data-driven model provides the concerned pressure within the cavitation region, where the density tends to zero when the pressure falls below the saturated pressure. The present model…

This https://arxiv.org/abs/2209.13104 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Neural Network Approach for Stochastic Optimal Control
We present a neural network approach for approximating the value function of high-dimensional stochastic control problems. Our training process simultaneously updates our value function estimate and identifies the part of the state space likely to be visited by optimal trajectories. Our approach leverages insights from optimal control theory and the fundamental relation between semi-linear parabolic partial differential equations and forward-backward stochastic differential equations. To focus …

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick
https://arxiv.org/abs/2405.02078

CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28x speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the…

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
Theodoros Theodoropoulos, Angelos-Christos Maroudis, Antonios Makris, Konstantinos Tserpes
https://arxiv.org/abs/2405.00570

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims at expanding upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions, as well …

ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures
Salim Rukhsar, Anil Kumar Tiwari
https://arxiv.org/abs/2403.03276

ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures
We proposed an Attentive Recurrent Neural Network (ARNN), which recurrently applies attention layers along a sequence and has linear complexity with respect to the sequence length. The proposed model operates on multi-channel EEG signals rather than single channel signals and leverages parallel computation. In this cell, the attention layer is a computational unit that efficiently applies self-attention and cross-attention mechanisms to compute a recurrent function over a wide number of state v…

This https://arxiv.org/abs/2209.07577 has been replaced.
link: https://scholar.google.com/scholar?q=a

Experimental verification of the quantum nature of a neural network
Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks have some actual hidden quantum properties that make them such suitable tools for a highly coupled quantum problem. I discuss here what makes a system quantum and to what extent we can interpret a neural network as having quantum remnan…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

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 …

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
Theodoros Theodoropoulos, Angelos-Christos Maroudis, Antonios Makris, Konstantinos Tserpes
https://arxiv.org/abs/2405.00570

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims at expanding upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions, as well …

Pre-training on High Definition X-ray Images: An Experimental Study
Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin, Yao Rong, Bo Jiang, Chuanfu Li, Jin Tang
https://arxiv.org/abs/2404.17926 https://arxiv.org/pdf/2404.17926
arXiv:2404.17926v1 Announce Type: new
Abstract: Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $\times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guarantee of effective solutions to difficult miscellaneous diseases. In this paper, we address these issues by proposing the first high-definition (1280 $\times$ 1280) X-ray based pre-trained foundation vision model on our newly collected large-scale dataset which contains more than 1 million X-ray images. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input, and the masked image patches are reconstructed by the Transformer encoder-decoder network. More importantly, we introduce a novel context-aware masking strategy that utilizes the chest contour as a boundary for adaptive masking operations. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition. Extensive experiments demonstrate that our pre-trained medical foundation vision model achieves comparable or even new state-of-the-art performance on downstream benchmark datasets. The source code and pre-trained models of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis.

A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers
Jiawei Liao, Oscar Toomey, Xiaying Wang, Lars Widmer, Cynthia A. Chestek, Luca Benini, Taekwang Jang
https://arxiv.org/abs/2405.02146

A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers
Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this paper, we propose a novel spiking neural network (SNN) decoder for regression tasks for implantable BMIs. The SNN is trained with enhanced spatio-temporal backpropagation to fully leverage its capability to handle temporal problems. The proposed SNN decoder outperforms the state-of-the-art Kalman filter and artificial neu…

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Fast Algorithms for Spiking Neural Network Simulation with FPGAs
Bj\"orn A. Lindqvist, Artur Podobas
https://arxiv.org/abs/2405.02019 https://<…

Fast Algorithms for Spiking Neural Network Simulation with FPGAs
Using OpenCL-based high-level synthesis, we create a number of spiking neural network (SNN) simulators for the Potjans-Diesmann cortical microcircuit for a high-end Field-Programmable Gate Array (FPGA). Our best simulators simulate the circuit 25\% faster than real-time, require less than 21 nJ per synaptic event, and are bottle-necked by the device's on-chip memory. Speed-wise they compare favorably to the state-of-the-art GPU-based simulators and their energy usage is lower than any other pub…

On the reduction of Linear Parameter-Varying State-Space models
E. Javier Olucha, Bogoljub Terzin, Amritam Das, Roland T\'oth
https://arxiv.org/abs/2404.01871

On the reduction of Linear Parameter-Varying State-Space models
This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary use…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta, Irene Amerini, Laura Palagi
https://arxiv.org/abs/2405.02089

Computational issues in Optimization for Deep networks
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classification performance. In particular, we aim to analyze the behaviour of state-of-the-art optimization algorithms in relationship to their hyperparameters setting in order to detect robustness with respect to the choice of a certain starting point in ending on different local solutions. We conduct extensive computational …

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
https://arxiv.org/abs/2404.03493

A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve …

A neural network-based approach to hybrid systems identification for control
Filippo Fabiani, Bartolomeo Stellato, Daniele Masti, Paul J. Goulart
https://arxiv.org/abs/2404.01814 …

A neural network-based approach to hybrid systems identification for control
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design. We propose a specific neural network (NN) architecture that yields a hybrid system with piecewise-affine dynamics that is differentiable with respect to the network's parameters, thereby enabling the use of derivative-based training procedures. We show that a…

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

us_air_traffic: U.S. air traffic
Yearly snapshots of flights among all commercial airports in the United States from 1990 to today. Metadata include passengers, distance, carrier, airport located city, state, and month of the flight.
This network has 2278 nodes and 6390340 edges.
Tags: Transportation, Airport, Unweighted, Metadata, Temporal

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted